Advanced Digital Marketing

Advanced Digital
Marketing Strategies in a
Data-Driven Era
Jose Ramon Saura

Rey Juan Carlos University, Spain
A volume in the Advances in Marketing, Customer

Relationship Management, and E-Services
(AMCRMES) Book Series
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Library of Congress Cataloging-in-Publication Data
Names: Saura, José Ramón, 1992- editor.

Title: Advanced digital marketing strategies in a data-driven era / José
Ramón Saura, editor.
Description: Hershey, PA : Business Science Reference, [2021] | Includes
bibliographical references and index. | Summary: “This book offers a
holistic overview of the main applications and uses of data sciences in
digital marketing strategies and business models, analyzing the new
strategies that influence digital marketing and offering insights
related to the creation of innovative data-driven and knowledge
discovery techniques applied to traditional and digital marketing
strategies”-- Provided by publisher.
Identifiers: LCCN 2021017670 (print) | LCCN 2021017671 (ebook) | ISBN
9781799880035 (hardcover) | ISBN 9781799880042 (paperback) | ISBN
9781799880059 (ebook)
Subjects: LCSH: Internet marketing. | Internet marketing--Statistical
methods.
Classification: LCC HF5415.1265 .A378 2021 (print) | LCC HF5415.1265
(ebook) | DDC 658.8/72--dc23
LC record available at https://lccn.loc.gov/2021017670
LC ebook record available at https://lccn.loc.gov/2021017671
This book is published in the IGI Global book series Advances in Marketing, Customer Relationship Management, and E-
Services (AMCRMES) (ISSN: 2327-5502; eISSN: 2327-5529)
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Advances in Marketing,
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ISSN:2327-5502
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er-relationship-management/37150
Blockchain Technology and Applications for Digital Marketing

Rohit Bansal (Department of Management Studies, Vaish College of Engineering, India) Pacha Malyadri (ICSSR,
Center for Economic and Social Studies, India) Amandeep Singh (Chitkara Business School, Chitkara University,
Punjab, India) and Asif Pervez (Jamia Millia Islamia University, India)
Business Science Reference • © 2021 • 329pp • H/C (ISBN: 9781799880813) • US $225.00
Big Data Analytics for Improved Accuracy, Efficiency, and Decision Making in Digital Marketing
Amandeep Singh (Chitkara Business School, Chitkara University, Punjab, India)
Handbook of Research on Future Policies and Strategies for Nation Branding

Victoria Pistikou (Democritus University of Thrace, Greece) Andreas Masouras (Neapolis University, Cyprus)
and Marcos Komodromos (University of Nicosia, Cyprus)
Developing Successful Global Strategies for Marketing Luxury Brands

Fabrizio Mosca (University of Turin, Italy) Cecilia Casalegno (University of Turin, Italy) and Rosalia Gallo (Uni-
versitat Autònoma de Barcelona, Spain)
Insights, Innovation, and Analytics for Optimal Customer Engagement

Samala Nagaraj (Woxsen University, India)
New Techniques for Brand Management in the Healthcare Sector

Ana Pinto Borges (European Business School and Research Group, ISAG (NIDISAG), Portugal) and Paula Ro-
drigues (Lusíada University of Porto, Portugal)
Impact of ICTs on Event Management and Marketing

Kemal Birdir (Mersin University, Turkey) Sevda Birdir (Mersin University, Turkey) Ali Dalgic (Isparta University
of Applied Sciences, Turkey) and Derya Toksoz (Mersin University, Turkey)
701 East Chocolate Avenue, Hershey, PA 17033, USA

Tel: 717-533-8845 x100 • Fax: 717-533-8661
E-Mail: cust@igi-global.com • www.igi-global.com
Table of Contents
Preface.................................................................................................................................................. xiv
Section 1
From Literature to Theory: Reviews, Concepts, and Definitions Linked to Digital
Marketing
Chapter 1
A Better Understanding of Big Data and Marketing Analytics: A Review............................................. 1
Francisco J. S. Lacárcel, Madrid Open University, Spain
Leticia Polanco-Diges, Rey Juan Carlos University, Spain
Felipe Debasa, Rey Juan Carlos University, Spain
Chapter 2
The Role of Big Data in Digital Marketing........................................................................................... 16
Neslihan Cavlak, Namık Kemal University, Turkey
Ruziye Cop, Bolu İzzet Baysal University, Turkey
Chapter 3
Digital Marketing vs. Traditional Marketing: Literature Review and Practical Lessons....................... 34
Vera Gelashvili, King Juan Carlos University, Spain
Chapter 4
Digital Loyalty Programmes: Pull Strategies in B2B Channel Marketing............................................ 51
Ammarah Marjan, London South Bank University, UK
Charles Graham, London South Bank University, UK
Margaret Bruce, London South Bank University, UK
Andrew Mitchell, Brandmovers Inc., USA
Section 2
Towards Practitioner Knowledge: Applied Digital Marketing Strategies
Chapter 5
Alibaba’s International Digital Marketing Practices and Strategies...................................................... 80
Sue Claire Berning, University of Ingolstadt, Germany

Chapter 6
Activism in the Digital Age: Social Movements Analysis Using User-Generated Content in Social
Media..................................................................................................................................................... 99
Laura Holgado-Ruiz, Rey Juan Carlos University, Spain
José Ramón Saura, Rey Juan Carlos University, Spain
Beatriz Rodríguez Herráez, Rey Juan Carlos University, Spain
Chapter 7
Understanding Google Ads Metrics for SME...................................................................................... 123
Belem Barbosa, University of Porto, Portugal
Zaila Oliveira, Unichristus, Brazil & Unifametro, Brazil
Sandrina F. Teixeira, CEOS, ISCAP, Polytechnic of Porto, Portugal
Vasco Paço Gomes, University of Aveiro, Portugal
Chapter 8
Big Data Optimization in Zara: How Zara Will Optimize Its Process With Big Data Due to the
COVID-19 Situation............................................................................................................................ 145
Paula González-Padilla, Rey Juan Carlos University, Spain
Carlota Galán Zori, Rey Juan Carlos University, Spain
Chapter 9
Confirming Digital Marketing Model Innovation Design: SEM in Post-COVID Social Impact
Startups, Mexico.................................................................................................................................. 158
Juan Mejía-Trejo, Universidad de Guadalajara, Mexico
Chapter 10
Do Spanish Family SMEs Make Appropriate Use of Their Organizational Websites? An Analysis
of Family Firms’ Brand Promotion and Website Quality Level.......................................................... 172
Rubén Martínez-Alonso, University of Almería, Spain
María J. Martínez-Romero, University of Almería, Spain
Daniel Rodríguez-Rodríguez, University of Almería, Spain
Chapter 11
Adapting Digital Strategies to a New Era: A Delphi-Based Analysis in the Fashion Industry........... 192
Rocío López Muniesa, Rey Juan Carlos University, Spain
Jose Ramón Saura, Rey Juan Carlos University, Spain
Eloísa Díaz-Garrido, Rey Juan Carlos University, Spain
Section 3
Case Studies: Advances in Digital Marketing Strategies Applied to Industries
Chapter 12
How Digitalisation Is Influencing Traditional Food Restaurants in the Management of Their
Marketing Strategies............................................................................................................................ 211
Juan-Gabriel Martínez-Navalón, Rey Juan Carlos University, Spain

Chapter 13
Personal Brand Benefits of Social Media Use for Researchers: A Case Study................................... 226
Alberto Prado Román, Rey Juan Carlos University, Spain
Iria Paz-Gil, Rey Juan Carlos University, Spain
Miguel Prado Román, Rey Juan Carlos University, Spain
Chapter 14
Digital Customer Journey in the Luxury Hotel Experience: A Case Study Approach........................ 246
Jonathan Gomez Punzon, Rey Juan Carlos University, Spain
Chapter 15
Approach to Social Media Marketing Strategies in Different World Regions: A Descriptive Study.. 261
Luis Matosas-López, Rey Juan Carlos University, Spain
Roberto Baelo Alvarez, University of León, Spain
Compilation of References................................................................................................................ 286
About the Contributors..................................................................................................................... 334
Index.................................................................................................................................................... 339
Detailed Table of Contents
Preface.................................................................................................................................................. xiv
Section 1
From Literature to Theory: Reviews, Concepts, and Definitions Linked to Digital
Marketing
This section presents chapters that develop reviews on digital marketing strategies, concepts linked
to the use of data-centric strategies, and the main definitions that encompass the digital ecosystem of
advanced digital marketing strategies in a digital era.
Chapter 1
A Better Understanding of Big Data and Marketing Analytics: A Review............................................. 1
Francisco J. S. Lacárcel, Madrid Open University, Spain
Leticia Polanco-Diges, Rey Juan Carlos University, Spain
Felipe Debasa, Rey Juan Carlos University, Spain
Data mining and analysis is consolidating as a crucial practice in economic, educational, social, and
business sectors. In this context, this study aims to identify and categorize the main strategies, metrics,
and concepts that are derived from big data analytics (BDA) and marketing analytics (MA). This study
follows a systematic literature review (SLR) of important scientific contributions made so far in this
research area. The authors have identified through this study 13 key concepts related to big data analytics
and 13 related to marketing analytics, which are classified and categorized according to their application in
technologies or actions in digital marketing. The chapter concludes with a discussion between theoretical
and practical implications on the results for future researchers.
Chapter 2
The Role of Big Data in Digital Marketing........................................................................................... 16
Neslihan Cavlak, Namık Kemal University, Turkey
Ruziye Cop, Bolu İzzet Baysal University, Turkey
Consumers perform their activities through digital channels more often as a result of technological
advancements where those advancements also allow marketers to reach excessive information about
consumers, store them, and use them whenever and however they consider necessary. These big data
provide businesses to understand the unmet demands and expectations of consumers and achieve a
sustainable business success. Despite the importance of big data analytics for marketing of businesses,
research on this issue is scarce. In order to contribute the literature, the purpose of this chapter is to
reveal the importance of big data in the digital marketing environment. In line with this purpose, a

comprehensive literature review including the definition, components, sources of big data, and the role
of big data in digital environments and the examples of businesses using big data is undertaken.
Chapter 3
Digital Marketing vs. Traditional Marketing: Literature Review and Practical Lessons....................... 34
Vera Gelashvili, King Juan Carlos University, Spain
Digitalisation has caused important changes that have affected people’s lives on a social, economic, or
personal level. These changes have affected both business and society in general. Therefore, companies
had to adopt different strategies to reach with their users, since traditional methods were no longer
effective in some cases. It is the case of marketing, where companies had to change or blend traditional
marketing with digital marketing (i.e., to promote their products and services through the internet). The
aim of this work is to analyse the existing literature on similarities and differences between traditional
and digital marketing. The methodologies used to achieve these objectives were a systematic literature
review and bibliometric analysis by Scopus. The results obtained have shown that both traditional and
digital marketing are important for companies, so firms must find out which of them is more compatible
with the company’s objectives. This chapter provides practical lessons for entrepreneurs or marketing
makers when choosing a type of marketing for their businesses.
Chapter 4
Digital Loyalty Programmes: Pull Strategies in B2B Channel Marketing............................................ 51
Ammarah Marjan, London South Bank University, UK
Charles Graham, London South Bank University, UK
Margaret Bruce, London South Bank University, UK
Andrew Mitchell, Brandmovers Inc., USA
Digital loyalty programmes are an increasingly common tool for business-to-business marketers hoping
to increase repeat sales through deeper customer engagement. In consumer markets, such programmes
do little to influence behavioural loyalty and disproportionately attract the firm’s existing heavy buyers.
Industrial buying, however, relies on direct sales channels and features negotiation and reciprocity.
Loyalty effects may therefore differ in B2B, and although no clear picture yet exists, such knowledge is
important as B2C digital loyalty programmes grow in popularity. Here, the authors describe programme
membership’s evolving characteristics over in a B2B scheme that was launched in the US metal-cutting
tools manufacturer customer base. Findings are consistent with the idea that the scheme recruited the
heaviest buyers earliest and had an insignificant effect on total revenue. The authors discuss managerial
implications, particularly about (1) managing the rollout of similar schemes and (2) refocussing on the
programme objectives to maintain sales from the lightest rather than the heaviest buyers.
Section 2
Towards Practitioner Knowledge: Applied Digital Marketing Strategies
In order to understand how practitioners should develop digital marketing actions in their industries,
this section presents several chapters that analyze from a business point of view the main actions linked
to decision-making that CEOs, executives, and other company managers have to consider for the
development and optimization of data-centric digital marketing techniques.

Chapter 5
Alibaba’s International Digital Marketing Practices and Strategies...................................................... 80
Sue Claire Berning, University of Ingolstadt, Germany
In this chapter, the main relationship between a company’s use of data-driven methods and its international
digital marketing strategies are examined. In particular, the question of how data-driven methods, like
consumer analytics, helped the company in its internationalization efforts are outlined. By following the
case study approach, the diverse digital business models, online advertising campaigns, and international
digital marketing practices of the Chinese company Alibaba are investigated. As China’s e-commerce
market currently became one of the most dynamic ones in the world, and as Alibaba is one of the leading
internet and e-commerce corporations worldwide, valuable insights are provided. Moreover, Alibaba’s
international digital marketing practices, underlying strategies, as well as adaptive capabilities are
systematically analyzed. In addition, Alibaba’s competitive behavior is investigated and compared with
international companies and peers. In this context, the standardization versus adaptation paradigm is
also revisited.
Chapter 6
Activism in the Digital Age: Social Movements Analysis Using User-Generated Content in Social
Media..................................................................................................................................................... 99
Laura Holgado-Ruiz, Rey Juan Carlos University, Spain
José Ramón Saura, Rey Juan Carlos University, Spain
Beatriz Rodríguez Herráez, Rey Juan Carlos University, Spain
Social movements have been transformed in the last decade by social networks, where the dynamics of
the social protests have evolved and have been structured and viralized through social media. They are
no longer just conversations between activists that stay on social platforms. The cyberactivism that takes
place on Twitter or Instagram can also play a significant role in general society by influencing government
decision making or shaping the relationships between citizens. In this chapter, the authors explore the
main activist movements that took place in social media in the last decade: Occupy, BlackLivesMatter,
and MeToo. The proposed approach used in this study facilitates the comparison of each movement while
focusing on the user-generated content in social media. This study suggests the presence of four major
categories to frame the content generated by the activists. The chapter concludes with the identification
of three different approaches to the research of a future research agenda that should be considered for
the study of the social movements from the UGC theory framework.
Chapter 7
Understanding Google Ads Metrics for SME...................................................................................... 123
Belem Barbosa, University of Porto, Portugal
Zaila Oliveira, Unichristus, Brazil & Unifametro, Brazil
Sandrina F. Teixeira, CEOS, ISCAP, Polytechnic of Porto, Portugal
Vasco Paço Gomes, University of Aveiro, Portugal
Despite its popularity, search engine advertising is a particularly complex and demanding technique.
One of the main challenges for Google Ads managers is to adequately monitor performance. Indeed,
the literature identifies a plethora of metrics to measure the success of a search engine ads campaign.
One research question arises: What are the metrics adopted by small and medium-sized companies to
measure the performance of a Google Ads campaign? This chapter includes a mixed-method study with

digital marketing professionals experienced in managing Google Ads campaigns for Portuguese SMEs.
Interviews helped highlight the main difficulties faced by SEM’s Google Ads’ managers and to identify
the performance measures they mostly control. Then, a survey enabled to analyse the association between
performance measures and campaigns’ perceived success. The insights produced by this chapter are
particularly interesting for researchers, teachers, business managers, and digital marketing professionals,
as it presents important clues on measuring the effectiveness of Google Ads campaigns.
Chapter 8
Big Data Optimization in Zara: How Zara Will Optimize Its Process With Big Data Due to the
COVID-19 Situation............................................................................................................................ 145
Paula González-Padilla, Rey Juan Carlos University, Spain
Carlota Galán Zori, Rey Juan Carlos University, Spain
This chapter will address the issue of online sales in the fast fashion sector, specifically Zara, the flagship
brand of the Inditex textile group. Since 2012, Zara has been working on a plan to close, restructure, and
optimize its physical shops, a process that was accelerated in 2017, and which has been affected by the
global pandemic that began in early 2020. These two events have caused online sales to exponentially
rise with, in turn, the percentage of returns. This is the objective of this chapter: to analyze where Zara
is in terms of online sales and returns and how, through digital marketing and the application of tools
such as big data, it can reduce the large volume of online returns that it has to deal with.
Chapter 9
Confirming Digital Marketing Model Innovation Design: SEM in Post-COVID Social Impact
Startups, Mexico.................................................................................................................................. 158
Juan Mejía-Trejo, Universidad de Guadalajara, Mexico
The COVID-19 pandemic is an unprecedented event that has ravaged emergent economies like Mexico
seriously. In this sense, several sectors like social impact startups (SIS) are called to participate in actions
to recover more quickly in their operations, income, and competitiveness in the post-COVID era. In fact,
digital marketing campaigns are alternatives for the Mexican SIS to raise its competitiveness again. Hence,
this study aims to confirm the digital marketing model innovation (DMMI) through covariance-based
structural equation modeling (CB-SEM) applied on a survey of 180 Mexican SIS during Dec-2020 to
Feb-2021. The study´s value is the model`s validity of DMMI and its capability to determine digital
marketing strategies to overcome emergency situations like COVID-19.
Chapter 10
Do Spanish Family SMEs Make Appropriate Use of Their Organizational Websites? An Analysis
of Family Firms’ Brand Promotion and Website Quality Level.......................................................... 172
Rubén Martínez-Alonso, University of Almería, Spain
María J. Martínez-Romero, University of Almería, Spain
Daniel Rodríguez-Rodríguez, University of Almería, Spain
The aim of this chapter is to analyse how family SMEs use digital media, particularly organizational
websites, to disseminate information about their products, services, history, etc. To this end, the authors
perform a descriptive analysis of 32 organizational websites from privately owned family SMEs located
in the region of Andalusia (Southern Spain), emphasizing two differentiating strategies: promotion of
the family firm brand and website quality level. On the one hand, the findings show that family firms are

to some degree reluctant to promote their family firm brand on their organizational websites, with the
indicator of being a family firm as the most communicated. On the other hand, the findings reveal that
family firms tend to develop organizational websites with an adequate quality level; however, the inclusion
of components, such as FAQ or help sections or a bilingual option, need to be considered in the future.
Chapter 11
Adapting Digital Strategies to a New Era: A Delphi-Based Analysis in the Fashion Industry........... 192
Rocío López Muniesa, Rey Juan Carlos University, Spain
Jose Ramón Saura, Rey Juan Carlos University, Spain
Eloísa Díaz-Garrido, Rey Juan Carlos University, Spain
Fashion brands are continuously reinventing themselves to adapt their business strategies to emerging
markets. In this paradigm, digital marketing becomes an essential tool for communication to the target
audiences online. In digital ecosystems, a new way of sharing information is taking place in which
brands interact with users to increase engagement and brand awareness. Accordantly, the objective of the
present study is to explore what is the evolution of digital marketing and how it has affected the strategies
applied by fashion brands on digital ecosystems after the COVID-19 pandemic. The research develops a
Delphi method with the participation of seven fashion digital marketing experts, whose conclusions and
analysis of results will allow future research to be linked to the objectives of the research. The results
propose and discuss nine future directions and four research proposals focused on digital marketing in
the fashion industry. In the future, these proposals may be used by research or fashion marketers as a
starting point for future research studies and practice.
Section 3
Case Studies: Advances in Digital Marketing Strategies Applied to Industries
This section presents case studies and future research lines for the digital marketing sector focused on
data analytics. The case studies encompass approaches to different business industries to, finally, show
the pros and cons of developing digital strategies according to the characteristics of each case.
Chapter 12
How Digitalisation Is Influencing Traditional Food Restaurants in the Management of Their
Marketing Strategies............................................................................................................................ 211
Juan-Gabriel Martínez-Navalón, Rey Juan Carlos University, Spain
There is no denying that digitalisation is a new revolution. At a time when technology is taking over all
aspects of society in an exponential way, not introducing it into the management of companies is a clear
mistake that could lead to their disappearance. But we cannot forget that the incorporation of technology
is a challenge for any type of company and sector. This study analyses the importance of digitalisation
in traditional restaurants and its influence on the management of marketing policies using case study
analysis. Therefore, the aim of this study is to show how digitalisation for order management in these
companies can also be applied to marketing policies. This is because, by monitoring and storing the
customer’s behaviour in the ordering process from start to finish, it is possible to know their preferences
and searches in a more concrete way. Such information enables the company to maximise its resources by
applying policies designed to meet customer needs, as well as to design targeted advertising to increase
the chances of success of the advertising campaign.

Chapter 13
Personal Brand Benefits of Social Media Use for Researchers: A Case Study................................... 226
Alberto Prado Román, Rey Juan Carlos University, Spain
Iria Paz-Gil, Rey Juan Carlos University, Spain
Miguel Prado Román, Rey Juan Carlos University, Spain
Social networks are a very relevant tool for businesses to connect efficiently with many users at the same
time. It means that in the second decade of the 21st century, companies have strengthened their strategies
to expand their influence. In the higher education context, social media can help develop teaching
strategies. Nevertheless, are they also relevant to expanding the professional capacity of researchers?
Given this, this research aims to determine whether they are relevant within the research field and how
they use them according to the researchers’ position and the professional objectives set.
Chapter 14
Digital Customer Journey in the Luxury Hotel Experience: A Case Study Approach........................ 246
Jonathan Gomez Punzon, Rey Juan Carlos University, Spain
Digitalization is one of the biggest changes in our fast-moving world. There are a lot of digital innovations
in many different industries, which are also affecting the hospitality industry. Taking into consideration
that travel and tourism is all about the combination between a wide range of experiences and on-site
benefits, the implementation of the digitalization in this industry is completely necessary. As a part of
the travel and tourism industry, the luxury hospitality segment is continuing to grow more than what
was expected, reaching figures of big spending and revenue regarding luxury hospitality bookings, and
it is even expected to grow more than 60% by 2026. Luxury hospitality brands have a lot of upcoming
opportunities to generate discussions through digital tools and innovations, not only about bookings,
but also covering the whole customer journey.
Chapter 15
Approach to Social Media Marketing Strategies in Different World Regions: A Descriptive Study.. 261
Luis Matosas-López, Rey Juan Carlos University, Spain
Roberto Baelo Alvarez, University of León, Spain
The present study, using a sample of university organizations from different world regions, aims to
provide an overview of social media marketing strategies used in different geographical locations. For
this purpose, the authors conducted a descriptive study of the communication patterns implemented
by university institutions in four regions: Africa and the Middle East, North America, Latin America,
and Europe. The study, which adopts a comparative format, contrasts the findings obtained in each of
the aforementioned regions, highlighting the existence of both similarities and differences in the social
media marketing strategies of the organizations observed. In line with previous research, the authors
took Twitter as the social media platform to be monitored.
Compilation of References................................................................................................................ 286
About the Contributors..................................................................................................................... 334
Index.................................................................................................................................................... 339
xiv
Preface
In the last decade, the Internet has dramatically changed how organizations and companies establish
and develop their business strategies in the digital ecosystem (Pastor-Satorras & Vespignani, 2007).
In this context, digital marketing has become one of the main domains for international business and
commercial communication (Flanagin, 2020). Specifically, the increase in the use of new technologies
and connected devices on the international level has led companies to use digital marketing strategies to
promote their products and services on all types of devices, including laptops, cell phones, smart TVs,
as well as in social networks (Bharadwaj et al., 2013).
The growing use of both digital marketing techniques and social networks has opened up novel op-
portunity for companies to collect and manage large amounts of data. These data, which include users’
interaction with web pages, search habits, and so forth, reflect users’ purchasing decisions and digital
behavior (Saura et al. 2021).
As digital marketing has become one of the most important communication channels for companies,
data-centric strategies have started to be used as key tools to improve decision-making, activity optimi-
zations, and economic performance (Morabito, 2014).
In parallel, recent decades have witnessed the growing use of social networks (Wai Lai and Liu,
2020). The resultant link between the increased use of connected devices, digital marketing strategies to
attract online leads, and the use of advanced data analysis techniques (Stone and Woodcock, 2014) has
allowed companies to adapt to a new ecosystem where prerequisites of a company’s business success on
the Internet are data analysis, user segmentation, exploration of new business models (Flanagin, 2020).
Furthermore, the approach of data sciences techniques to digital marketing (Saura, 2020) has enabled
digital marketing to take advantage of data-centric technologies and strategies, as data sciences tech-
niques can focus their user behavior predictions on digital marketing techniques (Langan et al., 2019).
In summary, in the new data-driven decision-making era, the success of promotional and sales actions
on the Internet largely depends on digital marketing and data analysis (Stone and Woodcoc, 2014). Digital
marketing strategies enable companies to trace users’ behavior and adapt their products and services ac-
cordingly to better match users’ intentions and expectations. To this end, appropriate data and behavior
prediction algorithms analyzing the results of digital marketing campaigns are used to optimize those
campaigns and to increase their efficiency (Ribeiro-Navarrete et al., 2021).
The above underscores relevance of digital marketing and data-driven decision-making remains
relevant for the creation and management of online knowledge in organizations.

Preface
DIGITAL CHALLENGES
As mentioned above, appropriate management and development of data-centric digital marketing

techniques is vital for the success of companies’ strategies in digital ecosystems (Saura et al., 2021).
Furthermore, the proper management of data from digital marketing techniques is essential in terms of
ensuring optimization and automation of digital marketing to improve return on investment (ROI) of
companies in the future.
In this context, in order to understand the main challenges of advanced digital marketing techniques
in the data-centric era, it is necessary to address the following five challenges:
• Exploring the use of the main data-centric digital marketing techniques

• Establishing the main pillars for successful digital marketing techniques using data-centric tools.
• Proposing future lines of research for practical and theoretical development of advanced digital
marketing strategies that work with data sources from the Internet
• Linking the business success to the use of social media marketing, digitalization, Big Data, among
others, to the know-how of companies working with digital marketing.
• Understanding practitioners’ experiences with the use and development of data-centric digital
marketing techniques
Several previous studies have already sought to explore the use of the main data-centric digital
marketing techniques (Saura et al., 2021; Ribeiro-Navarrete et al., 2021), from the analysis and collec-
tion of data techniques to the newest digital marketing approaches. Nowadays, the use of the Internet
has become a habit that can be analyzed, understood, and studied with these techniques. The resultant
knowledge can be meaningfully used to appropriately design advertisements, increase engagement, or
predict behaviors and purchases (Johnsen et al., 2017).
From the digital marketing investor’s perspective, understanding performance with the use of data-
centric strategies such as investments in different social networks or digital platforms is essential for the
development of trust in a highly competitive ecosystem.
While, from the traditional marketing point of view, there are different strategies to attract and maintain
customers (Saura et al., 2019), from the digital perspective, companies should understand what main
digital marketing techniques can be used to segment messages and promote the development of users’
acquisition and loyalty actions (Flanagin, 2020). Accordingly, priority should be given to new digital
marketing techniques that can work with data sciences and link them to a traditional perspective, in
which users can be impacted with advertising actions (Bharadwaj et al., 2013).
Upon identification and analysis of the main data-centric digital marketing techniques, the fundamental
pillars for their success must be established. As argued by several authors, from the theoretical point of
view, digital marketing strategies can be developed for different purposes (Stone and Woodcock, 2014).
However, in order to succeed in the performance development in digital ecosystems, companies should
also properly follow justified guidelines. Here, essential are specific objectives, such as increasing brand
awareness and sales, optimizing ROI, or attracting new customers (Tiago et al., 2014; Saura, 2020).
Furthermore, taking into account both the digital ecosystem approach and the traditional one, the
study of the industries where the activities are going to be developed and the success in the investments
in such business, can determine the fundamental pillars for the choice a digital marketing technique or
another (Saura et al., 2019).
xv
Preface
As argued by Langan et al. (2019), in order to learn how these actions should be correctly optimized
based on both industry and specific circumstances, digital marketing and data-centric decision-making
should first be analyzed. In this respect, promising approaches in terms of improving digital marketing
techniques are Artificial Intelligence (AI) linked to data automation and monitorization of data and ac-
tions in digital ecosystems, as these approaches are particularly helpful for data analysis and optimization
(Johnsen, 2017).
Accordingly, based on the study of business cases, it is important to understand and propose future
lines of research that target practical and theoretical development of data-centric decision-making (Ri-
beiro-Navarrete et al., 2021). At present, data sources available on the Internet are getting increasingly
larger, which has made user-generated content (UGC) and user-generated behavior (UGB), also known
as user-generated data (UGD), the main sources of data (Saura et al., 2020). To date, UGD has been
extensively used to understand the structure and development of digital marketing techniques, as well as
to train algorithms based on machine learning or Big Data to predict user actions in digital environments.
DIGITAL MARKETING DATA-BASED STRATEGIES:

FROM THEORY TO PRACTICE
In previous research, it has been frequently argued on the need to increase technical and knowledge
skills in digital marketing by members and employees in charge of the development of these strategies
in companies (e.g., Tiago and Veríssimo, 2014).
Therefore, of a particular relevance for practitioners is to have first-hand information through the
use of data analysis techniques and digital marketing techniques, such as social media marketing, SEO,
search engine rankings, inbound marketing, content marketing, branded content, video marketing, or
influencer marketing, and so on (Bock et al., 2017).
Similarly, on the managerial level, it has been argued that the top management of companies still
does not effusively support the use of digital environments (e.g., Sheikh et al. (2018). Therefore, there
is an urgent need to effectively combine traditional and digital marketing investments according to the
sales channel used by companies to develop their activities. Therefore, obtaining information from case
studies and experiences from other companies to define and monitor appropriate actions is a relevant
tool to develop innovative strategies and optimize companies’ digital processes (Ardito et al., 2019).
Of note, practitioners’ experiences are biased by different industries with specific characteristics
and influencers. Yet, as argued by Stone and Woodcock (2014), experiences on the development of
data-centric digital marketing techniques and their use should be shared among companies so that all
companies can benefit from the insights and knowledge accumulated in the field.
ORGANIZATION OF THIS BOOK
Taking into account the abovementioned considerations, this book is organized into 15 chapters. A brief
description of each of the chapters is as follows.
Chapter 1 reviews the role of Big Data Analytics and Marketing Analytics techniques in a digital
ecosystem characterized by digital marketing strategies. This chapter undertakes a systematic literature
xvi
Preface
review of the contributions in Big Data Analytics and Marketing Analytics. Based on the results, the
authors identify and discuss a total of 26 key indicators to measure actions in both areas.
Furthermore, Chapter 2 presents a comparative analysis of the main differences between traditional
and digital marketing to understand the uses that companies make of the techniques encompassed in
both perspectives. The literature review reveals that traditional and digital marketing are important for
companies, so they should be combined and developed to obtain satisfactory results in their marketing
campaigns.
The next contribution, Chapter 3, discusses the role of Big Data in digital environments from the
theoretical and practical points of view. Therefore, this chapter analyzes definitions, components, and data
sources related to Big Data that can be used in digital marketing by companies to boost their user acquisi-
tion strategies. Finally, based on the results of the review, several uses and best practices are presented.
Chapter 4 proposes an analysis of loyalty digital programs in Internet business. The authors describe
membership’s evolving characteristics over in a B2B scheme launched in the US metal-cutting tools
manufacturer customer base. The study identifies the main particularities and implications of B2B
loyalty digital programs.
From a different perspective, Chapter 5 focuses on the digital influence of social movements in social
networks. This chapter highlights the role of social networks such as Twitter or Instagram and analyzes
the social movements such as Occupy, BlackLivesMatter, and MeToo. Based on the results, the authors
identify four content categories related to user-generated content (UGC) n social networks. In addition,
three further research directions on social movements using the UGC theory framework are identified.
Next, Chapter 6 studies the evolution and use of digital marketing techniques in the fashion industry.
Specifically, in order to identify the main digital marketing actions in this sector, the chapter develops
an exploratory approach with the review and the Delphi method to interview fashion industry experts.
Based on the results, the authors outline 9 future directions and 4 research proposals, which can be a put
into practice by companies focused on the digital ecosystem in the fashion industry.
Furthermore, Chapter 7 presents the case study of Alibaba and its international strategies based on
data-driven methods. This chapter aims to understand how data-driven methods, such as consumer and
behavioral analysis or international digital marketing practices, are developed. To understand Alibaba’s
strategies in a dynamic and changing digital ecosystem, the authors explore how Alibaba develops strate-
gies on the international level, as well as compare the company with its main competitors.
Next, Chapter 8 reviews Google Ads to analyze the main challenges that managers should appro-
priately monitor in the performance of their campaigns. The authors present the main metrics used by
small and medium-sized enterprises (SMEs) to measure and monitor the digital advertising actions by
using Google Ads. Finally, using a survey, the authors analyze the association between performance
measures and campaigns’ perceived success.
The next contribution to this book, Chapter 9, reviews the digital strategy carried out by Zara. Specifi-
cally, the authors consider the data-centric strategies and decision-making performed by this international
fashion company. Among the conclusions, the authors highlight the initiatives to adapt the company’s
stores to a hybrid model where the company’s digital presence is gaining strength to increase sales. The
authors also emphasize flexibility and support for new innovative initiatives to adapt Zara’s business
model to the new era based on data and Interned sales.
In addition, Chapter 10 studies a Digital Marketing Model Innovation (DMMI) using the Covariance-
Based Structural Equation Modeling (CB-SEM) on 180 social impact startups. Social impact startups are
reported to more quickly recover in their operations, income, and competitiveness in the post-COVID-19
xvii
Preface
era, which reinstates the importance of digital marketing and data analysis for boosting visibility in
companies. The authors conclude with highlighting the DMMI value and the analytics capabilities of
post-COVID-19 digital marketing strategies.
From a similar perspective, Chapter 11 presents an analysis of how family SMEs use digital media,
focusing the analysis on the companies’ websites. Specifically, the authors explore the promotion of
the family firm brands and website quality levels. The chapter concludes with a discussion of relevant
promotion strategies of family firms on the Internet.
Next, Chapter 12 reviews the use of social networks as a channel to connect companies with their
clients. The specific focus of this chapter is on higher education social networks and its use by researchers.
The results reveal several challenges to increase the visibility of researchers’ profiles in social networks
and their objectives in higher education digital strategies.
Assuming a historical perspective, Chapter 13 reviews the digitalization process and its evolution
linked to digital innovations. This chapter focuses on the hospitality sector to understand how digitali-
zation can improve the processes in this area. The authors analyze the customer journey related to the
digitalization processes in hospitality and investigate its influence on the increase of tourism.
Furthermore, Chapter 14 presents an overview of the use of social networks by universities across
the globe. The authors compare the use of tools linked to social networks and the messages shared by
faculty to establish a common action protocol. The results identify similarities in social media marketing
strategies and highlight the use of Twitter as the basis for the use of social networks in this area.
Finally, Chapter 15 explores the use of digitalization in traditional restaurants and its influence on
marketing management strategies. The results of this chapter suggest that monitored strategies added to
restaurant marketing management improve processes, anticipate demand, and facilitate interpretation
and prediction of customer behavior. The chapter concludes with a discussion of implications and future
lines of research.
Jose Ramon Saura

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Flanagin, A. J. (2020). The conduct and consequence of research on digital communication. Journal of
Computer-Mediated Communication, 25(1), 23–31. doi:10.1093/jcmc/zmz019
xviii
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Johnsen, M. (2017). The future of Artificial Intelligence in Digital Marketing: The next big technologi-
cal break. Maria Johnsen.
Langan, R., Cowley, S., & Nguyen, C. (2019). The state of digital marketing in academia: An examina-
tion of marketing curriculum’s response to digital disruption. Journal of Marketing Education, 41(1),
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Pastor-Satorras, R., & Vespignani, A. (2007). Evolution and structure of the Internet: A statistical phys-
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collection: Assessing pandemic surveillance technologies to preserve user privacy. Technological Fore-
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Saura, J. R., Palacios-Marqués, D., & Iturricha-Fernández, A. (2021a). Ethical Design in Social Media:
Assessing the main performance measurements of user online behavior modification. Journal of Busi-
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Saura, J. R., Ribeiro-Soriano, D., & Palacios-Marqués, D. (2021). From user-generated data to data-driven
innovation: A research agenda to understand user privacy in digital markets. International Journal of
Information Management, 102331. Advance online publication. doi:10.1016/j.ijinfomgt.2021.102331
Saura, J. R., Rodriguez Herráez, B., & Reyes-Menendez, A. (2019). Comparing a traditional approach
for financial Brand Communication Analysis with a Big Data Analytics technique. IEEE Access:
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ACCESS.2019.2905301
Sheikh, A. A., Rana, N. A., Inam, A., Shahzad, A., & Awan, H. M. (2018). Is e-marketing a source of
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Stone, M. D., & Woodcock, N. D. (2014). Interactive, direct and digital marketing: A future that depends
on better use of business intelligence. Journal of Research in Interactive Marketing.
Tiago, M. T. P. M. B., & Veríssimo, J. M. C. (2014). Digital marketing and social media: Why bother?
Business Horizons, 57(6), 703–708. doi:10.1016/j.bushor.2014.07.002
Wai Lai, I. K., & Liu, Y. (2020). The Effects of Content Likeability, Content Credibility, and Social Media
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xix
Section 1
From Literature to Theory:
Reviews, Concepts, and
Definitions Linked to Digital
Marketing
This section presents chapters that develop reviews on digital marketing strategies, concepts linked
to the use of data-centric strategies, and the main definitions that encompass the digital ecosystem of
advanced digital marketing strategies in a digital era.
1
Chapter 1
A Better Understanding of Big
Data and Marketing Analytics:
A Review
Francisco J. S. Lacárcel
Madrid Open University, Spain
Leticia Polanco-Diges
Felipe Debasa
ABSTRACT
Data mining and analysis is consolidating as a crucial practice in economic, educational, social, and
business sectors. In this context, this study aims to identify and categorize the main strategies, metrics,
and concepts that are derived from big data analytics (BDA) and marketing analytics (MA). This study
follows a systematic literature review (SLR) of important scientific contributions made so far in this re-
search area. The authors have identified through this study 13 key concepts related to big data analytics
and 13 related to marketing analytics, which are classified and categorized according to their applica-
tion in technologies or actions in digital marketing. The chapter concludes with a discussion between
theoretical and practical implications on the results for future researchers.
INTRODUCTION
Undeniably, the environment in which society is living is increasingly dynamic and changing, which
according to many studies is called VUCA. This acronym is used to reflect the Volatility, Uncertainty,
Complexity and Ambiguity of the world we live in (Kaivo-oja and Lauraeus, 2018; van Tulder et al.,
2019; Patnaik, 2020).
DOI: 10.4018/978-1-7998-8003-5.ch001
Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

A Better Understanding of Big Data and Marketing Analytics
In this context, the study and analysis of data is acquiring significant importance in most sectors,
especially when they occur in a digital environment. Big Data is known as the large amount of data that
it is generated in these ecosystems, and it is characterized by the accessibility to them, making data a
valuable material for those who are able to interpret and use them optimally (Fosso Wamba et al., 2018).
On the other hand, the field of data science and analysis are areas that are in continuous growth and
evolution. There is an increasing demand for analytical and data science profiles in most companies and
even institutions (Saura, 2020). Therefore, it could be said that the so-called Big Data Analytics (BDA)
is considered an important element of differentiation and competitive advantage over competitors when
analytics and data interpretation is done correctly (Akter et al., 2019).
However, the quantity of information collected by organizations is so large which make it sometimes
difficult to manage and obtain conclusive results. These results favor for the implementation of corporate
strategies, decision making, innovation or the improvement of user experience (Dudycz, et al., 2019).
The benefits of extracting data, analyzing them and obtaining the so-called insights have a very
significant impact on many areas and departments of companies, both large and SMEs (Saura et al.,
2021). In contrast, either for reasons of ignorance, or little interest, data analysis still does not have the
consideration it requires in the daily tasks and strategies of organizations (Camargo et al., 2018).
Understanding consumer needs and transmitting correct message to the right people is one of the
main tasks of Marketing, a simple but at the same time quite complex task. Consumers’ tastes, interests,
needs and concerns are constantly changing, closely linked to the aforementioned VUCA ecosystem in
which the society is living (Saura et al., 2019). Through data analysis it is possible to obtain insights
and make better strategic decisions focusing on the consumer as the main target, an important feature
of the so-called Marketing Intelligence (Lies, 2019).
It is through social networks, smart devices and other devices where most users provide a large
amount of data and expose personal information such as their interests or location (Anshari et al, 2019).
From a business point of view, these channels are key to be able to keep up with what to offer, how to
offer it and especially how to improve consumers’ lives in a more personalized way (Zhang et al., 2019)
Users are spending more and more time on the Internet, shopping online, but above all using social
networks as channel of interaction with friends, family and even with the brands (Kang and Yang, 2021).
Through these channels, it is possible to analyze and better understand customers or potential customers
based on two important drivers: User-Generated Content (UGC) (Saura et al., 2019) and User-Generated
Data (UGD). According to some studies, these concepts are key to the development of advanced digital
marketing strategies, therefore this explains the importance of effective data analysis (Saura, 2020).
As technology advances, new methods, techniques, or processes focused on data analysis such as
machine learning or data mining appear (Gutnik, 2021). This is a challenge for companies, who should
understand what the type of data most useful and which techniques are the most effective according to
the companies’ objectives and products/services offered (Akter et al., 2019). However, overcoming this
barrier is decisive, for example for the optimization of resources, content personalization and for the
creation of just-in-time strategies to differentiate themselves from the competition, hence the importance
of studying this topic.
The main gap that this chapter aims to cover is to provide contrasted and relevant information on the
main techniques used in data analysis in digital environments, as well as the most relevant indicators,
metrics or concepts that play a determining role in companies.
The chapter will be developed in the form of a Systematic Literature Review and will follow the fol-
lowing structure: In the second section, a theoretical framework will be developed where the relationship
2

between Big Data Analytics (BDA) and Marketing Analytics (MA) in digital business will be explained
based on the contributions of other authors. Then, the methodology will be carried out using PRISMA
approach. Then, the results will be analyzed and the chapter will end with a conclusion where theoretical
and practical implications will be provided.
THEORETICAL FRAMEWORK
The Importance of Big Data Analytics and

Marketing Analytics in Digital Business
As mentioned above, data represents an essential source of information in companies, and even in govern-
ments and other institutions (Löfgren and Webster, 2020). Big Data (BD) does not only refer to a mas-
sive amount of data, but it is also a concept that relates to the difficulties, challenges, capabilities and/or
skills related to its management and processing to obtain meaningful information (Dina Darwish, 2020).
According to Chandarana and Vijayalakshmi (2014), the importance of understanding the charac-
teristics and nature of big data allows for better analytics, and therefore better results or insights. These
authors state that big data is characterized by (i) volume, (ii) velocity, (iii) variety, (iv) veracity and (v)
value. However, other studies suggest that these same characteristics are in turn big data challenges
(Hajjaji et al., 2021).
More and more companies are seeking to understand big data in depth in order to overcome these
barriers that hinder the clear understanding of the data, hence the importance of Big data analytics. One
of the main benefits of big data analytics in companies lies in the creation of Competitive Intelligence
(CI) (Ranjan and Foropon, 2021). Moreover, according to Gupta (2018), big data analytics represents
a key factor in organizations to acquire a greater competitive advantage and obtain a better positioning
in consumers’ mind.
On the other hand, Big Data Analytics serves as a starting point in companies to be able to establish
processes, develop strategies and make decisions according to business objectives, thus developing the
Competitive intelligence mentioned above (Ranjan and Foropon, 2021).
It is in this context, the concept of digital marketing is becoming broader due to the constant imple-
mentation and advancement of new technologies such as IoT, Smart technologies or Artificial Intelligence
among others (Herhausen, 2020). Digital marketing is no longer only considered as a strategy or means of
communication, but it is also used as a tool for obtaining user data, which is often more profitable than a
simple purchase (Polanco-Diges and Debasa, 2020). This is where Marketing Analytics comes into play.
It is a fact that big data serves as a digital marketing support for the implementation of strategies and
new ways of marketing in digital ecosystems. Therefore, the concept of marketing analytics gets a great
prominence to achieve greater efficiency and profitability in the practices that are carried out especially
in the marketing department and other areas of the organization (Wedel and Kannan, 2016).
Through the strategies implemented in online ecosystems such as social networks, e-commerce or
websites, companies collect a large amount of information from their customers or potential customers.
Based on this information, future strategies will be oriented according to the results obtained, all done
in a more accurate, consistent and most importantly in a more consumer-centric way (Saura et al., 2017).
This is where the importance between digital marketing and marketing analytics lies.
3

According to Wedel and Kannan (2016), marketing analytics objective is the collection, manage-
ment and analysis of data to obtain insights, maximize effectively the resources and optimize Return on
Investment (ROI). In these processes intervene different techniques such as Natural Learning Processing
(NLP), Sentiment analysis (SA) o Machine Learning approaches (ML) among others (Saura, 2020; Sheth,
2021). As well as key performance indicators in marketing strategies (Saura et al., 2021). Moreover, it
is refered terms like User Generated Content (UGC), User Generated Data (UGD), electronic Word of
Mouth (e-WoM) o data privacy (Gutierrez et al., 2019; Saura, 2020; Karegar et al., 2020; Sarin, 2021).
In this way, the main objective is to identify and categorize the main strategies, metrics and data-
driven oriented concepts, as well as their classification according to the relationship between Big Data
Analytics and Marketing Analytics in order to acquire a better understanding. The originality of the
study lies in how the results are presented, not having been studied in this way before. This chapter is
presented as a Systematic Literature Review where the results will be interpreted in an exploratory way
and will answer the following Research Question: What is the relationship between Big Data Analytics
and Marketing Analytics?
METHODOLOGY
Data Extraction
The methodology used is a Systematic Literature Review (SLR), whose main objective is to be able to
answer the research question formulated above. The SLR approach consists of identifying and synthe-
sizing the contributions of other authors that are sufficiently conclusive (Snyder, 2019). For this, we
followed the systematic sequential process proposed by vom Brocke et al. (2009) and vom Brocke et al.
(2015). Finally, PRISMA statement will be applied to facilitate the preparation of the literature review
(Moher et al., 2015).
Three important databases were used to search for articles and research: Web os Science, Scopus and
Science Direct. The searches were performed following a similar structure proposed by Saura, (2020).
First, the key terms for our research were identified, then the databases were searched. For greater
precision of the results, the searches were filtered by collecting only those articles whose search terms
appeared in the title, abstract or/and keywords. In addition, only research considered as scientific articles
was taken into account. Tables 1 and 2 provide more details on the search terms used in the SLR process
and the results obtained from the databases.
Table 1. Terms used in the databases
Database Searched terms Fields

Web Of Science Big Data Analytics AND Marketing Analycis Title
Scopus Business Analytics OR Digital Marketing Abstract
Science Direct Keywords
Source: Self-elaboration
4

Table 2. Number of article classifications by results
Database Number of results Number of relevant results - 2º

Web of Sciences 126 13
ScienceDirect 64 2
IEEE Explore 48 3
Total 238 18
In the SLR process, it has been followed the guidelines described in previous studies, such as vom
Brocke et al. (2015) presenting an SLR through the PRISMA diagram (Moher et al. 2015). Figure 1
shows the steps that were followed to extract the most relevant articles according to the object of this
study, some articles were excluded as they did not contain search terms or on the other hand, those found
were not related to the present study (vom Brocke, 2015).
Figure 1. The SLR process
Table 3 shows the total number of items identified according to the search parameters of the SLR
process (Saura, 2020).
5

Table 3. Results of the SLR
Authors Journal Category

Hallikainen et al. (2020) Industrial Marketing Management Marketing
Management of Technology and
Choi et al. (2018 Production and Operations Management
Innovation
Xu et al. (2016) Journal of Business Research Marketing
Erevelles et al. (2016) Journal of business research Marketing
Benoit et al. (2020) Journal of Marketing Management Strategy and Management
Liao et al. (2019) Asia Pacific Journal of Marketing and Logistics Business and International Management
Ducange et al. (2018) Soft Computing Software
Mariani et al. (2020) Journal of Business Research Marketing
Sivarajah et al. (2020) Industrial Marketing Management Marketing
Dong and Yang (2020) Information & Management Management Information Systems
Indrakumari et al. (2020) Advances in Computers Computer Science (miscellaneous)
Ciampi et al. (2021) Journal of Business Research Marketing
Buhalis and Volchek (2021) International Journal of Information Management Marketing
Xu et al. (2016) Journal of Business Research Marketing
Saidali et al. (2019) Procedia Manufacturing Artificial Intelligence
Lamberti et al. (2017) IEEE Transactions on Industrial Informatics Information Systems
2020 IEEE International Conference on Engineering,
Mohamed and Weber (2020) Computer Science
Technology and Innovation (ICE/ITMC)
2020 International Conference on Electronics and
Ekka and Jayapandian (2020) Computer Science
Sustainable Communication Systems (ICESC),
ANALYSIS OF THE RESULTS
Following the methodology, it has identified the most frequent key terms used in each of the articles
found, which have been defined in Table 4.
Each of these terms are related to the search keywords “Big Data Analytics” and “Marketing Ana-
lytics”. However, some present a more marketing-focused approach and others on data science itself.
Thus, in Table 5 we have listed terms with a more data science-oriented focus and those that have a
more direct application in digital marketing and ecommerce. However, due to the relationship that exists
between the two we could conclude that the results belonging to Big Data Analytics are the first step to
obtain the Marketing Analytics terms.
Once this classification has been made, Table 6 classifies each of these terms belonging to Big Data
Analytics in different categories: Techniques, systems and technology.
In Table 7, the terms included in Marketing Analytics have also been categorized but this time as
follows: Software, metrics/KPIs, strategies and concepts.
6

Table 4. Key terms definition
Key terms Definition
Technology that is able to mimic human functions based on a large amount of massive data and through a series
Artificial Intelligence (AI)
of intelligent algorithms that are able to automatically learn patterns of behavior.
It consists of the provision of services on the Internet. Information, data, processes, servers or software are
Cloud Computing
stored in the cloud, where everyone can access them without the need for large infrastructures.
It consists of the continuous management of a portfolio of customers through the influence of big data analytics
Customer Relationship Management (CRM)
in order to build and maintain customer relationships to maximize profits.
Data Mining (DM) It is the process of extracting insights to solve a defined problem.
It is a combination of systems that allow the integration of different business operations (logistics, production,
ERP (Enterprise Resource Planning)
finance...) which share the same database.
The process by which it is possible to identify or verify a person’s identity using biometric technology. It is a
Facial Recognition
process of comparison and analysis of people’s facial features.
Geographic Information Systems (GIS) Information system that integrates massive measurable data in a global context.
It is a navigation system that allows knowing the location of any object or person by means of the signals
Global Positioning System (GPS)
emitted by satellites.
It is considered a method within Artificial Intelligence (AI) that deals with the development and evaluation of
Machine Learning (ML)
algorithms for the extraction of patterns in a large dataset.
Marketing practice that consists of analyzing consumer touchpoints during their customer journey to identify
Marketing Attribution or Multi-touch attribution
which tactics are most cost-effective in achieving the company’s goals.
Marketing Behavior Marketing strategy that relies on the specific behaviors and actions of consumers and users to make decisions.
Marketing Intelligence Refers to the extraction, understanding and analysis of data in which the marketing department is involved.
Term used to describe the analysis and improvement of the effectiveness and efficiency of marketing based on
Marketing Performance Measurement
the correct alignment of activities, metrics and strategies with business objectives.
Marketing Touchpoint Touch points where a potential customer or potential client has some kind of interaction with the brand.
The technology that allows the transmission of data, voice and video through wireless devices without the need
Mobile Computing
to be physically connected.
Discipline based on Artificial Intelligence that is in charge of understanding, interpreting and manipulating
Natural Language Processing (NLP)
human language.
Databases that work with unstructured databases and do not address scalability and performance issues of big
NoSQL
data.
Online Consumer Reviews (OCR) Refers to the evaluations and opinions left by users in an online ecosystem about products, services, or brands.
It is an online advertising model whereby the advertiser pays a certain amount of money each time a person
Pay-Per-Click (PPC)
accesses a certain website through that advertising link.
Refers to the different cycles or phases that sales of a given product undergo during the time it remains on the
Product Lifecycle Cycle (PLC)
market.
Return On Investment (ROI) Metric used to evaluate the profitability of investments with respect to their cost.
It consists of the use of NLP and computational linguistics to find and extract information about positive,
Sentiment Mining (SM)
negative or neutral feelings in a text.
It is the combination of different intelligent technologies such as machine learning, or natural language
Social Media Analytics (SMA)
processing for the extraction of social big data.
A set of digital marketing strategies consisting of the use of social media channels to achieve the company’s
Social Media Marketing (SMM)
goals.
Programming language that works with structured data and allows faster and easier manipulation of elements in
SQL
databases.
A set of techniques that consists of the exploration of large amounts of text data with the objective of
Text Mining (TM)
discovering patterns or trends that explain behaviors in a text.
It is the content generated by the user on social networks or other digital platforms. The content can be
User-Generated Content (UGC)
distributed through comments, videos, opinions, reviews...
7

Table 5. Classification of terms in big data analytics and marketing analytics.
Big Data Analytics Marketing Analytics

Artificial Intelligence (AI) Marketing Performance Measurement
Cloud Computing Return On Investment (ROI)
Data Mining (DM) Product Lifecycle Cycle (PLC)
Facial Recognition Social Media Marketing (SMM)
Geographic Information Systems (GIS) Enterprise Resource Planning (ERP)
Global Positioning System (GPS) Social Media Analytics (SMA)
Machine Learning (ML) Customer Relationship Management (CRM)
Mobile Computing Marketing Intelligence
Natural Language Processing (NLP) Marketing Behavior
NoSQL Marketing Touchpoint
Sentiment Mining (SM) Marketing Attribution or Multi-touch attribution
SQL User-Generated Content (UGC)
Text Mining (TM) Online Consumer Reviews (OCR)
Table 6. Categorization of big data analytics terms
Techniques Systems Technology

Data Mining (DM) Geographic Information Systems (GIS) Artificial Intelligence (AI)
Machine Learning (ML) Global Positioning System (GPS) Facial Recognition
Natural Language Processing (NLP) NoSQL Mobile Computing
Sentiment Mining (SM) SQL Cloud Computing
Text Mining (TM)
Table 7. Categorization of marketing analytics terms
Software Metrics/KPIs Strategies Concepts

Enterprise Resource Planning Marketing Performance Social Media Marketing
Marketing Behavior
(ERP) Measurement (SMM)
Customer Relationship
Return On Investment (ROI) Social Media Analytics (SMA) User-Generated Content (UGC)
Management (CRM)
Marketing Attribution or Multi- Online Consumer Reviews
Marketing Intelligence Marketing Touchpoint
touch attribution (OCR)
Product Life Cycle (PLC)
8

DISCUSSION
The study compiles information from other authors by classifying and organizing it in a clearer and more
understandable way. In this way, the study is based on the results of others research that have already
been approved by the academy.
However, it is identified terms that have appeared less frequently showing relationship between BDA
and MA, such as the term Pay-Per-Click (PPC). The authors Xu and Ramírez (2016) state that big data
analytics plays an important role in online advertising strategies, since it is possible to obtain actionable
information to improve advertising investments.
On the other hand, Buhalis and Volchek (2021) consider that the Geographic Positioning System
(GPS), allows to know the location of customers and their activities. Its relationship with Big Data Ana-
lytics provides more detailed information about users in order to be able to carry out more personalized
digital marketing strategies depending on the location where they are. In addition, the combined use of
GPS and GIS may be useful to identify virtual market zones (Ducange et al., 2017).
Regarding the results of the study, Liao and Hsu (2019), consider that the application of big data
analytics in Social Media Marketing provides very relevant and accurate information about users’ mo-
tivations, interests, opinions. Thus, they state that marketers can develop strategies and make decisions
based on the results obtained from big data analytics when applied in social media marketing.
Mariani and Fosso (2020) explain that OCR together with big data analytics has a great relevance to
predict market potential or demand in digital networks such as social media or review platforms. This
is where specific Big Data Analytics techniques come into play such as Natural Language Processing
(NLP) that could in turn help to understand and comprehend the use of human language in these types
of channels (Ducange and Mezzina, 2018).
Moreover, according to Xu et al., (2016), Text Mining, Sentiment Mining, and Data Mining techniques,
which are important areas of BDA, are techniques that when they are applied in marketing strategies are
very useful tools for today’s managers.
In this context, the term UGC mentioned Dong and Yang (2020) could be studied and analyzed with
these techniques to identify and understand the preferences, interests or feelings that consumers have
regarding a brand, product or service.
CONCLUSION
This chapter provides an overview of the current state of the theoretical and empirical literature on Big
Data Analytics and Marketing Analytics. The continued development and growth of the Internet, e-
commerce and big data imply a shift in the paradigm and thinking of marketing practices, as traditional
parameters have had to evolve to new digital marketing techniques, performance analysis and measure-
ment processes.
In this systematic literature review, 26 key terms have been identified. Subsequently, these have been
classified, on the one hand, terms related to Big Data Analytics and on the other hand, to Marketing
Analytics.
Firstly, Big Data Analytics results have been specifically classified according to; techniques, systems
and technology. Secondly, the results related to Marketing Analytics have been grouped between; Soft-
wares, metrics/KPIs, strategies and concepts.
9

Based on these results and responding to the main research objectives, it has been obtained through
our results 13 key terms about Big Data Analytics. Through our classification it has been able to distin-
guish between; 5 techniques, 4 systems and 4 technology.
On the other hand, based on the 13 results on Marketing Analytics, the results obtaining at the end
have been classify into; 3 software, 4 Metrics/KPIs, 3 Strategies and 3 Concepts.
The results obtained make it possible to answer the Research Question formulated at the beginning
of the research: What is the relationship between Big Data Analytics and Marketing Analytics?
In an exploratory way we could say that Big Data Analytics and Marketing Analytics have a strong
common ground. Both concepts share data analytics as the main engine for obtaining insights, however,
in the case of Marketing Analytics it has a much more specific and determined application since it is
oriented to achieve specific objectives, and it is less complex with respect to Big Data Analytics.
Until now, Big Data Analytics techniques, technologies or systems (table 6) are not used as a funda-
mental axis in digital marketing strategies, however, they rely on them in their simplest form to improve
their (i) strategies, (ii) to evaluate their metrics, (iii) to understand new concepts such as UGC or (iv) to
incorporate them into software that serve as daily management tools as in the case of CRM (see table 7).
Theoretical Implications
The results obtained from the research might help the academia to understand, categorize and study the
concepts related to BDA and MA in a different way. Thus, as new terms appear, as technology advances,
and as new systems, techniques or strategies are created, the categorization developed in this study (table
4, 5, 6 and 7) would be updated and completed in parallel.
Practical Implications
The results will help future studies on data analysis focused on company strategies to improve their
marketing processes. From the applied perspective, the results of this study can be useful for commu-
nication agencies, digital marketing agencies, traditional companies and people who want to apply new
techniques, key concepts, strategies and much more in their marketing actions.
The present study classifies concepts between data mining and data analysis with a directly related
focus on marketing. The results of this study can be used by companies to improve their digital marketing
actions by analyzing the different strategies and actions that can be carried out. This will avoid creating
generic actions by companies in order to perform specific marketing strategies related to data analysis.
The increasing use of data mining strategies prior to marketing actions is linked to the development
and advances in technology. The rapid evolution of new data mining techniques and methods is leading
to new demands from companies and researchers.
The results of this study are key to new business models in research and development areas, and it
contributes to the effort to categorize metrics, techniques and applications of big data and marketing.
Going forward, this study will help researchers in both business research areas and technology sectors
related to big data and digital marketing.
10

Limitations and Future Research
In relation to the limitations of the study, the authors have found the classification of the terms into
categories to be complex, since there is no accurate and common classification for each term. This, each
author interprets and classifies them in the way that best interests him or her, depending on the research
approach or subject matter addressed.
On the other hand, other terms have been identified in the results, which have appeared less frequently,
and we consider of great interest for future research and thus improve and complete the studies of this
topic for academia and practitioners.
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16
Chapter 2
The Role of Big Data in
Digital Marketing
Neslihan Cavlak
Namık Kemal University, Turkey
Ruziye Cop
Bolu İzzet Baysal University, Turkey
ABSTRACT
Consumers perform their activities through digital channels more often as a result of technological
advancements where those advancements also allow marketers to reach excessive information about con-
sumers, store them, and use them whenever and however they consider necessary. These big data provide
businesses to understand the unmet demands and expectations of consumers and achieve a sustainable
business success. Despite the importance of big data analytics for marketing of businesses, research
on this issue is scarce. In order to contribute the literature, the purpose of this chapter is to reveal the
importance of big data in the digital marketing environment. In line with this purpose, a comprehensive
literature review including the definition, components, sources of big data, and the role of big data in
digital environments and the examples of businesses using big data is undertaken.
INTRODUCTION
With the Internet and digital media coming into our lives, the way businesses operate has radically
changed. More than 3 billion people in the world regularly use the Internet to search for products, enjoy
themselves, and find friends (Chaffey, 2019: 6). Consumers spend a large part of their lives in the digital
environment and mostly benefit from it, which causes both consumer behavior and marketing methods
of businesses to undergo a major change (Chaffey, 2019: 6). The development in digital technologies
causes the marketing activities of businesses, which aim to gain a competitive advantage by meeting the
demands and expectations of consumers and by complying with the present conditions, to transform each
day. From desktop to laptops to smart phones and tablet devices, there are technological platforms that
DOI: 10.4018/978-1-7998-8003-5.ch002

The Role of Big Data in Digital Marketing
consumers can easily use. The digital environment offers product, service, and price options of different
suppliers to customers, and provides them with a faster and an easier way to buy.
The increasing number of technological tools and the ability to perform operations, which normally
require different devices, using a single device in time has enabled new ways of marketing on the Internet.
In this way, a new user-centered, more measurable, global, and interactive digital marketing concept has
emerged (Piñeiro-Otero and Martínez-Rolán, 2016: 38). Digitalization of almost every aspect of business
operations today leads businesses to take action in order to create a sustainable value, increase business
performance, and gain competitive advantage by making use of big data and analytical tools (Wamba et
al., 2017: 357). With digital transformation, consumers are benefiting from more and more digital tools.
All kinds of operations performed by consumers using these tools are registered and stored. Datasets,
which are obtained through combination of the data that come from sensors and the Internet of Things
and created on web sites, social media, and mobile platforms and the data within the body of opera-
tions, have created the “big data” concept (Chen et al, 2014: 321). Big data indicates massive amounts
of data that have reached a greater audience thanks to the increasing number of operations performed
by consumers in the digital environment. As the volume of the data increased, the marketers started to
use not only the traditional operation data, but also advanced statistical modeling techniques developed
to analyze the data that are getting more and more complicated, including unstructured data such as im-
ages, conversations, pictures, audios, and videos obtained from digital and social media (Erevelles et al,
2016: 898). Social networks such as Facebook and Twitter have a great impact on customer decisions
and that’s why the leading businesses and brands endeavor to include information obtained from these
platforms into their marketing solutions (Moro et al., 2016: 3344; Amado et al., 2018: 2). The utility
of big data enables marketing tools to work in a stronger and a more innovative way (Grishikashvili
and Meadows, 2014: 27). Successful businesses regularly analyze their customer service records. Since
businesses always monitor the marketing environment, they are able to collect data regarding consumer
sensitivity to products and brands.
With the developments in big data, traditional marketing strategies are changing day by day (Fu et al.,
2020: 515). In the recent years, the use of data sciences, which makes it easier for businesses to reach
information through massive data clusters they obtain from the digital marketing environment and to
create decidable and actionable insight, has significantly increased (Saura, 2020). As new technologies,
channels, and consumption patterns become widespread, it gets more complicated to understand modern
consumer behavior. Also technological developments enable marketers to reach rich consumption data
with greater volume, velocity, and variety. These rich and new information resources (Big Data) allow
marketers to notice some elements they missed out or could not understand regarding consumer behavior.
As the data become richer, marketers are able to notice new gaps better and develop various approaches
to consumer behavior (Erevelles et al, 2016:900). Consumers live in a digital world where the data is
abundant and rapid technological advancements are experienced. The ease of accessing digitally sourced
data and the availability of advanced technology that can analyze this data lead many industries to digital
transformation. Many global businesses recognize the potential of big data and believe that analyzing big
data clusters can increase competitiveness of businesses and help them shape their marketing strategy
decisions (Grishikashvili and Meadows, 2014: 26; Saura, 2021: 92). In the big data environment, busi-
nesses can access to all kinds of information about their customers. Businesses can monitor the changes
in customer behavior and learn the reasons for those changes by monitoring customer data in detail. Big
data allows businesses to improve their operations, develop better pricing strategies, evaluate customer
feedbacks more quickly, and increase productivity and efficiency through cost reduction (Lee, 2017:
17

300). In short, technology enables proper access to customer data, a better focus on customer relations,
and improvement of customer relations management (CRM) opportunities.
Today, although big data has become one of the most important developments in the field of digital
marketing and data processing, studies on this subject are limited. To bridge this gap in the literature,
this book chapter aims to reveal the importance of big data in the digital marketing environment.
Some studies reveal the importance of using big data in digital marketing. For example; Camilleri
(2020), presents that the data-driven technologies can transform the businesses’ into customer-centric
marketing. (Wamba et al., 2015; Sivarajah et al., 2020) states that; big data and social media analytics
in a participatory web environment make the business more profitable and remain sustainable through
strategic operations and marketing related business activities. Saura (2021), defines the methods of analy-
sis, their uses and performance metrics based on data sciences as used in the strategies and techniques
of digital marketing. (Sharma et al., 2014), discusses the potential of data and analytics to create value
in their study. Businesses can create value through the use of big data and business analytics (Gillon et
al., 2014; Mithas et al, 2013). As can be seen, the studies discourse the benefits of big data, but do not
reveal the big data issue in detail in the marketing literature.
The purpose of this study is, to reveal the importance of big data in the digital marketing environ-
ment. In line with this purpose, the definition, components, and sources of big data are reviewed in
the first part of the study while the second part is focused on the digital marketing concept. The role
of big data in digital environments is discussed in the third part and the fourth part includes examples
of businesses using big data in the digital environment. The analysis in this study is, based on second-
ary sources of data primarily scholarly articles, news articles and survey reports and social media. The
researchers made a literature review and interviewed with the experts in the field for their insights on
usage and implications of Big Data.
BIG DATA
With the technological advancements, consumers are performing all kinds of activities using more and
more digital channels, which allows businesses to reach plenty of information about consumers, store
that information, and use it whenever and however they deem necessary. Today, it is almost impossible
to reach customers through classic marketing methods. This is mostly because of these methods fail to
discover customer demands and expectations and to reach adequate information about them. In today’s
business environment, where the competition is quite strong, businesses mostly need to make much
better speculations and decisions. That is why analyzing the market properly is of great importance to
the future of businesses.
Today, businesses can easily get all kinds of information about consumers from different sources.
Businesses can reach structured data (such as customer relations management) from traditional databases
that belong to their customers or unstructured data (such as video, text, image, e-mail) obtained from
new communication technologies and user platforms (Lansley and Longley, 2016: 271). While structured
data is the data that can be obtained, processed, stored, and analyzed by businesses, unstructured data
is the data that is really challenging to obtain and process (Balducci and Marinova, 2018: 585). The use
of structured data is quite easy. Collecting and analyzing unstructured data enables businesses to gain
competitive advantage, increases efficiency, and helps create innovations (Eberendu, 2016: 50). The
increase in consumers’ social media usage, online shopping, and the use of databases such as customer
18

loyalty offers businesses new opportunities to measure and model consumer behavior (Lansley and
Longley, 2016: 271). It leads to an increase in the data sources of businesses and allows them to reach
plenty of information about consumers.
Big data can be defined in many ways. Manyika et al., (2011: 3) defines big data as datasets in such
volumes that are impossible for general database software tools to reach, store, manage, and analyze.
Big data is regarded as massive amounts of information clusters that provide data about consumers with
the help of sources like social network posts, geotagging, and sensor outputs (Johnson, 2012: 21). As
e-commerce becomes widespread, the effect of consumers’ online research on their decision making
process regarding consumer behavior is gradually drawing attention of more businesses (Neirotti et al.,
2016: 1133). The eye-tracking sensor technology, which is constantly improving these days, provides
businesses with different insights regarding the online shopping process of consumers. Analyzing the
data obtained from sensors that record consumer activities during online shopping, it is possible to
customize web pages, send personal advertisements, and perform marketing activities accordingly (Fu
et al., 2020: 515). With the technological advancements, businesses have less face-to-face contact with
consumers but are getting more and more information about them each passing day by carefully analyz-
ing big data clusters.
While businesses used to serve consumer demands and expectations in the past based on the databases
they designed themselves, all the digital tools consumers use today provide businesses with detailed
information about them. Today, almost 70% of marketing survey data of businesses is obtained from
online platforms while nearly 15% of them are collected through mobile phone surveys (Hair and Sarstedt,
2021: 2). A proper analysis of these data is very important for businesses to achieve a sustainable success.
Thanks to data, clear consumer insight can be gained and turned into a market advantage. Analytics
indicates the tools that help analyze the data and discover the hidden elements (Erevelles et al. 2016:
897). Today, businesses are able to get much more data. In order to fully understand the unmet demands
and expectations of consumers and to increase the profit of the business, it is necessary to benefit from
big data and analyze it carefully (Erevelles et al. 2016: 899). Using big data, as many other new infor-
mation technologies, businesses aim to reduce operational costs, make important improvements when
necessary, and discover the elements required to develop new products (Davenport and Dyché, 2013:
3). Making use of new information technologies to the full extent can be quite beneficial to businesses
in giving the best decisions in the digital age.
The digital data and the techniques used to analyze them have created a great revolution and transforma-
tion in marketing research. Massive amounts of data offer countless opportunities to marketing research
teams to speculate and explain consumer behavior better (Hair and Sarstedt, 2021: 1). Before making
strategic decisions, businesses should internalize those massive amounts of data they have by adopting
different perspectives and then analyze them. The rise of such big data obtained from digital channels
provides businesses with detailed information about the lifestyles, activities, and behavior patterns of
consumers, which gives businesses a chance to analyze the data for various types of modeling (Wamba
et al., 2017: 357). 2021 statistics indicate that 4.66 billion people worldwide use the Internet and 4.20
billion people use social media (https://datareportal.com/reports/digital-2021-global-overview-report).
The use of the Internet and social media is increasing day by day. People spend approximately 7 hours
a day on the Internet (https://datareportal.com/reports/digital-2021-global-overview-report). This is a
clear proof that the Internet is an indispensable element of people’s lives.
Consumers share many positive or negative feelings and thoughts about businesses and products on
forums, social media networks, and various platforms (Abbasi et al., 2016). This offers businesses a
19

chance to learn users’ opinions on many issues. Consumers usually want to have different and notable
shopping experiences. Thanks to these platforms, businesses that want their customers to have unique
experiences can reach information about them and follow strategies accordingly. Businesses that adopt
a customer-oriented approach are collecting and analyzing more and more data they obtain from various
sources to improve the efficiency of their operations (Côrte-Real et al., 2017: 379). As long as they are
supported with the right technologies and organizational resources, massive amounts of data can pro-
vide positive insight and a competitive advantage (Morabito, 2015). The leading technology companies
such as IBM and Hewlett-Packard make great investments in big data (Barton and Court, 2012: 79).
The primary reason for businesses to use big data is that it has the potential to change the operation of
businesses significantly (Wamba et al., 2015: 234). Businesses that benefit from data analysis to the full
extent and use it for their operations can increase their efficiency by 5% and their profitability by 6%
(Barton and Court, 2012: 79). That is why most businesses invest in data analysis.
Big data and the developments in data analysis contribute a lot to the efficiency of businesses’ com-
munication with their target customers. Classifying customers by their demographic structures and
implementing marketing strategies according to the demographic structure of customers are marketing
strategies adopted by businesses. It is known that age and sex characteristics have a significant effect on
consumption behavior and that is why analyzing the information in datasets about these characteristics
of consumers is very important to marketers (Lansley and Longley, 2016: 271). Businesses, on the other
hand, mostly benefit from geographic datasets as a way to understand and classify customers (Lansley
and Longley, 2016: 271). Such information enables businesses to work more effectively and productively
on marketing activities.
Data is now created and collected in much larger volume and velocity than ever in almost each area
such as management, health, engineering, and education (Duan and Xiong, 2015: 1, Côrte-Real, et al.,
2017: 379). Due to its operational and strategic potentials, proper analysis of big data is considered one
of the most important elements that increase business productivity and efficiency (Wamba et al., 2017:
357). There is a positive relation between big data practice and company performance (Germann et al.,,
2014), and big data analysis adds value to businesses (Sharma et al., 2014: 433). This is mostly because
businesses that perform big data analysis acknowledge themselves and the market, and develop strate-
gies accordingly.
There are five main components of big data platform. These are variety, velocity, volume, verifica-
tion, and value. Big data must be handled with an integrated perspective based on these five components.
Variety: The source of big data involves variety and abundance. Data can be structured, semi-structured,
and unstructured. The examples of structured data are browser or sensor data, records, files, and databases
while unstructured data can be text data, videos, images, and audio records (Erevelles et al, 2016: 898;
Jin et al., 2015: 59). Most of unstructured data is collected from social media where individuals mostly
share their information with their friends and family (Erevelles et al, 2016: 898). Collecting, storing,
and analyzing such data is quite challenging.
Since it involves various types and structures of data, it can be very difficult to process (Zhou et al.,
2014: 62). In order to process the variety of data, different methods and techniques should be adopted.
Structured and unstructured data variety might result in heterogeneousness, experimental diversity, and
statistical prejudice in data process (Wang et al., 2016: 104). While electronic tables and databases were
the only sources of data in the past, there are many data sources today such as e-mail, photograph, video,
tracking device, PDF, and audio recording.
20

For instance; Procter & Gamble performs big data analysis in order to increase its operational per-
formance by bringing together more than 100 specialists who can make supply chain, marketing, sales,
and consumer behavior analyses (Davenport, 2006: 4). Tata Motors analyzes more than 4 million text
messages each month including customer complaints, appointments, customer comments on new prod-
ucts, and customer satisfaction surveys (Agarwal and Weill, 2012: 35).
Volume: Volume refers to the size of data created by systems or tools per second (Zhou et al., 2014:
62). As the name suggests, big data is the information that involves multidimensional data fields and
is created from various sources and formats. The Internet plays a major role in people’s lives. Sharing
content, sending messages, commenting, and liking shared posts; people take a very active part on social
media. Being in constant progress all day long, this cycle creates datasets that are too big for traditional
databases to store. As more people use computers, data is gaining more volume day by day.
More than 2.5 quintillion bytes of data are created every day in the world (Alani, 2021: 1). While 95
million photographs and videos are posted on Instagram daily, 510,000 comments and 293,000 status
texts are shared by Facebook users per hour (https://www.forbes.com/sites/bernardmarr/2018/05/21/how-
much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/?sh=7a63738760ba).
With the other communication platforms including Snapchat, LinkedIn, YouTube, and Twitter, the volume
of data created in a day, or a second worldwide can be extremely large.
Velocity: Another dimension of big data is the continuity of data creation speed (Erevelles et al, 2016:
898). The crucial element in data velocity is the rate of data flow speed (Lycett, 2013: 381). Today, the
velocity of data production is quite high and it is getting higher day by day. Sharing contents and their
feelings and thoughts about almost everything on various platforms, social media users create data at a
very high speed (Harrigan et al., 2021: 2). In short, data that are obtained from each field are changing
and improving so fast.
Amazon is the leading of all businesses that benefit from big data. Using big data, Amazon launches
new products, makes deals with new suppliers, delivers products on time, and has no trouble gaining
new consumers (Davenport, 2006). Today, businesses can monitor consumers and analyze consumer
behaviors by following online clickstream data (Wamba et al., 2015: 236). Analyzing clickstream data
of consumers, businesses can reach a lot of information about them. Clickstream includes information
about the timing and order of pages viewed by a customer. Making use of big data analysis, even small
and medium sized enterprises (SMEs) can renew and improve their website design, develop effective sales
techniques, and build personalized product offer systems (Gandomi and Haider, 2015: 138). Moreover,
businesses can update the differences in customer behavior elaborately using data they obtain near real-
time (Manyika et al., 2011). The products that are viewed, displayed, added to bag, removed from bag,
bought, and returned by the customer can be followed by businesses instantly and easily.
Veracity: It refers to the quality and safety of data that are created and processed. The accuracy and
safety of collected data are crucial. It might be impossible to check the quality and accuracy of vast
amounts of data created each second. As the volume and size of data increase, so does the uncertainty.
Therefore, it is of great importance to have a high level of safety and quality of data obtained from vari-
ous sources (Wamba et al., 2015: 235). When it turns out that the collected data are not of sufficient
quality compared to other data and information, the analyses of the business can be misleading, which
has a negative effect on business success (White, 2012: 211). Thus, businesses must be careful with
data quality. A research by IBM has revealed that poor data quality costs many USA-based businesses
nearly 3.1 trillion dollars per year. However, almost 27% of the participants state that they are not so
sure how much of data they think are of poor quality are inaccurate (Rubin and Lukoianova, 2013: 4).
21

Value: Value refers to the extent to which the outcomes of big data analysis contribute to value cre-
ation economically (Wamba et al., 2015: 236). Collecting, storing, and processing big data can create
an important value for the world economy, increase the efficiency and competitive power of businesses,
and create an economic value for consumers (Manyika et al., 2011: 1). While big data has only recently
come into our lives as an extremely popular concept, many businesses are aware of the potential com-
petitive advantage that the effective use of this collected and stored information offers to the business
(White, 2012: 219).
As long as it is collected, stored, and analyzed properly, big data is an element that brings great ben-
efits to businesses. With data that is of great volume and persistence, obtained from various sources,
proved to be accurate and safe, and likely to create value, businesses can gain competitive advantage by
developing strategies according to consumer demands and expectations.
DIGITAL MARKETING
Performing all kinds of activities through digital channels today causes businesses to experience data
explosion. To understand the effects of marketing activities, businesses are caring about data analysis
and digital marketing more than ever. Digital marketing involves many channels that do not require the
Internet including cell phone text messages, digital advertising, and digital media (Yasmin et al., 2015:
69). Digital marketing is considered as the means of achieving marketing goals by businesses using
digital technology.
Digital marketing can be defined as the use of any digital technology to make marketing process
easier along with the ultimate aim of customer interaction, participation, and evaluation. Using digital
marketing strategies, businesses have an interactive and attended experience with consumers (Zahay,
2021: 125). Establishing an interactive communication with consumers through digital channels is the
primary goal of digital marketing (Yasmin et al., 2015: 71). Creating value with consumers and other
partners through digital channels and forming a constant communication and interaction with them are
of great importance to businesses, which can offer new opportunities to businesses. Making effective and
productive use of digital marketing can lead to the transformation of production and delivery policies
and enable businesses to offer various consumption patterns (Munar and Jacobsen, 2013: 2).
Making use of data analysis and digital technologies can help businesses transform and expand the
role and extent of their marketing activities (Shah and Murthi, 2021: 772). Performing data analysis
helps businesses discover the effect of various marketing activities including sales development, delivery,
price, product qualities, TV, and print ads on the market share, sales revenue, or the brand value of a
product’s brand. It leads businesses to become more customer-oriented and work harder to make them
have notable experiences. Today, customer experience has replaced the concept of customer satisfaction
which forms a basis for marketing (Sheth and Kellstadt, 2021: 781). Attaching as much importance as it
takes to data analysis and benefiting from digital marketing techniques are the reasons why businesses
still exist in the digitalizing world.
22

THE ROLE OF BIG DATA IN DIGITAL ENVIRONMENT
People are living in a digital world where technology is advancing rapidly and data is becoming plentiful
day by day. Today, information obtained by digital means has a major part in marketing research and
activities. Integrating with the business life, technology causes marketing communication to become
complicated. The solution to this problem is possible only when businesses analyze marketing perfor-
mance in a detailed manner (Buhalis and Volchek, 2021: 1). Today, there are almost no industries on
which big data analysis has not created a great effect.
The results of big data analysis can offer detailed information, feedback, and personalized advice to
businesses about their operations in various sectors including health, tourism, commerce, and transpor-
tation (Benjelloun et al., 2015: 5). For example; tourism destinations, tourists, and tourism establish-
ments create large volumes of data every day. Thanks to digital data analysis, tourism establishments
are able to reach information about the demands, needs, expectations, and preferences of tourists, and
make decisions to please them and offer them unique and notable experiences (Mariani et al., 2018:
3516). In the health sector, on the other hand, big data analysis provides businesses with detailed infor-
mation to increase the efficiency and productivity of healthcare costs and services, reach more detailed
information about diseases and patients, and offer a more personalized service to patients (Benjelloun
et al., 2015: 1). Big data analysis also brings many benefits to the banking sector. The banks are able to
provide their customers with better service, store a lot of information about them, take safety measures,
and enable customers to perform all kinds of banking transactions through digital channels (Srivastava
and Gopalkrishnan, 2015: 643). Using big data in agriculture brings many benefits to the industry. Big
data analysis enables the review of production strategies, the adaptation of production plans to climate
prediction, the evaluation of supply and demand based on the area and customer profiles, decision mak-
ing according to climate prediction, and the evaluation of the results of smart receptors (Benjelloun et
al., 2015: 3). As can be seen, big data analysis can help various businesses make decisions, develop
strategies and policies, and increase their profit and competitive power.
Businesses can collect information from many digital channels. These are search engines, interactions
on social media accounts that are constantly used, activities on electronic banking accounts, all kinds of
interaction on the Internet including blogs, e-mails, sensors, and activities on e-government (Blazquez and
Domenech, 2018: 101). Some data sources, on the other hand, are not intentionally created by consumers.
These are the cookies on the Internet, the location data created by smart phones, GPS, call history and
Bluetooth signals, and personal data created as a result of activities on the Internet pages (Wamba et al.,
2017:339). These data are created by consumers in the digital environment and constantly updated. People
are living in a digital era when everything is smart. The Internet, smart phones, and smart sensors are the
essential elements of people’s lives and that is why many businesses perform their marketing activities
in digital environments (Blazquez and Domenech, 2018: 99). In order to collect various kinds of data
so that they can develop strategies to prolong the lifespan of their products, businesses are benefiting
from more and more sensors and wireless Internet (Kusiak, 2017: 23). Recording all kinds of activities
performed by consumers on the Internet makes it easier for businesses to develop marketing strategies.
Any activity performed by consumers on the Internet can be subject to all kinds of measurement for
marketing activities and consumer behavior analyses the businesses are planning to carry out. For example;
each and every one of the shopping searches of consumers, the stores they visit, the products they buy,
or have planned to buy but then change their minds about, and products they return is recorded as data
and can be analyzed by businesses (Xie et al. 2016: 1037). While surfing the Internet, consumers can
23

leave several digital footprints (Blazquez and Domenech, 2018: 101). Businesses which monitor them
meticulously are able to understand the consumer behavior, intent, demands, and expectations easily.
Today, marketers can access geographical data to map out the geographic mobility of consumers
and thanks to this mapping, they can speculate on where consumers might be (Zheng et al., 2021: 1). It
provides marketers with many advantages. On the one hand businesses are able to understand consumer
behavior, but on the other hand they get the chance to carry out various marketing activities including
offering ads or advice to consumers based on where they are or they will possibly be (Zheng et al.,
2021: 1). Bringing together the real-time (such as geospatial) and vast amount of data (such as purchase
history of consumers), businesses can reveal the consumer information that remained in the dark and
understand consumer behavior better (Erevelles et al., 2016: 900). In order to increase the efficiency
and productivity of their marketing activities, businesses must use their creativity and make innovations.
However, it is not enough for them to gain competitive advantage over their competitors in the market.
Businesses must benefit from big data to make innovations and radical decisions (Tellis et al., 2009: 3).
With the technological advancements, access to data is a lot easier and businesses’ perspectives on
data have changed. A constant increase and variation in data sources such as social media, sensors, and
computerization of machines causes an increase in the amount of data. Reaching greater volumes of
data enables businesses to obtain more information about them, which can allow them to adopt a more
detailed and wider perspective on themselves (Barton and Court, 2012: 80). As the content of data
becomes richer, marketers can detect new gaps and understand consumer behavior better (Erevelles
et al., 2016: 900). Businesses can notice spots they have missed or overlooked before, offer different
experiences to their customers, and develop various strategies (Barton and Court, 2012: 80). With their
new marketing and management approaches they have developed from elements such as clickstream,
web server logs, and social network relations, Google, eBay, LinkedIn, and Facebook are the businesses
which have benefited from big data first (Davenport and Dyché, 2013: 2). Factors including technological
advancements and constantly changing consumer demands and expectations cause modern marketing
structure to become complex.
In the digital age, the way consumers access information, communicate others, reach the brand they
like, and most importantly the way they purchase has changed and varied (Xu et al., 2016: 1563). This
situation enables businesses to collect data about consumers on a very detailed or even individual level
(Wooff and Anderson, 2015). Businesses can easily access all kinds of personal information includ-
ing the channels visited by consumers, the duration, time, and frequency of their visit (Li and Kannan,
2014: 43), IP addresses of customers, their view history, keywords they use to search, the device and
browser they use, the duration of their sessions, their location, education, gender, and age information
(Ghose and Todri, 2015: 28). Big data allows businesses to evaluate consumers in various aspects. For
instance, a business that launches a new product can develop some strategies by considering the effects
of advertisements on purchase. It is obvious that the purchases of consumers, who are a lot more social
today than ever, are inspired by a variety of factors. Thanks to tools that big data has provided us, we
can easily analyze what consumers buy personally, where they have discovered the product they bought,
and even how they are convinced to buy it.
Businesses can reach a lot of information through users who share positive or negative thoughts about
those businesses or their products on digital channels. Electronic word of mouth marketing is defined as
a positive or negative thought about a business or product expressed by potential, old, or real consumers
on the Internet (Hennig-Thurau et al., 2004: 39). Such comments not only guide other consumers through
the product and business, but also provide the business with information. E-WOM is one of the most
24

important and effective sources of information. Research has revealed that 95% of consumers definitely
read the comments of other consumers in digital environment thoroughly before buying a product, which
increases the interaction between consumers and sellers by 720% (Aakash and Gupta Aggarwal, 2020:
2). Reading such comments elaborately and answering them if necessary are quite important for both
marketers themselves and consumers. However, it can be very time-consuming and painful for businesses.
In such situations, businesses can benefit from a variety of analysis methods that have gained popularity
with the increasing use of Internet technology in the big data age (Aakash and Gupta Aggarwal, 2020:
2). Big data that is obtained from a wide range of sources can benefit businesses with the help of new
techniques and tools that have been developed in the recent years.
EXAMPLES OF BUSINESSES USING BIG DATA IN DIGITAL ENVIRONMENT
Big data guides business to make strategic decisions, organize their daily activities, and also guides
them through budgeting and marketing decisions, and helps them discover new business opportunities
and innovations (LaValle et al., 2011: 21). Therefore, businesses are benefiting from data collection,
registration, storage, process, and proper data analysis more than ever.
For example, Wal-Mart’s data analyses in 2004 showed that after a storm warning had been made, the
sales of some products including Pop-Tarts increased by 700% just before the storm (Hayashi, 2014: 34).
Such analyses can help retailers like Wal-Mart to realize the products that are popular during a specific
period, identify those products and increase the supplies in that period, and develop various strategies
to benefit from the expected rise in the best way possible.
According to the data analysis by NFLshop.com, women were unsatisfied with online shopping in
summer 2012. This was because the vendor had the wrong idea that most women shopped online to buy
presents for their loved ones (mostly men) rather than shopping for themselves. However, the analyses
have revealed the fact that women usually shop for themselves on online stores. According to data, the
store increased its sales by 25% in a year after designing its catalogues and intending their ads especially
for women (Thau, 2013).
Southwest Airlines records the conversations between its personnel and consumers to get information
about the business and employees. Analyzing those conversations which it believes would be helpful to
gain consumer insight, Southwest Airlines is able to improve the information flow and restructure the
business by training its personnel more often (Erevelles et al., 2016: 899).
In order to reach more detailed information about customers’ future shopping and predict an online
order before the customer actually does it, Amazon has obtained a patent right to access the order his-
tory, product search history, and shopping bag records of consumers shopping from businesses which
use big data (Banker, 2014). This attempt of Amazon is for re-building the delivery strategies rather
than improving marketing activities, which proves the potential power of big data analysis (Benoit et
al., 2020: 237). What Amazon is doing here is to understand consumer insight by using big data and
develop new marketing strategies. Making such radical decisions through big data, businesses aim to
create more value and increase business performance (Kunc and Morecroft, 2010: 1165).
Like many other businesses, Kroger offers a shopping card to its customers and keeps shopping re-
cords of customers in order to monitor them. But unlike many other businesses, Kroger sends customized
coupons to its customers considering their buying behavior instead of sending them the same coupons
25

for shopping. Analyzing the customer data and predicting what customers might buy in that month, the
updated coupons are being sent to customers constantly and automatically (Germann et al.,, 2014: 589).
E-Bay and Target use customer browsing history and loyalty card data to speculate consumer demands
and expectations and improve buying tendencies (Chen et al., 2012: 1169). While General Electric ben-
efits from big data to increase the efficiency and productivity of its gas and power systems (Wamba et
al., 2017: 357), Alibaba and Tencent have established a big data-based credit scoring system for their
traditional banking systems to perform better (Cappa et al., 2021: 52).
Like many other brands, Nike Company designs its products based on customer demand, does stock
control, prioritizes customer participation while carrying out marketing activities, pays attention to of-
fer customized products to its customers, and manages to increase its sales as a result of customer data
analysis (GalbRaith, 2014: 7). Collecting primary consumer data among almost four million drivers on
the road through sensors and remote application management software, Ford has achieved the necessary
product renewal and design (Erevelles et al., 2016: 901). Fresh Direct organizes its prices and sales de-
velopment activities on a daily basis according to the data stream of consumers’ daily online activities,
visits to Internet pages, and customer service interactions. Likewise, Ford Motor, Pepsico, and South-
west Airlines analyze what customers share about themselves on social media sites including Facebook
and Twitter in order to measure the instant effects of marketing activities and understand the changes
in consumer sensitivity to their brands, and develop strategies accordingly (Bughin et al., 2010: 82).
To sum up, the amount of data created over the Internet has increased in the modern world where
technology is advancing rapidly, and all kinds of electronic devices can be controlled by a computer.
Thanks to those electronic devices, businesses are able to access a lot of information about consumer
behavior and preferences (Perera et al., 2014: 83). As the amount and frequency of data increase, busi-
nesses are benefiting from big data more each passing day (Lynch 2008: 28). These examples reveal
how businesses benefit from big data in the digital age.
SOLUTIONS AND RECOMMENDATIONS
In this study, the importance of big data for the companies from the marketing perspective is presented.
The study reveals that the vast majority of consumers’ purchases are made through digital channels.
Nowadays, there are almost no industries of which big data analysis has not created a great effect. Big
data guides businesses in making strategic decisions and organizing their daily activities that can also
guide budgeting and marketing decisions and help businesses discover new business opportunities and
innovations (LaValle et al., 2011: 21). The study reveals that, businesses, serving in a wide variety of
industries, are increasingly focused on collecting, saving, storing, processing and analyzing big data day
by day. As a result, the benefits that businesses gain from using big data in the digital age are enormous.
This study highlights that in the digital age, big data is an important element for businesses on the
way to digitalization to achieve sustainable success. While surfing the Internet, consumers can leave
several digital footprints (Blazquez and Domenech, 2018: 101). Businesses which monitor them me-
ticulously are able to understand the consumer behavior, intents, demands, and expectations easily. As a
result, businesses need to benefit from big data analytics in order to satisfy their customers and achieve
sustainable success.
26

FUTURE RESEARCH DIRECTIONS
This study is limited to a detailed literature study on the benefits of big data to businesses. Big data is a
very important subject but the studies about it are very scarce. In future studies, purchasing behaviors of
consumers can be analyzed according to their demographic characteristics by using quantitative methods.
Authors can make interviews with the businesses from different sectors about how they benefit from big
data and their marketing strategies in this regard.
CONCLUSION
Big data comes in as a crucial element for businesses to maintain success on their way to digitalization.
Both in the past and the present, the Internet has created many digital marks about consumers that busi-
nesses can collect and process (Ducange et al., 2018: 325). As a result of rapid technological advance-
ments, consumers create a wide range and a large amount of data. The data created in such volume,
velocity, and variety enable businesses to gain insight into consumer behavior. A proper analysis of those
data helps businesses fill in the gaps about consumers and gain a competitive advantage in the market.
The importance of big data in digital marketing environment is revealed in this chapter. In line with
this purpose, the definition, components, and sources of big data are reviewed in the first chapter of the
study, while the second chapter is focused on the digital marketing concept. The role of big data in digital
environments is discussed in the third chapter and the fourth chapter includes examples of businesses
using big data in the digital environment.
In today’s highly competitive environment, being in constant interaction and communication with
their customers is of great importance to businesses in terms of establishing a long-term relationship
with them. In this sense, big data can offer businesses extremely detailed information about customers.
Thanks to the information they obtain, businesses are able to make better decisions, create different
and innovative ideas, and increase business performance. In order to maintain a consumer-oriented
approach, businesses need to use customer insight they gain from Big Data and constantly update the
information at hand.
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KEY TERMS AND DEFINITIONS
Big Data: Massive amounts of information which is difficult to store, manage, and analyze.
Consumer Behaviour: The examination of all activities related to the purchase of individuals or
groups.
Consumer-Centric Marketing: It is a kind of marketing strategy that focuses on consumer needs
and wants.
Data-Driven Marketing: Refers to strategies that are built on information derived from the analysis
of big data collected through consumer interactions to create predictions about their future behavior.
Digital Age: The time-period of every kind of technologies are providing users the skill to certainly
and quickly get information.
Digital Marketing: A marketing strategy that enables to reach consumers with the help of digital
channels.
Marketing Strategy: It is a long-term, forward-looking approach of a business that aims to gain a
sustainable competitive advantage by understanding the needs and wishes of its customers.
33
34
Chapter 3
Digital Marketing vs.
Traditional Marketing:
Literature Review and Practical Lessons
Vera Gelashvili
https://orcid.org/0000-0001-5951-6392
King Juan Carlos University, Spain
ABSTRACT
Digitalisation has caused important changes that have affected people’s lives on a social, economic, or
personal level. These changes have affected both business and society in general. Therefore, companies
had to adopt different strategies to reach with their users, since traditional methods were no longer ef-
fective in some cases. It is the case of marketing, where companies had to change or blend traditional
marketing with digital marketing (i.e., to promote their products and services through the internet). The
aim of this work is to analyse the existing literature on similarities and differences between traditional
and digital marketing. The methodologies used to achieve these objectives were a systematic literature
review and bibliometric analysis by Scopus. The results obtained have shown that both traditional and
digital marketing are important for companies, so firms must find out which of them is more compatible
with the company’s objectives. This chapter provides practical lessons for entrepreneurs or marketing
makers when choosing a type of marketing for their businesses.
INTRODUCTION
Since the Internet was born, has taken place to important changes that have affected people’s lives on a
social, economic or personal level (Haythornthwaite, 2005; Matthyssens et al., 2008). Not only people
have been conditioned by this change, but the business world, which has had to adapt to the new methods
of conducting business (Saura et al., 2019). Therefore companies had to change their way of reaching
customers, doing business or using different technological tools that have allowed greater effectiveness
and profitability (Martínez-Navalón et al., 2020; Ribeiro-Navarrete et al., 2021). One of the areas of the
company that has been specifically affected by these changes has been marketing (Lagrosen, 2005). In
DOI: 10.4018/978-1-7998-8003-5.ch003

Digital Marketing vs. Traditional Marketing
particular, companies had to implement digital marketing, i.e. to promote their products and services
through the internet. That is why traditional marketing has been affected in most cases, because compa-
nies had to invest more in digital marketing, leaving aside traditional marketing.
According to Morozan et al. (2009) through digital marketing it is possible to promote products and
services using digital distribution channels to reach consumers in a relevant, personal and cost-effective
way. Digital marketing strategies can measure multiple factors, such as sales or conversions, to time
spent viewing a website or preferences. Therefore digital marketing allows for much more flexibility
and full customer treatment. Also, it enables a personalisation of the strategy that is more tailored to the
target audience. In addition to this, digital marketing allows to have a clear information about products
and services, has a greater engagement of customers or enables instant purchase (Yasmin et al., 2015).
Therefore, companies should try to find out through digital channels what their customers want and
demand, as this is directly related to customer satisfaction and trust towards the product and services
(Gelashvili et al., 2021).
In the meantime, traditional marketing is based on promotional strategies such as direct sales, televi-
sion, radio, mail and print media (Cant and Wiid, 2016). In most cases, traditional marketing reaches a
local audience more easily than other types of marketing and longevity is the main reason why people
are used to traditional marketing (Todor, 2016). According to study elaborated by Kashani et al. (2005)
traditional marketing focuses on the immediate “consumers” of the company’s products and services
who are defined as the “next in line”. Companies using the traditional marketing system must plan,
implement and control the strategies to achieve the proposed objectives, in addition to this they must
set marketing goals for the company (Armstrong et al., 2009). Traditional marketing methods to reach
the customers are different among them it is possible to distinguish newspapers, radio, TV, billboards
along roads and highways, etc. (Sinha, 2018).
According to Pascalau and Urziceanu (2021) traditional marketing requires a lot of time, money and
manpower. In addition to this, traditional marketing is often not available, as it depends on the availability
of media such as TV, radio or newspapers. While, digital marketing is not time-bound, can be updated
quickly and consumers in different countries can have access to products that are not available in their
home country. Despite this, both types of marketing are useful depending on several factors that market-
ers must take into account. The authors of the study conclude that traditional marketing tends to be more
effective if it operates at a local level, whereas digital marketing is more effective at a global level as it
reduces communication barriers and users can access certain products and services from anywhere on
the planet. Other marketing studies (Kaur, 2017; López García et al., 2019; Roncevic et al., 2019; Todor,
2016) have concluded that for a company or product one type of marketing may be more efficient than
the other, or in some cases the best result may be obtained from a mix of digital and traditional marketing
(Srinivasan et al., 2016; Todor, 2016). Consumers of traditional marketing products and services have
the advantage such as possibility of see and touch the real goods or service (Salehi et al., 2012) which
in the case of digital marketing is not possible. In addition to this, in digital marketing the data is used
properly which makes it different from traditional marketing (Durmaz and Efendioglu, 2016).
In general, several studies have analysed traditional marketing and digital marketing separately,
although the literature review has shown that there is a lack of studies that go into the comparison of
these two different types of marketing (Durmaz and Efendioglu, 2016; Kumar et al., 2017; Pantano et al.,
2019), analysing the similarities or main differences that exist ((Durmaz and Efendioglu, 2016; Yasmin
et al., 2015). This leads to a lack of academic literature on the subject. However, it is worth noting that
more studies have been published in recent years on digital marketing alone (Nasiopoulos et al., 2019).
35

Although the number of studies on digital marketing has increased, it should be noted that most of these
studies are generic, despite the advances or increases in research on digital marketing there is a lack
of specific studies, such as on the improvement of management of Data Sciences in digital marketing
(Saura, 2020).
It is therefore worth asking what are the main differences and similarities between these two methods
of marketing? Can a company opt for both types of marketing or does it have to choose one of them?
Taking all of this into account the aim of this book chapter is to analyse the existing literature on
similarities and differences between traditional and digital marketing, check why in some cases tradi-
tional marketing is effective and vice versa. In addition to this, this research will analyse the number of
studies that have been published separately on digital marketing and traditional marketing taking into
account the type of document, the country of publication, the area of publication or the evolution of the
number of studies over the years.
In order to achieve the objectives set out, first of all, a review of the literature on the concepts of
digital marketing and traditional marketing will be carried out. The systematic literature review meth-
odology is a widely used methodology in the legal and social sciences in the recent years (Lim et al.,
2019; Ribeiro-Navarrete et al., 2021; Safarov et al., 2017; Saura et al., 2021b). It is a methodology that
studies different researchs on a specific topic that allows objective conclusions to be drawn based on
the literature analysed. In addition to this, bibliometric analysis by Scopus has been done. Scopus is a
bibliographic database that provides bibliometric analysis tools based on various criteria such as the
number of citations received by the articles, area of publication, publication entities, country of publi-
cation, evolution of the number of articles per year, etc. The Scopus database is increasingly used for
bibliometric analysis in recent years (Liu, 2020; Parlina et al., 2020; Schotten et al., 2017) as it allows
for fairly objective results and conclusions to be drawn on the basis of high-impact studies.
The systematic literature review of the literature has shown that there are several similarities and
differences between traditional marketing and digital marketing. Among the similarities it can highlight
that both are the way a company communicates with its customers with the objectives to get more users,
increase the volume of sales and increase the advantage over its competitors. Apart from that, both types
of marketing try to have a direct contact with the customers, to send a message through marketing and
to give detailed information about products and services and thus to increase the possibility of the sale
at the moment or in the future. The main differences of this two types of marketing are several, among
them it can distinguish the mode of communication by which companies act with their customers. In
case of traditional marketing there is no direct contact with the consumers, while in digital marketing
the communication is based on direct interaction between the company and the customers. Among other
differentiating factors between digital and traditional marketing are the marketing cost, marketing content,
the marketing target audience and the measurability of the marketing result.
Taking into account the type of companies, the type of activity or the products offered, it has been
determined that both types of marketing are efficient. Traditional marketing is specifically useful for
small and medium enterprises (SMEs), while big companies reach their customers faster with digital
marketing. However, most companies perfectly combine both types of marketing to achieve the desired
objective. Based on this it can be concluded that companies need to carefully study their customers, the
environment, the market they operate in, the competitors and then apply one of the marketing types or
combine the two to achieve the best result.
The results obtained for this book chapter have a great of importance in the academic literature, as
several studies have pointed out that there is a lack of studies on these two important types of marketing
36

used by most companies all over the world. Furthermore, the paper presents managerial implications
that can be of great help in establishing the right type of marketing for companies products and services.
Along with the managerial implications, practical implications are presented that are of great interest
to society on the whole.
This book chapter is structured as follows. The 1st chapter is the introduction, where the motivation,
justification, objectives, methodology and main conclusions and implications of the study have been
briefly described. The 2nd chapter is on traditional marketing, where the literature review together with
the Scopus bibliometric analysis has been elaborated. Chapter 3 analyses the existing and most updated
literature on digital marketing, as well as a bibliometric analysis of the subject is carried out. Chapter 4
compares digital marketing and traditional marketing, where the main similarities and differences are
presented. Chapter 5, which is the final part of this book chapter, presents discussions, conclusions and
managerial and practical implications. Moreover, the limitations and future research lines are presented
at the end of chapter 5.
TRADITIONAL MARKETING
The first attempts to define marketing discipline started at the beginning of the 20th century. According
to Carasila (2006) the three clearly differentiated periods have been identified for the marketing defini-
tion: the pre-conceptual period (1900-1959), the period of formal conceptualisation (1960-1989) and
the current period of the marketing concept (199 -2005). The pre-conceptual period is a question of
discovery, conceptualisation, integration, development, reapplication and reconception of the marketing
concept (Bartels, 1988). In the pre-conceptual period Clark (1922) publishes the book on marketing
principles, where for the first time marketing was defined as a discipline. The second period of formal
conceptualisation can be described as the period of major marketing advances, including the formal
definition provided by the American Marketing Association (AMA). In this second period many re-
searchers (Borden, 1964; Kotler, 1972; Kotler and Keller., 1982; McCarthy et al., 1979) in the area have
made significant contributions to the marketing research. In the last period of marketing development,
the concept has been clarified and divided by different types of marketing such as: traditional market-
ing, social marketing, rational marketing, digital marketing or social media marketing among others.
Taking into account the evolution of the concept of marketing over the last century, the numerous
studies and definitions, it is difficult to come up with a single definition. Therefore, the latest AMA
(2017) update on the marketing concept can be considered as a general concept, where the marketing is
defined as “the activity, set of institutions, and processes for creating, communicating, delivering, and
exchanging offerings that have value for customers, clients, partners, and society at large”1. Therefore,
we can say that marketing is a science that allows companies to interact with their customers, where a
set of techniques and strategies are used to study the behaviour of markets, the commercial management
of companies and the needs of consumers. Another definition provided by Kumar et al. (2017) considers
traditional marketing as “an offline form of advertising used to promote sales”.
Through the Scopus bibliometrics it has become clear that in the last decades the number of studies
on marketing has increased. The following graph shows on average, the number of research papers, books
or working papers that have incorporated the word “marketing” in their title by year.
37

Figure 1. Average number of documents that include the word “marketing” in title
Source: Own elaboration based on data provided from Scopus
As we can see in the graph 1, marketing studies have increased considerably in the last decade. Al-
though, the table shows the average number of documents that have used the word marketing in their
title, in total there are 49,534 documents. 67% of these documents are scientific research papers, 9.6%
are conference papers, 7% are book chapters and the rest of the percentage belongs to notes, reviews or
working papers among others. If we analyse these documents by subject area we see that more than 30%
are published in the area of management, administration and accounting, almost 12% are classified in
the social science area and others in different subject areas.
Over time the concept of marketing has evolved. With the advance of technology different market-
ing channels have appeared and among them is “traditional marketing” which is the marketing based
on the advertisements in newspapers and journals, radio, TV, brochures, newsletters, promotions, etc.
(Opreana and Vinerean, 2015; Salehi et al., 2012). Based on that, it can be said that traditional marketing
uses these tools to sell products and services. According to Schmitt (1999) Traditional Marketing refers
to “a canon of principles, concepts and methodologies gathered by scientists, consults and marketing
practices and it describes the nature of products, the behaviour of consumers, and competitive activity
in the marketplace”.
A review of the literature on traditional marketing shows that there are very few definitions of the
concept of “traditional marketing”. This may be due to the fact that traditional marketing is marketing
from the beginning, so its definition is directly related to normal “marketing” concept. In addition to
this, many authors refer to “traditional marketing” as “offline marketing” (Salehi et al., 2012; Sriniva-
san et al., 2016), which can be one of the disadvantages when analysing the concept. There are more
disadvantages of traditional marketing such as the difficulty to measure results, high costs especially for
some specific cases and if changes occur it is not easy to change the way of marketing (Todor, 2016).
Among the advantages of traditional marketing one can distinguish the possibility to physically see the
product and then make the decision to purchase it or not (Salehi et al., 2012) or faster results and level
of confidence generated in consumers (Todor, 2016).
38

Figure 2. Traditional marketing

Source: Own elaboration
The Scopus bibliometric review has shown that the term “traditional marketing” has been used in the
title of 32 research paper and 804 documents have included this term in their title, abstract or in key words.
Figure 3. Number of documents by type that include the concept “traditional marketing” in title, abstract
or keywords
As shown in the graph, 62% of the papers that have analysed traditional marketing are scientific articles.
23% are conference papers and 6% book chapters and review. Analysing by year, we have seen that 74
papers have been published in 2019 (the highest number), while in 2020, 70 papers were published on
this topic. The most published country on “traditional marketing” is the United States (198), followed
by China (111), United Kingdom (81) and India (43).
39

DIGITAL MARKETING
Since the digitalisation of the market, things have changed for both companies and consumers, and gener-
ally for society (Sehlin et al., 2019). For the companies, digitalisation offers too many opportunities for
growth in all areas, promotes longer life for products, minimises the transaction costs and waste, gain a
competitive advantage or contribute positively to economic growth (Antikainen et al., 2018; Myovella
et al., 2020; Szalavetz, 2019). But not all are advantages, according to Ahmad and Murray (2019) digi-
tisation is directly related to a certain cost for the company.
The digitisation of companies has contributed to the improvement of marketing strategies and has
made it possible to have a type of digitisation-oriented marketing called “digital marketing”. In general,
digital marketing (marketing 2.0) is the set of strategies aimed at promoting a goods and services on the
internet. It differs from traditional marketing in that it includes the use of channels and methods that
allow the analysis of results any time and facilitate the relationships between customers and companies
(Wymbs, 2011).
The concept of digital marketing has been defined by several authors. In the next table the system-
atic literature review on the definition of digital marketing is shown (there are shown the most quoted
definitions).
Table 1. Digital marketing definition: systematic literature review
Author Year Definition

“Digital marketing is the use of digital or social channels to promote a brand or
2019 reach consumers. This kind of marketing can be executed on the internet, social
AMA 2
media, search engines, mobile devices and other channels”.
Digital marketing refers to “achieving marketing objectives through applying
Chaffey and Ellis-Chadwick 2019
digital technologies and media”.
Digital marketing refers to “an adaptive, technology-enabled process by which
Kannan and Li 2017 firms collaborate with customers and partners to jointly create, communicate,
deliver, and sustain value for all stakeholders”.
“Digital marketing involves applying digital technologies which form online
Chaffey, Ellis-Chadwick, Mayer and channels to market to achieve following objectives: support marketing activities
2009
Johnston aimed at achieving profitable acquisition and retention of customers within a
multichannel buying process and customer lifestyle”.
According to study elaborated by Yasmin et al. (2015), there are several techniques by which digital
marketing is composed, like as: online advertising - internet adds that best suits the interests of consumers;
email marketing - when the content of an advertisement or offer is sent via email; social media - when
advertisements about a product or service can be seen on social media pages; text messaging - when
information on products and services is sent via mobile phone message; affiliate marketing - is a type
of performance-based marketing where a company rewards affiliates for each customer they bring in
through the marketing efforts they create on behalf of the company; Search Engine Optimization - affect-
ing the visibility of a web page in a search engine’s natural or un-paid search results; and Pay Per Click
– that use search engine advertising to generate clicks to websites instead of get those clicks organically.
Nowadays, the daily activity of companies operating on the internet includes such techniques to improve
40

the digital marketing strategy (Saura, 2020). It is worth noting the importance of social networks as the
information provided by them directly affects consumer behaviour (Saura et al., 2021a). Based on this
we can say that digital marketing is a quite complex type of marketing for marketers as there are differ-
ent ways and techniques to reach customers with a suitable product or service.
The main advantages of digital marketing for the companies are direct relationship with customers,
possibility to measure the effect of advertising more easily (Mogos, 2015), duration of the information
on the internet as it is permanently available in some cases, (Todor, 2016), low investment cost compar-
ing with traditional marketing (Tiago and Veríssimo, 2014), easy to get data on the specific customer
touchpoints with the company (Kannan and Li, 2017), or capability to record consumer behavior including
their preferences and responses to various products (Atshaya and Rungta, 2016), among others. Based
on Veleva and Tsvetanova (2020), main disadvantages for the company when use digital marketing are:
availability of information for everybody that is an advantage for competitors, the reputation of companies
can be destroyed by negative feedback provided by users, not to be suitable for all types of products and
services, inappropriate use of digital tools and applications, etc. In addition to this, security and privacy
issues in social networks can be a problem, so companies must take it into account (Saura et al, 2021c).
Taking all this into account, we can say that digital marketing has its advantages and disadvantages,
therefore companies should take into account when implementing it.
The Scopus bibliometric review about digital marketing has shown that the term “digital marketing”
has been used in the title of 434 research paper and 1,451 documents have included this term in their
title, abstract or in key words. From this 1,451 documents 59% of these are scientific articles, mean-
while, conference papers are 25%, and reviews and book chapters are 5% of the documents elaborated
on digital marketing.
Figure 4. Number of documents by type that include the concept “digital marketing” in title, abstract
or keywords
41

The result of the bibliometric literature review has shown that the country where the document about
digital marketing is most published is United States with 267 documents. After the United States, the
countries with the highest number of publications on digital marketing have been India (207), United
Kingdom (151) and Spain (71).
The first documents elaborated on digital marketing date back to the 21st century. Since then, the
number of documents published on this subject has increased year after year. As it is shown in the graph
4, since 2015, the number of documents produced has exceeded one hundred. In 2018, 183 documents
were prepared and published, while in 2019, 323 documents on digital marketing were created and in
2020, the number of documents has doubled compared to 2018.
Figure 5. Number of documents that include the word “digital marketing” in title, abstract or keywords
2004*- documents from 2004 and previous years; 2020* - includes 46 documents of 2021 January/February
Having this result it can be predicted that this number will increase in the next few years as digital
marketing is of great interest for both companies and customers. In relation to the documents published
by subject area the result has shown that 25.5% of documents were published in the area of manage-
ment, administration and accounting, almost 20% are classified in the area of computer sciences, 18.8%
in social sciences and 11.2% in the area of engineering.
DIGITAL MARKETING VS. TRADITIONAL MARKETING
Technological advances in the world have changed the rules of communication for everyone, including
businesses and consumers (Durmaz and Efendioglu, 2016). Companies have invested to reach their
customers more easily, efficiently and without high costs. With these changes companies had to adapt
their marketing strategy to digital marketing or make a marketing mix using both traditional as well as
42

digital marketing (Siakalli et al., 2017; Todor, 2016). Digital marketing techniques can be used alongside
traditional marketing techniques and this is one of the main advantages when looking at digital marketing
(Chaffey and Ellis-Chadwick, 2019).
According to Kayumovich and Annamuradovna (2020), in the case of traditional marketing, com-
panies provide consumers with information that they believe is useful to them, whereas in the case of
digital marketing, it is consumers who seek information about products and services and companies must
provide more detailed and extensive information if they want to be competitive in the market.
Taking into account the review of the literature on traditional and digital marketing in the previous
sections, the following differences and similarities of these two types of marketing can be distinguished:
Main Differences between Traditional and Digital Marketing
Means of Communication - the main difference between digital and traditional marketing are the chan-
nels through which companies act with their customers. Traditional marketing uses the more traditional
media such as television, radio and newspaper advertisements among others. Digital marketing uses
digital channels that are accessed through the internet, such as advertisements on websites, in social
media or through email.
Communication - in digital marketing, users can interact with the company, whereas in traditional
marketing the user is totally passive.
Target Audience - part of consumers that companies target in their marketing efforts to drive awareness
of their products or services. Today, for traditional marketing its target audience is a group of consumers
previously identified by specific products and services, and, also people who do not have access to or
do not use digital media. For digital marketing its target audience are consumers who use digital media
to make enquiries about product or service features or make purchases online.
Content - in case of digital marketing it is possible to give more specific information about some
products and services, while in traditional marketing the content of information is limited.
Measurement of Results - in this respect we are not talking about measuring the profitability result,
as this can be measured through both types of marketing. Measurement the results in this case refers to
measuring how efficient the marketing of a brand, product or service has been, whether it has reached
the customers and how well it has been accepted by them. The use of instant messages and comments
about products and services on the internet makes it possible to measure the effectiveness of digital
marketing, whereas in the case of traditional marketing it is difficult and in some cases almost impos-
sible to achieve such instant results.
Cost - digital marketing is cheaper than traditional marketing, making it accessible to any business.
However, it must be taken into account that the implementation of digital marketing (digitisation of the
company) involves a high cost for the business (Ahmad and Murray, 2019).
Main Similarities between Traditional and Digital Marketing
Reach an audience – in case of both types of marketing the main objective is to reach as many consumers
as possible, and thus be able to make higher sales volumes and compete with competitors in the market.
Send a Message – both digital marketing and traditional marketing aim to send a clear message to
their consumers. Through these messages consumers can have a first information about offered products
and services and evaluate their purchase at the moment or in the future.
43

Taking all that into account we can say that there are several differences between these two types of
marketing (Kayumovich and Annamuradovna, 2020; Siakalli et al., 2017), but both have as their main
objective to reach their consumers in an easy and fast way and provide clear, reliable and useful infor-
mation to its users (Todor, 2016).
DISCUSSION, CONCLUSION AND IMPLICATIONS
The aim of this book chapter was to make an analysis of the existing literature on the differences and
similarities between digital marketing and traditional marketing. In addition to this, a systematic review
of the literature on these two types of marketing is to be carried out. In general it can say that market-
ing studies have increased considerably in recent years. Every year there are more and more marketing
researches and more than 50% of them are published as research articles. When comparing digital mar-
keting with traditional marketing it is shown that in 2020 almost 300 more papers have been published
on digital marketing than traditional marketing. This implies the great interest of researchers in digital
marketing. This may also be due to the fact that digital marketing has the potential to reach consumers
in all corners of the world (Pascalau and Urziceanu, 2021). Additionally, the COVID-19 pandemic has
forced many companies to digitalizad (Faraj et al., 2021), this has been very noticeable in their type of
marketing (Almeida et al., 2020) as for several months almost everywhere in the world people could not
leave their homes freely and most of the companies were either closed or selling online (Kim, 2020).
Therefore companies did not have the possibility to promote their products and services through tradi-
tional marketing. This increase of studies on digital marketing could also be based on the demands of
companies, as most of the literature (Ahmad and Murray, 2019; Mogos, 2015; Pascalau and Urziceanu,
2021) has pointed out numerous advantages that digital marketing has over traditional marketing, among
these advantages can be distinguished the possibility to save costs (not in the first stage), to get consumers
from different countries or to manage historical data of consumers to achieve their stated goals.
Even so, it should be noted that both marketing methods are modes of communication between a
company and its consumers. Therefore, it is up to the company to decide which type of marketing is
best for its business strategies. For SMEs, which make up the majority of the business fabric, it has
been indicated that traditional marketing is most effective, especially at the local level (Pascalau and
Urziceanu, 2021; Todor, 2016), and it should be borne in mind that very few SMEs manage to grow and
internationalize (Gelashvili et al., 2019).
Literature review has shown that marketing strategies can be carried out through traditional or digital
channels (Chaffey and Ellis-Chadwick, 2019; Siakalli et al., 2017). Before the digitisation of companies
traditional marketing practices were successful and led many companies to grow and become big brands.
Therefore, with the change they had to adapt and change their strategies partially or completely. Although
we can say that with the advance of digital marketing, traditional marketing has not disappeared, it is
still a marketing strategy commonly used by both multinational and SMEs. International corporations
(e.g. McDonald’s, Burger King, etc.) still use traditional marketing together with digital marketing and
aim to reach their customers efficiently. In case of SMEs, traditional marketing is essential, as it is the
easiest way to make their products and services known locally.
Digital marketing can bring the company several advantages, firstly it is cheaper than traditional
marketing, secondly it can easily measure the results of the marketing strategy used and finally it is
sustainable over time. In addition to this, digital marketing is directly related to environmental sustain-
44

ability (Martínez-Navalón et al., 2019; Martínez-Navalón et al., 2020). Another advantage of using digital
marketing is the possibility of interacting with consumers in the moment, record consumer behavior
including their preferences and save consumer behavior data for future interactions.
Taking all of this into account, it is possible to propose some managerial implications / practical
lessons for companies or for entrepreneurs who have doubts about what kind of marketing strategies
to use. Therefore, the following ideas are suggestions that are proposed in order to run any marketing
strategy successfully in the company.
1. Companies must first study the environment in which they want to move their products and services.
Depending on this, a traditional marketing or digital marketing strategy could be used. A strategy
mix based on market demand can also be suitable.
2. The implementation of digital marketing does not guarantee low marketing costs. First of all the
digitalisation of the company and then the creation of the first internet marketing strategies (e.g.
website creation) is carried out with a considerable inicial investment.
3. With the implementation of digital marketing, companies should not forget about traditional mar-
keting as this last one provides several advantages and for some companies it could even be the
only marketing strategy option.
4. Company managers should consider the advantages of both marketing modes as well as their dis-
advantages in order to achieve better results.
Apart from these managerial implications, it is worth noting that there is a lack of academic literature
on the comparison of traditional marketing and digital marketing. Therefore, the academy should focus
on this very important issue, especially in this time of post COVID-19, which has made many companies
unable to continue to operate or has worsened their performance. Therefore the correct application of the
right type of marketing can save many of them and should be academic studies that promote and propose
practical and managerial implications in such an important area as marketing. Therefore, this study fills
the gap in the academic literature on the comparison of digital marketing and traditional marketing. At
the same time it is of interest for companies or managers of them to analyse marketing strategies well
and implement them in the right way to ensure the best results.
This study is not free of limitations. Bibliometric analysis of marketing strategies, specifically digital
and traditional marketing is done through Scopus tool, which is a disadvantage to use only one bibliomet-
ric tool, because in case of using another bibliographic tool it could be possible to make a comparison
of the results obtained. Another limitation of the study was language. In bibliometric studies, keywords
are normally used in only one language, as was the case in this book chapter. But there are some very
interesting and powerful studies in other languages such as Spanish, German, French or others. For future
research lines, it is interesting to make a bibliometric analysis using other bibliographic tools such as
Web os Science and Google scholar metrics and compare the results obtained with Scopus bibliometric
analyses. Apart from this, it will be interesting to use the SciMAT software that allows a mapping analysis
that allows a better visualisation of the results on the basis of maps.
45

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Marketing: The activity, set of institutions, and processes for creating, communicating, delivering, and
exchanging offerings that have value for customers, clients, partners, and society at large (AMA, 2017).
Traditional Marketing: Is an offline form of advertising used to promote sales (Kumar et al., 2017).
Digital Marketing: Is the use of digital or social channels to promote a brand or reach consumers.
This kind of marketing can be executed on the internet, social media, search engines, mobile devices
and other channels (AMA, 2019).
Marketing strategy: Marketing strategy refers to an organization’s integrated pattern of decisions that
specify its crucial choices concerning products, markets, marketing activities and marketing resources in
the creation, communication and/or delivery of products that offer value to customers in exchanges with
the organization and thereby enables the organization to achieve specific objectives (Varadarajan, 2010).
Digitalisation: Is a strategic tool used by company for continuous development based on the imple-
mentation of technology (Sehlin et al., 2019).
ENDNOTES
1
https://www.ama.org/the-definition-of-marketing-what-is-marketing/
2
https://www.ama.org/topics/digital-marketing/
50
51
Chapter 4
Digital Loyalty Programmes:
Pull Strategies in B2B Channel Marketing
Ammarah Marjan
London South Bank University, UK
Charles Graham
https://orcid.org/0000-0001-7603-9161
Margaret Bruce
Andrew Mitchell
Brandmovers Inc., USA
ABSTRACT
Digital loyalty programmes are an increasingly common tool for business-to-business marketers hoping
to increase repeat sales through deeper customer engagement. In consumer markets, such programmes
do little to influence behavioural loyalty and disproportionately attract the firm’s existing heavy buyers.
Industrial buying, however, relies on direct sales channels and features negotiation and reciprocity.
Loyalty effects may therefore differ in B2B, and although no clear picture yet exists, such knowledge is
important as B2C digital loyalty programmes grow in popularity. Here, the authors describe programme
membership’s evolving characteristics over in a B2B scheme that was launched in the US metal-cutting
tools manufacturer customer base. Findings are consistent with the idea that the scheme recruited the
heaviest buyers earliest and had an insignificant effect on total revenue. The authors discuss managerial
implications, particularly about (1) managing the rollout of similar schemes and (2) refocussing on the
programme objectives to maintain sales from the lightest rather than the heaviest buyers.
DOI: 10.4018/978-1-7998-8003-5.ch004

Digital Loyalty Programmes
INTRODUCTION
The chapter considers the effectiveness of end-user digital loyalty programmes in the business to busi-
ness (B2B) domain. Pull strategies of this type that create demand among end-users are common in
consumer markets but unusual in B2B marketing, where push tactics are more normally prioritised
to promote products through distributor channels. Advances in data science now make B2B loyalty
programmes both affordable and practical, but their benefits may not yet be fully understood in a B2B
setting. This chapter aims to set out those benefits by discussing the launch and diffusion of a novel
end-user loyalty programme that connected a heavy industrial manufacturer with its end users through
the existing distributor network.
The term “loyalty programme” captures a range of marketing initiatives, including discount vouchers,
reward cards, tiered service, membership levels, dedicated customer support, and other techniques (Hen-
derson et al., 2011), all designed to encourage repeat buying (Uncles et al., 2003). Loyalty programmes
share a common purpose: to attract new consumers to the brand and to lock in existing members (Juetten
et al., 2006; Ziliani & leva, 2019), and there is some evidence that they may successfully drive market
share growth (Humby et al., 2004).
Previous authors challenge the idea that in consumer packaged goods or retail, schemes targeting
consumer loyalty can create differential advantage. This is because it is normal for rival brands to share
customers, and those rival brands often also have rival schemes (Verhoef & Langerak, 2003; Liu &
Yang, 2009). In addition, the capacity of the heaviest users to increase purchasing is limited (Bolton et
al., 2000; Lal & Bell 2003); the schemes appeal mainly to existing, loyal customers (Meyer-Waarden
& Benavent, 2006); and loyalty strategies are in any case defensive, doing little to replace lost users
(Sharp & Sharp, 1997). In other words, “loyalty programmes are a poor marketing tool” (Shugan, 2005;
Magatef & Tomalieh, 2015) which reflect rather than change existing behaviours.
However, important differences exist between buying behaviours in B2B and B2C (business to con-
sumer) contexts. Industrial manufacturers and their distributors have a relatively smaller customer base
with bigger and more regular purchasing, some fixed contractual binding, professional buyers and sellers,
a close relationship between parties (Jackson & Cooper, 1988; Wilkinson et al., 2016), and an emphasis
on customer retention strategies (Dorotic et al., 2012). These differences may affect the operation of a
loyalty programme, or render it ineffective. They might also breach the as-if random interpretation of buy-
ing behaviour that underpins the generalised stochastic models used to assess and predict repeat buying.
The adoption of end-user digital loyalty programmes in industrial markets would seem to meet many
of the objectives of the B2B marketer, encouraging higher loyalty, reduced shipping costs, and increased
repertoire size (Capizzi, 2002; Lacey & Morgan, 2009; Kwiatek & Thanasi-Boce, 2019). Here we ask,
are loyalty programmes, a pull strategy, an appropriate and effective tool for channel marketers where
push strategies are more generally the norm?
In this chapter, we explore three questions. The first - are pull strategies a suitable tool for B2B
marketing at all? B2B marketing investment is usually designed to maintain distribution relationships
and competitive advantage within the distribution channels. B2B brands are not usually advertised to
B2B customers to the same degree as B2C brands because the one-to-many model is not justified by the
limited size of the B2B target market and its bulk buying. Instead, relationship marketing is the usual
technique designed to reward and develop the behavioural loyalty of distributors and particularly the
most valuable ones (Reichheld & Sasser, 1990). But just as in consumer markets, while the end-user
buys the brand, they are not directly the brand’s customer.
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Second, are loyalty programmes useful mechanic in industrial marketing? In B2C and services
marketing, some question whether loyalty programmes grow sales. Previous research has extensively
applied stochastic models of aggregate repeat-buying such as the NBD or NBD Dirichlet (Dowling &
Uncles, 1997; Sharp & Sharp, 1997) to establish that while there may be other benefits, programme
membership has little impact on expected purchase frequency or scheme adoption across the customer
base (Uncles et al., 2003). Applications of the models and the empirical generalisations they describe
have successfully extended to some B2B buying contexts (McCabe et al., 2013; Pickford & Goodhardt,
2000; Schmittlein & Peterson, 1994), but this work has not yet extended to the study of B2B loyalty
programmes and in particular, it pre-dates the rapid advance of advanced digital marketing techniques.
Our third question, then, is whether digital advances have changed anything. The greater marketing
efficiency noted by Saura et al (2017) has been achieved by the shift to online platforms (Russell, 2010),
and this creates two meaningful opportunities. Firms now have access to a vast array of digital tools to
communicate with customers, and second, such tools have made marketing activities more measurable
by improving marketers’ ability to access, gather, analyse, and report data (Pauwels et al., 2009) and
take action. But the complex nature of channel relationships means that end-user performance data is
often hard to come by, and ownership of detailed market insight is both limited and one-sided. A manu-
facturer/end-user loyalty programme that operates through the distributor network could address this
challenge to the benefit of all players. Therefore, while an online B2B loyalty programme might not
drive improvements in sales directly, as a result of the data-driven insights generated, it may improve a
manufacturer’s ability to manage sales performance through the whole channel. Advantageous distributor
relationships might then be developed based on allocations of end-user performance rewards generated
and managed digitally and at a low cost.
Following a recent call for further research into the effectiveness of loyalty programmes (Chen et al.
2021), this chapter raised three critical questions. Can a B2B pull strategy based on a loyalty programme
(1) solve the problem of customer and end-user management in complex distribution channels (2) build
sales through end-user loyalty, and (3) provide strategic customer relationship management (CRM) in-
sights that deliver a competitive advantage to the manufacturer while benefitting the distributor network.
To explore these questions, we offer a case study that describes the introduction and rollout of a novel
digital B2B channel loyalty programme in a heavy industrial setting and the unforeseen benefits achieved
for all channel members involved. We examined this, first evaluating changes in aggregate behavioural
loyalty in membership sales data as the new scheme rolled out and from contemporaneous interviews
with management at the global digital marketing agency that innovated the scheme.
In the following sections, we provide an overview of the literature framing the study then a summary
of the methodology and data sources employed. The results and their implications are then discussed,
with a summary of limitations and suggested further research.
THEORETICAL OVERVIEW
Channel Marketing
Most industrial manufacturers do not directly sell their goods or services to their end consumers. In-
stead, they turn to market intermediaries (wholesalers, distributors, retailers) to facilitate the flow of
manufacturing output to the market (Brocato, 2010), also known as channel marketing. In turn, mar-
53

keting intermediaries work as a mediating link between manufacturers and end-consumers (Kotler &
Armstrong, 2010) and can add significant marketing value. For example, intermediaries represent the
manufacturer and its brands in the marketplace and may provide logistic support by offering pre-and
post-sales services (Fayyaz & Azizinia, 2016).
In turn, intermediaries work with multiple, competing partners (Calantone & Gassenheimer, 1991;
Aman, 2017); therefore, a common strategy for rival manufacturers is to push their products through the
channel by incentivising their intermediaries through the promotion mix (Armstrong, Kotler & Opresnik,
2011). This approach is referred to as a “push” strategy (see Figure 1), and its primary purpose is to
increase repeat business at the expense of channel rivals (Dibb et al., 2012) while favourably developing
the channel relationship.
Figure 1. Example of the pull strategy versus the push strategy in the B2B market
Adapted from Sandybayev (2019).
Management may believe that push marketing builds strong channel relationships, which lead to loyalty
and mutual profitability (Morgan & Hunt, 1994; Payne et al., 2017). The strategy is clearly beneficial
for intermediaries as they receive competing and ever more lucrative deals. However, for manufacturers,
while such promotions represent a continuous and necessary investment, they appear to have no persistent
positive impact on sales (Nijs et al., 2001). They may even train intermediaries to become deal prone by
shifting focus from the brand to the promotions (Scriven et al., 2017).
In B2C marketing, manufacturers who are at arm’s length from their users redress this balance by
investing directly in consumer-brand relationships. These are designed to create demand - “pull” strate-
gies (Figure 1). Pull marketing differentiates and builds consumer-brand knowledge through advertising,
develops consumer loyalty with reward schemes, and attempts to strengthen consumer-brand relation-
ships through continuous online interactions (Lim et al., 2019; Thaichon et al., 2020). Customer loyalty
programmes provide a valuable platform for these interactions, and at their core, they share a common
54

purpose: to attract new consumers to the brand, lock in existing members (Dowling & Uncles, 1997;
Juetten et al., 2006) and deliver competitive advantage from the data they collect (Grewal at al., 2011;
Breugelmans et al., 2015).
Nevertheless, few have ever attempted to include an end-user digital loyalty programme in a B2B
setting, largely because of the relatively smaller number of end-users to be served. Significant differences
exist between B2B and B2C market (Zinkhan & Cheng, 1992; Liu et al., 2018). For example, impulse
buying is unusual because B2B firms purchase raw material to fulfil derived demand. The buying deci-
sion depends on professional buyers and sellers (Jackson & Cooper, 1988; Oakley et al., 2021); orders
are usually bigger and often more infrequent, sometimes scheduled using specialised software, while
the initial sales process often takes considerable time. It may also involve fixed contractual binding and
negotiations regarding technical support, credit, and delivery terms (Grewal et al., 2015). There is then
an emphasis on customer retention (Dorotic et al., 2012) throughout the channel, and channel effective-
ness then depends on leveraging relationships.
An end-user loyalty programme would offer a novel solution to many of the manufacturer challenges
with channel marketing in this setting. For example:
• it would be relatively cheap to set up, using new digital technologies

• it would operate permanently, unlike campaign based promotions
• it would provide a continuous intervention against competing channel promotions
• it would add long-run value and interest for end-user members, who would accumulate points in
a tiered membership hierarchy
• it would have the potential to improve relationships between manufacturer and distributor through
regular data sharing and analysis
Loyalty Programmes
Marketers use loyalty programmes hoping to retain their most valuable existing members. However,
previous researchers who examined repeat purchase rates pre and post the introduction of a loyalty
programme have found very limited evidence of “excess” behavioural loyalty when measured against
stationary market models of buying norms such as the NBD-Dirichlet (Sharp & Sharp, 1997).
Like everything else in competitive marketing, they suggest that if one brand introduces a successful
intervention, others must follow. In repertoire markets like grocery retail or consumer packaged goods
where switching barriers are low, loyalty programmes generally attract those who stand to benefit the
most: the heaviest shoppers. Since they already use several brands in the category over the course of
a year, they often subscribe to several competing loyalty schemes. If, as Meyer-Waarden & Benavent
(2006) conclude, loyalty schemes are simply more attractive to heavier rather than lighter users, then they
become a cost of doing business without creating an advantage by segmenting the market, particularly
if they do not attract or are not targeted at, lighter buyers.
The NBD Model of Repeat Buying
In established B2C markets, near habitual repeat buying of consumer packaged goods has successfully
been described and predicted at the aggregate level (i.e. across the customer base) with zero-order sto-
chastic models. The most commonly adopted of these (Ehrenberg, 1959) is the NBD, which assumes
55

that population buying propensities are distributed gamma across all buyers, and the timing of their
purchases is random around a fixed mean, following a Poisson distribution.
The NBD is simple to use to evaluate buying of a single brand or a category when buying is station-
ary. It is highly generalised and linked to empirical generalisations in repeat buying that has been widely
tested and supported in many marketing contexts (Uncles et al., 1995) across North America, Asia,
Europe, and Australasia. Recently, the NBD has been extended to different types of behaviour such as
gambling (Lam & Mizerski, 2009) and mobile phone service (Lee et al., 2011). While the use of the
NBD has been tested across various domains (e.g. Dawes et al., 2020; Trinh et al., 2014; 2016; 2018),
its application in the B2B context has been limited to only a few studies. Some of these have suggested
that since B2B customer behaviour is less random (especially within membership contexts), it may con-
stitute a boundary condition (Sharp et al., 2002); thus, the model may not explain B2B behaviour well.
In the study of loyalty programmes, the NBD and its more complex relative the NBD-Dirichlet
(Godhardt, Ehrenberg & Chatfield, 1984) provide a suitable benchmark to examine divergence from
stationarity. For example, to demonstrate if a particular class of heavier or lighter buyers change in
propensity resulting from adopting loyalty programme membership (Sharp & Sharp, 1997) and being
rewarded for purchasing more. In any given time period, to calibrate the NBD, only two inputs are re-
quired. The penetration of a customer group within its category and the average purchase frequency in
the given time. The model output then describes the distribution of buyer classes (1, 2, 3, 4….n), the
repeat purchase rate and the customer acquisition rate between periods; in other words, the key measures
of behavioural loyalty in the customer base.
Loyalty Programmes in Digital Marketing
In a B2B setting, buyers are not always primary decision-makers. In many cases, professional procurement
managers are moved from department to department with the express intent of discouraging buyer-seller
relationships from being developed. Therefore, there may be a first-mover advantage for a manufacturer
across its distribution channel in owning a B2B loyalty programme targeting end-user relationships to
develop sales at the expense of rivals. While any competitive advantage offered by launching the first
B2B loyalty programme may diminish over time, the benefit obtained by a constant flow of information
about end-customer behaviour would still remain valuable (Palmer et al., 2000), continuing to provide
leverage with the distributors.
The role of loyalty programmes has changed radically in recent years, owing to the web’s immediacy
and data-driven marketing. Today, customer loyalty programmes operate digitally with e-commerce
and online payment infrastructure and reward members for various desired actions. The programmes
have eliminated the need for physical cards, and digital evolution has made programme adoption much
simpler (Smith, 2000). The other benefits brought by new technologies benefit marketers by providing
detailed insights (Reyes-Menendez et al., 2018), identifying and targeting customised segments, and
providing an interactive platform where customers and brands can see progress towards rewards (Leva
& Ziliani, 2016).
Besides, the transition of loyalty programmes into the digital space has addressed some previously
stated challenges. In comparison with traditional schemes, Leva & Ziliani (2016) state that online loyalty
programmes may have distinct benefits: They can increase single-brand loyalty (O’Malley, 1998), provide
a freestanding brand touchpoint (Lemon & Verhoef, 2016), keep the memory of brand fresh (Romaniuk
56

& Sharp, 2016), and intuitively they are good value for money (Leva & Ziliani, 2016). Therefore, online
loyalty programmes may be more likely to increase loyalty and achieve the firm’s desired objectives.
B2B manufacturer’s products have a substantial impact on the quality and productivity of their end-
customers’ products. In their innovation, they should fully engage with their customers in parallel with
channel partners (Wright et al., 2019). In B2B channel marketing, manufacturers have more motivation
to create a relationship with intermediaries (Arnould et al., 2003; Sharma et al., 2020) and systemati-
cally offer profit sharing in the form of tiered and over-riding discount structures in return for larger
purchase volumes (Leung et al., 2020). Compared with B2C, B2B marketing involves large orders from
fewer customers, and it is more feasible for the seller to invest in these incentives (Zinkhan, 2001) with
the distributor. But in addition, an end-user digital loyalty programme would help the manufacturer
overcome high dependence on their distributors for transactional data. By promoting stronger end-user
relationships in this way, manufacturers might also regain control over this data and use the insights it
contains to manage channel relationships better.
Digital marketing has opened many opportunities for B2B marketers; however, scholarly research in
this area is still at the embryonic stage (Vieira et al., 2019). Online schemes capture detailed customer
information through various interactions across all channels – also known as big data (Stourm et al.,
2020), through the integration of intelligent analytics and communication technology (Saura et al.,
2017). Apart from interactive tools to connect with consumers (Chaffey & Smith, 2017), big data helps
generate precise insights reflecting customers’ behaviour (Pandey & Gudipudi, 2019). The two main
limitations to further research here, particularly for stochastic modellers, would both be addressed by
digital programmes in B2B marketing: the difficulties in obtaining B2B data from across channel part-
ner sources and the thin volumes of that data because of the smaller customer base sizes (Lilien, 2016).
A principal objective in B2B marketing is to build channel partner engagement through repeat activity
driven by end-user orders. The case for a cost-effective digitally-driven pull strategy in B2B seems quite
clear if it could improve distributor and end-user acquisition and retention. In this chapter, we describe an
investigation into the operation of such a programme over its first-year roll out, to answer the question;
IS A DIGITAL LOYALTY PROGRAMME A SUITABLE PULL

STRATEGY IN A HEAVY INDUSTRIAL MARKET?
Research Aims and Questions
Existing research has not yet addressed channel marketing issues from a digital perspective (Thaichon
et al., 2020), let alone in the B2B context (Liu, 2020). The aim here is to investigate whether knowledge
of B2C relationship marketing can help industrial marketers achieve a firm’s goals, by exploring how
digital technologies can effectively enable B2B pull marketing. Such research would contribute usefully
to the knowledge of industrial marketing.
Therefore the following research questions are proposed:
• RQ1. Is a pull strategy appropriate in B2B marketing?

• RQ2. Is a loyalty programme an effective pull tactic in B2B?
• RQ3. Is this now possible by applying digital marketing practices?
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Research Context and Dataset
A dataset provided by an industry-leading tools manufacturer and its marketing agency was used, con-
taining sales data in the customer base over the rollout of an end-user loyalty programme. This scheme is
one of the first in the heavy machinery industry and operates through a wide-ranging distributor network
across the United States. The programme is live, but the data provided is historic and describes the first
year of operation. All corporate buyers are anonymised in the files, identified only by coding, but sales
can be associated with programme rewards and viewed in time series.
The programme was initiated with selected customers as a trial in the United States in 2018 then
launched to the full customer base in 2019. The available dataset plots the development of member and
non-member transactions as the scheme was first offered to a limited number of sellers in a trial, then
a bigger pilot, then opened up and rolled out. The complete membership transaction records include
the purchases of over a thousand corporate customers across seven product categories sold through a
network of 90 distributors for a year. They can then be analysed at a continuous buying level in time-
series aggregated by month or quarter.
Programme Mechanics
The digital B2B loyalty programme offers points to end customers for purchases made in each calendar
month that can be redeemed for manufacturers’ products or other merchandise such as electronics and
shop appliances. Points are awarded based on quantity purchased across multiple categories in a given
month. Earnings per piece purchased are reset every month. Typically, customers earn more points for
every additional category purchased, though the point’s barriers vary across the categories.
The scheme has developed a 4-level customer tier programme with enhanced benefits at each progres-
sive tier, usually accompanied by initial base points, point multipliers and anniversary gift points. This
multi-tier scheme clearly distinguishes end-customer status (e.g. aluminium, steel, stainless, titanium)
based on past purchase behaviour—two primary reasons are identified for this. First, membership in a
particular tier provides customers with a sense of exclusive identity that can be converted into a tangible
status attribute (McCall & Voorhees, 2010). Second, the tier system segments customers according to
buying class (heavy, medium, light) to provide differentiated rewards (Rigby & Ledingham, 2004).
The reward scheme dashboard measures repeat purchase activity and offer information management
and business analytics tools for manufacturers and end-users with an interactive website or application
interface (Marjan et al., 2020). The manufacturer monitors business performance by accessing sales
trends and real-time redemption information and can aggregate and visualise data from multiple sources.
End-users enjoy exclusive benefits and can check their progress towards points and tiers, motivating
them to interact with the platform to encourage higher conversion rates and higher revenues.
METHOD AND ANALYSIS
To answer the three research questions, a multi-method approach was pursued to combine numerical
measurement and in-depth investigation (Harrison & Reilly, 2011). Johnson and colleagues (2007, p.
123) provide a general definition of mixed-method research:
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“…the type of research in which a researcher or team of researchers combines elements of qualitative
and quantitative research approaches (e.g., use of qualitative and quantitative viewpoints, data collec-
tion, analysis, inference techniques) for the broad purposes of breadth and depth of understanding and
corroboration”.
A mixed-method approach is suitable because analysing a loyalty programme based only on time
series quantifiable indicators may not provide the contextual understanding that carries background in-
formation and complex assumptions beneath the numbers (Atieno, 2009). The inclusion of a qualitative
approach can complement findings with inspiration for new ideas or possible explanations that can help
sharpen existing theory (Siggelkow, 2007).
This study utilised a threefold empirical analysis. First, the transaction data for the rollout period
was examined to uncover trends and associations within the data. Second, the data was then modelled
using the NBD to identify any divergence from expectation, following the method adopted in earlier
B2C studies. In the final phase of the study, the quantitative findings were explored in detail with the
reward programme agency directors in-depth interviews.
Stage 1 - Initial Observations. To examine the data for any regularities, identify trends in repeat
buying, and establish the roll-out dynamics, the sales data was summarised and tabulated in monthly and
quarterly time series over twelve months. Run plots were produced to describe the shape of the scheme
uptake and trends in top-line sales by value and by order volumes – purchase occasions. A number of
loyalty-based marketing metrics were then established for each stage of the rollout: the penetration of
programme members in the customer base, the relative average purchase frequencies of members and
non-members at each stage, the repeat rates from quarter to quarter and the average product portfolio
sizes across the manufacturer product categories. Initial comparisons could then be made simply between
member and non-member buying from these observations.
Stage 2 - Model Fitting. In a second research stage, partly to extend knowledge of repeat buying,
and partly to establish any unexpected deviations from stationarity, model fittings were made to the data
at the final stage of the programme roll out, i.e. at the point that membership was self-selecting by the
end-users rather than where it had been offered by the agency in partnership with the distributors to the
“best” customers. At this point, the NBD was fitted to each month’s customer base sales data to establish
its fit, and evaluate if and how repeat-buying at the manufacturer level was diverging from stationarity,
perhaps as a result of the scheme.
Stage 3 - In-depth Interview. Finally, in-depth semi-structured interviews were conducted with the
client account director at the agency. Interview topics were adapted from the gaps identified in a recent
study by Chen et al. (2021), but allowed the discussion to develop in different directions to ensure depth
and richness in responses. Discussion focussed on motivations for launching the loyalty scheme and the
objectives set by the manufacturer for its performance. The discussion, conducted online, focussed on
benefits for channel members obtained through the loyalty dashboard and the data it provides for the
client. It focussed particularly on how the rollout of the digital reward programme helped the industrial
marketer achieve its relationship loyalty objectives (Ojiaku et al., 2017).
The interview data were analysed under five themes:
• Motivations for launching the scheme

• Client and agency programme objectives
• Benefits to market intermediaries
59

• The insight obtained through the loyalty dashboard

• Solutions provided by the program to prevailing channel marketing issues.
RESULTS
The analysis proceeded with an investigation of the research questions described in the previous section.
Push and Pull Strategy in B2B Marketing
The first enquiry focussed on the appropriateness of a pull strategy in B2B marketing through a depth
interview with the agency account director responsible for rolling out the loyalty programme for the
manufacturer. Before moving onto pull strategies, the interview first examined push strategies in general
to examine the motivation for the programme. The director suggested that it was still normal, even in
the digital age, for:
Representatives from manufacturers [….] to have face-to-face interaction with market intermediaries,
to make the sales call.
This is still a standard tactic through the channel in a push strategy, practised worldwide, especially
with account-based marketing gaining traction in recent years. It is believed to be effective, particularly in
upselling and customer retention. However, it is well-known that face to face selling is the most expensive
method in the promotional mix due to costs associated with premises, travel, trade offers and training; it
requires a large sales force to carry out personal selling successfully at scale, and it is time-consuming
and has a limited reach in a given period.
The account director indicated another prominent issue with push strategies, that of data sharing:
Typically, distributors have been very cautious of sharing details of their end customers over the fears
of manufacturer bypassing the channels intermediaries and dealing directly with the end customer.
He further added:
Such practice has not been beneficial for the manufacturer since this incapacitated any honest, direct
feedback.
The arguments align with previous literature and highlight concerns that push strategies can be
counter-productive for B2B marketers while necessary. Even where other promotional tactics are put in
place, these may not translate into continuous repeat selling after the promotion period is over (Scriven
et al., 2017). Other specific risks for marketing productivity in push strategies are noted in the literature:
• Retailers’ hearty appetite for trade deals makes promotions expensive to operate with no long-
lived change in buying patterns (Martín-Herrán et al., 2017).
• Large trade-deal expenses reduce manufacturer’s profit margins and only permit a limited amount
for further marketing activities (Lassar & Kerr, 1996).
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• The focus on securing a one-time purchase hinders building relationship and reciprocity, and the
results are likely to be short-term.
• The shifting market power to channel partners at the expense of the manufactures due to trade
promotions; in return, increased dependence on the intermediaries (Aliawadi et al., 1995).
• Lack of interaction with end-user and difficulty in obtaining data affect the performance of critical
production decisions (Leng & Jiang, 2016).
• Limited visibility of channel partner’s transactional data decreases the opportunity for category
growth. Manufacturers are unlikely to interpret and benchmark behaviour without such informa-
tion (Wilkinson et al., 2016).
It was then argued that the fundamental challenge from the manufacturer’s standpoint is how to
compete more effectively and profitably in the B2B channel to achieve the firm’s growth objectives,
using push tactics alone.
Introducing digital loyalty programmes to the industrial customer base, a push strategy borrowed from
consumer packaged goods marketing addresses many of the challenges identified. Loyalty programmes are
cheap to set up, operate permanently, unlike price promotions, and accumulate loyalty by building points/
tiers/hierarchies and providing additional information not available before. The account director adds:
By offering this new kind of programmes not only do we get increased efficiency of the communication
where we can be more frequent, lower cost and more meaningful, we can also give feedback and insights
that we could not provide in the past.
Establishing a programme means that the manufacturer firm can engage directly with customers
and motivate their behaviours in meaningful ways. Using digital loyalty programmes, they can adopt
multi-channel communication strategies (such as online and mobile devices) through membership data
to encourage participation and retention.
The interview noted that loyalty programmes in the B2B industry are still a relatively new idea,
therefore:
Getting the first-mover advantage enables a firm to build a relationship quicker and deeper understand
what customers want before competitors enter the arena.
Having a unique platform that rewards buying gives the firm a competitive advantage. In summary, pull
strategies appear to offer a route to sustainable competitive advantage – as the following example shows:
Primarily because technology and innovation have opened numerous opportunities for operating the
platform for the industrial clients.
The benefits are threefold: “…the manufacturer benefits from consistent customer interaction and
new insights into end-user behaviour. Distributors can incentivise category expansion and retention,
allowing improved marketing strategy and the end-customer relish exclusive benefits and recognition
from both manufacturer and distributor”.
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Effectiveness of Digital B2B Loyalty Programme
To answer the second question is a loyalty programme an effective pull tactic in B2B, the available
dataset was explored to evaluate the buyer behaviour outcomes as the programme was rolled out. Sales
made to scheme members were analysed in three quarters over the first year of the programme opera-
tion, corresponding to the launch to widening customer base segments in three phases. Monthly sales
and scheme membership remained largely stationary within each phase, and Table 1 describes the main
buying metrics observed. In the left-hand section, the volume and value analysis of orders is shown, and
on the right, the aggregated behavioural metrics; buyers in each period, and two loyalty metrics, average
purchase frequency and month to month repeat rate of buyers.
Table 1. Summary time series analysis of sales and loyalty metrics
Sales Metrics Loyalty Metrics

Average
Qualifying Qualifying Average Average Monthly
Near-stationary windows Purchase
Sales Invoices Invoice Buyers Repeat
Frequency
$,000’s $’s %
July - September 21 147 148 6 23 92

November - March 231 666 345 37 18 93
June - September 845 2368 358 166 14 94
The table shows that programme sales grew rapidly as the scheme rolled out; from just $21,000 in its
first quarter, to over $800,000 in its third quarter. The table also shows that not only did the scheme sales
value grow, the average value of individual invoices to scheme members also increased by over two times.
Clearly though, looking at the right-hand side of the table, membership is the driver of programme
revenue growth. The data show a dramatic and rapid fall in average purchase frequency, while sales
and buyer numbers are highly correlated. Moreover, as the membership base grows in size, it holds a
steady monthly repeat purchase rate, such that on average, around 93% of buyers in the membership in
one month repeat in the next.
The membership is drawn from the existing buyers of the brand, and from across distributors. In the
third window analysed, there are 166 buyers, which is 16% of the customer base of end-users. The find-
ings are entirely consistent with the idea that the scheme is recruiting the brand’s heaviest existing buy-
ers. A near 100% repeat rate, a doubling of the invoice value, and near-stationary buying characteristics
in each window all suggest little room for improvement in behavioural loyalty! The decline in purchase
frequency is curious but may indicate either (1) a degree of order-bundling to obtain scheme rewards
or (2) simply that the very heaviest buyers were recruited first. Further analysis was then undertaken to
investigate the scheme in the context of the firm’s total customer base.
First, the investigation was extended outside the scheme membership data to examine the shape
of total brand sales through all distributors. Figure 2 shows a monthly sales analysis in time series for
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the complete brand customer base - non-members and loyalty programme members - to identify any
performance trend between July and January 2000. Across the period, total sales appear to be broadly
stable – excluding a seasonal Christmas dip - meaning that although scheme sales are increasing, they
are taking a bigger share of a stable whole. On the other hand, it is also possible to say that overall
manufacturer sales are maintaining their value in the face of competition.
Figure 2. Evolution of sales revenues in the total customer base*

Customer base * (n) = 1042 *refers to both members and non-members.
Whether or not the loyalty programme has built additional sales for the brand has been examined in
light of behavioural data; the results are mixed. One apparent reason is that sales remain stable in the
customer base data; it is still hard to untangle the scheme’s performance from the top line sales curve
alone. This is because, under the surface of stability, significant buying fluctuations for different buyer
classes and categories may exist that are worth exploring. An analysis of the total customer base would
identify significant departures from the norms of stationary buying and, in comparison with the loyalty
metrics of scheme members, whether the scheme was influencing loyalty in unusual and unexpected ways.
For example, there is some evidence that the scheme was changing buying patterns by encouraging
order bundling – bigger but less frequent orders. The qualitative research had noted that:
…customers were bulk buying to get better rewards at a reduced cost on freight.
This might explain the declining purchase frequency because of less frequent orders and reflect
increased efficiency due to the programme.
But fitting an NBD would establish whether the loyalty (the distribution of heavy and light buying
across the buyer base) was anything other than what might be expected in a stationary situation. Given
the stability in the top line sales data, NBD fitting was deemed appropriate for each month, and the
estimations are summarised as an average monthly result for the final quarter of the analysis in Figure 3.
63

Figure 3. Customer base heterogeneity: average month

Customer base = 1042 b = 0.40; w = 9.9
NBD Theoretical vs. Observed buying July 2019 to January 2020.
The model output (shown in the histogram) is a surprisingly close fit to the observed data (shown as
the continuous curve). This, therefore, reflects several important characteristics about repeat buying in
the B2B customer base and implies that the brand buyers are behaving as expected from the evidence
presented in many studies. Notably though, the behaviour of the customer base as a whole is somewhat
different from that of the programme members.
For example, where the membership buys every month (a 94% repeat rate on average), only 40% of
the total customer base buy in any given month. The repeat rate is also far lower within each month,
where the average customer purchase frequency in the membership is 14 orders each month; for the total
customer base, that average rate is only 9.9.
The distribution is typically reverse-J shaped so that most buyers are lighter buyers than average: the
most common order frequency is only once a month, the buying rate for nearly a quarter of the monthly
customers. Therefore, the main finding is that programme members are not typical customers. In addition,
the total customer base has not been skewed by particularly heavy buying to vary from the expectation.
In short, there is evidence that the scheme may have increased order values for certain buyers, but it has
not changed the distribution of purchase frequencies beyond what is expected.
Therefore, while a digital loyalty programme may be an appropriate pull tactic in B2B, it may not
necessarily be an effective tactic if the objective is to adjust the loyalty curve’s shape permanently. On the
other hand, if the B2B customer base is similar to the B2C customer base in its repeat buying behaviour,
the programme may deliver some unexpected benefits, which we discuss in the final section of the chapter.
Advances through Digital Marketing
In answer to the third question, we asked whether advances in digital marketing could now make such
programmes more widely applicable. These advances have created two critical opportunities for B2B
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organisations. First, a wide array of digital tools are available to target different customer segments.
Second, those digital tools in combination with data science have made marketing initiatives more mea-
surable by allowing marketers to access, gather, analyse, and report data (Järvinen et al., 2012; Pauwels
et al., 2009; Saura, 2020), shaping knowledge-based decisions.
Since their inception, loyalty programmes provided relatively inexpensive means for firms to collect
information about spending patterns (Hamilton & Howcroft, 1995). However, many traditional loyalty
marketers did not seek to obtain transaction data in the past but simply rewarded behaviour in propor-
tion to customer purchasing. Such programmes are less expensive to manage (because of no marketing
dollars spent on the analysis) and hinder the opportunities to gain valuable customer insights, develop
relationships, and have been seen as more tactical than strategic marketing tools (Palmer et al., 2000).
In order to inform our research question, we asked the agency whether new digital B2B loyalty pro-
grammes are more strategic as opposed to tactical. And if so, in what ways? The response:
“They are both - we can lay a strategic framework for long-term relationship building but can also use
tactical promotions and offers to drive behaviour”. With the digital advances, loyalty programmes have
allowed the B2B firm more customer base segmentation opportunities because of the data it provides.
The detailed analysis of customer profiles helps to identify different buyer classes and their distinct
buying behaviour. Moreover, the manufacturer can now use this information to adapt and customise
their product offer for individual segments. “…to increase category usage and consumption across the
customer base, we set customised points and bundles together to engage and upsell to the segments had
not actively bought before”.
That is, the nature of the data delivered through loyalty can be used to improve performance and
develop distributor and customer relationships.
In addition, digital loyalty programmes work as a useful tool for testing the effectiveness of different
promotions much quicker –
“It is easier to test and identify what offers customers respond to best”.
The firm is now also able to have multiple touchpoint interaction. An example from account director
follows:
“We can now use the desktop, laptop, mobile, all social media channels to engage with the customer”.
The marketers can spot the need for quick insight, “...if certain SKUs seem to be popular in one segment
and not the other, instant data analysis can answer why the behaviour is happening the certain way”.
The analysis can be cross-referenced over time and extend comparison by customer segments, size,
geography and industry. The key to effective strategies will be learning from the loyalty programmes
analytics platform (Saura et al., 2017) and understanding how to effectively target communication and
manage channel offerings to maintain repeat purchases.
But the analysis of the customer base made here demonstrates the critical limitation of the scheme.
The data analysed is only pertaining to the behaviour of the scheme membership and not to the non-
members. While it may seem intuitively essential to manage the repeat buying of the most loyal custom-
65

ers, it may well be the case that they may find it hard to order any more often! Total sales also depend
on the contribution of the lightest buyers, those least likely to be in the scheme.
The data analysis highlights the importance of a new focus, not on the repeat buying of the heavi-
est, but the acquisition of the lightest buyers into the scheme. Regardless of operating in a B2C or B2B
market, brand growth usually comes by attracting many light buyers to the brand (Warc, 2021), and
brand decline happens due to losing many light buyers. In this regard, the digital scheme could provide
an excellent mechanism to interact with light buyers and incentivise their purchases, to reduce churn.
If appropriately managed, the B2B marketers’ vision should maintain heavy buyers but encourage light
buyers to enter and stay in the scheme as a low cost means to “stay in touch” between purchases. This
is more easily managed in B2B than in B2C.
Digital innovation provides additional opportunities to improve organisational learning, and leverage
end-user knowledge (Abrell et al., 2016). For example, the online platform gathers information that can
be used to develop a database to recognise buyer behaviour patterns (Zinkhan, 2001) and predict future
purchase rates by incorporating the NBD. Most online platforms have integrated web analytics systems
that analyse and report data to help understand users’ activity (Saura et al., 2017). By studying corporate
buying in this way, a loyalty platform can recommend future purchases and encourage upselling in other
categories with customised offers and rewards.
The transition into the online platform and dataset availability has brought several benefits. Digi-
tal marketing is a catalyst for channel management (Dasser, 2019) because it builds new engagement
networks that stretch back from end-user to manufacturer and allow a deeper understanding of the rela-
tionship potential. For example, the loyalty programme seems to be a cost-effective way of maintaining
customers. Also, digital has opened lines of communication between a manufacturer and their end-users
by providing means to communicate directly to members (Zhang et al., 2019) via applications, emails,
direct messages, social media, push notifications. This has eliminated the reliance on intermediaries for
customer information and transaction assistance that was not possible using traditional push strategies
of the past (Mudambi & Aggarwal, 2003).
DISCUSSION
This chapter analysed the launch of a novel B2B channel strategy supported by advances in digital mar-
keting – we set out to find answers to three research questions: 1) Is a pull strategy appropriate in B2B
marketing? 2) Is a loyalty programme an effective pull tactic in B2B? 3) Is this now possible by applying
digital marketing practices? The research used sales data from a newly launched loyalty programme and
in-depth interview with agency directors to answer the questions.
For many, the appropriate marketing strategy in B2B sales involves push tactics to move goods through
distribution channels towards end-users by squeezing competitors out of the way. Typically, these tactics
include short term volume and price promotions, face to face interactions, and sharing of resources in
order to build relatively stronger distributor relationships. These tactics are likely to produce volatile
and short-run outcomes because they are usually matched by the rival brands in the same channel, who
then regain lost ground. In B2C markets, these efforts are augmented with pull investments designed to
create end-user demand, usually advertising or promotions, and again often with only short-term results
that serve only to maintain a long-run equilibrium rather than persistent growth. Pull investments are ap-
66

propriate in B2C markets where efficiency is gained through one-to-many investments in the context of
“arm’s length” customers. Our first question is whether the same tactics are appropriate in B2B markets.
An advantage of the loyalty programme as an end-user pull strategy is that it is designed to build a
competitive advantage for the brand in the form of incentives, and those incentives are related to perfor-
mance. That means that marketing investment is more closely aligned with profitability and, therefore,
more efficient. A second advantage is that the marketing pressure applied is continuous in the context
of a complex marketing channel where a manufacturer has become separated from the end-user through
a distributor network; a loyalty programme makes excellent sense if it rebuilds links with end-users and
provides a means to incentivise the intermediaries in addition. It resolves precisely the same challenge
faced by B2C marketers, as our interview has demonstrated.
Second, we used a model associated with B2C marketing to evaluate the behaviour of the B2B
customer base. We showed that the early adopters of the programme were the heaviest buyers in the
customer base, and this is hardly surprising given they have the most to gain. Nevertheless, membership
is relatively low a year after launch, and across the total customer base, the scheme appeared to have
left the regular distribution of purchase frequencies unchanged. Although the loyalty of heavy buyers
might seem to be immune to management, the fact that the NBD gave such a close fit points up a cru-
cial lesson: every customer base has far more light buyers than heavy, and in total, they contribute an
important sales volume.
Sales from light buyers are neither optional nor dispensable. In many ways, this presents a more
complex marketing challenge than that posed by the few heaviest customers. Brands grow and decline
by gaining and losing large numbers of the lightest buyers, and yet here; the loyalty programme is not
reaching this part of the customer base. If it did, it would offer a cost-effective solution to the problem
of keeping the brand mentally available in the mind of the industrial buyer, particularly where the pur-
chase interval is prolonged. It should help the brand stay in touch with all of its customers. A loyalty
programme is uniquely placed to implement this strategic pull task because it is a continuous mechanism
and not a campaign-based tactic.
Finally, we asked if the advances in digital science would make the implementation of a reward
scheme more efficient. Whether in B2B or B2C, managers look for new ways to increase business ef-
ficiency (Berry & Rondinelli, 1998; Abubakar et al., 2019). Interventions through data-driven loyalty
programmes allow opportunities to analyse customer base data and take prompt actions, test promotional
effectiveness, segment buyer classes. These schemes provide means to interact not only with heavy but
also the infrequent buyers. Moreover, an online loyalty programme creates an owned, incremental and
engaging brand touchpoint relevant for all customers (Lemon & Verhoef, 2016) even if it is only actively
targeted at the most loyal. Engagement with scheme by heavy and light buyers is refreshing brand memory
structures (Romaniuk et al., 2013) even if a purchase does not immediately follow, maintaining brand
salience among the membership. Online loyalty programmes should be broadly not narrowly targeted
among the customer base to achieve higher reach for the brands they promote. This would more likely
contribute to the overall brand growth (Sharp et al., 2009; Sharp & Sharp, 1997).
THEORETICAL IMPLICATIONS
The NBD fit to the B2B loyalty programme context suggests that the model is robust and practically
useful here. In addition, its reverse-J shape reflects the fact that most buyers in B2B as in B2C are far
67

lighter than the average purchase rate. Consistent with previous loyalty programme literature, the NBD
fit proposes that industrial purchasing follows similar repeat buying patterns as B2C, contrary to some
prior commentary, which has suggested that regular timings of orders might constitute a theoretical
boundary condition (Sharp et al., 2002)
This chapter extends the application of the NBD model, which has seldom been applied in manu-
facturing and industrial goods markets before (although one recent exception is Wilkinson et al., 2016).
The findings extend knowledge of several important buying characteristics to the industrial customer
base and imply that the brand buyers behave as expected from many similarly close fittings. Particularly
here, though, the behaviour of the customer base as a whole is to a certain degree different from that of
the membership base, which in this case appears to consist only of the brands heaviest buyers.
We also highlight an increase in business efficiency as B2B customers began to bundle their purchases
into bigger but less frequent orders to save shipping costs in order to enjoy exclusive rewards. Thus,
the scheme motivated some end-customer behaviour; however, overall buying propensities remained
essentially unchanged.
By capturing interview insights, we acknowledge managerial objectives and motivation to launch
the scheme to the industrial customer base, its benefits to the channel partners, and the solution pull
strategies can provide to industrial manufacturers. The chapter value contributes to understanding the
use of big data insights obtained through the loyalty dashboards that support manufacturers to innovate
and overcome prevailing channel marketing issues. Moreover, such innovation can help the B2B firms to
identify commercially valuable patterns to supply critical knowledge of channel partners and transform
customer experience at the end of the value chain – i.e. the end-customer (Wright et al., 2019).
MANAGERIAL IMPLICATIONS
B2B firms’ nature towards marketing planning and decision making based on intuition, flexibility, and
experience may be challenged by the formalised nature of digital loyalty programme data and the analyt-
ics it combines (Armario et al., 2008). Going forward, managers need analysis and interpretive expertise
to create unique insights into consumer behaviour to reap the maximum benefits of programme launch.
The key insight here is to recognise the value of light buyers and adapt the scheme to offer incentives to
this group, which make the scheme relevant. In line with current thinking, digital loyalty programmes
are a flexible, relatively cheap and efficient marketing instrument.
The digital B2B programme allowed the manufacturer to reach the distributor’s customer and worked
backwards to the end-user. This generated data-driven insights for the manufacturer, offered rewards for
the end-customer, and created a demand pull. In conclusion, B2B managers should not hesitate to launch
loyalty programmes as they make channel marketing more efficient and facilitate technology-enabled
dialogue between the manufacturer and the end customer. But they should be managed for the whole
customer base, not the few heavy buyers that constitute around 15% of it, enabling attractive rewards
and offers targeted towards the lightest, most infrequent buyers.
In summary, the initial evidence has highlighted the value of digital loyalty programmes for B2B
marketers. Managers must consider loyalty programmes performance in the round: do competing manu-
facturers have rival schemes, can they provide more opportunities for upselling and engagement among
channel partners, do they have the potential to manipulate order size and reduce costs through bundling?
68

The actual value for B2B marketers is not in creating data warehouses through digital schemes from
which they can mine potentially helpful information (Palmer et al., 2000). However, it lies in using big
data in analysing and identifying commercially valuable patterns or relationships to supply critical com-
petitive knowledge to all channel partners. Knowledge of these recurring patterns helps management
because, in the form of empirical generalisations or simple models, they can predict the likely outcome
from a given level of investment.
CONCLUSION
Loyalty programmes may be seen as old fashioned, and they have been frequently criticised in the literature
as doing little to manage loyalty which generally follows predictable patterns. This study examined the
rollout of a new and innovative digital loyalty programme in an unfamiliar context, a heavy industrial
setting where a major manufacturer reaches the end-user through a distributor network.
We found that a familiar model of consumer behaviour, the NBD, worked well to describe the distribu-
tion of purchase heterogeneity in the B2B brand’s customer base. However, its assumption of stationarity
remained intact – in other words, the scheme was unlikely to have changed the buying propensities of
existing customers.
On the other hand, this drew attention to another aspect of industrial buying, the predictable nature and
importance of the lightest brand users. A digital loyalty programme makes an excellent and appropriate
tool to deal with the vital marketing task of maintaining pressure on this large group of customers at a
low cost if it can be diffused across the whole customer base.
LIMITATIONS AND FUTURE RESEARCH DIRECTIONS
This study produces novel insights into the value of digital loyalty programmes in the industrial buying
context. A number of limitations suggest that further research is required that builds upon the results
and addresses gaps of the study.
The first limitation stems from the data used—the analysis comprises smaller and inconsistent B2B
datasets between programme membership and the entire customer base. Future researchers could in-
corporate rich longitudinal digital data to help assess schemes’ ability to build sales and loyalty for an
extended period. Assessments can also be extended to loyalty website and mobile application interface
to test whether engagement is predictable (Graham et al., 2021) and if the expected engagement can be
modelled on long-term repeat activity to compare against the NBD norms. Such research will contribute
to online behaviour knowledge, specifically regarding the predictability of acquisition and engagement
to online loyalty programmes.
Secondly, the conclusions are limited to a single brand loyalty programme. In order to test scien-
tific validity and strengthen the generalisability and robustness of the NBD model, future research is
now desirable to conduct a replication study at another, or better, at several industrial loyalty schemes
simultaneously. Longitudinal B2B membership data reporting usage occasions from a wide range of
firms would be preferable. If available across the B2B industry, this data would allow a greater number
of brands to be studied; and a category-level NBD-Dirichlet to be fitted, from which benchmarks for a
wider range of behavioural metrics could be estimated. Such analysis is crucial for marketers better to
69

understand the nature of competition in their market. The NBD fits well in extended time periods of
continuous buying (Dawes et al., 2020), and this warrants extension from B2C into B2B.
Lastly, further work could consider the privacy concerns associated with loyalty programmes data
that have received little academic attention to date. Digital loyalty programmes have opened doors of
incredible opportunities for B2B managers with the rapid growth of big data, market intelligence and
the precision of micro-targeting they provide (Donnelly et al., 2015). By nature, such programmes gain
insights from the large amount of data they collect daily; however, not much is known regarding end-
customers willing to share their activity information to receive personalised offerings and communica-
tion (Saura et al., 2021). Gaining such understanding is crucial as firms increasingly pay attention to
satisfying member’s privacy rights and regulators strictly scrutinising ways marketers approach targeted
communication in some industries. A recent example is regulations imposed on bookmaker firms in
Great Britain to control the use of the VIP schemes data (Gambling Commission, 2020).
ACKNOWLEDGMENT
We gratefully acknowledge the support provided by Dr Andrew Mitchell – CEO of Brandmovers Inc
and founding Chairman of the Brandmovers Institute for co-funding this study, Joshua Mitchell – Ac-
count Director Brandmovers Inc. for discussing B2B channel strategy and the anonymous B2B firm for
permission to access data.
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retailers) to achieve manufacturer’s distribution objectives (Rosenbloom, 2007).
Empirical Generalisation: A fixed relationship between variables that regularly occurs; hence they
describe what tends to happen to x if there is a given change in y. Such knowledge is useful for theory
building or making routine predictions (Sharp et al., 2017).
Loyalty Programmes: Structured marketing efforts designed to encourage behavioural loyalty to the
brand by rewarding members with incentives such as points redeemable for prizes or discounts (Uncles,
Dowling & Hammond, 2003).
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Negative Binomial Distribution (NBD): The NBD theory is a mathematical model used to predict
future purchase patterns for a single brand or category from data on purchase frequency and penetration
for any given period (Ehrenberg, 1988).
Penetration: The proportion of all buyers under investigation who buy brand x at least once in a
period, measured in percentage (Wilkinson et al., 2016).
Pull Strategy: A promotional strategy focuses on facilitating communication flow directly from a
manufacturer to end customers to create a demand “pull” for products suitable for their needs (Brocato,
2010).
Purchase Frequency: The average number of purchasing made by those who purchase at least once
in a period (Wilkinson et al., 2016).
Push Strategy: A traditional trade marketing strategy used to “push” manufacturers products through
the channel by incentivising market intermediaries rather than end customers through the promotion mix
(Armstrong, Kotler & Opresnik, 2011).
Relationship Marketing: Marketing efforts designed to establish, retain and enhance long-term
relationships with a firm’s existing customers to increase profitability (Grönroos, 1994; Peterson, 1995).
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Section 2
Towards Practitioner
Knowledge: Applied Digital
Marketing Strategies
In order to understand how practitioners should develop digital marketing actions in their industries,
this section presents several chapters that analyze from a business point of view the main actions linked
to decision-making that CEOs, executives, and other company managers have to consider for the devel-
opment and optimization of data-centric digital marketing techniques.
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Chapter 5
Alibaba’s International
Digital Marketing Practices
and Strategies
Sue Claire Berning
University of Ingolstadt, Germany
ABSTRACT
In this chapter, the main relationship between a company’s use of data-driven methods and its interna-
tional digital marketing strategies are examined. In particular, the question of how data-driven methods,
like consumer analytics, helped the company in its internationalization efforts are outlined. By follow-
ing the case study approach, the diverse digital business models, online advertising campaigns, and
international digital marketing practices of the Chinese company Alibaba are investigated. As China’s
e-commerce market currently became one of the most dynamic ones in the world, and as Alibaba is
one of the leading internet and e-commerce corporations worldwide, valuable insights are provided.
Moreover, Alibaba’s international digital marketing practices, underlying strategies, as well as adaptive
capabilities are systematically analyzed. In addition, Alibaba’s competitive behavior is investigated and
compared with international companies and peers. In this context, the standardization versus adaptation
paradigm is also revisited.
INTRODUCTION
During recent years, the digital economy has rapidly grown both in Western and Asian countries as
well. Especially in China, a tremendously fast increase happened. Consequently, China became one
of the biggest e-commerce markets in the world, for example by having 120% of compounded annual
growth from 2003 to 2013 (Kwak, Zhang & Yu, 2019). Therefore, the developments of digital markets,
digital business models and digital marketing strategies and practices of Chinese companies in China
and worldwide provide interesting and relevant insights for professionals and researchers alike.
The aim of this book chapter is to give a comprehensive overview of the international digital market-
ing strategies and international expansion of the Chinese company Alibaba by analyzing its key success
DOI: 10.4018/978-1-7998-8003-5.ch005

Alibaba’s International Digital Marketing Practices and Strategies
factors. There exits several papers on Alibaba’s development, its founder Jack Ma, its strategic decisions
and expansion (e.g., Kim, Zhang & Zhang, 2016; Hu & Chang, 2019; Yun, Zhao, Park & Shi, 2020),
however, a paper focusing on Alibaba’s latest internationalization activities regarding digital marketing
practices and expansion is missing.
Further, the relationship between data-driven methods and Alibaba’s international digital marketing
strategies is examined. In particular, the question how Alibaba’s international digital marketing strategies
benefited from the use of data-driven methods like consumer analytics is investigated.
The structure of this book chapter is as follows. After a literature review, a short overview of the
Alibaba Group, the huge digital ecosystem that Alibaba built in an unprecedented speed and manner is
outlined. This digital ecosystem is the foundation of Alibaba’s enormous success, nationally and inter-
nationally. The digital business models of Alibaba are described as well.
Then outstanding key points describing the strategist Jack Ma are delineated. His strategic decisions
and approaches right from the start, as well as online advertising campaigns are described.
The main part include the systematic description of Alibaba’s international digital marketing practices,
the underlying strategies, as well as the international expansion. All marketing mix elements (product,
price, place, promotion) are analyzed in detail. Further, the different internationalization entry modes
applied by Alibaba are discussed. In addition, the innovative approaches of Alibaba how digitalization
and data-driven methods can enhance its international businesses are covered as well. The chapter ends
with a discussion, limitations, future research directions as well as concluding remarks.
This book chapter might contribute to current knowledge in several ways. By following the case study
approach, operation practices of a very successful company are examined. As the roots of Alibaba and
its management lie in China, a non-Western way and view of doing business is presented.
In addition, the relationship between data-driven methods, like Big data and consumer analytics, and
adaptive capabilities in international marketing activities is carved out.
Further, Alibaba’s success in international markets is largely based upon its success in China. An
analysis of the approach of how far domestic successful marketing practices could be transferred to
foreign markets is therefore included in the paper.
Moreover, Alibaba’s competitive behavior is investigated and compared with international companies.
In this context, the standardization vs. adaptation paradigm is revisited.
LITERATURE REVIEW
The literature on digital marketing, especially on the role of data-driven methods grew constantly in
recent years (see e.g. Kannan & Li, 2017; Dwivedi & Kappor & Chen, 2015; Koiso-Kanttila, 2004).
Data-driven methods and technologies can help capture rich and plentiful data on consumer phenomena
in real time. The process of extracting consumer insights from Big Data became ever more important
in recent years (Lycett, 2013; Mithas, Lee, Earley & Murugesan, 2013). Hence, the study of consumer
analytics lies at the junction of Big Data and consumer behavior (Weber & Henderson, 2014). Consumer
analytics is defined as the extraction of hidden insights and patterns of consumer behavior from Big
Data and the use of these insights (Erevelles, Fukawa & Swayne, 2016).
Tech-savvy organizations have started to use Big Data with the goal to improve their decision-making,
their agility, and their customer-centric approaches (George, Haas & Pentland, 2014; Camilleri, 2020).
They use analytics to identify how exogenous variables, like the broader economy, competitive offerings,
81

or the weather can affect consumer sentiment toward products and brands (Cambria, 2016), as well as
their organizational performance (Akter et al., 2016). By collecting and storing data from each and every
customer transaction they use analytics to customize and personalize their offerings and improve their
customer engagement (Grover et al., 2018; Ransbotham & Kiron, 2018).
Successful marketing strategists exploit these customer insights by translating them into market advan-
tages (Camilleri, 2020). Even though Big Data is considered a new form of capital (Mayer-Schönberger
& Cukier, 2013), many companies fail to exploit its benefits as the right interpretation and consequent
uses need skillful managers and fast reactions, thus specific dynamic and adaptive capabilities (Mithas,
Lee, Earley & Murugesan, 2013).
In extant literature, the process of using consumer analytics to understand consumer behavior is
only seldom connected to the process of utilizing consumer insights to enhance dynamic and adaptive
capabilities. Focus is laid on the latter process. A company’s capability to respond to changes dynami-
cally incorporates skills and knowledge embedded within the company to alter existing resources and
create new value (e.g., Teece, 2007; Day, 2014; Kozlenkova, Samaha & Palmatier, 2014). A company
using novel consumer insights extracted from Big Data to understand unmet consumer needs enhances
dynamic capability (Erevelles, Fukawa & Swayne, 2016). When companies are proactive in responding to
changes in the external environment by capturing even weak signals from consumers to predict market and
consumer trends, their adaptive capability is strong (Day, 2014). When successfully exploited, Big Data
provides firms with opportunities to enhance adaptive capability (Erevelles, Fukawa & Swayne, 2016).
Both dynamic capability and adaptive capability achieved through consumer analytics facilitate value
creation in diverse marketing activities (Ambrosini & Bowman, 2009; Day, 2011). In particular, value is
created as a result of improved decision making enabled by Big Data. For example, consumer insights
enable advertisers to better measure and improve the effectiveness of digital advertising (Bharadwaj
et al., 2013). Overall, better understanding customers through Big Data improves the effectiveness of
existing marketing activities (Story, O’Malley & Hart, 2011; Baesens et al., 2014).
The internationalization process of companies has been changed in various aspects through digita-
lization, too. In order to face the challenges of the internationalization, companies need to understand
thoroughly the new digital business environment, including foreign markets and customer insights (Barton
& Court, 2012; Berthon et al., 2012).
Big Data as the new source of knowledge can help companies convert business information into
competitive advantages in the global market (Côrte-Real, Oliveira & Ruivo, 2017). Although the topic
of Big Data receives much attention from researchers, the adoption of Big Data in internationalization
is still an emerging research interest how Dam, Dinh & Menvielle (2019) found in their systematic lit-
erature review on Big Data adoption in internationalization. Therefore, in the following the relationship
between consumer analytics and adaptive capabilities in international marketing activities is analyzed
by taking a closer look at the Chinese company Alibaba.
COMPANY OVERVIEW
Alibaba Group Holding Limited (Alibaba) is often cited as one of the most successful and most innova-
tive Chinese companies (e.g., Yip & McKern, 2016). In 2019, Alibaba was the Chinese company with
largest market value with around 480.8 billion US dollar, and the second most valuable retail brand in
the world just behind Amazon (Textor, 2020).
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Alibaba is one of the biggest e-commerce companies worldwide specializing in retail, internet, and
technology. For the fiscal year ending March 31, 2020, Alibaba had 780 million consumers in China
and over 180 million consumers outside China. The total revenues were more than 509.7 billion RMB
(around 73 billion USD) (see Figure 1), and a year-over-year growth of its core commerce revenue of
35% and of its cloud computing revenue of 62% (Alibaba Fiscal Year 2020 Annual Report, 2020).
After its initial public offering at the New York Stock Exchange in September 2014, Alibaba’s mar-
ket value was 230 billion USD – more than Amazon and eBay taken together (Anwar, 2017). Founded
in 1999 in Hangzhou, Alibaba has had in 2020 more than 117,600 employees in more than 70 cities
worldwide (Alibaba Fiscal Year 2020 Annual Report, 2020).
Figure 1. Development of Alibaba’s total revenues worldwide (Source: Statista 2020)
Alibaba’s Ecosystem
In 1999, Alibaba started with launching Alibaba.com and 1688.com, two business to business (B2B)
online trading platforms for small and medium sized businesses. Alibaba.com is geared toward global
wholesale trade, while 1688.com is targeted at wholesale trade for the domestic Chinese market (Alibaba
Fiscal Year 2020 Annual Report, 2020). In China, both the numbers 6 and 8 symbolize good fortune
due to their similarities in pronunciation with the respective Chinese words.
Four years later, Alibaba established Taobao, a platform which enables online shopping from con-
sumer to consumer (C2C). Taobao also facilitates individual entrepreneurs and small businesses to open
online stores, and can be seen as a direct competitor to eBay as it follows the same digital business model.
In 2007, Alibaba’s monetization platform Alimama was launched. Alimama is an online market-
ing technology platform offering sellers of Alibaba’s marketplaces digital marketing services for both
personal computers and mobile devices.
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One year later, Tmall was launched to complement Taobao Marketplace. Tmall focuses on business
to consumer (B2C) online shopping of big brands and retailers.
Another subsidiary of Alibaba was established in 2009, Alibaba Cloud. Alibaba Cloud provides
cloud computing services to online businesses and Alibaba’s own e-commerce ecosystem.
In 2010, AliExpress, the third digital consumer marketplace was launched. AliExpress is a global
transaction-based retail website that allows smaller buyers to buy small quantities of goods at wholesale
prices with a fast delivery.
In 2013, Alibaba established Cainiao Network together with its business partners, six large Chinese
logistics companies. For delivery of packages in China, this network grew to 14 local logistics compa-
nies within one year.
In 2014, Alipay’s parent company Ant Group was formally established. Alipay is an online payment
platform providing an escrow service as well, often compared to PayPal as it offers the same digital
services.
In 2017, the first Freshippo (known as “Hema” in Chinese) store was opened. Freshippo is Alibaba’s
proprietary grocery retail chain leveraging New Retail to converge online and offline shopping by hav-
ing for example, unmanned checkout kiosks, mobile-scanned product details, or fast-running fulfillment
conveyor belts. Freshippo wants to offer its customers digitally enabled grocery-shopping experiences
with just one mobile app (Wang, 2020).
Other digital platforms belonging to Alibaba are DingTalk (a communication and collaboration
platform), Ele.me (a leading on-demand delivery and local services platform), Youku (an online long-
form video platform), Juhuasuan (a group shopping platform for Chinese customers), Lazada (an
e-commerce platform in Southeast Asia), Fliggy (formerly known as Alitrip, an online travel platform),
Alibaba LST (a one-stop shopping platform for ordering, logistics, marketing and other services for
offline retail shops), Kaola (an import e-commerce platform), and Damai (an entertainment ticketing
platform). Table 1 gives an overview over Alibaba’s businesses.
The business model of Alibaba consists of providing a digital market place which connects sellers
and buyers and of generating income from the arrangement of transactions. It does neither compete with
its sellers nor service providers. The growth strategy consists of increasing the number of customers
and their spending, the number of product categories, as well as the spreading of online trade (Alibaba
Fiscal Year 2020 Annual Report, 2020).
At the core of Alibaba’s business model is the group of online business platforms which are supported
by the cloud computing system. They enable transactions and facilitate numerous functions for suppliers,
customers, app developers, and related service providers. For instance, Taobao provides its online search
engine for users to find the product or service they want. Hence, it builds a network between buyers and
sellers and serves as a platform where they make transactions (Wu & Gereffi, 2018).
One crucially important complement for facilitating online transactions was Alibaba’s decision to
provide financing. On the one hand, Alibaba helped small and medium sized businesses to gain access
to financing by providing banks with their transactions and trading records. Without Alibaba using small
and medium sized businesses’ data and forwarding these data to banks Alibaba’s e-commerce platforms
would never have become such a success. On the other hand, Alibaba started its own loan business for
small and medium sized businesses with Sesame Credit, China’s first credit agency (Scheuer, 2018).
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Table 1. Overview of Alibaba’s businesses (Source: own compilation)
Core Commerce
Core Commerce
China
International
▪ Retail Commerce: Tmall, Taobao,
Retail Commerce: AliExpress, Lazada,
Freshippo, Tmall Supermarket, AliHealth
Tmall Global, Kaola
▪ Wholesale Commerce: 1688,
Wholesale Commerce: Alibaba
Lingshoutong
Digital Media and Entertainment: Youku, Alibaba Pictures, Xiami Music, Damai, Shuqi
Consumer Services: Ele.me, Koubei, Fliggy
Innovation Initiatives: amap.com, DingTalk, Tmall Genie
Logistics Infrastructure for Core Commerce and New Retail Initiatives: Cainiao, Fengniao Logistics
Marketing Services and Data Management Platform: Alimama
Payment and Financial Services Infrastructure: Ant Group
Technology and Systems Infrastructure: Alibaba Cloud
Another essential component of Alibaba’s success is its investments into logistics. Its Cainiao Smart
Logistics Network exemplifies Alibaba’s innovative mindset and strategic advancements. Cainiao speeds
up the digitization of China’s logistics industry by connecting smart devices, warehouse and delivery
robots with algorithm-based management systems. The flow of parcels ordered online is digitally managed
along all steps of the logistics value chain. This includes parcels tracking, robot- operated warehouses,
automation technologies, delivery drones, driverless delivery robots, as well as pick up stations and
lockers working with facial recognition for opening (Campbell, 2020).
Overall, Alibaba’s digital market places are very successful in attracting millions of buyers and sellers
worldwide. By concentrating on combing online platforms, financing, and data services, Alibaba has
built a huge ecosystem of platforms, connections, and agreements for logistics, advertising, marketing,
and cloud computing.
THE STRATEGIST JACK MA
A lot of information and articles can be found about the founder of Alibaba, Jack Ma (e.g., Grosse,
2012; Kim, Zhang & Zhang, 2016; Hu & Chang, 2019; Zhou, Kim & Rui, 2019). His personal history
and background as a failed and not very successful English teacher, his personality characteristics as
visionary and entrepreneur, as well as his management style and philosophy are described and analyzed
in numerous studies.
An outstanding attribute of Jack Ma is his clear strategic thinking and strategic business approach.
Reflecting his innovative and far-sighted attitude, some examples will follow to illustrate Jack Ma’s
strategic decisions which were crucial for Alibaba’s successful development.
First, before determining a company name Jack Ma discussed with American friends several ideas
because he wanted to build in the long run a truly global company and their assessments as potential
English-speaking buyers was essential. By finally choosing ‘Alibaba’, he could even get two goals in
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one go: Alibaba is easy to remember for an international audience, and is actually listed in alphabetical
order before his biggest competitor Amazon (Scheuer, 2018).
Second, right from the beginning of Alibaba Jack Ma put the focus clearly on small and medium
sized enterprises (Anwar, 2017). This explicit target group concentration combined with the aim to
provide a digital trading platform for the companies that entered his online database proved to be the
right decision.
Next, one key success factor for Jack Ma was his deep understanding of Chinese customers and his
derived conclusions there from. For example in 2004, his powerful US-American rival eBay entered the
Chinese market. eBay transferred its American principle of selling the products via auctions to China
without any adaptations, following an absolute standardization approach. Equally, the internationally
used product categories were just copied to the Chinese market in an unchanged way (Scheuer, 2018).
However, these standardized concepts did not work in China. Jack Ma’s prompt reply to eBay was not
only the incredibly fast foundation of Taobao but also the quick adjustment and adaptation to Chinese
customers’ preferences: Jack Ma realized that Chinese customers were not used to the bidding on auc-
tions and mostly overstrained, so as a reaction he offered the majority of products for direct sale very
soon. Additionally, he adjusted the arrangement of product categories in such a way that they followed
the same scheme Chinese customers knew from their local department stores. These digital marketing
techniques proved to be very successful. After two years, Taobao had managed to dominate the Chinese
market with a market share of 67%, while eBay only held 29% (Shao, 2019). This leading principle of
Alibaba of adaptations of foreign products and services to the Chinese market is seen as a crucial success
factor, and can be interpreted as a further extension of the standardization versus adaptation paradigm
of the international marketing literature (Ryans, Griffith & White, 2003).
Another example of Jack Ma’s strategic skills is the idea of the “Singles Day” on 11.11. (Chen &
Li, 2019). This online advertising campaign is by far one of the most successful ones. Lonesome single
students in China’s big cities formed that date due to the many ones / single digits as day of the singles.
Alibaba took up that idea and persuaded the city youth that this day should be celebrated by abundant
online purchases. With a mixture of incentives and compulsion, Alibaba induced its sellers to advertise
with big discount campaigns on 11.11.. For many companies, 11.11. became even more important than
the Chinese New year and pre-orders are placed months ahead. Hundreds of thousands of parcel deliv-
eries congest the cities every year shortly after 11.11.. The development of online purchases on 11.11.
shows that Chinese consumers spent 3.04 billion USD in 2012 on Alibaba’s e-commerce platforms and
this went up in 2020 to 74.1 billion USD (Ma, 2020). Alibaba hosts a grand gala for the Singles Day
with international guests’ appearance demonstrating its efforts to make it a holiday.
INTERNATIONAL EXPANSION
Right from the start, Alibaba set itself global aspirations and global goals (Anwar, 2017). This is also
reflected in Alibaba’s mission: “To make it easy to do business anywhere”. In order to achieve this,
Alibaba follows different approaches by focusing on different target groups.
Alibaba was able to build up a solid base for its global expansion with its growing business in China.
With its first website, Alibaba.com, Alibaba successfully managed to connect Chinese buyers with in-
ternational sellers (Wu & Gereffi, 2018). In addition, with Tmall Global and Kaola, Alibaba addresses
the growing Chinese consumers’ demand for international products and brands by offering high quality
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imported products, premium services, and an interactive online shopping experience (Alibaba Fiscal Year
2020 Annual Report). Examples of foreign companies selling on these platforms include Aldi, Henkel,
Adidas, WMF, Bosch and Zalando, as well as Mondelez, Nestlé, IKEA and many others.
Further, in order to target consumers worldwide by enabling them to buy directly from manufactur-
ers and distributors in China, AliExpress became the global digital marketplace. At the end of 2017,
the AliExpress platform served more than 100 million customers (Scheuer, 2018). Beside to the global
English language version, AliExpress is available in eleven other languages (Russian, Portuguese,
Spanish, French, Polish, Hebrew, Italian, Turkish, German, Korean, and Arabic) (AliExpress, 2020).
Top consumer markets where AliExpress is particularly popular are Russia, Brazil, the USA, Spain and
France (Alibaba Fiscal Year 2020 Annual Report).
Moreover, Alibaba.com is China’s largest integrated international online wholesale marketplace by
revenue since 2019. It connects online Chinese and overseas sellers to overseas wholesale buyers, such
as trade agents, wholesalers, retailers, manufacturers and small and medium sized enterprises. Alibaba
offers sourcing, online transaction, digital marketing, digital supply chain fulfillment and financial
services to these sellers (Alibaba Fiscal Year 2020 Annual Report). Sellers on Alibaba may purchase
the annual Gold Supplier membership, which was already launched in October 2000, to reach custom-
ers online, provide quotations, and transact on the marketplace (Anwar, 2017). As of March 31, 2020,
Alibaba.com had around 190,000 paying members from China and other countries. In the fiscal year
2020, over 20 million buyers from approximately 190 countries had sourced business opportunities or
completed transactions on Alibaba.com (Alibaba Fiscal Year 2020 Annual Report).
In addition, by using data-driven methods, like consumer analytics Alibaba identified overseas Chi-
nese consumers as important buyers. Therefore, Tmall Taobao World, a Chinese-language e-commerce
platform, offers overseas Chinese conveniently shopping products from China (Alibaba Fiscal Year 2020
Annual Report). By leveraging Alibaba’s existing commerce infrastructure, Tmall Taobao World aims
to serve tens of millions of overseas Chinese consumers.
Another target group of Alibaba are Chinese tourists. The number of Chinese tourists increased
from around 45 million people in 2008 to almost 162 million tourists in 2018 (Textor, 2020). For these
constantly growing groups and financially strong Chinese buyers, Alibaba provides with its payment
system Alipay an outstanding service. Chinese tourists can pay in foreign countries very conveniently
with their smartphone and do not even have to change any money. Worldwide, more than 80,000 re-
tail stores accept Alipay whereof around 10,000 retailers are in Europe and around 2,000 retailers in
Germany, e.g. Rossmann, WMF Group, Kaufhof, Breuninger and the Munich Airport (Scheuer, 2018).
For the international expansion of Alipay, these Chinese tourists play a crucial role. Alipay is also the
payment solution for Chinese tourists buying at UGG shops in Australia, King Power duty free shops
in Thailand, Lotte malls in Korea, or Takeya stores in Japan.
Besides focusing on different strategic target groups with different platforms, one further key ele-
ment for Alibaba’s fast and successful internationalization are strategic partnerships and acquisitions.
Strategic partnerships include international joint ventures, for example with MegaFon, Mail.ru Group
and RDIF in Russia, or with the Swiss company Richemont for luxury products, as well as international
cooperations with local partners, such as with CCPay in Singapore, or with Yahoo! Japan (Havinga,
Hoving & Swagemakers, 2016). The aim of these strategic alliances is to further advance Alibaba’s
global network, to facilitate and optimize cross-border transactions, as well as to enable merchants to sell
globally and consumers to buy globally. Further examples are partnerships with Apple’s iTunes Store that
accepts mobile wallet payments from Alipay’s local partners in Korea, the Philippines and Thailand with
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Mars Incorporated, one of the largest food manufacturers, with Accor hotels, with Deloitte Consulting,
as well as cooperations often with exclusive agreements, with Visa, MasterCard, with Antwerp World
Diamond Center, with Moet Hennessy and with Ford Smart Mobility, to name just a few.
Acquisitions played another important role for Alibaba’s rapid international expansion. On the one
hand, Alibaba acquired many China-based companies, like Xiami Music Network in 2013, 365 Trans-
lation in 2015, South China Morning Post in 2016, or Intime Department Store in 2017. On the other
hand, Alibaba acquired foreign companies as well, such as Tokopedia from Indonesia and BigBasket
from India both in 2017 (Johnston, 2020). One acquisition that is seen as key strategic investment from
Alibaba was the purchasing of Lazada from Indonesia in 2016. Lazada is a leading e-commerce platform
in Southeast Asia for small and medium sized enterprises that offer regional and global brands. Lazada
offers merchants and brands a one-stop marketplace as it has its own logistics network, which is the
determining success factor in that region (Scheuer, 2018). Lazada gives Alibaba access to consumers
in Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam with an estimated user baser
of 200 million people. From 2019 to 2020, the number of transactions increased by 170%. In particular,
Lazada saw a large demand in apparel, accessories and fast moving consumer goods categories, where
Artificial Intelligence recommendations and an efficient search engine were key drivers for growth
(Alibaba Fiscal Year 2020 Annual Report).
In order to support its international customers and provide adequate services, Alibaba had in 2020
117,600 employees worldwide and 13 office locations across 12 countries: China (Headquarters in
Hangzhou), Hong Kong, Taiwan, USA, UK, Italy, France, Germany, The Netherlands, Japan, India, and
Australia (Alibaba Group, 2020). In India, for example, Alibaba opened in 2010 its customers service
operations, while the office in London opened in 2015 and serves as central hub for the European busi-
ness (Anwar, 2017). Alibaba uses Alibaba.com and 1688.com in emerging countries to target there more
than one billion potential customers. In developed countries, Alibaba is competing with Alibaba.com,
1688.com, Tmall.com and Tmall global for its 400-500 million customers.
To expand its presence in key markets and serve international customers, Alibaba proposed in 2017
to build a global e-commerce platform, World e-Trade Platform (eWTP). The long-term goal is to elimi-
nate barriers to commerce in order to promote free trade and help businesses and consumers everywhere
participate in cross-border trade (Wu & Gereffi, 2018). eWTP is a vision of Jack Ma with which he
wants to revolutionize the world trade. Belgium was the first country in Europe to join eWTP in 2018,
having in Liege the largest cargo airport of Belgium which should become a smart logistics hub. eWTP
was presented by Jack Ma during the G20 summit in Alibaba’s headquarters in Hangzhou and has set up
partnerships and initiatives in China, Malaysia, Rwanda and Ethiopia (China Daily, 2019). Negotiations
and plans with other countries’ governments are running.
INTERNATIONAL DIGITAL MARKETING STRATEGIES
Product Strategy
Alibaba provides diverse digital products that facilitate convenient and fast management for merchants.
Information can be uploaded and / or updated freely into the database in order to contact buyers directly.
For instance, Alibaba.com shows company profiles, showrooms as well as contact information. Buyers
can use the search engine for products and registered users can easily create trade alerts. Further provided
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digital services for sellers are establishing blogs, forums and user groups for customers to communicate
and gather feedback regarding their products and services of their interests. Other communication chan-
nels are Alitalk and Yahoo message that enable the dissemination of updated information of demand
and supply in real time.
As described above, Alibaba offers a wide range of products that include e-commerce, logistics, pay-
ments, financial services, cloud solutions, data technology, entertainment, media, groceries, gaming, web
services, on demand delivery and health services. This diversity of online platforms and marketplaces
address different retail supply chain management and sales and marketing systems.
Another key success factor for Alibaba was its customer segmentation according to the geographi-
cal criteria. By utilizing data from Big Data analytics, Alibaba decided to divide its digital business
and platforms into the home market, China on the one side, and international markets on the other
side. Hence, some platforms and products are only available and adapted for the international market
(Havinga, Hoving & Swagemakers, 2016). For example, Alibaba provides a special website for custom-
ers in Japan, called Alibaba.co.jp, which is different from Alibaba.com. Another example is Lazada,
the e-commerce platform for customers in Singapore, Indonesia, Malaysia, the Philippines, Thailand,
and Vietnam. Lazada provides local language options and mobile apps to customers in each of the six
markets. It has developed its own logistics infrastructure with warehouses and a last-mile delivery fleet
to offer fast and reliable services to its customers (Wu & Gereffi, 2018). Further, the platform Taobao
has two different versions, one is for the Chinese market and the other one for the international market.
Pricing Strategy
As to Alibaba’s pricing strategy, in order to draw attention away from its higher priced competitors,
Alibaba follows a very aggressive pricing policy. Alibaba enters new markets with extreme low prices
and hence a penetration pricing strategy. For instance, when ebay entered the Chinese market to ad-
dress Chinese private consumers, one of the decisive strategic factors that Alibaba helped in the end to
succeed and win the competition against ebay with Taobao was the pricing. While ebay charges fees
and commission, Taobao is completely free of charge (Scheuer, 2018). Likewise, AliExpress does not
have any registration fee.
Moreover, Alibaba set itself right from the beginning the target to build up a big digital platform
system at a very rapid pace. Accordingly, it offers for example the services on Alipay at very advanta-
geous prices. At the beginning, those services were almost for free, and even though Alipay introduced
service fees, they are still much lower than those of its international competitors. Numerous discounts
and cash back opportunities for customers aim to win new customers and turn them after some purchases
into loyal regular buyers.
In summary, to establish its presence, Alibaba offers lower prices than its competitors even if that
results in an initial, albeit calculated income loss. However, the goal is to raise brand awareness, brand
loyalty and to occupy important future markets. This is again an example for Jack Ma’s strategic and
long-term thinking mentioned above infused and supported by data extracted from consumer analytics.
In the course of time, Alibaba gained substantial competitive advantage from having built its business
to a massive scale and following the cost leadership approach. Economies of scale enable Alibaba to
generate enough revenues to sustain its expansion to various new markets as well as is capability to sat-
isfy the market needs for different sectors. Alibaba provides merchants, retailers and small and medium
sized enterprises with cost effective digital platforms to market and sell their products.
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Distribution Strategy
Alibaba is at its core a technology company with special focus on e-commerce. The internet is used
by Alibaba as the main intermediary. The platforms and services are online, and therefore distributed
through websites. Digital platforms are the main distribution channel for Alibaba’s products and services.
They are constantly adapted and improved due to insights gained from data analytics.
The simultaneous development of countrywide logistics networks helped strengthening the e-com-
merce of Alibaba crucially (Scheuer, 2018). In terms of logistics, Alibaba entered on the international
level partnerships of strategic importance with post offices. For example, Alibaba has partnerships with
Australia Post, Singapore Post, and U.S. Postal Service. With its Cainiao Network, Alibaba builds and
operates a global fulfillment network together with network partners by offering domestic and international
one-stop-shop logistics services and supply chain management solutions. To support its cross-border
retail commerce businesses, which are mainly Lazada, AliExpress and Tmall Taobao World, Alibaba
uses a combination of bonded warehouses in China and direct shipment from markets outside mainland
China (Alibaba Fiscal Year 2020 Annual Report).
Besides the online distribution channels, Alibaba also has physical distribution stores and distribu-
tion channels. In 2017, Jack Ma publicly announced a strategy to combine online and offline commerce,
called “New Retail” (Wang & Ng, 2020). Shopping experiences from physical brick-and-mortar space
are merged with those shopping experiences of virtual e-commerce stores that are identified as most
popular by consumer analytics. This omni-channel experiential retail is seen as the future of the retail
industry. Alibaba wants to enhance physical stores that are indispensable during and for the customer
journey, through data-driven technology and personalized services of the digital economy.
New Retail examples include thousands of mom-and-pop stores across China using Alibaba’s retail
management platform Ling Shou Tong which helps store owners optimize product procurement and boost
sales by giving recommendations based on sales analytics on what to buy and how to display goods.
Alibaba’s cloud computing and logistics businesses create digitally connected inventory management
systems where products are directly shipped from warehouses eliminating the need for middlemen and
are provided for free for Chinese store owners as long as they use Alibaba’s storefronts as fulfillment-
and-delivery centers and hand over data on theirs customers’ shopping habits (Hao, 2018).
Another example of Alibaba’s New Retail initiatives are tech-enabled Hema grocery stores where
customers can use a mobile app to get product information and recipe ideas personalized to their prefer-
ences based on consumer analytics. At the end, they can pay with their smartphones using Alipay. Hema
stores are also distribution centers where assigned employees go around filling bags with online orders
which are then placed on a convey belt to the delivery center (Li, 2019).
Moreover, Alibaba opened Tao Cafe which are cashier-less cafes using artificial intelligence and
facial recognition-enabled cameras to allow customers to skip the checkout line. Tao Cafes are staff-less
cafeterias selling drinks and snacks as well as backpacks, notebooks, plush toys and the like for Taobao
customers paying with their app (Schögel & Lienhard, 2020).
Further, Alibaba set up vending machines to let customers buy sample-size consumer products which
helps Alibaba to collect data on consumer preferences and follow up with customers online. Beyond
groceries, fashion, and electronics, is the vending machine that Ford agreed to set up with Alibaba in
China to sell and test-drive their cars. This cat-themed car vending machine is unstaffed, digital and works
with the Tmall app. Users can select the car model they are interested in, leave a deposit electronically,
schedule a pickup time, make a selfie for the necessary facial recognition and then have a test drive for
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free. Prerequisite is a credit score of at least 700 which is scored at Ant Financial’s credit ranking system
Sesame Credit (Ong, 2018).
In international markets, Alibaba has also physical stores that aim to convince both international
customers to use Alibaba’s platforms as well as international enterprises to sell on Alibaba’s online
platforms. For example, AliExpress PLAZA opened in 2019, its first physical store outside China and
in Europe in Madrid, Spain. The 740 sqm store in Madrid offers products of around sixty brands, in-
cluding Chinese products, multinational brands as well as local Spanish brands (retaildetail.eu, 2019).
Customers can purchase their products through their AliExpress account and enjoy the same benefits
and promotions available online as well as cash or card payments.
Moreover, Taobao has opened a physical store in 2019 in Singapore selling home furniture goods.
This Taobao Store is a partnership with the local company Virmall and sells products from merchants on
Taobao. Similarly with above described New Retail initiative, customers can use mobile apps for product
information, pick up their online orders, and payment services. Additionally, there is a complimentary
virtual reality service for costumers to better visualize how a piece of furniture will fit into their homes
(Ng, 2019). Another Taobao Store can be found in Kuala Lumpur, Malaysia. Unlike its counterpart in
Singapore, not only furniture goods but diverse items from electronics, to baby products are sold there
by local and Chinese merchants. The aim is to further localize services and experiences to Malaysian
customers, to show popular products and introduce new ones, as well as to win new online customers
who are unfamiliar with online shopping experiences (Cheema, 2019).
Promotion Strategy
Alibaba uses a number of different channels to promote its brand. It advertises through various chan-
nels, like traditional print media, radio, TV advertisements as well as trade fair booths with catchy
taglines. Alibaba also utilizes the rather outdated TV shopping channels, but integrated the possibility
that potential customers can ask live questions and let the presenters show the products benefits live as
well. Hence, everything aims to increase direct interaction (Scheuer, 2018).
For promoting its online products and services, Alibaba uses in addition digital media. Thousands
of video channels are online where young sellers present the latest trends and newest products. Another
sales promoting tactic is the use of live streaming involving an influencer who demonstrates a product
and responds to questions from the digital audience. This real-time format is a highly effective way to
impact online purchasing. Further, in accordance with its low pricing penetration strategy, all Alibaba
platforms offer diverse discount purchases, festive sales and cash back opportunities on a regular basis.
Sometimes offline retailers are to join Alibaba’s online promotion as well. For example, in 2015 more
than 300,000 brick-and-mortar stores in China and around the world participated in the December 12
sale by offering discounts to consumers who use their mobile phones to make a wide range of so-called
“on-demand” purchases, from fast food to movie tickets to taxi rides to car washes to haircuts to fresh
fruit. This was to push the use of Alipay on the one hand, and on the other hand to get traditional retail-
ers involved in omni-channel retailing (Wang, 2015).
Moreover, Alibaba is sponsoring international events, particularly in the sports sector. Alipay’s spon-
sorship of UEFA in 2018 to 2026, Tmall’s engagement in Renault F1 as well as Alibaba’s sponsorship
of the Olympic Games are examples of this promotion tactic. The target is to create brand awareness
and improve brand perception.
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DISCUSSION, LIMITATIONS AND FUTURE RESEARCH
Data-driven methods and technologies helped Alibaba tremendously to capture abundant and meaningful
data on their consumers. The process of extracting significant consumer insights from Big Data became a
cornerstone for Alibaba’s international marketing strategy. Particularly, consumer analytics were utilized
to build market advantages by fueling Alibaba’s international marketing decision-making, its agility, as
well as its consumer-centric approach.
By extracting and detecting patterns from large databases, Alibaba could successfully forecast and
steer customer behavior, for example as he did with massive digital marketing campaigns for 11.11.
A specific strength of Alibaba is that the valuable consumer insights benefits its process to enhance
dynamic and adaptive capabilities. Alibaba is able to react to market changes in a very dynamic way.
Additionally, Alibaba acts in a proactive way as well, for example in enlarging its distribution channels
continuously by combining offline and online options. In doing so, Alibaba uses numerous opportunities
to enhance its adaptive capability.
Both dynamic capability and adaptive capability achieved through consumer analytics enables Alibaba
to constantly customize its marketing activities to local markets’ demands and improve its effectiveness
of digital marketing tactics.
As to internationalization, Alibaba uses data analytics to always better its understanding of local
consumers’ behavior and preferences. Jack Ma optimized the process of consumer analytics to gain a
deep insight into international consumer behavior differences. This resulted in improved adaptations
of the marketing mix to international consumer needs wherever needed on a constant basis. Thus, he is
able to exploit benefits from data analytics to a great extent.
Alibaba shows a high level of marketing adaptability by adapting different aspects of its digital
marketing strategy to different international target markets. The capability to improve its offerings by
rethinking and redesigning its products to better match the diverse demands of different international
markets is deeply incorporated in Alibaba’s digital marketing strategy.
Marketing data mining techniques and consumer analytics helped Alibaba to accomplish such goals
as adapting its products to international markets as compared to its home market China. For instance,
the platform Taobao has two different versions, one is for the Chinese market and the other one adapted
for the international market.
Alibaba also conducted international marketing analysis on promotional data, such as price cuts or
coupons. Alibaba could find out how different types of customers (national, international, overseas Chi-
nese) responded to different promotional strategies or how different categories of products affected the
effectiveness of promotional strategies. Enhanced by mobile technologies and location-based services,
Alibaba could also use customers’ location information to improve its promotion strategy and select
targeted customers.
To improve product awareness and promote products to potential international customers, Alibaba
developed and supported recommender systems as well. User rating-based collaborative filtering methods
and content-based association mining methods enabled Alibaba to reach different international customers
through different versions adapted to their preferences.
This paper also clarifies how data data-driven methods can lead to opportunities for networked
businesses to gain a competitive advantage. It explains how they are leveraging themselves by utilizing
contemporary marketing strategies and tactics that are customer-focused.
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Alibaba’s comprehensive ecosystem infused by Big Data and consumer analytics has been a tremen-
dous driver and accelerator for its international digital marketing and international success.
CONCLUSION
Alibaba has become within a relatively short period of time, one of the world’s largest retailer and e-
commerce companies and one of the most successful Internet companies worldwide. It successfully owns
and runs a multifaceted portfolio of companies around the world in diverse business sectors. Alibaba hosts
the world’s largest digital marketplaces, is rated as one of the largest artificial intelligence companies
worldwide, is also one of the biggest venture capital firms and investment corporations in the world,
and claims to be the biggest IT corporation of China (McLaughlin, 2018; Patrizio & Maguire, 2018).
By referring to the aspects mentioned above, the five main decisive key success factors for this im-
pressive development can be summarized as follows.
First, by building its own huge digital ecosystem which is constantly improved due to insights gained
from data analytics, Alibaba was and is able to act independently. With every new digital product or
service, Alibaba adds another important building block for its national and international success. By
related and unrelated, horizontal and lateral diversification, Alibaba continues to grow and to ensure
that the single parts of the ecosystem are mutually beneficial which in turn provides possibilities for
further growth.
Second, by separating the offers of digital products and services into the home market China on the
one hand and international markets on the other hand, Alibaba is able to focus on the respective customer
segments effectively. This strategic decision was made due to insights gained from consumer analytics.
By tailoring its digital products and services, Alibaba is able to respond to a country’s specific demands
and hence reach consumers. This differing segmented marketing approach proved to be very successful.
Third, a thorough understanding of Chinese customer needs and consequent adaptation of offers based
on consumer analytics and Big Data helped Alibaba enormously in outpacing its Western competitors.
This aspect may also contribute to the standardization versus adaptation paradigm discussion. So far, the
perspective of the standardization versus adaptation paradigm has been when a company enters another
country and how this company is to conduct marketing in this host country. Now, we may also observe
and learn from a company’s international entry and see how host countries’ companies can react accord-
ingly, meaning to standardize or adapt their own marketing efforts.
Fourth, the long-term and strategic big thinking of founder Jack Ma is another key success factor.
His strategic approaches were leading to a very early international expansion and digital marketing
practices, tactics and strategies that used the knowledge acquired through these international activities.
Lastly, the integration and merging of online and offline products and services can be seen as crucial
success factor as well. By building a solid online foundation first in China, the fast expansion into other
countries and business sectors could follow. The interconnection and the combination of online with
offline goods and services demonstrates Alibaba’s innovativeness and pioneering spirit. The fast use
of new learnings and knowledge proved to be very beneficial for Alibaba so far, and new technology
innovations are expected to continue to bring out new products in the future as well.
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ADDITIONAL READING
Morgan, N. A., Feng, H., & Whitler, K. A. (2018). Marketing capabilities in International Marketing.
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491–507. doi:10.1016/j.ibusrev.2010.09.003
Business-to-Business (B2B): A business relation where one company makes a commercial transac-
tion with another.
Business-to-Consumer (B2C): A situation where a company is selling products or services directly
to consumers.
Consumer-to-Consumer (C2C): A situation where a consumer is selling products directly to an-
other consumer.
Digital Ecosystem: A group of interconnected information technology resources that can function
as one unit.
E-Commerce: The activity of electronically selling or buying products or services over the Internet.
International Expansion: Corporate expansion into other countries including trade and transactions
of goods and services.
International Marketing: A marketing done on the international level, based on a strategy created
in the home country of the company and distributed to other affiliations.
Standardization vs. Adaptation: Crucial part of the international marketing strategy, decision
whether to sell products and services overseas in a standardized or adapted way.
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Chapter 6
Activism in the Digital Age:
Social Movements Analysis Using User-
Generated Content in Social Media
Laura Holgado-Ruiz
https://orcid.org/0000-0003-0217-7574
José Ramón Saura

https://orcid.org/0000-0002-9457-7745
Beatriz Rodríguez Herráez

ABSTRACT
Social movements have been transformed in the last decade by social networks, where the dynamics of
the social protests have evolved and have been structured and viralized through social media. They are
no longer just conversations between activists that stay on social platforms. The cyberactivism that takes
place on Twitter or Instagram can also play a significant role in general society by influencing government
decision making or shaping the relationships between citizens. In this chapter, the authors explore the
main activist movements that took place in social media in the last decade: Occupy, BlackLivesMatter,
and MeToo. The proposed approach used in this study facilitates the comparison of each movement while
focusing on the user-generated content in social media. This study suggests the presence of four major
categories to frame the content generated by the activists. The chapter concludes with the identification
of three different approaches to the research of a future research agenda that should be considered for
the study of the social movements from the UGC theory framework.
DOI: 10.4018/978-1-7998-8003-5.ch006

Activism in the Digital Age
INTRODUCTION
Social movements represent the collective effort of people to voice their concerns over the civil, economic
and political welfare of a population of people (Snow et al., 2004). Although social movements in 20th
century history have been marked by individuals such as Emmeline Pankhurst to Martin Luther King to
Mahatma Gandhi promoting change in the awareness of people (Montefiore, 2014), the modern social
movements in the 21st century have evolved with the advancement of technology and social media and
have been marked by the often anonymous collective power (Velasquez & LaRose, 2015).
As George and Leidner (2019) study, from street demonstrations and marches, movements marked
by technology have evolved, achieving a greater reach and being carried out in many different ways such
as clicktivism, digital petitions or data activism. These social movements promoted or executed through
the Internet are also called digital activism. Although, as Joyce (2010) points out, there are numerous
terminologies such as cyberactivism, e-activism or online advocacy that are also used to refer to all forms
of social or political campaigns using digital infrastructures.
Social movements have been transformed in the last decade also due to social networks, where the
dynamics of social protests have evolved and have been structured and viralized through the channels
offered by social networks (Tan et al., 2013). It is no longer exclusively about conversations between
activists that emerge and are maintained on social platforms. Nowadays, cyberactivism taking place on
Twitter or Instagram can also play a crucial role in society at large, by influencing government decision-
making or shaping relationships between citizens, brands and other stakeholders (Sandoval-Almazan
& Gil-Garcia, 2013).
Social networks facilitate leveraging and maximizing the collective power of all like-minded individu-
als by disseminating information quickly and expansively (Hwang & Kim, 2015). Social networks are
considered to provide an effective method not only for disseminating information but also for involving
new volunteers in social movements (González-Bailón et al., 2011), hence they can be considered one
of the main current platforms for exercising activism.
Following Yang (2016), one of the most interesting developments in digital activism in recent years
is the rise of “hashtag activism” (HA). HA occurs when users create a large number of posts on social
networks under a common word or phrase, with a hashtag that acts as a social or political rallying cry.
This “networked power of hashtags,” together with the power of collective storytelling, can turn the
sharing of a personal experience online into an action that makes a collective movement more powerful
(Clark, 2016).
Despite the social importance of this digital activism, research defining the trends, comparisons,
and relationships between different activist movements in social media remains limited. Understanding
modern activism requires determining the patterns and commonalities across social movements and their
use of these digital channels like Twitter and Instagram for driving social movements, demonstrations
and protests. Furthermore, in the same way that research on user-generated content (UGC) applied to
brands has great potential value for communications, advertising or customer engagement (Liu et al.,
2017), the relationship between UGC and HA has not yet been widely investigated in academia covering
a gap in the literature. Therefore, the originality of the present study lies in the relevance of our object
of study, as it compares and contrasts hashtag movements by exploring patterns in the use of hashtags
during periods of three distinct social justice movements: Occupy, MeToo and Black Lives Matter.
Specifically, we analyze UGC from the beginning of each movement to assess homogeneous features in
the generation and sharing of Twitter content during different social movements.
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Likewise, a user-generated data (UGD) research provides a broad view of each social movement as
it contains rich information about users’ attitudes, opinions, and experiences (Liu et al., 2019; Ribeiro-
Navarrete et al., 2021). Along these lines, this study highlights the usefulness of UGC to provide insights
and a better understanding of the complexity of major social movements, for social justice and politics,
in the digital sphere. Accordingly, this study proposes the following research question (RQ): what homo-
geneous features are found in the purpose pursued by participants of different social movements when
generating and sharing content on Twitter?
The chapter is structured as follows. First, it reviews previous studies published on social movements
in social networks and user-generated content on digital platforms. Specifically, it highlights research
that focuses on the analysis of the discourse of cyberactivist movements in social networks. Next, the
methodology carried out is presented and the results obtained through a multiple case study are reported.
Subsequently, a discussion is presented as well as a detailed agenda for future research. Finally, the
conclusions of the study are offered.
Social Movements in Social Media in the 2010s
The decade of 2010 began with one of the main social revolutions brought about by social networks, the
Arab Spring (Howard et al., 2015). In this movement, social networks played a key role in changing the
media agenda and fostering an unprecedented political debate. Thanks to platforms such as Facebook,
Twitter or YouTube, democratic ideas not only played an integral role in the defeat of the Egyptian
and Tunisian governments, but also crossed international borders, generating hundreds of thousands of
messages calling for democracy in North Africa and the Middle East (Bruns et al., 2013; Howard et al.,
2015; Wolfsfeld et al., 2013).
Ten years later, in 2020, the dynamics of social protests continue with the same essence. Conversa-
tions in social networks go beyond dialoguing and sharing resources, they also serve to explicitly propose
actions, such as voting for or against someone or trying to change a policy (Li et al., 2020). For example,
this very year and along these lines, the #BlackLifesMatter movement has re-emerged, which can already
be considered a technological as well as a cultural phenomenon (Jackson, 2016).
Following Paul (2019), the largest social movements of the 2010s have been: criminal-justice reform,
occupy, LGBTQ+ rights, marijuana decriminalization, gun control, Black Lives Matter, native american
rights, new populism, #MeToo and climate strike. In this study, we have selected as a case study those
that have quickly transcended the borders of North America to become a worldwide movement sustained
over time (Amesu, 2021; Arriaza-Ibarra, 2019; Theocharis et al., 2015), and which in turn are focused
on a goal related to political and social justice. In this case: Occupy, Black Lives Matter and #MeToo
(See Table 1).
The Occupy movement was influenced as much by the Arab Spring as by the “indignados” movement
in Spain or the “aganaktismenoi” movement in Greece, all of them identified with the idea of demanding
“real democracy” through social mobilization (Theocharis et al., 2015). As Van Gelder (2011) studies, the
protests were organized around the idea that the protesters were the 99% of society, placing themselves
in confrontation with the remaining 1% that agglutinates wealth and generates systematic inequality.
The strength of this movement lay in the activists’ use of social networks. As Gamson and Sifry (2013)
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analyze, without user-generated and non-corporate owned media, it is quite possible that the Occupy
movement would not have achieved its level of notoriety.
In the case of Black Lives Matter, it is a movement that has made clear the importance of networks
as platforms to discuss police violence and systemic racism (Carney, 2016). Following data from Pew
Research Center, this movement, which emerged in 2013, has had different moments of great agitation
in networks. For example, with the Ferguson protests after the death of Michael Brown, in 2014, or the
attack on police officers in Dallas, in 2016, but it has undoubtedly been in 2020 when this movement
has overwhelmed the networks at the international level (Anderson et al., 2020). After #COVID19, the
second most tweeted hashtag of 2020 was #BlackLivesMatter. The world mobilized and echoed the
need for racial equality and social justice following the death of George Floyd, who was the third most
tweeted person globally (McGraw, 2020).
Regarding the #MeToo movement, as indicated by Mendes and colleagues (2018), it was started by
Tarana Burke in 2006, an African-American women’s rights activist. It would not be until a year later
when it reached worldwide attention following a tweet by actress Alyssa Milano. The hashtag quickly
became a way for users to unite and recount experiences of sexual violence suffered by women, while
standing in solidarity with survivors of sexist attacks. Research such as Modrek and Chakalov ’s study
(2019) indicates that more than 5% of tweets in that period of time unveiled experiences of sexual abuse.
#MeToo was widely used on Twitter, Facebook, Snapchat and other platforms. According to CBS
(2017), Facebook alone garnered more than 12 million comments and reactions during the first 24 hours.
Meanwhile, the official Twitter Data (2017) account confirmed 3 million tweets with the hashtag dur-
ing the first week of the movement. Table 1 shows the most prominent social movements of the 2010s
related to social justice found in the literature.
Table 1. Most prominent social movements of the 2010s related to social justice.
Social
Representative hashtag Year of creation Main message Relevant authors
Movement
Calhoun, 2013; Theocharis et
Occupy #OccupyWallStreet September, 2011 Wealth equality al., 2015; Tremayne, 2014; van
Gelder, 2011.
Black Lives Carney, 2016; Ince et al., 2017;
#BlackLivesMatter July, 2013 Racial equality
Matter Rickford, 2016.
Reporting sexual assault Mendes et al., 2018; Rodino-
#MeToo #MeToo October, 2017
and harassment Colocino, 2018.
As indicated by Hwang and Kim (2015), social movements find in networks an efficient and effective
platform for their development. There are two main reasons for this. On the one hand, social network
users can contribute to the globalization of social movements by transmitting their messages to a wider
audience, so that communication spreads over larger and larger territories (Wright, 2004). Secondly,
social networks empower users to share messages that seek social activation, achieving public engage-
ment in a horizontal and democratic manner (Hwang & Kim, 2015).
A common issue of many of the large social movements in the 2010s is the connection through a
hashtag as a key to their identification and identity. Often referred to as “Hashtag Activism” (HA).
Hashtags allow Twitter users, and those visiting the Twitter website without being registered, to follow
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in real time all messages containing the hashtag (Bruns & Stieglitz, 2013). This idea has evolved into a
tool for those who want to comment on social and political change (Chen et al., 2018). Although hashtags
initially emerged in the social network Twitter, we can currently see it in many others, such as Instagram
or Facebook, generating the same empowering effect of activist messages.
HA has not been without its critics (Meikle, 2014) .While there are those who support the useful-
ness of this connection between users united by a cause (Jackson et al., 2020), certain concerns can be
found about the possibility that this form of digital activism can replace real actions that require greater
commitment (van Laer & van Aelst, 2010)
User-Generated Content
As its name suggests, UGC is all that material that comes from standard users who voluntarily create and
publish different data, information and content, which is subsequently published and may be of interest
to others (Krumm et al., 2008). It is considered one of the main data sciences used in digital marketing
strategies, and it can be used to analyze the content publicly published by users on social networks and
all kinds of digital platforms (Saura, 2021).
Following Luca (2015), UGC platforms allow their users to interact with each other by generating
content and being able to share it publicly and interact with it. Depending on the platform, different
modalities of content are generated, considering not only text, photos and videos, but also content from
crowdfunding platforms or sharing platforms (See Table 2).
Table 2. Popular user-generated content platforms
Types of UGC Prominent platforms

Pictures Instagram, Pinterest, Snapchat, Flickr
Personal updates and networking Twitter, Facebook, LinkedIn, Qzone, Sina Weibo
Reviews for products and services Encyclopedia and reference Google my Business, Amazon, TripAdvisor, Yelp
sites Wikipedia, Wikia
Videos YouTube, TikTok/Douyin, Kuaishou
Comments on news articles NY Times Online, WSJ Online
Crowdfunding Kickstarter, IndieGoGo, GoFundMe
Sharing platforms Uber, Airbnb, Couchsurfing
Social payments Venmo, Cash app, Paypal
Discussion/question and answer Reddit, Quora, Baidu Tieba
Blogs WordPress, Tumblr, Medium, Blogger
UGC in social networks can have themes of diverse nature, from content related to self-presentation
and self-promotion (Fox et al., 2018), consumption and branding (Christodoulides et al., 2012; A. J. Kim
& Johnson, 2016), related to personal experiences, health-related (Fergie et al., 2016), travel-related ex-
periences (Chung et al., 2015; Reyes-Menendez et al., 2019) or political opinions (Bennett & Segerberg,
2012; Peña-López et al., 2014), among others.
Studies as Naab and Sehl (2017) show how the UGC has a clear predisposition for political topics and
the users preference is to focus on messages with a societal orientation. As social media has provided users
the opportunity to make public their private political opinions, the combination of UGC and Activism has
a considerable research potential but also it can be a methodological challenge. However, although UGC
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provides an excellent opportunity to include historical and real-time data on a broad variety of topics, its
semi-structured or unstructured nature makes information extraction difficult (Meneghello et al., 2020).
Users don’t necessarily consider keywords or names that are search engine friendly. As a result, the lack
of guidelines or criteria surrounding UGC makes data collection for research purposes more difficult.
For this reason, the analysis of content generated by activists on social media is often analyzed by using
their popular hashtags, as they serve as an indexing system (Bonilla & Rosa, 2015; Xiong et al., 2019).
User-Generated Content Analysis Applied to Hashtag Activism
Since the beginning of the use of hashtags in a wide variety of social movements, several studies have
been conducted investigating the content of Hashtag Activism (HA) movements through different meth-
ods (Kim et al., 2020).
One of the typologies of studies focuses on hashtag ethnography, conducting an ethnographic descrip-
tion of the possibilities and needs of this modality of activism (Bonilla & Rosa, 2015). There is also
research that focuses its study on the analysis of images, through networks such as Instagram or Twitter
(Kim et al., 2020; Stefanone et al., 2015).
Another group of studies of HA focuses on analyzing the textual content published and created by
users of social networks. This field of analysis can be quite broad as different areas and platforms where
users can post text can be analyzed. Such as, for example, comments in Facebook groups (Woolley et
al., 2010), posts on Weibo (Shi & Chen, 2014) or tweet analysis (Gleason, 2013)
Regarding the analysis of UGC in social networks, different studies have been carried out that have
applied different techniques to analyze content in various social movements united by a hashtag as
shown in Table 3.
METHODOLOGY
Case Study
The case study is one of the most appropriate study strategies for the purpose of this research, as it
studies a contemporary phenomenon in depth and within its real-world context (Yin, 2014). Context is
fundamental information when talking about social movements as the political situation can often create
the opportunity for their formation or strengthening (McAdam & Tarrow, 2018).
Snow and Trom (2002) have analyzed the case study methodology applied specifically to social
movements. They indicate that the research objective of a case study is to understand and shed light on
how certain initiatives, processes or events have been generated within a particular context, as well as
to examine how they are reproduced and how interactions are generated with other elements that are
related to the movement.
Following Yin’s (2011) extensive study of case study research, the author classifies the types of case
studies as explanatory, exploratory or descriptive. Likewise, depending on the selection of cases, it can
be considered single or multiple-case studies, and depending on the depth of the study, it can be holistic
or embedded case study (See Table 4).
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Table 3. Studies focused on UGC analysis applied to HA
Title Authors Hashtag Analysed Description Methodology

This study determines
Using the
Exploring key indicators the key indicators of
methodological
of social identity in social identity in the
Reyes-Menendez et al., approaches of corpus
the# MeToo era: Using #MeToo #MeToo movement in
2020 linguistics and discourse
discourse analysis in Twitter using textual
analysis to study a
UGC analysis and sentiment
corpus of 31.305 tweets.
analysis of UGC.
“# IAmGay # What
About You?”: This study investigates Performing a textual
Storytelling, Discursive the #IAmGay online analysis of 798 posts
Politics, and the protest on Weibo against generated by users
Liao, 2018 #IAmGay
Affective Dimension of the platform’s proposed on Weibo and code
Social Media Activism ban on homosexual them into six different
against Censorship in content. categories.
China.
This study sought to
Performing a thematic
characterize the use of
Sexual victimization content analysis on 281
the hashtag #UsToo
among men: a qualitative tweets and categorizing
Bogen et al., 2020 #UsToo on Twitter to disclose
analysis of the twitter them as the disclosure
or comment on men’s
hashtag #UsToo. of victimization or
experiences of sexual
response to the hashtag.
victimization.
This article explores
Extracting 0.58 million
#377Scrapped LGBT- related Twitter
Tweeting in Support tweets and classifying
#Section377 UGC to understand the
of LGBT? A Deep Khatua et al., 2019 them into supporting
#377verdict societal acceptance of
Learning Approach. tweets or opposing
#IndiaRejects377 sexual minorities in the
tweets.
Indian context.
Collecting 9000
This paper examines
hashtags from 700 posts
Language as pride, love, multilingual hashtags as
on Instagram. These
and hate: Archiving #雨傘運動(Chinese discourse of emotions
hashtags were coded
emotions through Lee & Chau, 2018 word for ‘Umbrella about social movements,
by language choice and
multilingual Instagram Movement”) with a focus on the 2014
their broad discourse
hashtags. Umbrella Movement in
functions of fact,
Hong Kong.
opinion, and emotion.
In this research, a qualitative and exploratory methodology is used, where the main objective is to
expand the information of the problem under study, without a clear and concrete single set of outcomes
(Thomas, 2020; Yin, 2014).
A multiple case study is conducted since, unlike the single case study, it makes it possible to explore
differences and similarities between the selected cases (Baxter & Jack, 2008; Yin, 2014). The objectives
of multiple case studies, as Yin (2014) indicates, can be either predict similar results (a literal replication)
or predict contrasting results but for predictable reasons (a theoretical replication). For this reason, cases
have been carefully chosen, both in terms of scope and relevance as well as subject matter, to support
the prediction of contrasting results.
In this study, three cases are analyzed along with their actual contexts (major political and social
justice movements in the 2010s), and an embedded unit of analysis of each (in this case, user-created
105

content on twitter). Unlike the holistic model, the identification of a subunit of analysis allows for a
rather more concrete and detailed level of investigation (Gustafsson, 2017).
Figure 1 shows the research model used, where the dashed lines show that there is no clear differ-
entiation between the different layers, as the context of each case can permeate to each unit of analysis.
Table 4. Case studies types.
Case study type Definition

A study used to resolve research questions with presumed causal linkage but considered
Explanatory
too complex to resolve through experimental strategies.
A study used to explore situations where there is no detailed preliminary research, so
Exploratory there is
no concrete hypothesis to be tested through the study.
Descriptive A study used to describe a phenomenon within the real context in which it takes place.
A study that analyzes a single case, either to test a theory or to analyze an extreme or
Single
exceptional case.
Multiple A study that analyzes two or more cases and explores the differences between cases.
Holistic A study that analyzes a single unit of analysis comprehensively.
Embedded A study that analyzes one or more subunits within each selected case.
Source: Baxter et al., 2008; Gustafsson, 2017; Robert E. Stake, 2013; Yin, 2011, 2014
Content Analysis
Following the content analysis method applied by Gleason (2013), who carries out a case study of a so-
cial movement through two samples of 144 and 150 tweets, in the present study 3 samples of 200 tweets
containing the main hashtag of the movement in question were collected (See Table 5 for details). Thus,
through a content analysis (Schwartz & Ungar, 2015), an exploratory coding of the sample collected
from Twitter has been carried out. This is a standard procedure in studies that work samples collected
from social networks and that use UGC as the foundation for the study (Saura et al., 2021).
As indicated by Reyes-Menendez, Saura and Alvarez-Alonso (2018), the social network Twitter is
optimal for conducting a content analysis. From a privacy point of view, and unlike others such as Face-
book, Twitter offers users an open communication that can maximize the reach of their opinions. Easy
access to published information makes Twitter a valid platform for measuring public opinion.
The samples have been collected through Twitter’s advanced search tool. In all cases, the English
language and specific time frames have been established. In the case of the Occupy and MeToo move-
ments, the samples were collected two months after the beginning of the movement and the use of the
hashtag on Twitter. This prevents messages from being conditioned by specific events, such as, for
example, the first gathering of the Occupy Wall Street movement.
In the case of BlackLivesMatter, the sample is collected two months after the Ferguson mobilizations
that took place from August to December 2014, as it is considered the first moment with great impact
of the hashtag, regardless of whether it was created a year earlier (Anderson et al., 2020). The sample
selection excludes all those tweets that merely share the hashtag, without expanding the content with
text or images.
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Figure 1. Multiple-case study research design with an embedded unit of analysis

Adapted from Yin (2011)
Subsequently, following the study method carried out by Gleason (2013) and initially conducted
by Kohlbacher (2006), systematic analysis of the selected UGC based on different categories has been
performed. It includes a systematic analysis based on the creation of categories of text that are initially
founded and then revised. First, for data coding, an inductive model of category development has been
initially carried out after reading 50% of the tweets. Four categories were created on the basis of the
emergent textual meanings of the tweets.
Table 5. Embedded units of analysis selected for the study
Movement Hashtag Sample Language Date

Occupy Wall Street #OccupyWallStreet 200 tweets English 15th November 2011
Black Lives Matter #BlackLivesMatter 200 tweets English 15th - 16th January 2015
Me Too #MeToo 200 tweets English 15th December 2017
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Table 6. Key properties of each family grouping
Category Main textual properties

Informative messages without loaded language (emotive language, high-
Explanation of the movement itself
interference language) language-persuasive techniques).
Social and political justice Messages with loaded language. Main focus on claims and petitions.
Messages with loaded language. Main focus on disclosure of personal
Exchange of personal experiences
information or personal opinions. First person singular.
Messages with loaded language. Main focus on communicating as part of a
Community building
group while offering support or specific help. First person plural.
Other Messages unable to be categorized in the major categories above.
Figure 2. Summary of the methods developed
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Then, after finding and reviewing four major categories, we classified in Table 6 the entire sample
of tweets based on the textual properties of each one of them.
In order to summarize the methodological process carried out, Figure 2 presents a summary of the
methods developed as well as their characteristics.
RESULTS
The result of the present study has made it possible to compare the UGC of the different selected move-
ments, as well as to explore the main thematic areas regarding the objective pursued by each user’s
publication, aligned with the research results regarding twitter content classification by Dann (2010)
or by Gleason (2013). Thus, the exploratory analysis carried out suggests the existence of four cross-
cutting areas that are present in the three cases studied. (See Table 7 for the examples of representative
tweets of each area)
Firstly, the (i) explanation of the movement itself. This category focuses on informative communi-
cation, and here have been classified all those messages related to information about the movement,
presentations of events that have taken place, real-time messages about rallies, media comments, updates
with links to informative pages or videos.
Secondly, claims for (ii) social and political justice. This category classifies all messages related to
specific claims and petitions in relation to the given movement. On many occasions, citations to politi-
cians or personalities with greater visibility and power for change are used.
Thirdly, (iii) exchange of personal experiences. This category includes all those singular first-person
messages that either convey how an activist within the movement is doing or reveal a significant event
that the user has experienced. For example, disclosure of a past sexual assault, in the case of the MeToo
movement, or of an experience of racist discrimination, in the case of BlackLivesMatter.
Finally, (iv) community building. This category brings together different messages that foster com-
munity building and are often expressed in plural first-person. The contents selected are intended to
share resources, offer specific help, or provide general support messages to other activists.
Table 7 below shows examples of tweets representative of each area and presents how the thematic
classification of the analyzed sample has been carried out.
Once data samples had been coded and quantitatively analyzed, homogeneous features were found
between the movements. The results showed very similar percentages of tweets in each category. On
average, 36.1% of the messages were classified within “explanation within the movement”, while 20.1%
are “claims of social and political justice”, 24% are “exchange of personal experiences”, 16.1% are
“community-building messages”. There are 2.8% of tweets that were not likely to be classified in the
previous categories. Figure 3 shows the data deduced from the analysis of the 3 movements.
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Table 7. Representative tweets
Purpose Movement Representative Tweet

@Acampadaparis
OccupyWallStreet Camp destroyed tonight by police is being rebuild #OccupyDefense #ows
#occupywallstreet
@ancklein
Explanation of the Powerful statements from the organizers of the #Boston #BlackLivesMatter
BlackLivesMatter
movement demonstration on I-93 today: http://bostonglobe.com/metro/2015/01/15/
demonstrators-issue-statement/EE1fqG7tk3NooOHCu5dhOM/story.html
@AJEnglish
MeToo #MeToo was founded by an African American woman, whose aim was to reach
women in underprivileged communities. https://aje.io/veh2s - #AJOpinion
@haywoodwhy
Tell the Dept of Justice to protect the rights of #OccupyWallStreet protesters. Sign
OccupyWallStreet
here: http://bit.ly/uBjh2v#OWS #p2
@DFAaction
@NYjusticeleague
Claims for social and We march 4 the #StolenDreams of #TamirRice no more execution of our children
BlackLivesMatter
political justice by police!
#BlackLivesMatter #ReclaimMLK
@CobaltDaisy
There is a sexual harassment/abuse scandal in the oval office no one is paying
MeToo
attention to... why not deal with that one while they “nervously wait” for the next
one on the hill? #MeToo
@janelynne
#occupywallstreet #occupyphiladelphia
OccupyWallStreet
I am wary of so- called anarchists who are planted to discredit Occupy. They are
undermining
@kinkyintellect
black women are not a group who people recognize as being victims of rape.
Exchange of personal BlackLivesMatter
we’re never damsels in distress.
experiences
#BLMconf #BlackLivesMatter
@TheWomensWatch
Dear #CarterPage, As a #MeToo survivor I assure you that nothing you have
MeToo endured over the last year is nothing, NOTHING, compared to the constant mental
anguish a survivor goes through on a day to day basis. No one cares about your
poor me act.
@brentnhunter
OccupyWallStreet It’s irresponsible to be pessimistic in field of infinite possibilities. Let’s build
bridge to future. #occupywallstreet
@iAskMonica
Community building BlackLivesMatter “Alone we can do so little; together we can do so much.” Helen Keller
#FERGUSON #blacklivesmatter
@RepresentPledge
MeToo We believe you and stand with you
@realdylanfarrow. #metoo
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Figure 3. Percentage of tweets in each thematic area
DISCUSSION AND RESEARCH AGENDA
This study shows a number of common characteristics regarding what objectives social movement par-
ticipants pursue when generating and sharing Twitter content. Like other social networks, Twitter offers
multiple opportunities for engagement in social movements, either by creating content, citing, sharing
links to text or video content, following a hashtag, etc. (Gleason, 2013). This, in turn, also offers many
possibilities for academic research, as it allows to analyze both users and the various content that can
be generated and shared.
Scholars have previously studied the users’ purposes while interacting on social networks (Karahanna
et al., 2015). Specifically, regarding activism and social movements, the use of social media has been
evidenced, highlighting the aims of mobilization, persuasion, information exchange and distribution of
resources (Miller, 2017; Pang & Goh, 2016). The present study agrees with those previous results found
by Pang and Goh (2016) but broadens those conclusions by specifying the field of analysis thanks to
developing a textual and comparative study.
As indicated by González-Bailón (2013), digital technologies are generating an unprecedented level
of information. This fact might have great implications for the social sciences at a theoretical level, but
also at a practical level, in fields such as policy making. In this regard, the significance of social move-
ments under investigation is that it creates a great deal of attention on digital platforms as a large number
of people see, share and interact with it. The information that results from these interactions, may be
relevant for businesses, institutions, governments, decision-makers or public figures who want to engage
with the numerous digital activists, as suggested by Shah, Sivitanides and Mehta (2013).
These findings are highly relevant to understand the patterns of how communication works between
social movement activists, so they offer plenty of opportunities to create and share content aligned with
the movement. As proposed by Reyes-Menendez, Saura and Filipe (2020), the mentioned stakeholders
can use the insights of this research to successfully join the social media conversations and to better
frame their communication and advertising through defining relevant messages and strengthening the
companies’ marketing, advertising, and reputational strategies.
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While these results can be valuable to better acknowledge the main areas of communications behind
the conversations held in past movements, they also are of great worth to theorize about how future move-
ments would structure their messages on social media, as previously highlighted by Harlow (2020). In
this way, all communications managers could take into consideration our findings in order to better plan
their communications and marketing strategies when it comes to joining the social media conversation
and position themselves within actual or future activist movements.
Furthermore, the findings of this research, by identifying homogeneous traits among different move-
ments, are valuable for researchers as they offer the opportunity to develop new research areas related
to the combination and comparison of social movements, aiming to achieve a more holistic view. In this
way, in order to establish future research in this field of research, Table 8 presents future research areas
and research questions.
Table 8. Future research areas
Approach Future research areas Future research questions

● How have some social movements allowed and enabled the rise of
others?
● Activist Engagement
Comparative analysis ● What is the activist archetype present in the social networking social
● Discourse analysis
between movements movements? Are they replicated throughout the different movements?
● Public opinion
● Which movements have received the most public support on social
networks?
● Has the ‘filter bubble’ had an impact on activist engagement on
social media?
Analysis of the evolution ● Activist Engagement
● Is there an evolution of topics and expressed sentiments in the UGC
over time ● Discourse analysis
over time?
● How does the gender of activists shape social network discourse?
● Have any policy initiatives related to the claims of social movements
been carried out?
● How has the media presence and visibility of social movements
evolved?
● Public agenda and policy
Analysis of changes ● Is there any dissonance between UGC and news published in
reforms
resulting from the traditional media regarding a social movement?
● Media
movement ● Has the visibility of specific topics (in relation to minorities,
● Companies
feminism, etc.) increased in posts created by brands on social
networks?
● What impact does the involvement of brands in activist movements
have on users’ perception?
Research Propositions to Address the Challenges and Opportunities

of Social Media Analysis using UGC in Social Media
Aiming to guide future research in the area, we formulate different research proposals based on our
study, which blends both the combined study of different social movements and their analysis from the
perspective of UGC in social networks.
One of the areas explored by researchers in recent years is sentiment analysis. This is a relatively
new technique that processes natural language through an algorithm and categorizes text-based data into
positive, negative, or neutral sentiment (Kearns, 2020). It can be of interest in order to obtain insights into
users’ attitudes, thoughts, and emotions towards a particular issue. In this regard, it can also be applied
112

to social network content related to activist movements for the sake of deeper understanding (Al-Rawi,
2016). Therefore, the following research propositions are put forward:
Proposition 1. The sentiment present in UGC varies over time depending on the context in which
the movement is located.
Proposition 2. The social movements of the 2010s show similar UGC characteristics, featuring
mainly negative sentiments in the messages shared in social networks.
There is a term that has grown in popularity in recent years: filter bubble. This is a filter generated
by algorithmic personalization systems whereby users would be exposed to a lesser extent to that news
not fitting their ideology (Spohr, 2017). In the same way that this concept has been previously analyzed
with the aim of investigating its role in political campaigns (Groshek & Koc-Michalska, 2017), it may be
of great interest to analyze its impact on the evolution and network structures within social movements.
Therefore, the following research proposal is put forward:
Proposition 3. Social media algorithms and the ‘filter bubble’ have had an impact on the UGC of
social movements by creating a more polarized and radical discourse.
Social networks play an increasingly important role in the consumption of information and news
(Spohr, 2017). Although the differences in information consumption habits between traditional media and
social networks have been analyzed previously (Zhao et al., 2011), the possible particularities depending
on the news’s nature remain unknown. Along these lines, it is relevant to analyze whether, in the case
of social movements, there is a preference when it comes to users’ access to information. Therefore, the
following research proposal is put forward:
Proposition 4. Users prefer to be informed about social movement issues via the UGC of social
networks rather than by traditional media.
One area of research related to data visualization, and explored by Costanza-Chock and Rey-Mazón
(2016), is the comparison between front pages of big media and the number of tweets published about
a particular social movement. Following this research line, it is of interest to investigate the occurrence
of homogeneous traits between the key messages and the sentiment expressed by traditional media and
UGC in social networks. Therefore, the following research proposal is put forward:
Proposition 5. The coverage provided by the media is not proportional to the reach achieved by UGC
in Social Media, nor is it aligned to the key concepts and sentiment expressed by users.
In the same way that social network users have a great power to mobilize for social causes, they can
also organize themselves both for and against institutions or brands. In turn, brands can find in social
movements a line of dialogue with their public by aligning themselves with the same purpose. This has
been investigated both from a user perspective (Romani et al., 2015) and from the search for insights to
apply to companies and marketing activities (Reyes-Menendez et al., 2020). Therefore, the following
research proposal is put forward:
Proposition 6. Users’ perception of brand value has improved after the incorporation of messages
related to social protests by companies and institutions.
The impact of gender on different dimensions of social networks, such as lexical variations (Bam-
man et al., 2014), representation of images (Rose et al., 2012) or social identity (Wang, 2017) has been
previously studied. In this line, knowing that gender can create a substantial variation in the use of social
media (Volkova et al., 2013), the following research proposal is put forward:
Proposition 7. Gender modulates participation in social movements as well as the features and senti-
ment of the UGC.
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CONCLUSION
In the present study, the main social movements of the 2010s were analyzed through the UGC in Social
Media. Regarding our main research question (“What homogeneous features are found in the purpose
pursued by the participants of the different social movements when generating and sharing content on
Twitter?), an exploratory approach has been carried out through a case study and a systematic analysis
of the textual content of three text-based data samples.
The results show homogeneous features between the movements, concerning four categories where
the data collected from Twitter have been classified: an explanation of the movement, claims for social
and political justice, exchange of personal experiences, or community building. In this way, not only
is the research question answered, but the presence of homogeneous features between movements is
confirmed. An area to discover through possible future research.
For this reason, the article proposes a detailed research agenda that is articulated around three main
areas of study: Comparative analysis between movements, Analysis of evolution over time, and Analy-
sis of changes resulting from movement. Always taking into account the wealth of the UGC of social
networks as a source of data for its study.
In terms of theoretical implications, the present study offers a new perspective on the analysis of social
movements in social networks. Not only from the theoretical framework of the UGC analysis of a move-
ment, but the combination and comparison of the different social movements that occur worldwide.
Therefore it is possible to generate a more global vision of how this phenomenon works. Accordingly,
future studies can address the questions included in the proposed research agenda.
From the theoretical perspective, the results of the present study offer meaningful theoretical implica-
tions for individuals, organizations and institutions. The finding of an homogeneous thematic structure
of messages generated by users opens the door to new research that broadens the similarities between
movements and delves into the social implications that this fact may have. Researchers should focus on
the future research areas proposed in order to gain a deeper understanding of social movements in which
millions of people around the world are voluntarily involved.
Likewise, the worldwide social situation caused by Covid-19 has motivated that movements for so-
cial justice proliferated and were particularly active during the months of restrictions and confinement
(Pleyers, 2020). This unprecedented situation, in the same way that favors social movements, may favor
further research on this area.
From a more practical point of view, communications and marketing managers, as well as decision-makers
from all kinds of institutions, can use the results of the present study as the starting point to develop their
data-driven communications strategies. Nowadays, the population is increasingly demanding social and
environmental involvement by brands and institutions. This is why it is essential for managers to have
insights based on data, so they can join digital conversations held by numerous activists on social media.
Thanks to a deeper understanding of the opportunities and potential of big data of UGC available on
social media channels, different stakeholders can design, adapt and elevate their marketing strategies.
114

Furthermore, as stated in recent research (Saura et al., 2021), in the digital ecosystem, data would be the
new currency for businesses and companies, as the analysis and use of UGD can predict user behavior
and analyze their actions.
The limitations of the present study are related to the number of cases selected in the study, the sample
analyzed in each case and the types of analysis used to analyze the data. In terms of future research
objectives, the research propositions described above should be taken into account as starting points to
establish new directions and lines of research focused on gaining a better understanding of the social
movements in social media based on the analysis of UGC.
Furthermore, the categorical classification of the tweets, as well as the exploratory selection, can be
taken into account as a limitation, since other studies have shown statistical significance and additional
quantitative analysis in similar approaches. In the future, researchers can use these results to propose
new constructs or variables for quantitative models, as well as follow the research proposals revealed
in the present study.
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information to a large audience (Sandoval-Almazan & Gil-Garcia, 2014).
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sentence (Yang, 2016).
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publish different data, information and content, which is subsequently published and may be of interest
to others (Krumm et al., 2008).
Case Study: research methodology based on an empirical inquiry that investigates a contemporary
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Content Analysis: research methodology that comprises techniques for reducing texts to a unit-by-
variable matrix and analyzing that matrix quantitatively to test hypotheses (Kohlbacher, 2006).
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Chapter 7
Understanding Google
Ads Metrics for SME
Belem Barbosa
https://orcid.org/0000-0002-4057-360X
University of Porto, Portugal
Zaila Oliveira
Unichristus, Brazil & Unifametro, Brazil
Sandrina F. Teixeira
CEOS, ISCAP, Polytechnic of Porto, Portugal
Vasco Paço Gomes

University of Aveiro, Portugal
ABSTRACT
Despite its popularity, search engine advertising is a particularly complex and demanding technique.
One of the main challenges for Google Ads managers is to adequately monitor performance. Indeed,
the literature identifies a plethora of metrics to measure the success of a search engine ads campaign.
One research question arises: What are the metrics adopted by small and medium-sized companies
to measure the performance of a Google Ads campaign? This chapter includes a mixed-method study
with digital marketing professionals experienced in managing Google Ads campaigns for Portuguese
SMEs. Interviews helped highlight the main difficulties faced by SEM’s Google Ads’ managers and to
identify the performance measures they mostly control. Then, a survey enabled to analyse the associa-
tion between performance measures and campaigns’ perceived success. The insights produced by this
chapter are particularly interesting for researchers, teachers, business managers, and digital marketing
professionals, as it presents important clues on measuring the effectiveness of Google Ads campaigns.
DOI: 10.4018/978-1-7998-8003-5.ch007

Understanding Google Ads Metrics for SME
INTRODUCTION
Search Engine Advertising (SEA) is currently one essential tool for companies that wish to gain online
visibility (Jafarzadeh et al., 2019; Winter & Alpar, 2020) and to attract more visitors to their websites.
Being also known as search engine marketing or programmatic advertising (Saura, 2020), it comprises
strategies to place adverts in search engines and banners or displays in partner websites. SEA consists of
developing and managing ads to be featured in the top of search engine’s results pages that are generated
after the user entered a certain phrase in the dialogue box.
Although it is an instrument with great popularity among companies of almost any sector, the fact
that the ads are shown based on relevance and not based on the highest bids (Geddes, 2014), enables
smaller companies to compete with large companies using ad quality instead of budget (Geddes, 2014).
Obviously, this creates a great opportunity for small and medium-sized enterprises (SMEs), to reach po-
tential customers in a relevant way (Geddes, 2014; Gong et al., 2014; Klapdor et al., 2014; Lynch, 2015).
To use this tool effectively, companies should carefully follow users’ conversion process and evaluate
the return on your investment (Alby & Funk, 2011; Geddes, 2014; Google, 2021b; Jansen et al., 2009;
Sculley et al., 2009). Monitoring the SEA campaign takes advantage of digital marketing measurability
(Alby & Funk, 2011; Geddes, 2014; Google, 2021a; Jansen et al., 2009; Sculley et al., 2009) and enables
real-time adaptation and improvement of campaign performance. However, due to their limited resources,
SMEs may not be able to deal with the tool’s complexity (Geddes, 2014; Jafarzadeh et al., 2019) and
usually have insufficient resources to track all available metrics to measure their SEA efforts (Barry &
Charleton, 2009; Jansen & Clarke, 2017).
Despite the plethora of metrics that the literature suggests measuring the success of a SEA campaign,
this chapter proposes the control of five main metrics, which are associated with different stages of user
interaction with the ad, from the beginning of his research until reaching one or more conversion points.
These metrics are: clickthrough rate, bounce rate, conversion rate, return rate, and return on investment.
As this chapter demonstrates, this proposal was validated by a group of marketing professionals, and
the relationship of controlling these performance metrics on perceived success of the SEA campaign
was explored.
This chapter includes an empirical study using mixed-method approach comprising interviews and
an online survey. By combining qualitative and quantitative techniques, this study enabled to fully tackle
the research question. Participants were digital marketing professionals with experience in managing
Google Ads campaigns for Portuguese SMEs.
The insights produced by this chapter are particularly interesting for academics investigating and
teaching topics related to SEA. They are also valuable for business managers and digital marketing profes-
sionals, as the chapter presents essential clues on measuring the effectiveness of Google Ads campaigns.
BASICS OF GOOGLE ADS
Google is the most popular search engine in many countries around the world, including in Europe. Search
engines have commonly one main source of income: advertising, in the form of sponsored links, that are
featured in their results pages. For managing sponsored links, Google created on October 23, 2000 the
Google Adwords platform, which was renamed and is nowadays called Google Ads. It allows advertis-
ers to create, manage, and control their campaigns. According to Statista (2021), the revenue generated
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by Google Ads platform totalized 146.92 billion US dollars in 2020. Google Ads comprises two types
of advertising: search and display ads. The latter refers to advertising that is placed in partner websites
and platforms beyond search engines and shown to users while they are browsing on the internet. These
ads can be interactive and have various formats, such as image, video, and sound (Kotler & Armstrong,
2020). A search ad is displayed after the user initiated a query, by typing a phrase on the search engine
dialogue box to find information on a specific topic. Search ads are then included in the search engine
results page along with the organic results. These ads are usually in text format and may be confused
with the search engine’s organic results (Kotler & Armstrong, 2020). Although Google Ads enables to
manage these two types of advertising, this chapter focuses only on search ads.
Main Advantages of SEA
Search advertising is characterized by being a consequence of searches that the user performs on a
search engine (Google, 2021b). Google’s objective is to deliver advertising that perfectly tackles to the
problem that the user is trying to solve with the search (Ghose & Yang, 2009; Lu & Zhao, 2014). This
objective portrays SEA’s added value - the relevance to the user (Chan & Park, 2015; Fain & Pedersen,
2006; Geddes, 2014). In addition to this, SEA also conveys several advantages that make it very attrac-
tive for companies.
Payment
The payment method for SEA is one of the first advantages that a company faces (Google, 2021a). The
company pays for each click received by the ad (Geddes, 2014; Google, 2021b). As in the case of display
advertising, marketing strategy may enable the company to pay per action. Hence, the payment will be
calculated so that it is close to the clicks necessary for an action to take, and the company will pay the
sum of those clicks (Google, 2021b). In traditional advertising or display advertising, payment is defined
in terms of impressions. In that case, the company will pay for an estimated number of people who will
see your ad, but that it does not know for sure whether they will be affected by it or not.
In contrast, SEA uses a model of pay per click. The click corresponds to a lead for the company’s
website. This lead is closely related to a valuable action for the company (Lynch, 2015), which means
that the cost per acquisition in this type of advertising tends to be lower than in other advertising types
of advertising (Animesh, Ramachandran, & Viswanathan, 2010).
Control
Related to the payment characteristics is another advantage of SEA: control. In this type of advertising,
it is possible to control daily and monthly budgets and control how much the company pays for each
click on any of the keywords (Geddes, 2014; Google, 2021b; Laffey, 2007).
Measurement
The most significant difference that Internet advertising has brought about from all other traditional
advertising is measurement. This is particularly notorious in the case of SEA (Jansen et al., 2009). SEA
enables the use of several metrics that will help measuring campaign performance and controlling each
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keyword’s performance (Geddes, 2014; Google, 2021b). In SEA, the advertiser can control its invest-
ments in detail and identify the strategies with most significant profits. As such, this is a major difference
from other types of advertising (Sculley et al., 2009), allowing a very precise calculation of the return
on investment (Alby & Funk, 2011).
Flexibility and Speed
Cost control and the ability to measure any element of the campaign’s success are leveraged by two other
advantages inherent to SEA: flexibility and speed (Laffey, 2007). In fact, the ability to control costs and
measure success would have a more negligible effect if it were not possible to perform real-time adjust-
ments to any factor of the campaign such as ad copy, keywords, targeting, budget, and maximum bid,
among others (Jansen et al., 2009). In SEA control is ongoing, as during the campaign the company can
check the factors that are not having the desired performance and immediately change them so that its
performance can be improved. These changes and the improvement of results are performed in real-time,
allowing the company to quickly test various strategic options until reaching the desired performance
(Geddes, 2014).
Relevance
All the advantages discussed above add much more value to the company because they are combined
with the most significant advantage of SEA - relevance (Geddes, 2014). This characteristic is the one that
most distances SEA from other types of advertising (Gong et al., 2014). Indeed, the greatest advantage
of SEA for advertisers is that their ad is shown only to relevant users who are looking for the informa-
tion or benefit featured by the ad. So, the company can be confident that its investment is spent only
show its ads to users looking for them (Klapdor et al., 2014). And that relevance is achieved through
the segmentation available on Google Ads. The first targeting filter is the keywords that the user uses
to perform a search (Lynch, 2015), but the company can also define the type of users that will see their
ads. Hence, the advertised is allowed to target the person who sees your ads, including the following
characteristics (Google, 2021b):
• Language;
• Geographic location;
• Devices used for performing the search;
• The time when the research is performed;
• Various demographic characteristics.
A wide range of targeting criteria gives the company great control over how it spends its search
advertising budget and enables that almost everyone who would not be interested in reading its ads to
be excluded (Geddes, 2014).
Consumer-Centered Process
The ability to return results relevant to a user’s search is almost exclusive to search engines (Klapdor
et al., 2014). Although there are other ways to research a product or subject, their cost is much higher
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for the user (Chen & He, 2011). Search engines offer users the ability to find information and enables
companies to send advertising messages that are relevant for the issue faced by the user (Gupta & Ma-
teen, 2014), reducing the level of intrusion that is usually associated with online advertising (Ghose &
Yang, 2009). In fact, some authors (e.g., Fain & Pedersen, 2006; Gupta & Mateen, 2014; Laffey, 2007)
suggest that the main determinant of SEA success is the ability to create synergy between consumer’s
need of information that helps solving his problem and the company’s willingness to provide relevant
information to its target audiences. Without surprise, SEA is the most significant source of income for
search engines (Statista, 2021) and that organic results are their way of attracting enough users so that
search engines become relevant advertising platforms for companies (Laffey, 2007). So, this strategy
provides benefits for the three-party involved (Gupta & Mateen, 2014; Klapdor et al., 2014; Laffey,
2007), search engines, users, and companies. The essence of SEA resides in this intersection of benefits,
since all stakeholders are interested in making the process go smoothly. As argued by Laffey (2007), the
user is interested in finding information relevant to his problem, while the company needs to find the
users with specific problems that can be solved by the company’s products and services. As for search
engines, they are service providers, and their profit depends on the satisfaction their service provides to
both search engine users and advertising companies. Karande, Mehta and Srikant (2013) explained that
search engines are interested in meeting the needs of both users and advertisers, while maintaining a
long-term sustainable relationship with them. Thus, search engines must show the most relevant results
to their users in order to increase the customer base it can offer to companies. By providing relevant
results, search engines ensure that customers will continue to use their services because it facilitates
their search process. The company continues to be interested in investing in SEA because it allows it to
reach specific consumers.
Opportunities for SMEs
Overall, it is important that companies actively collaborate in this process, providing relevant ads for
search engine users. This is also essential for digital marketing results. Indeed, according to the findings
by Sculley et al. (2009), the bounce rate of companies that follow good practices in SEA is on average
25% lower than the ones that don’t focus on providing relevance. SMEs are no exception to this need
to be guided by good search advertising practices. Murphy and Kielgast (2008) alert that SMEs often
lose money with SEA, but that is because they do not make good strategic use of that advertising while
large companies do. In fact, SMEs tend to compete directly with large companies in this advertising type
when they should be doing the opposite (Geddes, 2014). Many of the keywords searched on a search
engine occur infrequently, and SMEs have an opportunity to explore those low competition keywords
that large companies do not usually explore. Large companies are generally concerned with the small
percentage of words that are searched very often. SMEs must then take advantage of search advertising
without competing directly with large corporations (Laffey, 2007).
Evaluating the SUCCESS of a Google Ads campaign
Measuring the effectiveness of a SEA campaign is crucial (McDonald, 2014). All decisions made to
improve the ad, change budgets, and change the various of a campaign must be based on its performance.
As much as the manager’s perception is that the campaign is being successful, if the numbers do not
agree with that perception, he will have to make changes (Geddes, 2014). If there is no way to evaluate
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the campaign effectiveness or if the control procedures are not adequate, the changes will be made based
on wrong information. They may harm rather than improve the campaign’s performance.
Numerous metrics are identified in the literature to measure the success of a Google Ads campaign
and its various components:
• Impressions (Geddes, 2014; Google, 2021b; Zenetti et al., 2014);

• Traffic volume (Moral et al., 2014);
• Number of pages per visit (Moral et al., 2014);
• Average visit time (Moral et al., 2014);
• Bounce Rate (Moral et al., 2014; Sculley et al., 2009);
• Return rate (Lu & Zhao, 2014; Moral et al., 2014);
• Lifetime value of the customer (Geddes, 2014; Google, 2021b; Lu & Zhao, 2014; Smith, 2002);
• Click through rate (Barry & Charleton, 2009; Geddes, 2014; Google, 2021b; Jansen & Clarke,
2017; Jansen et al., 2009, 2013; Kim et al., 2014; Lu & Zhao, 2014; Sculley et al., 2009; Zenetti
et al., 2014);
• Conversion rate (Barry & Charleton, 2009; Geddes, 2014; Google, 2021b; Jansen & Clarke, 2017;
Jansen et al., 2013; Lu & Zhao, 2014; McDonald, 2014; Rutz & Bucklin, 2007; Zenetti et al.,
2014);
• Return on investment (Alby & Funk, 2011; Barry & Charleton, 2009; Geddes, 2014; Google,
2021b; Jansen et al., 2013; Kim et al., 2014; Lu & Zhao, 2014).
Clearly, there are too many metrics for SMEs to follow efficiently. A set of metrics should be chosen
to allow the company to measure the success of certain key elements of the campaign, to identify its
weaknesses, and to amend deficiencies. Considering the consumer journey from the beginning of his
search until reaching one or more conversions, a set of particularly important metrics stand out, and for
that reason are proposed by this chapter to be the priority, especially for SMEs and other companies that
lack resources for a throughout analysis of performance. This reduced set of metrics are: click-through
rate, bounce rate, conversion rate, return rate, and return on investment. The next sections explain each
of those metrics in detail.
Click-Through Rate
Click-through rate is the metric most used by managers to measure the performance of Google Ads
campaigns (Abrahams et al., 2014; Barry & Charleton, 2009; Chan & Park, 2015; Gupta & Mateen,
2014; Jansen & Clarke, 2017; Sculley et al., 2009). Saura (2020) explains that, by using data sciences, it
is possible to measure and predict click-through rate, and consequently improve advertising profitability
and overall success in terms of impressions and clicks. As point of fact, this metric is also the one that has
more prominence in the Google Ads tool, mainly because it is the metric that most interests the search
engines to measure (Chan & Park, 2015; Gong et al., 2014); this is the main metric to determine search
engine’s revenue. Still, the prominence of this metric causes many errors in measuring the efficiency
of campaigns, as the company does not gain value per click, but rather by conversion (Abrahams et al.,
2014; Jansen et al., 2013; McDonald, 2014). This does not mean that this metric has no use – on the
contrary, it is very important. But is must be well-understood by ads managers.
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Click through rate is a metric that allows analyzing the user’s perception of the company’s ads (At-
kinson et al., 2014; Jansen et al., 2009, 2013). This metric is suitable for measuring consumer satisfac-
tion with the displayed ad, but it does not serve to measure the experience that user will have with the
landing page (Klapdor et al., 2014; Sculley et al., 2009) for the simple fact that when the user clicks
on the ad, he has not yet interacted with the company’s website. Kim et al. (2014) demonstrate that the
click-through rate is critical to measure the ad’s success, but that it cannot anticipate what happens after
the click, and consequently cannot be used as a substitute of the conversion rate to calculate the ad’s
return on investment.
Bounce Rate
The bounce rate measures the number of times that a user leaves the page shortly after entering it (Kotler
& Armstrong, 2020). This means that the user did not find what he expected on the landing page and
returned to the results page to find another alternative. This usually happens because expectations were
created in the ad that were not met on the landing page. Hence, the ad was not clear about what was of-
fered by the company and its website. As Sculley et al. (2009) stated, the bounce rate helps to measure
user dissatisfaction, not satisfaction. Hence, the ads manager must use this metric to define whether the
user’s expectations are being met.
Conversion Rate
The conversion rate is another metric frequently acknowledge as the best way to measure the performance
of a Google Ads campaign (Abrahams et al., 2014; Barry & Charleton, 2009; Chan & Park, 2015; Gong
et al., 2014; Jansen & Clarke, 2017; Sculley et al., 2009). This indicator measures the percentage of us-
ers who, when clicking on the displayed ad, also performed an action that brings value to the company
(Geddes, 2014). This action is, thus, associated with one of the calls-to-action that the website provides,
both on the landing page and on other pages that the user subsequently visited.
The problem is that, in order to assess ad performance, the conversion rate has to be something in-
stalled and defined by the company and directly associated with the campaign, which makes it less used
as a measurement indicator (Barry & Charleton, 2009) despite being an essential metric to calculate the
return on investment (Klapdor et al., 2014; Rutz & Bucklin, 2007; Smith, 2002).
Return Rate
The return rate is what allows the customer’s lifetime value to be calculated (Geddes, 2014; Smith,
2002). Regarding ads campaigns, if the company considers only the value of the first purchase that the
customer makes, instead of the total value of the customer, it will not be able to make enough profit to
cover the cost of acquisition (Smith, 2002). The return rate is considered a long-term rate that better
calculates the value SEA creates for the company (Abrahams et al., 2014; Klapdor et al., 2014; Lu &
Zhao, 2014). To be able to calculate the total value of each acquired customer, and consequently to set
budgets correctly, the manager must measure the return rate and to understand the level of retention he
gets on each acquired customer, that is to say, the rate of customers who make more than one purchase.
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Return on Investment
The return on investment is the metric that shows the global success of the campaign and is the metric that
most interests managers (Ghose & Yang, 2008; Jansen et al., 2013). While the other metrics discussed
above serve to diagnose low performances at certain stages of the customer journey, this one shows the
campaign’s success as a whole. It measures the profit that the company obtains for the amount invested
in the campaign. It is the only indicator that allows the company to verify whether its campaign is profit-
able (Geddes, 2014; Google, 2021b). This indicator is also highlighted in the study materials of Google
(2021b) and by several authors (e.g., Geddes, 2014) as the only one that effectively verifies whether the
campaign performance is according to the perceived success.
THE VIEWS OF SMES ADS MANAGERS ON GOOGLE ADS SUCCESS METRICS
Overall, analyzing the performance metrics of a Google Ads campaign is essential because it takes ad-
vantage of measurement opportunities provided by SEA (Alby & Funk, 2011; Geddes, 2014; Google,
2021a; Jansen et al., 2009; Sculley et al., 2009). However, due to their limited resources, SMEs may not
calculate and track all available metrics to measure their search advertising efforts (Barry & Charleton,
2009). Thus, for this study, a research question was defined:
Research Question: What are the metrics adopted by SMEs to measure Google Ads campaign
performance?
Based on the existing literature’s contributions and considering the proposed research question, five
research hypotheses were proposed, considering the relationship between the chosen metrics and the
perceived success of the Google Ads campaign, similar to what Klapdor et al. (2014) did in their study.
Hypothesis H1: There is a positive relationship between the click-through rate and the perceived
success of a SEA campaign.
The click through rate is the metric most used by companies to measure the performance of a SEA
campaign (Barry & Charleton, 2009; Jansen & Clarke, 2017; Sculley et al., 2009). It allows to measure
the user’s perception of the ad, indicating the ad’s success, thus contributing to measure the success of
an important campaign item (Jansen et al., 2009; S. Kim et al., 2014; Sculley et al., 2009). A higher click
through rate will cause an increase in the quality score, which in turn causes a decrease in the cost per
click, decreasing the acquisition cost per customer, and thus contributing to the increase in the return
on investment and consequently the perceived success of the campaign (Geddes, 2014; Google, 2021b).
Hypothesis H2: There is a negative relationship between the bounce rate and the perceived success
of a SEA campaign.
The bounce rate is a metric used to measure user dissatisfaction (Sculley et al., 2009), which is re-
lated to the expectations created in the ad not being met on the landing page. When a customer gives
up barely looking at the offer on the website, the company ends up buying an unqualified click, for a
user that never had the intention to become a customer. This has a negative impact on the return on
investment because resources are spent on a user who the company will not have a chance to convert.
Consequently, there will be an increase in the bounce rate, which will decrease the perceived success
of the campaign (Geddes, 2014).
Hypothesis H3: There is a positive relationship between the conversion rate and the perceived suc-
cess of a SEA campaign.
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The conversion rate is a metric that shows the success of the landing page and the advertised product
or service in converting a user into a customer (Google, 2021a). Due to the need to configure it, this
metric is not widely used by managers (Barry & Charleton, 2009; Jansen & Clarke, 2017), but it is an
essential metric to measure the return on investment of a campaign, because when it is absent, it is im-
possible to understand the number of customers were acquired by the campaign and the cost at which
they were acquired (Google, 2021a). The higher the conversion rate, the lower the cost of acquisition
for each customer, and consequently the higher the return on investment and the perceived success of
the campaign (Geddes, 2014).
Hypothesis H4: There is a positive relationship between the return rate and the perceived success
of a SEA campaign.
It could happen that in a particular campaign the return on investment of the customer’s first purchase
is negative, since the acquisition cost exceeds the profit obtained by that acquisition (Smith, 2002). It is
then necessary for the manager to monitor the return rate to be able to calculate customers’ lifetime value
and thus ensure that the investment made allows to maximize the profit obtained from the customer. A
higher return rate normally associated with a higher lifetime value for the client, contributing to a greater
perceived success of the campaign (Geddes, 2014).
Hypothesis H5: There is a positive relationship between the return on investment and the perceived
success of the SEA campaign.
The return on investment is directly linked to the manager’s perception of the success of his cam-
paign, because the greater the percentage of profit obtained by each monetary unit invested, the greater
the perception of success (Geddes, 2014; Google, 2021b). After choosing the method of calculating the
return on investment according to the company’s typology (Alby & Funk, 2011), this metric allows to
verify if the campaign is being profitable and if that profit is in accordance with the defined objectives
(Geddes, 2014; Google, 2021b).
Methodological Approach
Considering the proposed research question, a mixed method approach qual → quan (Bryman, 2012)
was carried out, in which each of the method typologies will serve to answer the research question in
a complementary way (Bryman, 2012). In the first phase, semi-structured interviews were conducted,
and in the second phase, an online survey was implemented.
The qualitative stage comprised semi-structured interviews with eight Google Ads campaign manag-
ers of Portuguese SMEs. Participants were recruited through the social networks of researchers’ contacts
and also by searching and contacting companies that were running Google Ads campaigns. The sample
was selected through purposeful non-probabilistic sampling, using typical case sampling as a selection
criterion (Patton, 1990). The interviews made by telephone and were recorded, transcribed and uploaded
into the qualitative analysis program NVivo for coding according to the parameters that were sought to
be researched.
The quantitative stage comprised an online questionnaire. The unit of analysis was one campaign
carried out for a SME. With the adoption of the convenience sampling technique and using LinkedIn
social network, all the profiles of Portuguese professionals who declared competencies with Google
Ads were analyzed, and an individual and personalized email were sent to them. The questionnaire was
applied only to Portuguese SMEs that used the Google Ads tool to carry out SEA campaigns. 144 valid
responses were collected. From the metrics found in the literature review on Google Ads, five were
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chosen that are linked to the path that a user takes in a Google Ads campaign, with the dependent vari-
able “perceived efficiency of a SEA campaign” being added (Jafarzadeh et al., 2011), renamed to “the
perceived success of a SEA campaign” (Table 1).
Table 1. Variables considered in the study
Success Metrics Supporting literature

(Barry & Charlaton, 2009; Geddes, 2014; Google, 2021b; Jansen et al., 2009; Jansen et al., 2013;
Click-through rate
Lu & Zhao, 2014; Kim et al., 2014; Sculley et al., 2009; Zenetti et al., 2014)
Bounce rate (Moral et al., 2014; Sculley et al., 2009)
(Barry & Charlaton, 2009; Geddes, 2014; Google, 2021b; Jansen et al., 2013; Lu & Zhao, 2014;
Conversion rate
McDonald, 2014; Rutz & Bucklin, 2007; Zenetti et al., 2014)
Return rate (Lu & Zhao, 2014; Moral et al., 2014)
(Alby & Funk, 2011; Barry & Charlaton, 2009; Geddes, 2014; Google, 2021b; Jansen et al.,
2013; Lu & Zhao, 2014; Kim et al., 2014)
Campaign perceived success (Jafarzadeh et al., 2011)
The survey requested that the respondent considered one single campaign that he/she had conducted
for a Portuguese SME, and that he had access to the campaign statistics. Then, for each of the five success
metrics, one question was posed “For the following metrics, please indicate which you have controlled
for that specific campaign”, with three response options: I controlled; I did not control; I don´t know.
For the metrics that the respondent had controlled, the percent value of the metric was also requested.
Perceived success of the campaign and some data for sample characterization was also requested.
The statistical analysis techniques were chosen considering the scales used in the questionnaire and
the type of hypotheses that were sought to be tested. Three methods of statistical analysis were then
used: univariate analysis, bivariate analysis, and multivariate analysis.
Qualitative Results
The eight participants in the qualitative study have varied profiles in terms of professional experience
and job (Table 2). The purpose of this option was to guarantee the diversity of the sample and a more
comprehensive collection of information and opinions.
Participants Preferences Regarding Success Metrics
Participants in this study unanimously recognized the usefulness of the five success metrics explored
in this study, although not all of them used them regularly to control the performance of the Google
Ads campaigns they conduct. Next sections explore in detail the views and experiences shared by the
participants regarding each of those metrics.
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Table 2. Interviewees’ profile
Interviewee Google Ads Experience (in years) Job

Interviewee 1 0.25 Marketing assistant
Interviewee 2 2 Project Manager
Interviewee 3 1 Head of Marketing
Interviewee 4 1.5 Digital marketing consultant
Interviewee 5 5 Company manager
Interviewee 6 3 Head of digital marketing
Interviewee 7 7 Product manager
Interviewee 8 8 Head of search
Click-Through Rate
In line with extant literature that indicates that click-through rate is the most used metric in Google Ads
(Abrahams et al., 2014; Barry & Charleton, 2009; Chan & Park, 2015; Gupta & Mateen, 2014; Sculley
et al., 2009), this mestric was top of mind for all participants in this study when the topic of success
metrics was introduced. Indeed, all respondents mentioned that they frequently checked Google Ads’
click-through rate without even being mentioned by the interviewer. For example, Interviewee 1 said that
“… every day the first thing I do in the morning is to see this, how were the impressions and the clicks
from the previous day.” Some participants also confirmed the growing awareness that the click-through
rate does not measure the total success of the campaign (Atkinson et al., 2014; Jansen et al., 2009, 2013)
with statements such as that of the Interviewee 8 who said that
A few years ago, the main metrics where the cost per click, impressions and clicks or click-through
rate, but they are that are now less important. I mean, they are still important, but now we look more at
conversion metrics, such as conversion cost and conversion rate.
Regarding the usefulness of this metric, Interviewee 5 supports the idea that the click-through rate
helps to evaluate users’ perceptions on the ad, but in some cases companies advertiser companies decide
to focus on this metric anyway depending on the main objective of the campaign “… if [the company] just
wants to generate traffic to the website (…) the main [metric] to follow will be the click-through rate”.
Bounce Rate
Bounce rate was mentioned by most of the interviewees, although two interviewees mentioned that they
did not follow it, and one said that they followed it, but only occasionally. Interviewee 3, for example,
justified the lack of monitoring of this metric due to integration difficulties, referring that “This is
something that I have to explore better. I tried to connect analytics with Google Ads, but (…) it didn’t
work”. Interviewee 5 agreed with the views that the primary utility of bounce rate is to measure whether
or not the user’s expectation was met on the landing page (Sculley et al., 2009), stating that it serves “to
assess the quality of the landing page, but mainly to validate the quality of the ad in conveying the cor-
rect information”. Interviewee 4 revealed that bounce rate is also followed for other purposes when he
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stated that “it is a metric that we use a lot, not only in terms of the campaign but also in search engine
optimization”.
Conversion Rate
Almost all respondents agreed that conversion rate’s control is important (Abrahams et al., 2014; Barry
& Charleton, 2009; Chan & Park, 2015; Geddes, 2014; Gong et al., 2014; Sculley et al., 2009), although
some were unable to control it. Interviewee 4 described that “the most important sign is conversions,
and, in the end, this is what matters to the [company]” and added that it is essential to calculate the
return on investment because “with it we see the cost we have for conversion and if the investment in
the campaign is worthwhile.”.
Some reasons were given for not controlling this metric. Interviewee 3 revealed that he had difficul-
ties connecting the website to Google Ads, and Interviewee 2 reported that he also had to wait a long
time to install conversion tracking. However, he was able to start controlling it afterwards. Interviewee
1 admitted that he did not intensively control the conversion rate because “we are working more on the
objective of increasing impressions, clicks, but conversions are not yet a goal for us”.
It was verified in these interviews that, although not everyone uses it, the interviewees recognize the
importance of return on investment (Ghose & Yang, 2008; Jansen et al., 2013). For example, Interviewee
3 stated that “The ultimate goal is selling. Visits are always of interest, but what matters is that at least
in the medium term there is a return on investment”. In addition to the reasons already mentioned above
by the interviewees for not measuring the conversion rate, Interviewee 6 explained that in his case the
return on investment is not yet estimated because “management still considers that we are in the launch
phase, we are in the market for a short time, and we are aware that initially we will have to make a big-
ger investment”.
Unpopular Success Metrics
Several metrics were often less popular for different reasons, namely because they are time consuming
and difficult to implement. Still, conversion rate was stood out for being disregarded by all participants.
Return Rate
Despite the need mentioned in the literature review to calculate the return rate to understand the true
potential of a customer (Abrahams et al., 2014; Klapdor et al., 2014; Lu & Zhao, 2014,) almost none
of the interviewees controlled it. Interviewee 4 stated that he did not carry out this control for small
companies, referring that “some [agencies] do it, but more so for medium and large companies… From
my experience in managing ads accounts, small and medium companies do not do that job so much”.
The same interviewee justifies the low adherence to this metric with the time needed to control it: “in
my opinion, it pays off (...), but it takes a lot of work, it does, but I understand that (...) small companies
cannot do it “.
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Additional Metrics Considered by the Interviewees
During the interviews, some respondents repeatedly mentioned two additional metrics that admittedly
could be considered to understand user’s search path: cost per click and impression share. Their views
and arguments are shared in the following subsections.
Cost per Click

The cost per click consists of the amount paid by company for the clicks that the ad receives from us-
ers (Saura, Palacios-Marqués & Iturricha-Fernández, 2021). In the case of Google Ads, it cannot be
higher than the maximum bid defined by the company (Geddes, 2014; Google, 2021a). According to
the interviewees, this metric essential to control the success of the campaign because it allows them
to observe the limits of their spending when it is not possible to calculate the return on investment. As
explained by Interviewee 5 “the cost per click is also a fundamental metric. We don’t want to spend too
much money on these companies, money they actually don’t have”. Hence, the interviewees assumed that
having a low cost-per-click is often considered one of the main indicators of campaign success. Still, it
is important to note that this metric is not possible to associate cost per click with any digital marketing
objectives, so considering it a main indicator for SEA campaign success is controversial, despite the
common views of interviewees.
Impression Share
Impression share is a metric provided by Google Ads that indicates the percentage of times an ad was
shown when eligible to do so (Google, 2021b). For example, an ad may have been offered a hundred
times, but there were two hundred searches for that ad, in which case the impression share would be fifty
per cent. An ad may not be shown when it is eligible for budget reasons only. Either the daily budget
ran out, and the ad can no longer be displayed on that day, or the maximum bid was so far below the
competition that it ended up not being qualified for the ad slots (Geddes, 2014; Google, 2021b).
According to some respondents, this metric is essential to understand the potential of the ads and the
keywords being featured. As Interviewee % explained, “for a company that fills the impression share by
fifty per cent, I know that it still has another fifty per cent of the search that can be worked on”. So, this
metric provides useful information to understand the campaign and plan future campaigns. As Inter-
viewee 8 said, “for those campaigns, we were able to define for which are the competitors what is their
position, and what impression share they are achieving”. So, the impression share enables to analyze, for
a particular ad that is having good results, if increasing the budget or maximum bids allows an increase
in the number of conversions while maintaining the return on investment stable (Geddes, 2014). Clearly,
despite the undeniably usefulness of this metric, its ability to measure success of a campaign is very
limited. Still, it is interesting to note the importance it was given by the interviewees.
Final Notes on the Qualitative Results
The qualitative study enabled to identify the metrics that were more supported by the Google Ads man-
agers that participated in this study, along with the reasons for adopting some metrics and neglecting
others. Only one of the metrics was commonly adopted by all participants: click-through rate. Other
metrics were considered valuable but presented difficulties in implementation or were considered very
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time consuming, and for that reason were often discarded. Moreover, one of the proposed metrics, return
rate, was deemed as more adequate for medium and large-size companies, and hence not a good fit for
SMEs. Except for the return rate, the participants acknowledge the relevance of the set of metrics pro-
posed by this article to measure the success of Google Ads campaigns conducted by SMEs.
Finally, the participants stressed the importance of two other metrics, which they pay a lot of atten-
tion to: cost per click and impression share. Despite their importance, it is important to note that these
two metrics cannot be directly associated neither with digital marketing objectives, nor with the user’s
journey. In fact, both those metrics have a more operational nature, and depend on the decisions made
by the manager namely regarding the budget, as well as the decisions made by the competitors. For
that reason, those two metrics were not included in the set proposed by this chapter, and are considered
complementary metrics, especially useful to plan future campaigns.
Quantitative Results
A total of 144 Google Ads campaign managers participated in the quantitative stage of this study. Par-
ticipants had diverse experience with Google Ads. 25% had up to one year of experience, and 19.4%
had been working with Google Ads for more than 4 years (Table 3). All of them had executed SEA
campaigns with Google for Portuguese SMEs.
Table 3. Participants’ experience with Google ads
Years of experience Frequency Percent Cumulative percent

From 0 to 1 36 25.0 25.0
From 0 to 1 36 25.0 25.0
From 2 to 4 28 19.4 60.4
From 4 to 6 29 20.1 80.6
From 6 to 10 20 13.9 94.4
From 10 to 15 8 5.6 100.0
Total 144 100.0
Metrics Controlled by the Respondents
Table 4 presents the metrics controlled by the respondents of the survey, and the reasons for not con-
trolling certain metrics. In line with the findings of the qualitative study, this study found that the most
controlled metric by the respondents is the click-through rate (77.1%). This great adherence to click-
through rate is also explained by that fact that this is the only metric in this study’ list that is provided
by Google Ads without any further preparation (Geddes, 2014; Google, 2021a). Hence, it is surprising
that the biggest reason for not controlling this metric is the lack of access (6.3%), which might indicate
that these managers only created the campaigns but did not control their performance.
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Table 4. Controlled metrics and reasons for not controlling
Click Through Return On

Bounce Rate Conversion Rate Return Rate
Metrics Rate Investment
Frequency % Frequency % Frequency % Frequency % Frequency %
Controlled 111 77.1 67 46.5 84 58.3 30 20.8 52 36.1
Not controlled 20 13.9 56 38.9 40 27.7 89 61.8 71 49.3
No access 9 6.3 23 16.0 20 13.9 41 28.5 45 31.3
It is difficult 6 4.2 10 6.9 10 6.9 17 11.8 16 11.1
It is not important 5 3.5 23 16.0 10 6.9 31 21.5 10 6.9
Do not know / No
13 9.0 21 14.6 20 13.9 25 17.4 21 14.6
answer
TOTAL 144 100 144 100 144 100 144 100 144 100
The conversion rate was the second most controlled metric (58.3% of the respondents). Of the managers
who did not control this metric (27.7%), most reported that they did not do it because of lack of access,
which can be explained by their difficulty in installing this control, as suggested during the qualitative
phase of this research and pointed out by extant literature (e.g., McDonald, 2014).
The bounce rate occupies the third place of the most controlled metric (46.5% of the respondents).
Still, more than half of the managers participating in this study do not control it. The main reasons for not
controlling this metric are the lack of access, which was explained in the interviews by the difficulty of
managers in combining website analytics with Google Ads, and also considering the metric unimportant.
The return on investment appears in fourth place, with 36.1% of managers controlling it, which is
something surprising given the difficulty of obtaining data from other metrics to calculate them. Pre-
cisely for this reason, it appears that the biggest reason for not controlling this metric (49.3% of the
respondents) is the lack of access.
Lastly, 20.8% of the respondents control the return rate. After the interviews’ conclusions, it was
already expected that this would be the least controlled metric. This metric proved to be quite unpopular
among managers, with the highest percentage of unawareness, and it is also the metric that most manag-
ers considered unimportant for their campaigns.
Table 5 presents the descriptive statistics for the metrics controlled by the respondents of the survey.
Table 5. Descriptive statistics
Click-Through Return On
Variables Bounce Rate Conversion Rate Return Rate
Rate Investment
N 111 67 84 30 52
Mean 18.4% 31.9% 16.3% 21.2% 90.9%
Median 10.0% 25.0% 7.7% 13.0% 22.5%
Standard deviation 0.2306 0.2385 0.2208 0.2494 1.6520
Minimum 0.0% 0.0% 0.0% 0.0% -19.4%
Maximum 100.0% 100.0% 100.0% 100.0% 100.0%
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Test of Hypotheses
Considering the dimension of the sample and the characteristics of the variables, Spearman’s correlation
was chosen for testing the hypotheses defined for this study. As explained by Bryman (2012), Spear-
man’s correlation enables to identify the relationship’s strength, the meaning of this relationship and its
statistical significance. As described above, the hypotheses defined for this study focus on the relation-
ship between each of the five performance metrics proposed by this chapter for SMEs (Table 5) and the
perceived success of a SEA campaign that the respondents had performed.
So, regarding the most popular metric used by managers in this study, it was found that the correla-
tion between click-through rate and perceived success of a Google Ads campaign is very low (0.095)
and that this correlation is not statistically significant (p> 0.05). Hence, this study does not empirically
support the hypothesis H1.
In the case of bounce rate, the relationship with perceived success of a SEA campaign was negative
as expected, but it was very low (-0.130) and that this correlation was not statistically significant (p>
0.05). Consequently, Hypothesis H2 is not supported by this study.
It was also found that the correlation between the conversion rate and the perceived success of a SEA
campaign is low (0.141) and that this correlation is not statistically significant (p> 0.05). Consequently,
it is concluded that this study does not provide empirical support to research hypothesis H3.
Regarding the metric that was shown less popular both in the qualitative and the quantitative study,
return rate presented very low and negative (-0,076) correlation with the perceived success of a SEA
campaign. Moreover, this correlation is not statistically significant (p> 0.05). As such, this study does
not support research hypothesis H4.
Finally, it was found that the correlation between return on investment and the perceived success of a
SEA campaign is strong (0.449) and that this correlation is statistically significant for a 99% confidence
level (p <0.01). Consequently, this study provides empirical support to the research hypothesis H5.
Final Remarks on the Quantitative Results
The quantitative results are consistent with the ones obtained in the qualitative stage of this study, namely
by confirming the unpopularity of the metric return rate. The possible reasons for this unpopularity refer
to the difficulty of computing this metric, and the fact that Google Ads managers do not consider it essen-
tial to measure their campaigns’ success. This has emerged in both qualitative and quantitative analyses.
Overall, this study demonstrates that Google Ads campaign control is often disregarded by SEMs ads
managers. Indeed, even the easiest metric to control, click-through rate, which is provided at no cost by
Google Ads tool, is not adopted by all the participants in the quantitative study.
More importantly, only one metric, return on investment, was strongly associated with the perceived
success of the campaign.
FUTURE RESEARCH DIRECTIONS
Despite the importance of search engine marketing in general and SEA in particular for practitioners,
research on the topic is still scarce. This chapter provides some interesting cues on how managers as-
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sess campaign performance and provided rich anecdotal evidence from both qualitative interviews and
quantitative survey.
Still, this study suffers from several limitations that prevent results from being generalized, and that
need further validation from future research. One main limitation faced by this study is related with the
sample dimension. The fact that the survey had 144 valid responses did not enable the use of multivari-
ate analysis techniques and the comparison between groups (e.g., type of SEA experience or sectors
of activity of the advertiser company). It is recommended that future research considers the inclusion
and comparison of campaigns from different sectors. Indeed, most literature focuses on the analysis of
one specific company (e.g., Abrahams et al., 2014; Alby & Funk, 2011; Atkinson et al., 2014; Ghose &
Yang, 2008, 2009; Klapdor et al., 2014; Moral et al., 2014; Rutz & Bucklin, 2007; Zenetti et al., 2014).
Surveys enable to collect data not only from several companies but also from several sectors, which could
be compared in the case the samples are large enough. Additionally, future studies may compare these
performance measurements with the ones used in social media (Saura, Palacios-Marqués & Iturricha-
Fernández, 2021), considering that some social networking sites are gaining importance as search engines.
It is also recommended that future research considers other metrics and different indicators of campaign
success. In the case of this study, it was considered the perceived efficiency of a SEA campaign, following
Jafarzadeh et al., (2011). Obviously, managers’ perceptions might not be the best indicator for campaign
success and performance, so future research could test alternative indicators for that dependent variable.
CONCLUSION
This chapter identified the main difficulties faced by Google Ads managers and the main performance
measures they use. Interviews with managers enabled to collect information on the popularity of Google
Ads metrics and the justifications for choosing the metrics used. It was possible to observe a general
acceptance of the metrics, but the return rate was particularly unpopular. It was also found that Google
Ads managers face two difficulties in managing their campaigns - insufficient budget and limited access
to the website and other company data. During the quantitative phase of this research, it was possible
to understand which metrics SMEs use the most to measure the success of their campaigns. The metric
with the most significant adoption by Google Ads managers was the click-through rate, which is in line
with what is advocated in the literature (Abrahams et al., 2014; Barry & Charleton, 2009; Chan & Park,
2015; Gupta & Mateen, 2014; Sculley et al., 2009). It was followed by the conversion rate, which was
also adopted by the majority, but not by all, despite being an essential metric to measure the success
that the SEA campaign in terms of reaching new customers (Abrahams et al., 2014; Barry & Charleton,
2009; Chan & Park, 2015; Gong et al., 2014; Sculley et al., 2009) and to assess the profitability of this
type of advertising (Klapdor et al., 2014; Rutz & Bucklin, 2007; Smith, 2002). In the respondents’ case,
the return on investment was controlled by a little more than one third of the respondents. This percent-
age is low for the importance that this metric has (Ghose & Yang, 2008; Jansen et al., 2013), because it
shows managers whether their campaigns are succeeding and making a profit for the company (Geddes,
2014; Google, 2021b). The return rate was the metric least controlled by respondents. Although this rate
is essential to measure the long-term success of the company’s investment in Google Ads (Abrahams et
al., 2014; Klapdor et al., 2014; Lu & Zhao, 2014), this result was not surprising due to the difficulty in
gathering the necessary information for its calculation (Geddes, 2014).
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In terms of the link between the manager’s perception of success and the metrics used to measure
the success of the campaign, we found that the return on investment was the only one that had a strong
correlation with perceived success, which is in line with the literature (Geddes, 2014; Ghose & Yang,
2008; Jansen et al., 2013; McDonald, 2014).
Another difficulty encountered during this research was the difficulty in configuring the metrics and
linking the account to the managers’ analytics data. Measurability is one of Google Ads’ advantages
(Geddes, 2014; Google, 2021b) and may be lost due to this difficulty. This conclusion about the difficulty
in calculating the metrics and their lack of access being one of the main reasons for not being controlled
can justify an intervention by Google and other search engines with similar services to facilitate their
configuration. Indeed, facilitating the articulation between analytics and SEA tools will enable managers
to optimize their resources better and to make the most informed decisions possible (McDonald, 2014).
This chapter makes two main contributions. It offers valuable information for managers involved or
intending to perform SEA. Indeed, the chapter explains the importance of adopting adequate metrics
to control and evaluate the campaigns, and makes important suggestions on which to choose, even in a
context of limited resources, which is often the case of SMEs. It also makes an important contribution
to the literature, by systematizing contributions scattered in the literature on SEA, by sharing relevant
empirical data, and by stressing the urgency of expand research in this topic.
Overall, it is recommended that managers carefully monitor their SEA campaigns, particularly by
adopting the set of five measures that are suggested in this chapter. By doing so, it is possible to assess
different stages of user interaction with the ad, and adequately monitor its efficacy and return on investment.
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com/blog/display-ads-versus-search-ads/
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advertising/worldwide
Bounce Rate: The percentage of visitors to a website that leave it (i.e., returns to the search engine)
without navigating on the website. It is an indicator of website rejection.
Click-Through Rate: Percentage of the ad impressions that resulted in a click, and consequently the
user was redirected to the website (i.e., the landing page).
Conversion Rate: The percentage of visitors to the website that perform a certain action intended
by the ad (e.g., subscription or purchase).
Return on Investment: Profitability of the ad campaign that results from the difference between the
revenue of the campaign and its cost.
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Return Rate: Percentage of visitors to the website that repeat their visit.
Search Engine Marketing: Digital marketing activities that enable a company or brand to be featured
in search engines result pages and encourage users to click and visit their web pages. It comprises two
main tactics: search engine advertising and search engine optimization.
Search Engine Results Pages (SERPs): The pages presented to search engine users after they type
a certain phrase on the dialogue box. It includes natural results and often also sponsored links (i.e., ads).
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Chapter 8
Big Data Optimization in Zara:
How Zara Will Optimize Its Process With
Big Data Due to the COVID-19 Situation
Paula González-Padilla
Carlota Galán Zori

ABSTRACT
This chapter will address the issue of online sales in the fast fashion sector, specifically Zara, the flagship
brand of the Inditex textile group. Since 2012, Zara has been working on a plan to close, restructure, and
optimize its physical shops, a process that was accelerated in 2017, and which has been affected by the
global pandemic that began in early 2020. These two events have caused online sales to exponentially
rise with, in turn, the percentage of returns. This is the objective of this chapter: to analyze where Zara
is in terms of online sales and returns and how, through digital marketing and the application of tools
such as big data, it can reduce the large volume of online returns that it has to deal with.
INTRODUCTION
This chapter will address the issue of online sales in the fast fashion sector, specifically Zara, the flag-
ship brand of the Inditex textile group, founded by Spanish entrepreneurs Amancio Ortega and Rosalía
Mena in 1974, and home to other brands such as Massimo Dutti, Pull & Bear, Zara Home, Uterqüe,
Stradivarius, Bershka, and Oysho.
The Inditex group currently sells in 96 markets through more than 7,000 physical shops, as well as
through its online sales platform, present in 202 markets. Specifically, 2,208 physical shops belong to
the Zara brand, which opened its first shop in 1975 in A Coruña, Spain.
As its corporate website states, “Zara’s priority is to offer attractive and responsible fashion to a wide
range of customers, at the time and place that best suits their needs. Its designers have the capacity to
DOI: 10.4018/978-1-7998-8003-5.ch008

Big Data Optimization in Zara
respond with agility and new ideas to their demands and comments in its women’s, men’s and children’s
collections”.
Since 2012, Zara has been working on a plan to close, restructure and optimize its physical shops,
a process that was accelerated in 2017, and which has been affected by the global pandemic that began
in early 2020. These two events have caused that online sales have exponentially raise, and in turn, the
percentage of returns.
This is the objective of this article, to analyze where Zara is in terms of online sales and returns and
how, through digital marketing and the application of tools such as big data, can reduce the large volume
of online returns that it has to deal with. Despite having an efficient platform in terms of online sales,
possibilities will be sought to improve some aspects that will increase sales and, above all, decrease the
high percentage of returns, thus causing a positive effect on the overall results of the company.
With a digital marketing strategy based on techniques for obtaining results through big data, the objec-
tive of achieving greater optimization of sales can be covered, using consumer segmentation resources
combining morphological, image and fashion parameters.
Since 2012 Zara has been working on a plan to close, restructure and optimize its physical shops, a
process that was accelerated in 2017 (Contreras, 2019), and which has been affected this year by the
global pandemic.
In June 2020, three months after the pandemic broke out, with the consequent temporary closures of
shops around the world, Inditex announced a loss of 409 million euros in the financial year from February
to April of the same year. It was the first time in the group’s history that it made a loss. Consequently,
the plan was accelerated and it was announced the closure of between 1,000 and 1,200 shops worldwide
during 2020 and 2021, and the opening of another 300.
With these closures and openings, the aim is to gain in sales area but reduce the number of shops,
thus continuing with the plan to update the spaces already defined in 2012, where larger shops with a
greater possibility of customer flow, spaces already prepared and designed to work with the latest com-
mercial integration technology, and of course, always under the concepts that define their shops and
online platforms: beauty, clarity, functionality, and sustainability.
With this plan, the company also announced the investment of 1,000 million euros to boost online
sales so that they account for 25% of the company’s total sales, through a strategy of anticipation of digital
transformation. And 1,700 million euros to update the integrated platform from which the shops, the
head office and the distribution centers work, its own technological base called Inditex Open Platform
(IOP), which is being updated and on which all the company’s digital operations are carried out, from
orders and distribution management, inventories, purchases, etc. (Neira, 2019).
The company’s objective with the implementation of this plan is to advance the total implementation
of the so-called integrated shop, a concept in which the customer can have a continuous and permanent
link with the shop, wherever he is, whenever he is, and from any device.
As mentioned above, the evolution of the fast fashion giant was shaken by the global pandemic that
has affected the entire planet (Kumar, Raut, Narwane & Narkhede, 2020). Covid-19 began to impact the
company in February 2020, when some countries began to close borders, businesses, enforce curfews,
and everything that in the following months would become the daily routine in all developed countries.
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Despite the historic losses, as it had never recorded a negative year, the company managed to over-
come and obtain a total profit of 671 million euros in the first nine months of the year, and 866 million
in the third quarter, despite having 5% of the shops closed, and a total of 88% of the remaining shops
around the world with restrictions on capacity and opening hours.
Sales were -44% in the first quarter, -31% in the second quarter, and -14% in the third quarter, and
online sales continued to grow strongly, reaching 75% at constant exchange rates in the first nine months,
and 76% in the third quarter.
Because of the shop closures, the fact of stopping distribution and/or reducing it considerably to adjust
to the new and scarce demand, operating expenses were reduced by 17% during the first nine months
of the year. Inditex’s strong recovery in the third quarter of the year allowed to increase its net financial
position by 7%, reaching 8,265 million euros, the highest in Inditex’s history.
Despite the difficulties faced, Inditex managed to open shops in 25 markets last year, including China,
Saudi Arabia and Russia. And it increased to 85 the markets with online sales integrated to the local
network of shops, which are added to the 106 markets that already have online sales through the website.
Ending the year with these good figures only denotes the company’s great management and coordi-
nation capacity between its different areas, logistics, design, product, manufacturing, shops and online.
It also demonstrates the company’s great capacity to adapt to change, and how its digital transformation
strategy has enabled to achieve remarkable results.
Another example of how the pandemic affected the sector can be found in Mango, a few days before
the state of alarm was declared, the firm presented its best results in three years and was immersed in a
plan of changes to adapt to customer demands. During 2020 the Catalan textile company had a 43% drop
in sales in physical shops, obtaining a turnover of 1,842 million euros, while the previous year it reached
2,374 million. If it were not for the performance of online sales, with an increase of 36%, the figures
would be even more negative. Achieving 42% of turnover through online sales is a great achievement
for Mango, as it is above the average for the sector.
Within this capacity for adaptation, and focusing on Zara, it has been seen how the collections pro-
posed by the design team captivated customers as they introduced a lot of comfortable clothes following,
as always, the market trends. While Zara has always been characterized for having a very cosmopolitan
line, with more sophisticated and trendy options and a wide variety of clothes focused on the different
needs of the day, whether they are more casual lines, or office wear, or even events, as the months went
by, by mid 2020 the focus and bulk of the garment offer was focused on comfortable clothes, options
that could be used both at home, teleworking, or going for a walk in the street. The clothing options for
events or parties were limited, and the more casual collections gained ground.
Despite all the positive aspects in terms of profit figures and customer response in terms of sales,
the truth is that 2020 was the hardest year for the company, having to continually adapt to the different
changes that took place, restructuring teams, laying off staff, hanging production and distribution times,
and losing a large volume of sales in physical shops.
One of the main problems concerning the online sales is the returns, the rise of internet consumption
and the return policies, favorable to the consumer, causes the customer to purchase products being less
conscious than when they do it in person (Cullinane, Browne, Karlsson, & Wang, 2019). This extrapo-
lated to a global purchasing and return trend has large economic and environmental costs (Frei, Jack,
& Krzyzaniak, 2020).
Spain is currently in fourth place in the ranking of product returns due to new consumer habits, now
it is common to buy product in order to try them on, and this habit means 20% of returns in e-commerce
147

and up to 30% in the case of online commerce in the textile sector (Komorova, 2020). The percentages
are very altered if we compare them with the figures that are given in shop, as these are usually between
6% and 10%.
RESEARCH QUESTIONS
As mentioned before, the current situation is bringing up some questions the author might give answers to.
In this respect questions such as how the psychology and sociology of the consumer is changing and
have changed due to this recent complex situation, or how does the shutdown of stores is affecting the
company and the consumer will be raised.
Therefore, the research questions addressed in the present study are as follows:
RQ1: How does the shutdown of stores will affect the customer?
In order to work with the objective of improving sales and reducing the online returns it is important
to analyze how these stores shutdown will affect the customer’s purchase options, what are their purchase
habits, what does influence in their decision making and what characteristics are the ones that define
the Zara’s customer.
RQ2: How does the shutdown of stores will affect the companies?
Also it is extremely important to analyze how Zara works in order to evaluate what does implies
these shutdowns for the company, how is their ecommerce channel, what characteristics does manifest
in this moment and see the possibilities for improvement.
RQ3: How is the consumer affected and how the companies could help?
As previously mentioned, the relationship marketing will run an important role here, as the psychol-
ogy and sociology of the consumer is one of the most affected aspects. In this sense, the relationship
marketing will advocate for a focus on the customer and not into a product-price approach as it was done
in the past.(Ryals, L., & Knox, S.,2001).
The environment, concerns and priorities of the customers have changed in these last decades due
to the internet or globalization, but the biggest change has occurred now with the pandemic situation.
According to a recent study of the OCU (Users and consumers organization), compared to the previ-
ous situation prior to COVID 19, 53% of the consumers are no longer going to cultural events, 51% does
not practice any sport in close places, 31% doesn’t even use public transport.
This is drastically changing the way the consumer behaves and interacts with the goods and service
and at the end with the brands behind it.
The companies, in this sense, are experimenting new needs in consumer behavior. In this sense, the
main objective of these ones will be to understand their needs, their new fears and try to find new ways
to reach them.
RQ4: How to obtain a sale of better quality? How could a sale not divert into a return?
With the previous extracted data and examples from other cases, a line of work could be drawn up
in order to give answer to this big volume of returns that the company is facing
RQ5: What tools will be used?
Zara has already some strong technological resources that well focused can generate a change in
the customer’s purchasing process, and therefore, in the success of the sale, influencing in reducing the
percentage of return possibilities.
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METHODOLOGY
The “exploratory case study” methodology is chosen for this analysis. This is an “investigation strategy
focused on understanding the actual trend in individual environments” (Eisenhardt, 1989). The study we
present is focused on a recent phenomenon and extremely topical, therefore we consider important to
use this methodology that measures the people’s behaviour through qualitative tools and not just quan-
titative, which means an added value in variety in the investigation and richness of it (Martínez, 2006).
The documentary observation is part of this study. The nature of the consulted sources is very wide
but it predominates the documents published on the internet. We will find studies, scientific articles,
academic journals and reports focused on fashion. We can’t forget the bibliographic documents, adver-
tisement and public relations’ articles, marketing and new technologies, blogs and websites of the sector.
RESULTS
In this section, we report the results concerning the mentioned questions.
RQ1: How does the Shutdown of Stores will Affect the Customer?
Inditex’s plan to close and restructure its fleet of physical shops and its commitment to e-commerce,
together with its commitment to the digitalisation of services, has led to a change in the company’s re-
lationship with customers, who now have greater expectations of a more convenient and faster shopping
experience (Surplise, 2020).
This adaptability towards the customer is also marked by the tendency of customers to use both the
more traditional shopping channel, with physical shops, and online sales channels. These last ones are
gaining in popularity day by day thanks to the convenience they offer and the time savings invested in
shopping.
However, even though there is a large part of consumers who have adapted to new technologies and
now live with them as part of their daily lives, there is still a percentage of consumers, those of older
generations, who are resistant to online shopping.
Physical shops are still the bulk of Zara’s sales, and the closure, whether it is temporary due to the
current health situation or permanent due to the restructuring carried out by the company, is leading to
a change in the customer’s shopping options.
Thanks to the pioneering technological strategy it has been developing over the last few years, creat-
ing a homogeneous experience and maintaining omnichannel coherence, Zara is the brand in the textile
sector with which consumers interact the most, reaching 21.8%, according to an article published in the
IE University.
Zara’s customers are young, ranging from teenagers to middle-aged people, both men and women,
and have a clear tendency to follow fashions and to be able to wear clothes without spending a lot of
money. They are usually customers who know the brand well, know that every few weeks they receive
merchandise and offer new products, go to the shops (whether physical or online) on a recurring basis
to look, see what’s new, keep up to date on trends, and usually end up buying an article they like.
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Having a predominantly young target audience, very adapted to the digital era, it is not such a negative
problem to close shops, but it is important to take a step further in the personalisation of the website so
that it affects as little as possible in terms of total sales of the brand (Nisar & Prabhakar, 2017).
RQ2: How does the Shutdown of Stores will Affect the Companies?
In the last decades the textile industry has changed and evolved in a voracious way, being a traditionally
physical sector. Sixty years ago, fashion products were bought exclusively by the upper class of society;
with the appearance of shops like Zara, the course of the industry changed completely and, for some
years now, it has been a mass market. A market that adapts to the purchasing capacity of its customers,
and which relies on technological advances to make their shopping experience as satisfactory as possible
while achieving greater profitability (Kim, 2020).
The sector is immersed in a profound business transformation, focusing its efforts on the development
of a digital strategy (Pedrajas, 2020). Not having such a large number of shops spread across different
parts of the cities has had and will continue to have a negative impact on sales, as not as many people
will be able to access their physical shops and have the option to buy.
The strategy of closures combined with the strengthening of the digital strategy means that Zara
wants to redirect the customer’s shopping process. The aim is to maintain shops with characteristics
more similar to a showroom, spaces with a wide variety of garments but with very little stock of each
one of them, prioritising shop space over warehouse space, and redirecting sales to the online channel.
The customer, therefore, will go to the shop to see the product, look at the fabric, try on the size, and
finish the purchase process by ordering it online.
Zara has been working for some months now with tools that allow interaction between the physical
and online channels and vice versa, i.e. when a customer sees a garment on the website, they can locate
the exact point where the garment is located in the shop they select, having the option of arriving at the
shop and going directly to the point where their product of interest is located, they can also reserve a
fitting room for a specific time (as long as the customer is in the shop at the time of making the reserva-
tion and with a few minutes to go).
Similarly, a customer can reach the specific web page of the garment they are holding in their hand
simply by using their mobile phone and without the need to have the Zara’s app. By taking a photo of the
QR on the label, the web page opens with the selected garment, allowing them to obtain all the detailed
information about the garment, its composition, care, the other colours in which it is available, washing
care, variety of sizes, and also to be able to buy it online.
The customer experience plays a fundamental role in Zara’s new strategic plan.
RQ3: How is the Consumer Affected and how the Companies could Help?
Taking into account the current situation, the mentality of the customer about the consumption of any
service or good has changed. The two main reasons are the fear about the health situation and the second
one will be the preoccupation about the economic situation of not just his/her country, but also about
the whole world.
Questions such as “will I get infected if I go outside my house?” or “should I save money just in case
the situation gets worse?” are appearing daily in the mind of the consumers. The uncertainty about the
situation is one of the main pains for the consumer nowadays, that makes him less impulsive, having
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a more thoughtful shopping conduct. Also, this new consumption is characterized by being in a local
environment and introducing more and more the digital platforms. The introduction of digital platforms
has been one of the main tools to keep connected to the outside world and combat the loneliness.
It does worth to mention that many of these new ways will continue to exist after the pandemic, they
came to stay. For example, home office is one of the practices that is increasing the most, compared to
the previous situation. Now the companies have realized that they can save money avoiding expenses
such as the rent of the office.
So, this new paradigm is changing the dynamic of the customer daily. Therefore, companies such as
Zara needs to change the perception about how the customer is now.
What is clear is that the digital field is becoming more present in the minds of consumers, trying to
solve the new fears and needs that the author mentioned they have. But, even that, the companies are not
fully adopted to that situation, only 5% of companies consider that they dominate digital to the point of
outperforming their competition (Accenture, 2020).
Companies must create positive experiences for their customers in their channels, the improvement
of the digital experience and the customer experience will be strictly related. Therefore, according to
Accenture, the key drivers for the digital transformation would be: Profitability, customer satisfaction,
customer experience, trade speed a nd the revenue increase.
So, what are the main mistakes the companies are making that makes that percentage of digital adop-
tion so low? First of all, they don’t measure the results. To make any change, you should draw the action
points or guidelines in order to measure the results. For that, the objective should be clear. The second
error will be not knowing what the following steps on a digital transformation process are and the third
one not knowing who should be leading this change. (Reinares, P., 2018).
These errors make the approach of the companies to the consumer much more difficult. In this sense,
the companies should start changing their concept and ways of recapping information from them. The
traditional ways could not be bridging the expected results so new ways such as the use of social networks
or big data could be the solution to their problems.
Analysis techniques such as User Generated Content (UGC) based on an extraction of content in social
networks such as Twitter or Facebook, could bring the patterns the consumers conceived about certain
topics. For instance in this study, we could be using the LDA method (Latent Dirichlet Allocation) to
gather the keywords related to fashion, stores or Zara. With this information then the company could
analyze the sentiment behind each of those concepts with a technique called SA (Sentiment Analysis)
in order to know how the customer feels about each of those concepts.
All these mentioned analyses are not just bringing more real and accurate results but also they are
less expensive than traditional ways of learning about our customers.
Another important aspect will be to understand how loyal Zara’s customer is (Shoemaker, S., & Lewis,
R. C.,1999). For that it is important to analyze the purchase repetition and the attitude of the custom-
ers about Zara’s product and brand. In this sense the classification of the customers will be divided in:
• Loyal - high repetition and high attitude

• Latent loyalty - low repetition and high attitude
• False loyalty - high repetition and low attitude
• No loyalty - low repetition and low attitude
Then, the classification of the customers of Zara will be identified in Loyal and Latent Loyalty.
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Other variables of rational classification of the customer are the consumer volumes, the purchase
potential, the rentability, the duration of the purchase, the importance of the alternative brands and the
level of satisfaction and how Zara can influence in it.
RQ4: How to Obtain a Sale of Better Quality? How

could a Sale not Divert into a Return?
Zara’s strategy is based on an exhaustive control of all processes, design, manufacture, distribution,
and sales, as mentioned above, always aimed at getting as close as possible to customer demand. In this
way, decisions are made quickly, for example, if a garment is successful, a replacement product or a
new version of the same product, in other colours, fabrics, etc., can be in the shop in a matter of days.
This process, which is also common in other competing shops, can take between three and five months
on average. Zara, and Inditex in general, has so much information on its sales and customers, and such
a large production and logistics capacity, that it can speed up the processes until they are reduced to a
minimum.
One of Zara’s most outstanding characteristics is that it has taken this concept to its maximum expres-
sion. It is no longer only decided at a global level whether to produce more of a garment or collection,
but it is the shops themselves who, according to their sales data, request and decide on the assortment
of garments, sizes and models. In this way, we work in a much more localized way, based on the target
of that specific shop, adapting not only to the customer, but also to the climate and even to the events
that take place in that city during a specific period of time. These details are what generate great brand
value, increasing the brand’s competitive advantage.
Today, Zara works under three premises that define its positioning and business model: high product
turnover, customer experience in its physical and online shops, and the adaptation of the use of new
technologies in all its processes and spaces.
Online sales in the fashion industry were already well established before the global pandemic, the
numbers reflected this, and the upward trend meant that company teams were working tirelessly to im-
prove the online customer experience.
There is no doubt that e-commerce, as well as being one of the great challenges of the last few years,
has been a turning point for many companies once they have found themselves in a situation like the
current one. Closed shops, restricted opening hours and capacity, reduced mobility of the population,
low tourism, all of this has led to a boom in online commerce, which in turn has been of vital importance
to save the serious economic situation caused by the lack of physical sales.
Although ecommerce has many advantages, it is important to highlight that there are some key points
in this type of sale that determine its success. Specifically, in the textile sector, it has certain character-
istics that make it difficult and affect the percentage of returns that shops accumulate. It is not the same
to buy an electronic device with all the functions detailed on the website and all you have to do is wait
for it to work as described, as it is to buy a piece of clothing, which in addition to complying with the
premises and descriptions on the website, has to be adapted to your measurements.
Returns in this type of products, and in the online market in general, are one of the problems that
the brand must face.
The most common are due to sizing, even if the customer knows his or her size in Zara, there are
always variations from one model to another depending on the design or fabric, and there are many
times when they do not get it right. Other times, precisely because of these doubts about the size, it is
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the same customer who prefers to buy two units in different sizes, to finally keep the one that fits him or
her well, generating a safe return. Then there is the issue of colours, the photos on the web do not always
accurately represent the reality, it is difficult to get it because it can affect the light or the retouching of
photography, and another important factor is that although the fabric is detailed, it is not so common for
customers to know the names or mixtures of compositions.
Other causes are the fact that the product arrives defective, and a very common one, which is re-
peated especially in periods of sales or discounts, is the return due to compulsive buying. Customers
attracted by prices buy more than they need or what they had planned, not having the garments in hand
makes them lose awareness of the real number of garments they are acquiring, and once the shipment
arrives it also brings a regret of part of the purchase made, which leads to a return. At this point we can
highlight a practice that also directly affects returns, the so-called wardrobing, which consists of buying,
wearing and returning the garment. Despite the fact that teams are trained so that there is no room for
these returns, it is very common for used garments to be returned every day in shops all over the world.
It is not easy to reduce the number of returns, especially considering the current situation, but there
are strategies that, if applied properly, can help reduce the number and prevent them from increasing
(Thaw, Mahmood, & Dominic, 2009). Zara already applies some of them:
Provide detailed information: the website specifies many details of each garment, obviously the avail-
ability of sizes, the colour, if there are more colours of the same model, the description, the price, the
composition of fabrics, the place where it has been manufactured, the reference, access to the size guide
and equivalences with other countries, a system to define what your size is based on your characteristics,
the care for washing and preserving the garment, etc.
Aligning the quality perceived on the website with the quality of the product: another reason that
happens frequently is the “gap” between the perceived quality online and the real quality of the product.
Zara works very well on its web staging, taking care of the photography and editing so that the cut, the
colour and the fabric are perceived in a representative way. It is necessary to avoid any possibility that
could lead the customer to feel deceived. High-definition images and videos should therefore be offered
to help the customer better appreciate the details of a product. Disappointment and the feeling of having
been cheated are two reasons for opting to return the garment.
Monitor returns: working with systems that allow you to know which customers return which gar-
ments is very important to keep track of returns and to be able to work on that basis (Orghazi, Karlsson,
Hellström, & Hjort, 2018). This information can both tackle problems and create or add information
to a customer’s profile, being able to suggest future purchases of garments that ensure greater success
by avoiding the purchase and return of similar garments. In addition, even though garments undergo
controls before arriving at the shop, on numerous occasions manufacturing defects have been detected
through repeated customer returns.
Cross-selling: cross-selling is a technique that offers or shows the customer products related to each
other, it is widely used in ecommerce as it is very easy to apply it in a very visual way on the web, it
is usually found under labels such as “related products”, “other customers also bought”, or in the case
of Zara a: “maybe you are interested in”. The particularity of this technique is that it is focused on the
product, that is to say, on crossing garments that can complement each other, but it does not apply or
interfere with any other tool that takes into account the customer’s need or preference. Therefore, this is
carried out thanks to a good study of the brand’s catalogue of garments, and to have a clear prescription
of which garments can be crossed and suggested to each other (Ansell, Harrison, & Archibald, 2007).
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The cross-sell represents a benefit of between 10% and 30% of sales for an online shop, and Amazon
states that they are at 35%. As can be seen, this is a very important factor to consider as it increases the
average spend per customer, provides an outlet for products that are not so widely viewed, and provides
purchasing patterns for different customers.
RQ5: What Tools will be Used?
To talk about big data is to talk about companies obtaining a large volume of data, which can be obtained
through different means, but in this study is particularly interesting the obtention of data through the
actions and interactions of network users (Saura, 2020). This data by itself does not have a great value,
it is when it is analyzed and divided according to the needs of the companies, when it is very useful to
make strategic decisions and optimize the different processes, reduce costs, solve internal problems, or
for example, bring to the market new products totally focused on what the consumer demands.
The architecture of big data covers so much and has so many nuances that, despite having been imple-
mented in large companies for several years now, there are still certain doubts about how to optimize
its use. Dealing with the management of all the data that a company can collect is a job that requires a
lot of planning and strategy.
Through case studies, an analysis has been done about how companies have implemented big data
effectively. One such pioneering and successful company is the e-commerce giant Amazon.
Amazon already has a great deal of experience in handling data, and it is common for what the com-
pany does to be extrapolated to other companies with common interests. Amazon gives masterclasses
in collecting, analyzing, and then implementing that data analysis successfully.
The customer experience from the moment they open the Amazon homepage, provided they are
logged in, is fully personalized. They are presented with products similar or related to purchases and/or
searches they have made previously. In addition, the user periodically receives e-mails with news about
products that may be of interest to them. These practices are very common nowadays in ecommerce,
but Amazon was one of the first to put them into practice.
Over the years, it has been proven that offering a more personalized online experience leads customers
to buy more than they would if they had not previously received specific product information. Amazon
makes concrete and efficient use of the vast amount of data it obtains through big data thanks to its key
consumer-driven approach (Hewage, Halgamuge, Syed, & Ekici, 2018).
This last point is one of the most important in Amazon’s use of big data. While other companies
also have the ability to collect large amounts of data, not all of them make such useful use of it. The
ecommerce giant has an extreme approach that has brought the company to the successful place where
it is today. When you are talking about collecting and analyzing so much data per second, it’s easy to
lose track of what to do with it and how to make it work for your business. You have to make a good
plan to optimize it and find a use for it for the customer and, of course, to translate it into sales. It is also
important to be able to implement changes and updates constantly.
Amazon is undoubtedly one of the great examples of how big data works, and how this technology,
put at the service of both the customer and the company, can lead to business success regardless of the
size of the company that applies it.
As we have seen previously, cross-selling is a very good technique for increasing sales, and if you
add all the information available to Zara with big data, you can reach a perfect alliance. Not only by
visualizing a product of interest to the customer, they will see other related products, but the product or
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products that are suggested to them will be more personalized, knowing which patterns they buy most
often, what type of garment or fabric, and other variables that are sure to make the shopping experience
successful.
Customized cross-selling generates a greater possibility of a sale and its consequent success as it is
something more related to the customer, so you are less likely to be returned.
CONCLUSION
The main objective of this study was to analyze Zara’s strategy to be able to respond and solve the prob-
lems facing ecommerce, the drop in sales in physical shops, and the large volume of returns.
As a consequence of all of the above, the most important point to highlight is the possibility and
recommendation to optimize such a powerful tool as big data. Zara receives a very significant amount
of data through each of its purchases, especially from online sales.
All this data, for now, is used to optimize its production processes based on the sales statistics of each
product, evaluating by area, city, country, and personalising the garments it has in each of its shops. In
addition, these data are used in the design and distribution processes.
What has been detected and is proposed as a point of improvement is to use this data to influence
customers’ next purchases, taking advantage of the strong competitive advantage it has and all the tools
it has available.
Through a digital marketing strategy based on data analysis of each customer who shops on the
website, patterns can be created to ensure a higher volume of purchases and a lower volume of returns,
for which specific products could be suggested for each customer:
• Newsletters: Zara already uses this tool to send emails to its customers with the latest news on
the website, it does so on a weekly basis, and includes special collections according to the dates
on which it is sent, but it does not make a specific selection of items for the specific customer,
nor does it differentiate between sections. It would be more efficient to send newsletters with new
items of related garments to their customers, as well as to select garments according to the section
in which they most frequently shop, whether in men’s, women’s or children’s wear.
• Zara.com home page: the website does not suggest clothes at any time according to previous
searches by the consumer, this is a differentiating factor in how Amazon and Inditex use big data.
If it were to introduce this change on the website, it could obtain great results, just as Amazon has
done.
• Personalized cross-selling: the fast fashion giant does use this resource on its website, but not in
a personalized way; as soon as the potential customer accesses a specific garment, garments or
products related to it appear, with the aim of completing an outfit. However, this selection does
not take into account the data that the customer may have left previously, either by preferences in
terms of colours, shapes and design of the garment according to their size, etc.
• Customer page: in this section of the website where the customer can consult their orders, previ-
ous purchases, return policies and other data, it would be very efficient to suggest garments based
on previous purchases, again applying a personalised cross-selling technique.
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With all these recommendations the author suggests to keep investigating in big data, knowing better
his customer and how this customer is changing. In this sense, spending more time where the customer
is Zara will obtain better results and could mitigate issues they had such as the strong figure on returns.
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KEY TERMS & DEFINITIONS
Omnichannel retailing: omni-channel means establishing a presence on several channels and plat-
forms and enabling customers to transact, interact, and engage across these channels simultaneously or
even interchangeably.
Ecommerce: is the sale of goods and services through the Internet, requires companies to tailor their
business models to capture Internet sales.
Big Data: refers to massive complex structured and unstructured data sets that are rapidly generated
and transmitted from a wide variety of sources. Will play a key role in the future of fast-moving industry
like fashion (Kim & Lee, 2018).
Marketing strategies: a business’s overall game plan for reaching prospective consumers and turning
them into customers of their products or services.
Cross selling: is a technique that offers or shows the customer products related to each other, it is
widely used in ecommerce as it is very easy to apply it in a very visual way on the web.
Internet of things: IoT refers to management and collection of daily use data from connected devices.
This also includes order and identification of new features that help personalize and offer new products
and services and to create new needs (Saura, 2020).
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Chapter 9
Confirming Digital Marketing
Model Innovation Design:
SEM in Post-COVID Social
Impact Startups, Mexico
Juan Mejía-Trejo
Universidad de Guadalajara, Mexico
ABSTRACT
The COVID-19 pandemic is an unprecedented event that has ravaged emergent economies like Mexico
seriously. In this sense, several sectors like social impact startups (SIS) are called to participate in ac-
tions to recover more quickly in their operations, income, and competitiveness in the post-COVID era. In
fact, digital marketing campaigns are alternatives for the Mexican SIS to raise its competitiveness again.
Hence, this study aims to confirm the digital marketing model innovation (DMMI) through covariance-
based structural equation modeling (CB-SEM) applied on a survey of 180 Mexican SIS during Dec-2020
to Feb-2021. The study´s value is the model`s validity of DMMI and its capability to determine digital
marketing strategies to overcome emergency situations like COVID-19.
INTRODUCTION
The Covid-19 crisis and the next normal are unprecedented phenomena that have severely affected all
sectors of the industry’s products and services in Latin America. It has hit the productive and business
structure with weaknesses that have arisen over decades. The region’s productive structure presents a
significant heterogeneity between sectors and companies. More than a third of formal employment and
a quarter of Gross Domestic Product (GDP) are generated in sectors strongly hit by the crisis. The in-
dustry’s impact would lead to a change regressive structural with the closure of 2.7 million companies.
However, the measures adopted by the different governments of Latin America have been important but
insufficient. To face the crisis, government institutions, business chambers, and academic centers have
called for innovation initiatives, such as launching startups (CEPAL, 2020).
DOI: 10.4018/978-1-7998-8003-5.ch009

Confirming Digital Marketing Model Innovation Design
However, in Mexico, 75% of startups closed their business after the second year of existence, which
means that only 25% of them remain up-to-date (El Financiero, 2016).
The Covid-19 pandemic and the next normal have triggered and accelerated the shift to the automa-
tion and digitization revolution to the next normal. Several manufacturing companies are reconfiguring
their production lines and supply chains. On the other hand, service organizations are emphasizing their
adaption to digital customer travel and contactless operations. These changes will impact the workforce’s
skills requirements and capabilities, from the impressive increase in remote and homework to new tools
and urgent safety and health requirements. Approximately 39% to 58% of work worldwide in operation-
ally demanding sectors can be automated using currently demonstrated technologies (McKinsey, 2020a).
Thereby, the study’s originality lay in the update and usefulness confirmation of each of the factors,
variables, and indicators involved in the DMMI proposed by Mejia-Trejo (2018) and applied to SIS
determine advanced digital strategies for the next normal post-Covid era in Mexico.
LITERATURE REVIEW
This section describes the social impact of startups (SIS), its meaning in Mexico, the Oslo Manual and
the concepts of innovations and how is related with the Digital Marketing Model Innovation (DMMI)
as a pre-Covid era.
The Social Impact Startups in Latin America
In Latin America, several online platforms are generating information about social impact startups.
For instance, AngelList (ANL, 2020), the investors are using this database looking for information
about startups for their decisions, being Brazil, the country with the highest number of startups in Latin
America, followed by Mexico. According to ASPEN (2017), in Mexico, are registered 416 SIS, with
more than half aimed to work with social impact interest; Mexico is the country where SIS ecosystems
are more distributed in its territory, with 32% of startups in Mexico City, 10% in Guadalajara, and 8%
in Monterrey (OECD, 2016).
The social impact startups (SIS) have emerged as key drivers of job creation due to economic growth
and are often the source for radical innovation. During the coronavirus (Covid-19 pandemic and the
next normal) crisis, the SIS has played a critical role for economies to the next normal. Some innovative
new SIS have responded quickly and flexibly to the pandemic, which is essential to help many coun-
tries switch to digital education, work, and health services, provided innovations in medical goods and
services. Some examples include adjusting commercial products (such as snorkeling masks for oxygen
supply in hospitals); launching a series of digital health services, including Covid-19 and the next normal
trackers, remote patient monitoring and remote consulting tools; the introduction of “no-contact” food
delivery; and provide researchers and scientists with artificial intelligence solutions, remote working
tools or online learning and entertainment, in some cases free of charge. (OECD, 2020).
The Meaning of the Social Impact Startup in Mexico
The SIS is also known as technology-based firms. It represents projects born from the detection of needs,
such as marketing knowledge, which is applied to provide market or industry solutions. An SIS is usu-
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ally defined as a new business initiated by an entrepreneur through a combination of business ideas and
resources. An SIS is a temporary organization that aims to search for scalable and repeatable business
models (Blank & Dorf, 2012).
Ries (2011) explains that SIS are organizations established to create new services or products under
highly uncertain conditions including new government business units, large companies, non-profit or-
ganizations, and commercial enterprises.
Due to their articulation and sophistication, they usually originate within a higher education institution
due to their ability to carry out basic and applied research (Monge-Aguero & Briones-Peñalver, 2012).
Startups’ origin is based on the spin-off concept in the mid-twentieth century in the US, particularly in
higher education centers like the Massachusetts Institute of Technology (MIT) and the University of
Berkeley.
These institutions were the pioneers in the commercial exploitation of the investigation results operating
a model where they received government support. Many countries have considered this model’s success
for wealth generation, and they are committed to university reforms to increase the commercialization
of publicly generated research results (Monge-Aguero & Briones-Peñalver, 2012).
It is interesting to observe that the self-employed with tertiary education (OECD, 2017), a precondition
to conform an SIS for Mexico, has a 21.3% of men and 15.6% of women (ahead of Spain: 17.3%/10.2% and
behind of Italy: 30.2%/19.9%), establishing a clear competitive advantage about the rest of the countries.
In general terms, the development of new companies that are managed by any of the actors (students
or research teachers) is similar. However, it changes direction in the name of the final result, which is
marked by rights over the intellectual property of knowledge between those involved.
Finally, and this will lead to classifying the new company in one of two options: spin-off, if the tech-
nology belongs to the institution of higher education and its research teachers, or startup if the idea and
knowledge that it is exploited is the property of the student (Gómez-Zuluoaga, 2019).
Additionally, the social impact startups concept is defined here as a “social impact startup (SIS)
that is aimed to solve one or several of the 17 sustainable development goals” determined by the United
Nations (UN, 2015).
Unfortunately, the Covid-19 pandemic and the next normal ravaged that economic backbone by fail-
ing to contain the loss of 12.5 million jobs in Mexico. The country’s employed population fell from 55.7
million in March to 45.4 million in April of 2020 (El Financiero, 2020); this means 2.1 million formal
jobs versus 10.4 million informal jobs.
The fall was caused by the following causes: a) The closure of supply chains, b) The fall in tourism,
c) The stoppage of non-essential activities in the country, d) The lockdown of Mexican families in their
home caused internal consumption to fall, lowering production and affecting employment and e) Sup-
port actions by the Mexican government were insufficient for microenterprises to be able to preserve
employment. However, what about the startups? Besides all of the above, most startups have a common
denominator: they usually fail. However, there is a minority that beat the odds and experienced some
common traits. They work tough right from the start and move extremely fast to attract great talent,
first-time customers, and extra funding. Hence, this study aims to determine factors and indicators in-
volved as a reliable business model innovation scale, capable of maintaining the successful momentum
of the startups that respond quickly to market changes, focus on results, and deliver value to customers
(McKinsey, 2020b).
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The Oslo Manual Business Innovation Model
The Oslo Manual is an essential reference for the analysis and collection of data on technological in-
novation. It is a guide that defines concepts and clarifies the activities that are part of the innovation
process, including its different types and the performance impact at the organization, thus advancing
the knowledge of the global process. The updating and use of the Oslo Manual contribute to the imple-
mentation of a technological culture currently under constant development. The Oslo Manual strongly
affirms that innovation must be measured. The last edition, published in 2018, describes the concept of
innovation as an activity and its result, giving the following definition:
“An innovation is a new or improved product or process (or a combination thereof) that differs sig-
nificantly from the unit’s previous products or processes and that has been made available to potential
users (product) or brought into use by the unit (process)” (OECD, 2018, p.20).
The business model covers several aspects such as the manufacturing, logistics, marketing, and
collaboration solutions used (the core business processes). To achieve strategic goals and objectives, it
includes the top products that a company is selling now or in the future. For instance, ta company may
use one or several business models simultaneously, for different markets or product lines (OECD, 2018,
par. 3.51). On the other hand, a business model innovation has not a single recognized definition due
to variations from the business model with partial innovations affecting only its business functions or
products to comprehensive innovations involving the business model for both business functions and
products (OECD, 2018, par. 3.52).
Of great interest are the comprehensive business model innovations due to they can substantially af-
fect economic production and supply chains, transform and create new markets. They can affect how the
company creates for users, utility (product innovation) and how the product is manufactured, marketed
or priced (business process innovation) (OECD, 2018, par. 3.53).
Hence, here we adopted the concept of a social impact startup as a business model innovation accord-
ing to OECD (2018) “…it relates to changes in a firm’s core business processes as well as in the main
products that it sells, currently or in the future” (p.242) and it is inspired by one or several sustainable
development goals (UN, 2015).
The Digital Marketing Model Innovation (DMMI)
Digital marketing campaigns are alternatives for the Mexican SIS to raise its competitiveness. The DMMI
is a model that describes and identifies all the variables and indicators involved in designing and driving
a digital marketing campaign. It was a product of a previous bibliometric analysis using VOSviewer
software on the SCOPUS and Web of Science databases to detect variables and items. A Delphi Panel
Focus Group and the Analytic Hierarchy Process (AHP) (Saaty,1997), was applied under three digital
marketing professors (academic vision) and three CEOs’ digital marketing to identify final different
variables and indicators that support the DMMI. The importance of such model is the capability to de-
sign a plan of action to achieve a long-term or overall aim (Kingsnorth, 2019; Mejía-Trejo, 2017) to the
improvement of reduction and lower costs, price, and placement (Goldfarb & Tucker, 2019).
A final questionnaire survey was applied between Jan-Apr 2017 over 400 subjects (200 CEO digital
marketing experts, 100 consultants, and 100 academics). In that time, as a quantitative stage (empirical
evidence), multiple linear regression and correlation techniques were applied to determine the relation-
ships in this model, proving the model’s reliability in the pre-Covid era.
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In this paper, the DMMI was analyzed under the Covariance-Based Structural Equation Modeling
(CB-SEM) to prove its model’s validity in the post-Covid era. See Figure 1 and Table 1.
Figure 1. DMMI scheme

MVS. Mission-Vision; VAL. Value Proposition; MKT. Market; GST. Goal Settings; STG. Strategy; TAC. Tactics; DMT.
Digital Marketing Tools; PLN. Planning; PER. Performance; PRO. Profitability
Source: Mejía-Trejo (2018)
RESEARCH METHOD
The procedure to prove the DMMI’s validity implied a final questionnaire survey applied between Dec-
2020-Feb 2021 over 180 social impact startups CEOs’ intervention (expert vision) in the relationship
of the model’s ten variables. Also, the CB-SEM was applied based on EQS 6.2 (Mejía-Trejo, 2019) fol-
lowing the recommendations suggested by the OECD (2008) to prove the model’s validity. Furthermore,
determine the digital marketing strategies as the final result in post-Covid Mexico’s era.
RESULTS
Before starting the CB-SEM, it is necessary to compute the Confirmatory Factor Analysis (CFA) that
specifies a “measurement model”, which describes how the measured variables “reflect certain latent
variables.” Once these measurement models are considered satisfactory, researchers can explore path
models (called “structural models”) that link “latent variables” (Thompson, 2004). CFA is strongly
suggested to be used as a construct validity tool that examines the constructs’ relationships in the broad-
est sense. Constructs are unobservable and theoretical (latent factors or variables).
Nonetheless, due to their unobservability, related theories often describe the relationships amongst
the constructs. The “construct validity” refers to a measure of checking attributes (or constructs) that are
not operationally defined or directly measured (Harrington, 2009). In CFA, researchers can “constrain”
or “fix” specific parameters to mathematically “allowable” values and “release” the use of input data
to obtain estimates of other model parameters (Thompson, 2004).
The CFA-SEM Conceptual Model Measurement Validity
The measurement scale’s validity used the CFA-SEM with EQS 6.2 software to apply the maximum
likelihood method (Byrne, 2006). To prove the measurement scales’ reliability, we computed for each
factor the Cronbach’s Alpha and Composite Reliability Index (CRI) (Bagozzi & Yi, 1988) with results
that exceeded the recommended value of 0.7 for both. This means that there is evidence to prove the
scale’s internal reliability (Nunnally & Bernstein, 1994; Hair et al., 2010). Average Variance Extracted
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Table 1. Digital marketing innovation model (DMMI) underlying factor
Item Variable Indicator

• Your firm considers the mission and vision involved in the digital campaign for competitiveness.
Mission-Vision
1 • Your firm considers the trademark, as a strategic asset to be used in the digital campaign design for
(MVS)
competitiveness
Value
2 Proposition 1. Your firm identifies and applies the value proposition in the digital campaign design for competitiveness
(VAL)
Market 2. Your firm has an specific market segmentation as a target to be attended for the digital campaign for
3
(MKT) competitiveness.
3. Your firm determines in the digital campaign design for competitiveness, as a goal to reach, to increase:
-The branding positioning
Goal Settings
4 -The number (real & potential) of customers database
(GST)
-The sales
-The product & services (current and new ones) information
4. You firm determines in the digital campaign design for competitiveness, as strategies to apply:
-Awareness
Strategy
5 -Engagement & Loyalty
(STG)
-Desire & Experience
-Effectiveness on Call to Action
5. Your firm considers the use of Digital Marketing Tools for each strategy in the digital campaign for
competitiveness, such as:
-Awareness (SEO/SEM; Affiliate & Partner Marketing; On line Advertising; On line PR; Social Media)
Tactics -Engagement & Loyalty (Content Marketing; Newsletters & eMail Marketing; e-Contact Strategy;
6
(TAC) Customer service & support; Mobile Marketing; Social CRM; Blogging)
-Desire & Experience (Augmented Reality; Virtual Reality, Wearable Marketing)
-Effectiveness on Call to Action (Home & Site-Wide Page; Landing page design; Search and Browse
Page; Basket and Checkout; Social Commerce)
Digital
Marketing 6. Your firm is in constant surveillance to determine what kind of digital marketing tools are ready to use in
7
Tools the digital campaign design for competitiveness
(DMT)
Planning 7. Your firm design a strong program, with schedule and times to implement the digital marketing tools, in
8
(PLN) order to obtain obtain results in the digital campaign design for competitiveness
Performance 8. Your firm determines the KPIs for performance monitoring to determine on real time, the current
9
(PER) performance of the digital campaign for competitiveness. Use of the Web Analytics.
Profitability 9. Your firm makes profitability analysis, on permanent way to determine on real time, the current
10
(PRO) profitability of the digital campaign for competitiveness.
(AVE) is represented from the fundamental construct and the observed variables (Fornell & Larcker,
1981); mainly, the values desirable are high than 0.6 (Bagozzi & Yi, 1988). We used in this research the
comparative fit index (CFI), the non-normed fit index (NNFI), the normed fit index (NFI), and the root
mean square error of approximation (RMSEA) (Bentler & Bonnet, 1980; Byrne, 2006; Bentler, 1990;
Hair et al. 2010). The values in the range from 0.80 to 0.89 represent a good fit of CFI, NNFI, NFI,
and (Hair, et al., 2010); values equal to or higher than 0.90 are considered a good fit of the theoretical
model (Byrne, 2006). For RMSEA, values below 0.08 are acceptable (Hair et al., 2010). See Table 2.
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Table 2. CB-SEM results or internal consistency and convergent validity of latent variables in the theo-
retical model for DMMI and SIS
Theoretical Model Consistency and Convergent Validity Theoretical Model Discriminant Validity
Id Load Robust t Cronbach’s CRI AVE
Variable MVS VAL MKT GST STG TAC DMT PLN PER PRO
Factor Value Alpha (>=0.7) (>=0.7) (>=0.5)
0.32- 0.5- 0.34- 0.27- 0.38- 0.67- 0.45- 0.48- 0.44-
1 MVS 0.785*** 1.000a 0.639 0.736 0.518 0.518
0.43 0.67 0.45 0.48 0.56 0.79 0.57 0.67 0.65
0.35- 0.39- 0.34- 0.21- 0.38- 0.56- 0.71- 0.65-
2 VAL 0.821*** 6.876 0.877 0.798 0.657 0.679 0.657
0.76 0.58 0.59 0.45 0.55 0.67 0.82 0.78
0.49- 0.56- 0.23- 0.17- 0.36- 0.28- 0.33-
3 MKT 0.993*** 5.682 0.783 0.795 0.589 0.559 0.591 0.589
0.66 0.78 0.35 0.28 0.45 0.45 0.67
0.56- 0.71- 0.67- 0.52- 0.68- 0.71-
4 GST 0.756*** 10.112 0.765 0.788 0.726 0.622 0.776 0.718 0.726
0.8 0.8 0.72 0.67 0.75 0.81
0.45- 0.21- 0.65- 0.76- 0.56-
5 STG 0.850*** 9.841 0.789 0.801 0.670 0.794 0.673 0.869 0.713 0.670
0.61 0.43 0.73 0.89 0.78
0.13- 0.27- 0.37- 0.65-
6 TAC 0.810*** 7.422 0.767 0.778 0.796 0.824 0.776 0.658 0.613 0.810 0.796
0.28 0.35 0.56 0.82
0.36- 0.22- 0.42-
7 DMT 0.926*** 12.587 0.865 0.895 0.577 0.881 0.791 0.813 0.697 0.624 0.890 0.577
0.56 0.45 0.57
0.65- 0.66-
8 PLN 0.891*** 5.876 0.812 0.827 0.692 0.759 0.867 0.682 0.730 0.821 0.857 0.687 0.692
0.77 0.82
0.34-
9 PER 0.694*** 7.871 0.765 0.770 0.786 0.765 0.775 0.651 0.566 0.689 0.578 0.651 0.881 0.786
0.56
10 PRO 0.707*** 8.876 0.751 0.759 0.671 0.678 0.654 0.609 0.678 0.873 0.651 0.871 0.778 0.765 0-671
Source: Own, using EQS 6.2
The CB-SEM results are depicted in Table 2 indicating that the model offers a good fit to the data
as follows: S-B χ 2 . 917.022; df=204; p<0.005; NFI=0.828; NNFI=0.801; CFI=0.871; RMSEA=0.081.
CRI and Cronbach’s alpha higher than 0.70 suggested by Nunnally & Bernstein (1994); for each pair of
constructs, the value of Average Variance Extracted (AVE) was calculated with results higher than 0.50
(Fornell & Larcker, 1981). For CB-SEM items, factor-related are significant (p <0.001), and the results
pointed out as convergent validity. The values of all the load factors are higher than 0.60 (Bagozzi & Yi,
1988).
DISCUSSION
The unusual appearance of COVID-19 as an emergency context and the next normal have brought essential
changes in the SIS behavior from emerging countries like Mexico to define new strategies in the digital
marketing field and facing the loss of economic growth levels of the SIS. As a result of this study, we
argue that DMMI fulfills the digital marketing strategists’ requirements to design a digital campaign in
their operations, income, and competitiveness in the post-Covid era. These DMMI variables are Mission-
Vision (MVS), Value Proposition (VAL), Market (MKT), Goal Settings (GST), Strategy (STG), Tactics
(TAC), Digital Marketing Tools (DMT), Planning (PLN), Performance (PER) and Profitability (PRO).
Here, we expose that the DMMI empirical model able has important contributions:
First, it determines the crucial role of MVS, VAL, MKT, GST, STG, TAC, DMT, PLN, PER, and
PRO called here DMMI when these variables are related.
Second, we determined how interacting such variables according to the SIS in post-Covid era in
Mexico elicit digital marketing strategies in designing a digital campaign to recover for the SIS opera-
tions, income, and competitiveness.
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This paper contributes to the knowledge based on the Digital Marketing Model innovation (DMMI)
proposal describing the underlying variables for the design that elicit digital marketing strategies for
social impact startup (SIS) in the next normal. The model has been proved empirically in several stages.
Stage 1 (or Pre-Covid Era). It implied a previous qualitative study (Jan-Apr 2017) based on a literature
review involving consistent research on DMMI. The configurational approach enables the understanding
to detect factors, variables, and indicators as a set of components to serve a conceptual model empiri-
cally proved.
The literature review results were compared by three SIS professors (academic vision) and three
CEOs leading SIS as specialists (expert vision). Through the Delphi Panel focus group and AHP, there
were detected, finally, ten variables: Mission-Vision (MVS), Value Proposition (VAL), Market (MKT),
Goal Settings (GST), Strategy (STG), Tactics (TAC), Digital Marketing Tools (DMT), Planning (PLN),
Performance (PER) and Profitability (PRO). A questionnaire was designed as a final result (see Table 1).
This stage finally proved the model’s reliability through multiple linear regression and correlation
quantitative techniques on survey questionnaires over 400 subjects (200 CEO digital marketing experts,
100 consultants, and 100 academics).
Stage 2 (or Post-Covid Era) The procedure to prove the DMMI’s validity implied a final question-
naire survey applied between Dec-2020-Feb 2021 over 180 social impact startups CEOs’ intervention
(expert vision) in the relationship of the model’s ten variables under the CB-SEM based on EQS 6.2.
Furthermore, determine the digital marketing strategies as the final result in post-Covid Mexico’s era.
The CB-SEM load factor results of the social impact startup (SIS) (see Table 2) highlight the im-
portance of the sublayer variables on DMMI. The results, according to the load factor magnitude per
variable and the most relevant indicators, allow us to see the following digital marketing strategies as
a post-Covid era:
1. Market (MKT, 0.993). This is the most relevant variable for the SIS. The SIS is trading all about the
market segmentation as a target. It comprises the heart of any business model. Without a (profit-
able) market, no company can survive for long. To better satisfy the market, the SIS group them
into distinct segments with common needs, common behaviors, or other attributes. The SIS aims
to define specific market segmentation as a target to be attended for their digital campaign to raise
their competitiveness.
2. Digital Marketing Tools (DMT, 0.926). The SIS is in constant surveillance to determine what digital
marketing tools are ready to use in the digital campaign design for competitiveness. The first results
under “touchless, cashless and hygiene” in the post-Covid era pointed out about digital marketing
tools, in descending category the uses of APPs; Mobile Marketing; Search Engine Optimization
(SEO); Search Engine Marketing (SEM); Social Media Marketing; Augmented Reality; Virtual
Reality; Wearable Marketing; Social CRM; Affiliate and Partner Marketing; Online advertising;
Online Public Relations; Home & Site-Wide Page Effectiveness; Landing Page Design Effectiveness;
Search and Browse Page Efficiencies; Category and Product Page Efficiencies; Basket and Checkout
Efficiency; Social Commerce; Content Marketing; Newsletters; eMail marketing; e-Contact Strategy;
Customer and Service Support.
3. Planning (PLN, 0.891). The SIS, in this step, gathers all the digital marketing tools and techniques
of the tactics is programmed logistically, to be implemented in practice as a strategy for digital
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marketing. The strategy is defined to integrate communications across different customer touch-
points. The planning involves setting goals, creating a coherent strategy to achieve them, and
evaluating evaluation tools. This means the SIS design a strong program with a schedule and times
to implement the digital marketing tools in order to obtain results in the digital campaign design
for competitiveness.
4. Strategy (STG, 0.85). The SIS in this stage represents the “how to do” to achieve the goal settings
(GST) in descending order for post-Covid era:
a. Engagement & Loyalty. Capture and retention as a growth strategy to build customer and fan
relationships to encourage repeat visits and sales.
b. Awareness. Acquisition strategy to build awareness off-site and in offline media to drive to
web presences.
c. Effectiveness on Call to Action. Conversion strategy to achieve marketing goals of leads &
sales on web presences and offline.
d. Desire & Experience. Strategy based on the sample and testing of a service or a product, with
a novelty presentation, increases the acquired sensations and emotions.
5. Value Proposition (VAL, 0.821). It is why customers turn to one company over another solving
their problems or satisfying their needs. It consists of a selected bundle of products or services that
cater to specific customer segmentation requirements. In this sense, it is an aggregation, or bundle,
of benefits that a company other customers. The SIS identifies and applies the value proposition
in the digital campaign design for competitiveness.
6. Tactics (TAC, 0.81). This represents all the activities to be implemented to follow the strategies,
involving mainly the use of the digital marketing tools (DMT). See Table 3.
Table 3. Digital marketing tools vs. strategy
Strategy
Awareness Engagement & Loyalty Desire & Experience Effectiveness on Call to Action
Augmented
SEO/SEM Content Marketing Home & Site-Wide Page
Reality
Affiliate & Partner Newsletters & eMail
D Virtual Reality Landing page design
Marketing Marketing
M
T On line Advertising e-Contact Strategy Wearable Marketing Search and Browse Page
On line PR Customer Service & Support Basket and Checkout
Social Media Mobile Marketing Social Commerce
Social CRM
Blogging
The SIS considers the use of Digital Marketing Tools (DMT) for each strategy in the digital campaign
for competitiveness, such as:-Awareness (SEO/SEM; Affiliate & Partner
Marketing; On line Advertising; On line PR; Social Media)-Engagement & Loyalty (Content Mar-
keting; Newsletters & eMail Marketing; e-Contact Strategy; Customer service & support; Mobile Mar-
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keting; Social CRM; Blogging)-Desire & Experience (Augmented Reality; Virtual Reality, Wearable
Marketing)-Effectiveness on Call to Action (Home & Site-Wide Page; Landing page design; Search and
Browse Page; Basket and Checkout; Social Commerce
7. Mission-Vision (MVS, 0.785). The mission is a written declaration of an organization’s core purpose
and focuses that normally remains unchanged over time. It is the cause of the firm’s campaign,
day-to-day operational objectives. The vision is the effect of the firm’s campaign. It expresses the
high-level goals for the future. The SIS considers the mission and vision involved in the digital
campaign for competitiveness and also considers the trademark as a strategic asset to be used in
the digital campaign design for competitiveness
8. Goal Settings (GST, 0.756). All digital marketing campaign requires objectives to be reached, in
descendent order, for post-Covid era: The sales; The branding positioning; The number (real &
potential) of customers database; The product & services (current and new ones) information.
9. Profitability (PRO, 0.707). It is expressed in terms of return on investment (ROI). It is about how
the digital campaign is working in the short, medium, or long term. The SIS permanently makes
profitability analysis to determine the digital campaign’s current profitability for competitiveness
in real-time.
10. Performance (PER, 0.694). It implies knowing how well the digital campaign is working. Practically,
it involves the measurement and assessment of all the previous stages. Its support is the web analyt-
ics to obtain full control of the digital campaign. he SIS establishes the key performance indicators
(KPIs) for performance monitoring to determine the digital campaign’s current performance for
competitiveness in real-time. Here they are using the web analytics digital marketing tools.
The creation of new SIS, particularly those that use technology and sustainable tenets, like the 17 sus-
tainable development goals (UN 2015) based on their products or services, generates competitiveness
and economic growth (Matson, 2006). The social impact startups fail so badly everywhere we look
due to several causes, mainly the allure of a good plan, a solid strategy, and thorough market research
(Ries, 2011). Due to the uncertainty, all of them must be judiciously analyzed and quickly applied (Ries,
2011; Pomerol, 2018). In an emergency context (like the Covid-19 pandemic and the next normal), the
uncertainty boost for social impact startups creation and development: “startups increase uncertainty
and uncertainty encourages people to feed the process of startup creation.” (Pomerol, 2018). Success
is not delivering a feature; success is learning how to solve the customer’s problem (Valencia, 2014).
The research findings provide useful implications for academics, digital marketing innovation
managers, and professional practitioners of innovation activities about the relationship of DMMI to
design digital marketing strategies for the next normal in Mexico. The DMMI proved its model´s valid-
ity based on CB-SEM assessment and empirically provides new insights on how the combinations of
the variables: Mission-Vision (MVS), Value Proposition (VAL), Market (MKT), Goal Settings (GST),
Strategy (STG), Tactics (TAC), Digital Marketing Tools (DMT), Planning (PLN), Performance (PER)
and Profitability (PRO) create digital marketing strategies for the SIS in the next normal in Mexico and
other several emergent countries.
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CONCLUSION
This study verifies how events like the Covid-19 pandemic and the next normal are considered emergency
context by 180 social impact startups (SIS) survivors in Mexico (an emergent country), in the scenario
of Dec-2020 to Feb-2021. The Covid-19 has elicited economic, employment ravages with missing em-
ployment, competitiveness, productivity, and worse yet, the loss of the SIS itself.
Digital marketing campaigns are alternatives for the Mexican SIS to raise its competitiveness again.
Therefore, as theoretical implication for academics considering all mentioned above, we started a study
to validate the underlying factors, variables, and indicators of a previous digital marketing innovation
model (DMMI) to be applied to the Mexican SIS to obtain digital marketing strategies for the next
normal and proved to be a solid model with high values of consistency, convergent and discriminant
based on CB-SEM.
Finally, the practical results are helpful for professionals and consultants to design permanent digital
marketing innovations to be adopted by the SIS economically affected in the next normal or post-Covid
era in Mexico. The results suggest a permanent revision of the segmented market (MKT), with a wide
use of digital marketing tools (DMT) to apply a digital campaign design for competitiveness. The first
results under “touchless, cashless and hygiene” in the post-Covid era pointed out about the use of digital
marketing tools, mainly based on APPs; Mobile Marketing; Search Engine Optimization (SEO); Search
Engine Marketing (SEM); Social Media Marketing; Augmented Reality; Virtual Reality; Wearable
Marketing; Social CRM, amongst other options. The planning (PLN) involves setting goals, creating a
coherent strategy to achieve them, and evaluating evaluation tools. The planning generates the strategy
(STG), and it represents the “how to do,” the goal settings (GST) in descending order for post-Covid
era: engagement & loyalty; awareness; effectiveness on call to action and desire & experience.
The SIS promotes the update of their value proposition (VAL) to highlight in the digital marketing
campaign and define the tactics (TAC) to be implemented. In this case, mainly the use of
Digital Marketing Tools such as:-Awareness (SEO/SEM; Affiliate & Partner Marketing; On line
Advertising; On line PR; Social Media)-Engagement & Loyalty (Content Marketing; Newsletters &
eMail Marketing.
The Mission and Vision (MVS) reinforce the goal settings (GST) aimed at sales and branding position-
ing. Profitability (PRO) and performance (PER) are the last underlying variables reviewed in real-time
by the SIS, when they are activated by the use of web analytics tools to make decisions.
LIMITATIONS AND FUTURE STUDIES
All empirical studies have some limitations:
1. One of them is the industry and the social impact startups sectors as sources of information. Not
all of them are accessible to provide information under equal conditions and times.
2. The results consisted of a questionnaire survey of self-reported data to remind their perceptions.
Further studies could combine this questionnaire with survey data from direct semi-structured
interviews and direct observations of specific social impact startups (SIS) from other emergent
countries.
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3. Future investigations may also include other different factors, variables, or indicators. This would
be very useful to complement the digital marketing model innovation (DMMI) to design digital
marketing campaign strategies for the next normal in other social impact startups and environments
that could offer more helpful information.
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KEY TERMS AND DEFINITIONS:
Startup company: Business model based on innovation and technology.

DMMI: It is a model that describes and identifies all the variables and indicators involved in design-
ing and driving a digital marketing campaign.
Digital Marketing Tools (DMT): Software to organise and develop digital marketing strategies.
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Chapter 10
Do Spanish Family SMEs Make
Appropriate Use of Their
Organizational Websites?
An Analysis of Family Firms’ Brand
Promotion and Website Quality Level
Rubén Martínez-Alonso
https://orcid.org/0000-0001-7389-762X
University of Almería, Spain
María J. Martínez-Romero
https://orcid.org/0000-0001-7891-1558
Daniel Rodríguez-Rodríguez
ABSTRACT
The aim of this chapter is to analyse how family SMEs use digital media, particularly organizational
websites, to disseminate information about their products, services, history, etc. To this end, the authors
perform a descriptive analysis of 32 organizational websites from privately owned family SMEs located
in the region of Andalusia (Southern Spain), emphasizing two differentiating strategies: promotion of
the family firm brand and website quality level. On the one hand, the findings show that family firms are
to some degree reluctant to promote their family firm brand on their organizational websites, with the
indicator of being a family firm as the most communicated. On the other hand, the findings reveal that
family firms tend to develop organizational websites with an adequate quality level; however, the inclusion
of components, such as FAQ or help sections or a bilingual option, need to be considered in the future.
DOI: 10.4018/978-1-7998-8003-5.ch010

Do Spanish Family SMEs Make Appropriate Use of Their Organizational Websites?
INTRODUCTION
In recent decades, the continuous renewal of digital media, such as organizational websites or social
networks, as well as the emergence of new information and communication technologies, have completely
revolutionized the business world (Alves et al., 2020; Gruner & Power, 2018). Furthermore, family firms
represent a form of organisation that has been attracting significant attention from scholars and prac-
titioners in global markets (De Massis et al., 2018). This increased interest is not surprising given that
family firms, defined as those businesses ‘dominantly controlled by a family with the vision to potentially
sustain family control across generations’ (Zellweger, 2017, p. 22), have become the backbone of the
majority of the world’s economies by generating around 70–90% of annual global GDP and creating
approximately 50–80% of employment in most countries worldwide (Casado-Belmonte et al., 2021; IEF
& Red de Cátedras de Empresa Familiar, 2018; Martínez-Alonso et al., 2020a).
Extant literature on this theme points to the rise of digital media as one of the possible factors that has
enabled family firms to gain considerable momentum within the business sphere (Zanon et al., 2019).
The application of digital media has contributed to making work more efficient and easier, improving the
image and reputation of family firms, as well as increasing their firm profitability (Gallucci et al., 2015;
Sageder et al., 2018). Nevertheless, the incorporation of these digital media in the context of family firms
has also provoked heated debates among family firm owners and managers (e.g., Barroso-Martínez et
al., 2019). On the one hand, some family firm owners and managers consider that digital media allows
them to establish differentiation strategies and even reach broader markets (Binz Astrachan & Botero,
2018). On the other hand, certain family firm owners and managers question the utility of these digital
media for their organizations, as they believe that instead of ensuring control of the firm and guarantee-
ing its subsistence, digital media might bring negative consequences for the business family (Treem
Jeffrey & Leonardi, 2012).
In order to shed new light on this debate, this chapter aims to conduct a descriptive analysis of the use
and dissemination of information through organizational websites by family firms. To do so, this study
focuses on examining the promotion of family firm brand and website quality level. First, the promotion
of family firm brand, conceived as the action through which a family firm promotes its family status as
part of its communication strategy (Binz Astrachan & Astrachan, 2015), is an emerging issue on which
empirical work is still lacking (Botero et al., 2013; Leonardi & Vaast, 2017). Therefore, in an attempt
to deepen in the promotion of family firm brand, this chapter will examine the following indicators in
family SMEs’ organizational websites (Barroso-Martínez et al., 2019): being a family firm, ownership
level, presence of family members in top management teams, family history, generation in control, and
family name as part of the firm name. Second, website quality level is also a topic of growing debate, as
having a website with a certain degree of quality could be very beneficial to offer a better image to the
market and the competition (Krappe et al., 2011; Sageder et al., 2018). In this regard, and to get further
insights on the website quality level of the analysed firms, this chapter will assess such quality through
four areas (Cober et al., 2004): content, form, functionality, and connectivity to social networks.
To develop the descriptive analysis, a sample of 32 organizational websites from privately owned
family SMEs located in the region of Andalusia (Southern Spain) is used. Regarding the promotion of
family firm brand, the results show that less than 50% of the sampled firms communicate their family
status on their organizational website. The findings reveal that there are family firm owners and manag-
ers who do not consider the inclusion of their family firm brand in their communication campaigns as
a differentiating strategy. This is quite surprising, insofar as recent research has shown that promoting
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family firm brand leads to very positive consequences for family firms (e.g., Binz Astrachan & Botero,
2018). Therefore, it is likely that family firm owners and managers consider that mixing family and
business is not entirely positive when it comes to the image they want to offer to their customers and
the rest of stakeholders. Concerning the website quality level, the findings reveal an increasing trend
on the part of family SMEs to offer easy and intuitive organizational websites. Nevertheless, there is
one area, i.e., connectivity to social networks, which is barely used, but that will be increasingly crucial
whether family firms want to establish quality, long-lasting relationships with their customers and other
stakeholders (Berrone et al., 2012; Miller & Le-Breton-Miller, 2005), and thus increase the likelihood
of success in achieving better performance outcomes (Gallucci et al., 2015).
In this light, this study offers important contributions to existing literature, as well as interesting
implications for practitioners. First of all, this study contributes to the literature of digital media, and
specifically, organizational websites within the family firm research field. In this regard, we answer
the call for studies investigating family firms’ communication strategies through their organizational
websites (Barroso-Martínez et al., 2019; Binz Astrachan et al., 2018; Botero et al., 2013). Namely, this
chapter focuses on two indicators, i.e., family firm brand and website quality level, which have been
underexplored so far in privately owned family SMEs. Second, this chapter extends prior research
regarding the communication practices through organizational websites in family firms (Botero et al.,
2013), which are known to influence stakeholders’ perceptions and attitudes towards firms (Cheney,
1991). Finally, inasmuch as a sample composed exclusively of family firms is analysed, this study deals
with the trendy issue of family firm heterogeneity (Chua et al., 2012) with regards to website promotion
policies. Regarding the implications for practitioners, the present study could be very useful for family
firm decision makers, who should contemplate the possibility of enhancing the family firm brand and
the quality level in the organizational websites to improve their firms’ outcomes.
This chapter has the following structure: first, it conceptualizes and explains the promotion of family
firm brand on the family firms’ organizational websites and their quality level. Next, the methodology is
presented, and then, the results are shown and discussed. Finally, the chapter ends with the conclusion.
STUDY OF FAMILY FIRMS’ ORGANIZATIONAL WEBSITES: PROMOTION

OF THE FAMILY FIRM BRAND AND WEBSITE QUALITY LEVEL
Concept of Family Firm Brand
Family firm brand is often conceptualized as a policy by which the family firm promotes its family
status (Barroso-Martínez et al., 2019; Binz Astrachan & Astrachan, 2015). Previous studies show that
communicating the family firm brand as part of corporate communication activities can lead to positive
perceptions of the family firm (i.e., benevolence, responsible behaviour, authenticity) and increased
intentions to purchase from such family firm (Beck & Prügl, 2018; Binz et al., 2013). In addition, some
scholars indicate that the communication of the family firm brand can exert an impact on firm perfor-
mance factors (Gallucci et al., 2015; Zellweger et al., 2012). Despite these benefits, prior research has
found that family firms greatly differ in the way in which they use their family firm brand as part of their
communication effort (Botero et al., 2013). Specifically, previous studies reveal that not all family firm
owners and managers agree that family firm brand would be the best formula for the growth of their
firms (Barroso-Martínez et al., 2019).
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According to Binz Astrachan & Botero (2018), there are two types of factors by which family firms
may decide to communicate their family status: those driven by the firm’s own identity and those prompted
by the firm’s expectations. Regarding the first factors, organizational identity theory suggests that family
firms differ in their proclivity to actively promote their family background. Depending on the degree
of identification and commitment of family members to their firm, their involvement in the creation
and promotion of their family firm brand will be encouraged to a greater or lesser extent (Memili et al.,
2010; Zellweger et al., 2010). In relation to the second factors, it is evidenced that some family firms
choose to communicate their family background because they consider that being a family firm will act
as a differentiating resource, thus bringing positive consequences for the organization (Binz Astrachan
& Botero, 2018).
Indicators of Family Firm Brand
The family firm brand can be communicated through direct channels, such as advertisements, or through
indirect channels, like external representatives (Botero et al., 2013). Normally, direct channels are
preferred, as they allow family firms to control the type of information to be presented to the different
stakeholders (Barroso-Martínez et al., 2019; Botero et al., 2013). One of the most important direct chan-
nels to communicate the family firm brand nowadays is the organizational website (Braddy et al., 2008;
Schlosser et al., 2006). Thus, this study is focused on the communication of the family firm brand through
organizational websites. According to Barroso-Martínez et al. (2019) and Binz Astrachan et al. (2018),
family firms’ organizational websites should contain information related to the following indicators to
properly communicate the family firm brand:
• Being a family firm: the recognition of the family firm status must appear on the organizational
website. An example would be: “Cuadraspania is a family firm established in Almería 30 years
ago...”.
• Ownership level: it relates to the percentage of ownership that the family or family members have
in the family firm, a condition that should appear on the organizational website. An example
would be: “At Cuadraspania, the firm is 100% owned by members of our family...”.
• Presence of family members in top management teams: it refers to the occupation by family mem-
bers of management positions. An example would be: “Cuadraspania’s top management team is
composed of Joséphine Cuadras as Chief Executive Officer, Antoine Cuadras as Chief Operating
Officer...”.
• Family history: it relates to the creation of the family firm and its history since then. An example
would be: “Jean Cuadras created the firm in 1990, with the objective of...”.
• Generation in control: it refers to the number of generations that have led the family firm, and
more specifically, the generation that currently leads the family firm. An example would be: “The
second generation of the Cuadras family is the one that currently leads Cuadraspania...”.
• Family name as part of the firm name: it indicates that the family name is part of the firm name. An
example would be: “In honour of our family name, our firm was given the name Cuadraspania...”.
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Concept of Website Quality Level
Website quality level refers to the degree of usability of organizational websites by customers and other
stakeholders (Barroso-Martínez et al., 2019). The website quality makes a website profitable, user
friendly and accessible, by offering useful and reliable information and by providing good design and
visual appearance to meet the users’ needs and expectations (Anusha, 2014).
Despite the current relevance of website quality, this concept is still vastly undefined (Semerádová
& Weinlich, 2020). Indeed, several definitions have been used during the last decade to clarify the web-
site quality term (e.g. Hasan & Abuelrub, 2011; Rocha, 2012). Finally, last year, Morales-Vargas et al.
(2020, p. 2) proposed the following formal definition, website quality can be considered the ability of a
website to meet the expectations of its users and owners, as determined by a set of measurable attributes.
The firms’ goal is to create a secure, useful, accessible and ultimately beneficial organizational website
(Signore, 2005). It is very important for firms to take care of the quality issue, as it can be extremely
valuable for coping with the market and the competition, as well as for providing both good public im-
age and reputation (Krappe et al., 2011; Sageder et al., 2018).
Since the beginning of the current century, the number of firms with website has increased ex-
ponentially (Fernández-Uclés et al., 2020; Marín Dueñas & Lasso de la Vega González, 2017). It is
evident that not all organizational websites are structured in the same way. Indeed, each firm provides
information in a different manner, but the quality level of such websites is a key issue to be considered.
The following section discusses in depth the areas and components that determine the quality level of
organizational websites.
Areas and Components of Website Quality Level
Following the proposals of Cober et al. (2004) and Barroso-Martínez et al. (2019), four main areas are
identified when analysing the website quality level: content, form, functionality and connectivity to social
networks. These areas, in turn, are divided into a total of 37 components, which are detailed as follows.
Content
The content area refers to the amount and type of information included in the organizational website.
This area is of utmost importance because it entails the main structure of the website, through which to
search and research in it. In addition, a good, high quality content will have a greater influence on cus-
tomers and other stakeholders (Keller & Block, 1997; Kivetz & Simonson, 2000). The ten most notable
components that any organizational website should include in this area are:
• About us section
• Blog
• Newsletter
• Copyright
• Legal disclaimer
• FAQ/Help
• News
• Privacy policy
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• Trust mark/Trust seal

• Terms of use
Form
The form area concerns the way in which information is presented on the organizational website. As
suggested by certain authors, if the information is presented in an attractive and evocative way, it can
lead to a higher firm profitability (Cyr, 2008; Keller & Block, 1997). The six components included in
this area are:
• Animation
• Colour background
• Pictures
• Colour photographs
• Colour text
• Video
Functionality
The functionality area refers to the ease of navigating through the organizational website, as well as inter-
acting and establishing communications with the firm (Barroso-Martínez et al. 2019). Thus, functionality
can be delineated along a number of variables, like the extent to which a site is navigable, playful and
instrumental (Cober et al., 2004). The twelve fundamental components in this area are:
• Search function
• E-mail option
• Fax Information
• Postal address
• Telephone
• Last update
• Forums
• Languages
• Site map
• Navigation menu
• Register
• RSS
Connectivity to Social Networks
The connectivity to social networks area relates to the presence of the firms’ social networks on the
organizational website (Barroso-Martínez et al., 2019; Cober et al., 2004). This area is gaining increas-
ing importance nowadays, as more and more firms are expanding their online structure through these
tools, being aware of the social and economic benefits that social networks can bring to them. This area
includes the following nine social networks as components:
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• Facebook
• Flickr
• Connection to Google +
• Instagram
• Linkedln
• Tumblr
• Twitter
• Weibo
• YouTube
METHODOLOGY
Sample
We decided to analyse the organizational websites of a sample of 45 privately owned family SMEs lo-
cated in the region of Andalusia (Southern Spain). The data were derived from a wider study analysing
general characteristics of a representative sample of Andalusian family owned firms (Rojo-Ramírez et
al., 2015). The study of family firms in the region of Andalusia is of great importance, inasmuch as
these businesses generate 78% of the GDP in such region and 57% in Spain as a whole (AAEF, 2021).
The family nature of privately owned SMEs was determined following the proposal of Diéguez-Soto et
al. (2015), which has been applied in many studies to identify and classify family firms (López-Delgado
& Diéguez-Soto, 2015; Martínez-Romero et al., 2020b; Rojo-Ramírez & Martínez-Romero, 2018).
Diéguez-Soto and colleagues, to differentiate between family and non-family firms, took as reference
the involvement approach (Chrisman et al., 2005) that considers both family control and family involve-
ment enough to consider a firm a family firm. Moreover, the abovementioned authors, as well as other
authors using this approach to identify family firms (Gómez-Mejía et al., 2001; Martínez-Romero &
Rojo-Ramírez, 2017; Rojo-Ramírez et al., 2011), took advantage of the Spanish conventional practice
of giving children two surnames, one from each parent. These authors develop a comparison among the
surnames of all internal firms’ stakeholders involved in the management and governance of the firm
(CEO, managers and shareholders). Depending on the match between the surnames, firms were classi-
fied as family or as non-family firms. Accordingly, based on the SABI1 (Sistema de Análisis de Balances
Ibéricos) database, we proceed similarly to Diéguez-Soto et al. (2015) to identify family firms.
Analytical Procedure
As previously mentioned, within the field of digital media, this study focuses on the analysis of organi-
zational websites. For this reason, the initial sample of 45 family SMEs was reduced to 32, due to the
non-existence of organizational websites in the case of 13 family firms. According to the number of
employees, the sample is composed of 20 small-sized firms (10-49 employees) (62.50%), and 12 medium-
sized firms (50-249 employees) (37.50%). In terms of industry, the sample firms pertain respectively, to
the agriculture (15.63%), manufacturing (21.88%), commerce (50.00%) and services (12.50%) sectors.
The analytical procedure applied consisted of an internet manual search for each of these family SMEs
to verify whether they had an organizational website. The data were collected in September 2019. Af-
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ter identifying those family firms with organizational website, we proceeded to examine the two main
aspects proposed in this research: the promotion of family firm brand and the website quality level. To
do so, a set of variables have been created to assess the proposed strategies, which are explained in the
following section.
Measures
Promotion of Family Firm Brand
The information to evaluate the promotion of family firm brand was obtained through an exhaustive
analysis of the organizational website of each of the firms in the study sample. For this purpose, a set of
six variables was built to determine the existence or not of the abovementioned indicators, representative
of the family firm status, in each organizational website (e.g., Barroso-Martínez et al., 2019). Specifi-
cally, all these variables were coded with the value 1 if the indicators were present on a family firm’s
organizational website, and 0 otherwise.
Website Quality Level
Similarly to the promotion of family firm brand, the required information to assess the website quality
level was collected through a comprehensive review of the family firms’ organizational websites (e.g.,
Barroso-Martínez et al., 2019). In this regard, a total of 37 variables representing those components
related to the areas of content, form, functionality and connectivity to social networks were created.
Particularly, all these variables were coded with the value 1 if the components were present on a family
firm’s organizational website, and 0 otherwise.
RESULTS
After describing the methodology utilized, this section shows the obtained results. First, we present
the results related to the promotion of family firm brand on the sampled family SMEs’ organizational
websites. Figure 1 shows that the most common component appearing on organizational websites is the
one that explicitly indicates that the firm is a family firm, that is, being a family firm. A total of nine
family firms in the sample carry out this action. Nevertheless, most family firms, despite indicating
their family status, do not provide information on their ownership level. In fact, only one family firm
reports the ownership level (in percentage) on its organizational website. On the other hand, it has not
been possible to gather much information on other family firm brand indicators, such as the presence
of family members in top management teams, nor on family history. Although they represent the second
and third most frequent indicators, they are only reported by seven and six family firms, respectively.
Additionally, very few family firms provide information on the generation in control of the firm and
on the use of the family name as part of the firm name. This is because if there were any kind of problem,
such problem will be automatically associated with the family, and this would make the family to incur
an enormous responsibility for their actions on an ongoing basis. Thereby, many family firm owners
and managers do not want to take this risk (Binz Astrachan & Botero, 2018), and try to avoid merging
the family name with the firm name.
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Figure 1. Number of firms including each family firm brand component on their organizational websites
Second, we show the obtained results regarding the website quality level of the sampled firms. Figure
2 illustrates the components of the content area and the number of firms using each of them. Family firms
are revealed to attach great importance to report certain components on their organizational websites,
such as legal aspects (e.g., copyright, privacy policy, etc.), information about the firm or even to blogs
and news. However, greater importance should be attached to the development of FAQ or help sections
for customers and other stakeholders in the future, since, as shown in Figure 2, they are scarcely present
on the analysed organizational websites.
Regarding the form area, Figure 3 shows the different components and the number of firms using each
of them. In general terms, the form of family firms’ organizational websites is quite attractive. Many of
the family firms under study have organizational websites with lots of animation, colour and images.
Nevertheless, there are not many family firms that include videos on their organizational websites.
The functionality area and its corresponding results are shown in Figure 4. It is revealed that family
firms tend to provide their contact (e.g., telephone, e-mail, fax…), and location data (e.g., site map,
postal address…). All this is essential to contact and/or locate the family firm. On the other hand, very
few family firms’ organizational websites are available in more than one language, an aspect that, if uti-
lized, would make these websites more attractive and useful (as well as enabling family firms to reach a
wider market). Finally, it should also be noted that few family firms’ organizational websites have been
found to include search functions, which makes it rather difficult to look for specific issues within them.
Figure 5 shows the results regarding the connectivity to social networks area. This analysis reveals
that the most included social network in family firms’ organizational websites is Facebook. This can
be explained by the fact that Facebook is one of the most widely used social networks worldwide by
institutions, governments and businesses, as Facebook allows to interact directly with all the individu-
als (Haro-de-Rosario et al., 2017; Ji et al., 2017). The second and third most popular social networks
on family firms’ organizational websites are LinkedIn and YouTube. Family firm owners and managers
also embrace these social networks because they allow them to generate, disseminate, and exchange
information about their products, services, or history with different online communities (e.g., profes-
180

sionals, customers, supporters, employees or funders) in an efficient and economical way (Mumi et al.,
2019; Sullivan & Bendell, 2020). Other social networks used by the sampled firms to a lesser extent are
Instagram and Twitter. Despite the above, very few family firms have been found to include their social
networks on their organizational websites, which can become a competitive disadvantage, due to the
increasingly digital nature of today’s firms.
Figure 2. Number of firms including each component of the content area on their organizational websites
Source: own elaboration
Figure 3. Number of firms including each component of the form area on their organizational websites
181

Figure 4. Number of firms including each component of the functionality area on their organizational
websites
Figure 5. Number of firms including each component of the connectivity to social networks area on their
organizational websites
182

DISCUSSION AND CONCLUSION
The last years have witnessed the continuous and exponential growth that family firms have experienced,
becoming one of the fundamental pillars of most of the world’s economies (Casado-Belmonte et al.,
2021; Martínez-Alonso et al., 2020a; Martínez-Romero, 2018). These businesses have consolidated
their position in global markets, evolving from being organisations created by a family with the pur-
pose to ensure their survival over the years, to being potential drivers of any country’s economy and
employment (IEF & Red de Cátedras de Empresa Familiar, 2018; Martínez-Romero et al., 2019). Part
of family firms’ growth has been due to the emergence of digital media, that is, a set of tools such as
organizational websites and social media, which have revolutionised the business world in the current
century (Kaplan & Haenlein, 2010; Kietzmann et al., 2011; Tiago & Veríssimo, 2014). With these new
digital tools, family firms have been able to make a significant leap in the markets, achieving a notable
improvement in image and reputation (Alberghini et al., 2014; Sageder et al., 2018; Uyar et al., 2018).
In addition, the use of these media has also enabled a significant increase in the economic and financial
outcomes of such firms (Chukwujioke Agbimi, 2019; Gallucci et al., 2015).
Particularly, the aim of this chapter was to study the use of organizational websites, as well as the dis-
semination of information through them, within the family firm field, focusing on the examination of two
differentiating strategies: the promotion of family firm brand and the website quality level. The empirical
analysis was carried out using a sample of 32 organizational websites from privately owned family SMEs
located in the region of Andalusia (Southern Spain). Regarding the promotion of family firm brand, the
research findings reveal that there are family firm owners and managers who do not consider the inclu-
sion of their family firm status in their marketing campaigns as a differentiating strategy. Nevertheless,
there are also family firm owners and managers who believe that communicating such status could bring
significant competitive advantages, as well as enhancing the family firms’ image and reputation in the
global market, and increasing the likelihood of successful firm growth (Barroso-Martínez et al., 2019;
Binz Astrachan & Botero, 2018; Sageder et al., 2018). In this respect, and contrary to what might be
expected, the empirical analysis reveals that only a small proportion of the sampled firms include the
different indicators representative of family firm brand on their organizational websites, suggesting that
this not a popular strategy for many family firms today. These findings are quite surprising, as recent
studies have shown that the promotion of family firm brand leads to very beneficial consequences for
family firms (Barroso-Martínez et al., 2019; Binz Astrachan & Botero, 2018).
Concerning the examination of the website quality level, the research findings show an increasing
tendency in the part of family firms to enhance the quality level of their organizational websites. How-
ever, it has also been found that certain components, such as a FAQ or help section, a bilingual option,
or the inclusion of firms’ social networks on the website, are issues that family firms need to consider
implementing on their websites in the future. In this regard, the development of a well-organised and
attractive organizational website, with different options for individuals to choose from, and with great
usability will be crucial if family firms want to establish quality, long-lasting relationships with their
customers and other stakeholders (Berrone et al., 2012; Miller & Le-Breton-Miller, 2005), and therefore,
achieve superior firm performance (Gallucci et al., 2015).
This chapter offers some very important contributions. First, it contributes to the literature of digital
media in family firms, a topic that has been barely investigated so far (Barroso-Martínez et al., 2019;
Binz Astrachan et al., 2018; Botero et al., 2013). Specifically, this chapter is focused on whether privately
owned family SMEs make an appropriate use of their organizational websites, by examining the promo-
183

tion of family firm brand and the website quality level. In this regard, this is one of the pioneer studies
exploring whether privately owned family SMEs communicate different family attributes through their
organizational websites and the level of quality in those means of communications. Second, another
contribution of this chapter is that it extends prior research regarding the communication practices of
family firms (Botero et al., 2013). Family firms’ communication practices are of utmost importance
because they can influence stakeholder’s perceptions concerning firms’ principles, values, impressions,
and practices, and consequently their attitudes towards firms (Cheney, 1991). Finally, as this chapter
examines a sample of privately owned family SMEs, it highlights the existing family firm heterogeneity
(Chua et al., 2012) with regards to their website promotion policies.
Additionally, this chapter has important implications for practitioners. To the extent that accurately
communicating the family firm brand and having a high level of website quality have been shown to
exert a positive impact on firm performance (e.g., Barroso-Martínez et al., 2019), family SMEs decision
makers should further contemplate leveraging both their family firm brand and website quality, as part
of marketing communication strategies towards stakeholders, in order to improve their performance out-
comes. Moreover, the obtained findings can motivate managers of family SMEs or branding consultants
to improve family members’ participation in the development of appropriate organizational websites.
Despite the interesting results obtained, the present study has some limitations, which in turn give
rise to fruitful lines of future research. First, the empirical study has been carried out with a sample
of family SMEs located in the region of Andalusia (Southern Spain). Thus, future studies should be
developed in other Spanish regions or even in other countries to test the validity and significance of our
findings. Second, we analyse the promotion of family firm brand by focusing on only one communication
channel, i.e., the organizational website. Given that the family firm brand can be communicated through
multiple channels (Barroso-Martínez et al., 2019; Botero et al., 2013), future research should analyse
how family firms communicate the family firm brand through interpersonal (i.e., sales representatives,
point of sales interactions…) and other channels (i.e., advertisements, social media…) to extend the
generalization of this chapter findings. Thirdly, more research is needed on the impact of organizational
websites, and other media such as mobile applications and newsfeeds, on the marketing actions of family
firms (Christofi et al., 2021). In this regard, very little is known about how family firms carry out their
marketing plans, whether digital or offline. Therefore, addressing this issue is fundamental for this type
of businesses to exploit their full marketing potential. Finally, it would be interesting that future studies
analyse how the two strategies analysed, i.e., the promotion of family firm brand and the website quality
level, by means of their indicators and components, affect other important aspects that are also acquir-
ing great importance in the family firm field, such as such as innovation efficiency (Martínez-Alonso
et al., 2020b), internationalization (Morales-López et al., 2019), value creation (Martínez-Romero et
al., 2020a), sustainable performance (Martínez-Alonso et al., 2019), or entrepreneurial orientation
(Hernández-Linares et al., 2020), among others.
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Andalusia: It is a geographical region located in the southern of Spain. It is the most populated
geographical region in Spain and the second in territorial extension.
Communication Strategy: It is a tool that helps firms align their general goals with a marketing
plan focused on achieving a set of results.
Customers: Individuals or firms that purchase goods or services from other firms.
Digital Media: It refers to any form of media that can be created, viewed, modified, and distributed
through electronic devices.
Family Firm Heterogeneity: Differences in the behaviours, objectives, and values of family firms
due to high or low degree of family involvement in the firm.
190

Family Managers: Family members of a family firm actively participating in the top management
team.
Family Owners: Family members of a family firm participating in the firm’s ownership.
Firm Performance: Economic outcomes obtained because of the interaction between firms’ char-
acteristics, actions, and environment.
Organizational Website: Websites whose purpose is to manage a firm presence on internet, facilitate
information about the firm, display and sell their products or services, and serve as a means of com-
munication with suppliers and customers, among other stakeholders.
ENDNOTE
1
SABI is an economic and financial dataset, the data from which were compiled by Informa D&B in
collaboration with Bureau Van Dijk, including financial statements, ratios, activities, shareholders
of more than 2.600.000 Spanish and 800.000 Portuguese firms (January 2020).
191
192
Chapter 11
Adapting Digital Strategies
to a New Era:
A Delphi-Based Analysis in
the Fashion Industry
Rocío López Muniesa

Jose Ramón Saura

https://orcid.org/0000-0002-9457-7745
Eloísa Díaz-Garrido
ABSTRACT
Fashion brands are continuously reinventing themselves to adapt their business strategies to emerging
markets. In this paradigm, digital marketing becomes an essential tool for communication to the target
audiences online. In digital ecosystems, a new way of sharing information is taking place in which brands
interact with users to increase engagement and brand awareness. Accordantly, the objective of the pres-
ent study is to explore what is the evolution of digital marketing and how it has affected the strategies
applied by fashion brands on digital ecosystems after the COVID-19 pandemic. The research develops a
Delphi method with the participation of seven fashion digital marketing experts, whose conclusions and
analysis of results will allow future research to be linked to the objectives of the research. The results
propose and discuss nine future directions and four research proposals focused on digital marketing in
the fashion industry. In the future, these proposals may be used by research or fashion marketers as a
starting point for future research studies and practice.
DOI: 10.4018/978-1-7998-8003-5.ch011

Adapting Digital Strategies to a New Era
INTRODUCTION
Society is aware, one way or another, that the digital age has come to stay, and that even if we want to
stay out of its evolution, it is inevitable. In this way, authors such as Pedrajas Trucharte (2020) indicate
that digitization refers to the process of including new digital technologies and their complementary tools.
For this reason, Chica, and González (2020), pose another challenge. How to design the customer’s
marketing strategy, if the market no longer has a physical location, referring to the presence of a virtual
market, where products, services and information are exchanged through existing technological networks,
which grow rapidly detached from time and space.
According to Pedrajas Trucharte (2020) it is possible to state with propieding that the main economic
effects of digitization on the marketing of the environment are: (i)innovate its structures and function,
(ii)increase productivity, (iii)expand markets and (iv) improve yields. In addition, the digitization of
trade makes available to customers or consumers a wider range of products and services that they can
access more quickly and information, also favoring competition between their suppliers (Chica and
González, 2019).
From the above it can be indicated that the Digital Age, the marketing areas, are being forced to
rethink strategies, functions and operations within a company (Saura, 2020; Saura, Palacios-Marqués
and Iturricha-Fernández, 2021). On the other hand, companies must take advantage of the technological
tools available, so that by analyzing the information provided by customers, they allow them to make
sound decisions, in the identification of new businesses or development of products and services, whose
characteristics are attached to the needs, trends and tastes of consumers (Alcaide, 2015).
As Pérez-Curiel, Clavijo-Ferreira, Luque-Ortiz, and Pedroni, (2017) claim, fashion is communica-
tion. Fashion is intrinsically related to social habits and is the most accurate indicator of time and time.
This way of understanding fashion has not changed since its birth, what it has done is the way to com-
municate it (Pérez-Curiel et al., 2017).
The recent most social and interactive scenario has generated the emergence of new agents and
strategies, ending the one-way conception that dominated communication in the business world (Pérez-
Curiel et al., 2017).
Therefore, the main objective of this research is to explore what will be the evolution of digital
marketing and its strategies in the fashion sector following the economic crisis arising from covid-19.
This is intended to identify the most relevant aspects of digital marketing and changes to a virtual en-
vironment as presented by Ribeiro-Navarrete, Saura and Palacios-Marqués (2021). As a result of the
problem raised, this research proposes the following research question: RQ1: What are the main digital
marketing techniques used in the fashion sector? If so, RQ2: What is the influence of Digital Marketing
on the evolution of the fashion sector in the development of digital business models?
The methodology used in this research is that of the Delphi Method, a method of decision analysis
proposed by Rand Corporation in 1948. Delphi is characterized by being a method for structuring an
effective communication process that allows a group of individuals to reach a group consensus. The
originality of this research is that as companies in the fashion sector must understand the changes that
are taking place in digital marketing today as well as the new ways of consuming customers.
The rest of this work is structured as follows. Section 2 presents the theoretical framework and dis-
cusses relevant concepts related to digital marketing and the fashion sector. In section 3, we present the
methodology used in this study, the Delphi Method. Section 4 reports the results after being analyzed.
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Section 5 proposes a series of research questions for future research. Finally, section 6 describes the
conclusions, theoretical and practical implications, as well as additional research directions.
Digital marketing (or online marketing) encompasses all those advertising or commercial actions and
strategies that run in the media and internet channels (Aguilera y Bolaños, 2018). This phenomenon has
been applied since the 1990s as a way to transfer offline marketing techniques to the digital universe
(Aguilera y Bolaños, 2018).
In parallel with the tremendous development and evolution of digital technology, online marketing
has been experiencing, progressively and very quickly, profound changes, both in the techniques and
tools used (and in its complexity) and in the possibilities it offers to receivers (Aguilera and Bolaños,
2018). According to authors such as Saura, Ribeiro-Soriano and Palacios-Marqués (2021) traditional
marketing has undergone a change due to the emergence of a new consumer who decides what to buy,
how, when and where and why to use it and what type of service it wants to receive and when it loses
confidence, abandons it and changes it.
“Likewise, authors such as Kamba and Rahman (2017) show that the customer is the decisive factor
that defines the success or failure of a business although broad in technologies and the development
undertaken by today’s competitive business world, the central concentration for the customer remains
equal to or greater than in previous times.
In the context of the fashion industry in the digital age, consumer behavior has changed to a digital
culture where they became more informative with increased access to information. In their research,
they indicate that consumers are not true to fashion brands and focus on the momentum and information
gained through social media. In 2014, a study reported that 90% of the data in the world, were generated
in the last two years (Plummer, Fiering, Dulaney, McGuire, Da Rold, and others, 2014). This high growth
in information data, consolidated by the growth of social networks, the Internet of Things, geolocation,
increased broadband, smartphones, gives way to Big Data technology (Joyanes, 2012).
Digitization has been growing in recent years as the world economy has done, but this has not
always happened, as, in times of economic crisis, many companies have opted to digitally transform
their business model to survive (Palos-Sanchez, Saura and Correia, 2020; Saura, Ribeiro-Soriano and
Palacios-Marqués, 2021a).
But at the present time, digitization helps boost economic growth and has become the engine of many
of the economies of this planet (Chica and González, 2019). The digital transformation of a company
must be carried out efficiently taking into account as Chica and González says (2019), it is not easy to
make an effective digital transformation, either at the moment when the company decides to make the
change, the way to bring it to its consumers.
The main disruptive effect of the introduction of digital technologies or marketing digitization is
to change the nature of supply, demand or both (Dawson, Hirt and Scanlan, 2016). Second, disruptive
innovations are those that have the ability to change the existing paradigm of a business model (Will-
mott, LaBerge and Schwartz, 2017). In short, it is intended to analyze the difficulties and challenges
of today’s Marketing and the opportunities provided by technologies to meet the challenges existing in
the Digital Age.
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Table 1. Similar research
Authors Description
They believe that digitization helps drive economic growth and has become the engine of many of
Chica and González (2019)
the economies, but it must be done efficiently.
They explain how digital marketing encompasses all the actions and strategies necessary for
Aguilera and Bolaños (2018) advertising to be carried out in online media and channels, that is, how to transfer offline marketing
to the digital universe.
Kamba and Rahman (2017) They show that the customer is the decisive factor that defines the success or failure of a business.
Willmott et al. (2017) Innovations are those that have the ability to change the existing paradigm of a business model.
In their research, they point out that consumers are not loyal to fashion brands and focus on
Plume et al. (2014)
momentum and information obtained through social media.
The main disruptive effect of the introduction of digital technologies or the digitization of marketing
Dawson et al. (2016)
is to change the nature of supply, demand or both.
Joyanes (2012) They explain how the high volume of information in online media leads to the creation of Big Data.
METHODOLOGY
Delphi Method
Within the different qualitative techniques, this study uses the Delphi method for its suitability to deal
with novel and complex topics (Lindstone and Turof, 1975). Although the traditional or classic approach
of the Delphi method seeks to obtain lines of consensus identified by the participants (Lindstone and
Turof, 1975; Okoli and Pawlowski, 2004), the development of this technique and its application to dif-
ferent areas of knowledge has resulted in different forms or variants of the Delphi method (Skulmoski,
Hartman and Krahn, 2007).
In this work, the proposal of Kendall, Smithson and Angell (1992) was followed, emphasizing the
ability of this technique to contrast opinions and approaches that allow to identify different alternatives
or scenarios for the future in a specific topic of study. By applying this method to the digital fashion
marketing industry, we will be able to contrast the opinions of industry experts and new ways of con-
suming customers.
For Gaitán and Piñuel (1998), the Delphi method is a form of evolutionary qualitative approach
research, seeking consensual answers from a group of experts to a number of specific questions.
This technique proposes, a prediction of events in the sector in question (Garrido – Pintado, et. al,
2018).
The Delphi method has a dialogical dynamic in which researchers have a mediating work (Ruiz Ispizúa,
1989; Gaitan and Piñuel, 1998)The studies carried out, as well as their results, through the Delphi method,
on the evolution from traditional marketing to digital marketing, according to Zambrano (2018) are of
great importance because companies can know what the current and potential customer thinks, based
on a market study that allows to create strategies that generate competitive advantages superior to the
competition. According to the results obtained by Zambrano (2018), creating a digital marketing strategy
is the success for many companies to have a greater and more effective impact. Interviews with digital
communication and marketing experts show the consequences of intangible management in new media.
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The management of intangible information helps differentiate and strengthen the brand and increase
the company’s reputation. (Villagra, N. Et. to 2015) In research using the Delphi method, Villagra (2015)
aims to deepen the new scenarios of brand communication through digital marketing strategies.
Considering the new situation that brands face in front of their audiences, business communication
is understood as a strategy of the company (Argenti, 2014). The methodology chosen in this research is
that of the Delphi Method, a method of decision analysis that originates in 1963, when two American
mathematicians (Norman Dalkey and Olaf Hermes) designed a technique with the purpose of reaching
consensus among experts on a future event. This method (a kind of expert research method) depends on
professional knowledge and the experience and judgment of the participants.
Since experts in certain fields are equipped with abundant knowledge, practical experience and
subjective judgments, the answers obtained from them are practical and effective (Tsai, et al., 2020).
In addition, with this method, the answers are organized scientifically, which allows consensus to be
reached on complicated issues as explained (Tsai, et al., 2020).
Delphi is characterized by being a method for structuring an effective communication process that
allows a group of individuals to reach a group consensus (Tsai, et al., 2020).
Currently, the Delphi method has evolved into a fundamental tool in the areas of forecasting, evalua-
tion and development of concepts/frameworks, when there is a need to incorporate subjective informa-
tion directly into evaluation models. (Zheng, et al., 2020)According to explain, (Zheng, et al., 2020), the
traditional Delphi method consists of six phases: (1) appointing a group facilitator to select a group of
experts based on the subject under consideration; (2) identify experts and assemble a panel of experts;
(3) define the problem and develop a questionnaire; (4) exchange ideas on alternatives through Round
1 questionnaires; (5) analyze, summarize and delimit alternatives through controlled feedback; and (6)
classify alternatives in subsequent rounds of questionnaires and reach closer consensus. At the end of
each round of questionnaires, all questionnaires are returned to the facilitator, who decides whether
another round is needed or whether the results are ready to support decision-making.
The rounds of the questionnaire can be repeated as many times as necessary to achieve a general sense
of consensus of research. As a subjective and qualitative method, the Delphi method produces reliable
results and draws unified conclusions from sufficient data, providing a solid basis for identifying key
indicators of research.
Within the framework of the Social Sciences, according to Flores (2011) cited in Huitraleo, Calisto,
Mansilla and Gutiérrez (2019) the Delphi method was considered “appropriate” for qualitative research,
even if the prospective approach was sought, which is why through this method it was proposed to unite
“a knowledge”, which was increased by the participation of the different specialists (Varela-Ruiz, Díaz-
Bravo and García-Durán, 2012, cited in Andrés et al., 2019), being this group of people, who will have
an opinion on a certain research objective (Gil-Gómez and Pascual-Ezama, 2012), in this way, reliability
will increase (Rosas, Sánchez and Chávez, 2012, quoted in George and Trujillo, 2018) and trustworthy
(Pozo, Gutiérrez and Rodríguez, 2007, quoted in Gutiérrez-Artacho and Olvera-Lobo, 2017), will re-
duce the intersubjective judgment (Reguant-Alvarez and Torrado-Fonseca, 2016), the autonomy of the
participants will be verified (Véliz, et al, 2013), using statistical procedures (Zartha, et al, 2015).
The characteristics that distinguish this technique from other group techniques are three: (i) anonymity,
(ii) having controlled feedback and (iii) group statistical response. In this way, the elements of the method
are: (i) the existence of an interventionable problem, (ii) a coordinating group (may be the principal
investigator alone or with a small group of persons) and (iii) A group of experts (a group of people with
appropriate knowledge, characteristics and experience for the achievement of the objective of the study).
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In consensus methods, experts are those who have knowledge and experience on the subject of study.
A good description of the defining characteristics of whom we consider an expert is necessary, similar
to the inclusion criteria of any study. (Romero-Collado, 2020).
Non-standardization and adherence to the Delphi methodology can jeopardize the validity of the
results obtained from its use in research. Researchers should consider the basic methodological aspects
that define the Delphi method to include in their work. (Quezada et al, 2020). After determining the
objectives of the study, the first task was to determine and choose the experts and academics belonging
to the digital marketing sector in fashion.
Data Sampling
For two weeks, in November 2020, researchers contacted different industry agents by phone and email.
The selection criterion was based, first, on the interviewee’s ability and secondly, it was intended to
give voice to all the new uncertainties raised in the fashion sector. The approximate duration of the pro-
cess and the use of the information anonymously was also reported. Researches motivated the answer,
explaining the potential benefits of the study not only for the academic environment, but also for the
professional field.
Following the round of previous contacts, a total of 15 private emails and messages invitations were
sent on social media profiles to the expert tha composed the panel. To this communication, 7 experts
responded so the final sample was reduced to 7 professionals and academics experts in digital marketing
in fashion, designed fashion, fashion history, fashion advertising, among others areas. After setting the
sample in December 2020, the first questionnaire was sent with an open question:
How do you think digital marketing is changing and will change the evolution of the fashion sector?”
I am currently conducting this research with a trajectory for five years to this day, but if there is one
thing, we are all clear about is that the situation in the past 2020 has turned around all the plans and
strategies established, or perhaps not, it is simply the opportunity to launch those ideas earlier than
expected. Therefore, it is very interesting to know the opinion and vision of professionals dedicated to
digital marketing, e-commerce, influencer marketing... of the fashion and luxury sector.
The first questionnaire was analyzed during January 2021 by the coordinating group finding con-
sensus for a second questionnaire in February 2021 consisting of open and valuation grade questions
explained below in results analysis.
Finally, in March 2021, all the necessary data were obtained to begin the analysis of results and be
able to reach conclusions, which will be detailed below despite being a fairly current topic that lacks
previous information within the fashion sector.
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Table 2. Delphi methodology questionnaire summary
Type of survey Month/Year Number of Participants Number of Answers

Open question November 2020 15 7
Quiz 1 December 2020 7 7
Quiz 2 January 2020 7 7
Source: The authors
ANALYSIS OF RESULTS
After having exposed the state in question, objectives and justifying the object of study, we are going to
present the most remarkable results:
As a result of the methodological process developed, it has been proven that digital marketing provides
viable opportunities for change in the fashion sector for brands to establish relationships with consumers
and promote brand loyalty.
Quiz 1: How Do You Think Digital Marketing is Changing and

will the Evolution of the Fashion Sector Change?”
Thanks to digitization and automation of digital marketing, marketing has been transformed, it allows
us to reach those who identify with our brand, our values, our designs, being able to impact each one
of them at the right time, in the right place., with the appropriate advertising format and the creative
message adapted to your communication style, which allows a more relevant and intelligent marketing.
On the one hand, 40% of the experts (3 of the 7 members), after their answers, come to the conclusion
that digital marketing allows brands and large firms to get to know better each of the people they are in
contact with. On the other hand, it helps them to understand them better, to know their aspirations, their
fears, their concerns, their desires, their beliefs, their evolution in ideas ... which allows them to create
and design products and services adapted to the target audience.
On the other hand, 60% of the experts (4 of the 7 members), consider that the digital environment
has allowed the creation of new forms of business: such as the sale of second hand, the subscription to
a brand to be able to have of a certain number of models per month, and the rent.
Quiz 2: According to the Research Topic, What Variables

Do You Think is Necessary to Take into Account, apart
from what has Already been Mentioned Above?
After the round of surveys corresponding to the research methodology, the experts consider that one
of the most important variables to take into account in the digitization of the fashion sector through
digital marketing is the type and quality of communication and the use of strategies related to influencer
marketing, therefore 70% (obtaining the following results: SEE TABLE 3) of the experts consider that
the sector is facing a process of change in the way of communicating after the Covid-19. Influencer
Marketing is a very important variable to take into account due to the influence in the sector. Today
many of the fashion brands use influencers to promote their product launches and this is automatically
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reflected in sales. This type of marketing is direct and precise and it achieves brand awareness, loyalty
and increased sales.
Table 3. Obtaining results after the questionnaires.
Strategic variable Result goal

10% of experts consider that the form of communication is what will determine an
Quality in digital communication.
effective customer service until customer loyalty online.
60% of experts consider that the use of new influencer marketing strategies will be
Influence Marketing
decisive to create brand positioning, visibility and attraction of new customers online.
5% of experts consider that the determining factor in new digital marketing strategies are
New shopping trends
new shopping trends such as renting or selling second-hand
The current new economic and social situation will be decisive for 25% of experts when it
Economic and social situation
comes to making new digital marketing strategies.
Source: The authors
What Grade would you Assign to the Following Variables?
After proposing the highlighted question in the questionnaire, the experts had to answer the according
to the degree of importance, with 1 being very unimportant, 2 not very important, 3 neither very impor-
tant nor not very important, 4 important and 5 very important (See Figure 1). Consumer adaptations to
online shopping, accelerated by the pandemic, are unlikely to end or slow after COVID-19 passes. In
this regard, the pandemic may be a trigger that induces the late majority, who have been reluctant to the
new way of buying, to finally buy online.
Figure 1. Degree of importance according to strategic variables.

Source: The authors
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As can be seen in Figure 1, all the variables have obtained a high degree of importance for most of
the experts participating in the research. It should be noted, on the one hand, the high importance of
almost 100% of the creation of online content by parts of fashion brands when developing the digital
marketing strategy and on the other hand, the slow and slight decrease in the degree of importance in
the development of offline strategies, which means that currently they are still necessary but that the
evolution to digital marketing must be mandatory and imminent on the part of companies.
What Motivations does a Fashion Company Find to Maintain, at

Present, after the Covid-19, the Maintenance of Physical Stores?
The previous study, after consensus with the professionals of the sector, of the experts finds that the
two dominant motivations to buy in physical stores instead of buying in online stores are immediate
possessions and social interactions, while online purchases tend to be substantially more convenient
and economical. However, they agree that online shopping offers greater flexibility in terms of time,
location and variety of products.
Although late users are likely to be more skeptical when evaluating new experiences, their online
shopping experience is likely to be a positive one as it provides a relatively safe way to shop during the
pandemic and is therefore likely to continue. shopping online even after the pandemic passes.
Despite creating and changing purely digital strategies and going through, as has been said before,
a health crisis after Covid-19 where they bet on online shopping and take advantage of the advantages
that this business model offers, 70% of the Respondents consider the continuity of physical stores by
brands and the creation of offline strategies to be very necessary.
Ultimately, brands have had to adapt their way of communicating, their business models, their strate-
gies and even the production of new products and collections.
The research has also been able to verify that the experts have been able to see that retail fashion is
undergoing a series of significant changes caused by: new digital marketing technologies, online sales
platforms, new forms of communication that allow reaching a young public and transmit the ethics of
sustainability. The pandemic will change not only the rhythm of daily life, but also consumption practices.
In addition, new business models, such as second-hand rental or sale platforms are increasingly
powerful thanks to the new challenges in the era of Covid-19 due to changes in work and lifestyles.
Digitization has also allowed the creation, as mentioned above, of apps for the sale of second-hand
garments, but which currently consumers seem to doubt in terms of hygiene despite being a model that
favors the sustainability of the sector.
What Digital Marketing Techniques are Important

to Consider in a Fashion Company?
As a result of the methodological process developed, it has been found that digital tools (See Table 4)
provide viable opportunities for brands to establish relationships with consumers and promote brand
loyalty.
Tools on social media and other online platforms provide real-time data on consumer behavior and
interests and how and where they spend time and money. Fashion brands get a high return on investment
through digital marketing.
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The SEO tool allows fashion brands to carry out their web positioning strategy and thereby detect
sales opportunities, through data analysis, keywords, etc., that users may be using.
The use of social networks in fashion: Instagram, Twitter and Facebook is one of the main digital
marketing strategies in the fashion sector, since in addition to generating quality content, it has also
allowed dialogue with consumers to be continuous to achieve loyalty from them, making it the most
important asset of a company. Fashion is the sector that invests the most in paid platforms for the creation
of online ads: Google Ads, Social Media Ads.
On the other hand, the role of the influencer in fashion brands refers to the attraction of fans since
the success of these figures allows them to attract consumers who are reflected in a specific identity and
personality. This social characteristic of the industry makes the digital environment and the social media
environment the ideal space for users to share their tastes and interests in fashion.
Table 4. Main techniques of digital marketing in fashion
Marketing Technology Description

It is the process of maximizing the number of visitors to a website, ensuring that the website appears
SEO
in the top search list.
In the fashion sector, e-commerce through Google Ads, provides an increase in sales that can grow the
SEM
web business
Instagram This social network helps to increase search engine optimization, which increases brand awareness.
It is used to provide products and fashion brands, but also, to establish relationships and create
Facebook
communities of followers.
It is one of the social networks most used by fashion brands, since it has the ability to achieve dynamic
Twitter
communication between the brand and the consumer.
It enables companies to create and maintain long-term relationships with existing clients while
CRM
optimizing corporate performance with the organization.
QR Code It is a technology that simplifies the purchase process by scanning the QR code on mobile devices
Fashion brands can use data analytics. It is the science that allows information to be extracted and can
Big Data reveal trends and metrics to influence current affairs and thus plan future content in the narrative of
communication with consumers
Influencer Influencers are the people who show social media trends directly to consumers
Source: The authors
DISCUSSION
As mentioned above, digital marketing and its evolution in the fashion sector after Covid-19 is a very
recent research topic which does not allow us to recapitulate enough data at present, but it will allow
us to observe how the Companies and consumers are transformed to new shopping behaviors and how
new strategies such as sustainable fashion production can help to favor digital marketing and commu-
nication tools in the fashion sector. From now on, not only marketers will observe their customers and
their behavior, but it will also be consumers who will observe companies to see how they adapt to new
circumstances and new ways of producing their products.
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According to Castaño and Jurado (2016), the arrival of the online generated a change in the way we
communicate and interact. Digital marketing has emerged as a solution to new technologies and a new
way of using and taking advantage of the internet.
The Covid-19 pandemic has not only been a challenge for public health, but also for the world’s
socioeconomic system. 28% of GDP depends on SMEs (Applied Economics, 2019), therefore, it is
important that they have a presence on the internet and continue to be in force and survive in this crisis.
(Hoyos-Estrada and Sastoque-Gómez, 2020)
Furthermore, it can be seen from the research results that a significant part of late users who were
opposed to buying online entered e-marketplaces after COVID-19. Among those surveyed, 11% of Gen
Z (Gen Z), 10% of Millennials and 12% of Gen X (Gen X), and 5% of Boomers have bought some-
thing online for the first time because of to the pandemic. (Kim, 2020). Fashion companies and digital
marketing professionals should be attentive to the purchasing behaviors of new audiences from now on
since their digital marketing strategy will depend on the demands of online shopping and the way these
audiences communicate.
Authors such as Brydest T, Retamar M. and Hanlon M., (2020) talk about whether Covid-19 and
digitization will and will allow a transition to a more sustainable environment in the fashion industry.
By pursuing economic and commercial objectives (Keller et al., 2017), companies can achieve a change
in user behavior, or behavior modification (Zuboff, 2019). Therefore, we will have to be attentive for
future research as sustainability will be part of both the production strategy and the digital marketing
strategy for fashion companies when communicating their products.
Adriana Domínguez, executive president of Adolfo Domínguez, anticipated an anomalous 2020
exercise for Europapress.es in July 2020 as the coronavirus crisis caused the closure of 390 stores in
22 countries around the world, which led to the launch of a contingency plan to replace stores with the
closure of those that were not profitable and create a new profitability plan that was increased by 30%
thanks to the fact that online sales increased 158% in this period.
On the other hand, a problem that derives from virtual commerce is detected since the lack of trust on
the part of users, many times discourages the consumer from making a purchase. “Storytelling techniques
and quality content from fashion brands can persuade consumers and present reasons to buy products
based on consumer conversations about what they want.” (Dreidame, 2018).
Consumer buying behavior or decision making is a final decision of the customer, which takes it
based on the impact produced on the purchase stimulus. The seller plays an important role in inducing
the customer through external marketing efforts through product, price, location, or promotion. But as
Udayangani Rathnayaka (2018) says “consumer behavior in the digital age has changed to a digital
culture where they have become more informative, therefore, it has become a great challenge for com-
panies, to retain customers for a long period of time”.
One of the experts interviewed indicated: “On the one hand, thanks to digitization and the automa-
tion of digital marketing, marketing has been transformed, it allows us to reach those who identify with
our brand, our values, our designs, being able to impact each one of them at the right time, in the right
place, with the right advertising format and the creative message adapted to your communication style,
which allows a more intelligent and relevant marketing. Digital marketing nowadays allows us to reach
quality people and not only reach people, that is, quality vs quantity prevails)”.
Although digital marketing and content personalization make it possible to quantitatively evaluate
the effectiveness of advertising campaigns and social ads in digital environments, they also lead to the
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emergence of new trends in fake news and abusive activities related to advertising (Lies, 2019; Liu and
Terzi, 2010)
Content personalization, that is, segmented targeting based on data-based models and the testing of
new market segments, includes actions focused on the analysis of user data applying innovation models
that study their actions online (He, Bahirat, Knijenburg and Menon, 2020). In this regard, another of our
interviewees pointed out: “Digital marketing and digitization, technology, bring firms and brands much
closer to users. Digital means: globalization, capillarization (it reaches any point that has an internet
connection), freedom of decisions for the people who use it, multi-connection, experiential moments,
such as 3D immersions, which activate different senses through sound, 360º image and video; It allows
activating the imagination of those who find or are interested in our brand, it allows us to strengthen
ties with them, it allows a more fluid communication. It is infinite because it is in constant movement
and innovation. Nowadays you can attend a parade without having to travel, you can get to know the
backstage, the workshop where the designs are created “
In summary, based on the results of this study, we formulate the following agenda for future research
on the behavior of digital marketing in the fashion sector (See Table 5).
Table 5. Research questions
Research areas Authors Research Questions

• How should the changes be when communicating with users in the
Kannappan (2020)
digital age?
Kantar Indonesia. • What changes in consumer behavior does digital marketing produce in
Digital Marketing
(2020) users?
Čiarnienė and Vienažindienė. • What are the digital marketing strategies that fashion companies should
(2014) adopt today?
Balram. • What is the margin of adaptation of digital marketing in the fashion
(2020) sector in a next pandemic?
TradeGala.
Covid-19 • How has Covid-19 impacted on the fashion sector?
(2020)
Thornton.
• Is the maintenance of physical stores still profitable?
(2021)
• Until when will influencer marketing be profitable for fashion
Aguilera y Baños. companies?
Influence (2016) • Will influencer marketing create a profitable business model for the
Marketing fashion industry in the future?
Stelzner. • Will influencer marketing make traditional marketing strategies
(2015) disappear?
Source: The authors
5.1. Research Propositions
In order to guide future research in this area, we formulated several research proposals based on our
results. The research proposals are aligned with the current digital marketing categories shown in Table
2, which are the results of the literature review and the framework consulted to establish the theoretical
foundations of the research. Therefore, considering the fact that the fashion industry is one of the global
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industries in the world where several processes are interconnected with each other to satisfy end users.
Also, Aiarnien and Vienna-indien (2014) pointed out that fast fashion is customer-driven, where retailers
have to change according to the changing demands, desires, desires and needs of consumers. Change is
inevitable when fashion brands must develop their leadership qualities by being agile. This inevitable
change can be divided into smaller versions to understand the complexities involved. Thus, based on the
complexities you can make the best decision to achieve the results in a simple way.
Proposal 1. The increase in online sales has led to a massive change in consumer behavior, as con-
sumers now have new shopping facilities and payment methods.
The Covid-19 has caused fashion companies to redo their marketing strategies to orient their compa-
nies to the digital age. Fashion brands planned strategically pretty well earlier this year 2020 and started
production, but due to COVID-19, they struggle to boost inventories until October, he says (Balram,
2020). In addition to existing problems, the COVID-19 pandemic is a major challenge for fast fashion
brands because stocks at outlets have become old and faded from their originality. Therefore, the de-
struction of old stocks and the planning of a new production is hectic. However, the result will be win
or lose (TradeGala, 2020).
Proposal 2. Covid-19 has caused fashion companies to redo their marketing strategies to guide their
companies to the digital age.
It is proof that social media has changed the way consumers buy, think and act. The main consequence
of this process of profound changes, to which the traditional marketing paradigm has been subjected,
is the emergence of a completely new consumer, an enormously empowered consumer who decides
what he wants to buy, how, when and where to buy it, how and for what do you want to use it and what
type of service do you want to receive; And when he loses confidence, he abandons it and changes it.
(Aguilera and Baños, 2016).
Proposal 3. Increased brand awareness and customer engagement is a direct and indisputable con-
sequence of using social media.
CONCLUSION
This research has proposed the study of how brands in the fashion industry are redesigning their busi-
ness models and their marketing departments after Covid-19 and digitization through new technologies
and digital marketing. The methodology developed has been an overview through the participation of
experts from the fashion and digital marketing sector using the Delphi Method.
According to the research question proposed (RQ1: What are the main digital marketing techniques
used in the fashion industry?) fashion companies, today, continue to use the same digital marketing
strategies to attack new consumers and communicate their products online. A large investment is still
being made in SEO and SEM techniques. However, it is increasingly common, since the appearance of
Covid-19, that fashion companies invest more budget in influencer marketing, since it has been shown
that it is one of the strategies that best reaches the consumer in the purchase process, since the form
of communication is changing. According to RQ2 (What is the influence of Digital Marketing in the
evolution of the fashion sector in terms of the development of digital business models?) fashion brans
are taking care of the image and communication in the media facing the audience can be a turning point
for brands and thus declare a new beginning as well as invest a greater percentage of budgets in new
business models related to marketing digital (Čiarnienė and Vienažindienė, 2014).
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Currently, fashion companies are redoing their marketing plans based on the daily observation they
make of consumers and their purchasing behaviors after the appearance of Covid-19, trying to understand
what are the changes that have arisen in the sector and how they will evolve. over time.
After the first round of the study using the Delphi Method, the experts believe that we must not only talk
about the influence and impact of Digital Marketing in the fashion sector, but also about digitization in
general. Digitization and digital marketing have made it possible to integrate the virtual world into the
real world. Both are like gears on a Swiss watch that fit together perfectly.
Digital marketing is nothing more than marketing, accompanied by technology, with the advantage
that said technology advances rapidly. Likewise, the internet and digital marketing create a point of union
between brands and customers, a world virtual connection, where thanks to technology this virtual world
can be integrated into the real world.
As always, changing cycles with media coverage, it remains to be seen whether the pandemic will
have a lasting effect or whether the fashion industry will move on to the next crisis, leaving behind the
lessons learned so far.
It is clear that fashion faces a very uncertain future, what is perhaps also clearer is that the industry
of tomorrow looks very different from the one we once knew. Future research could examine what this
change will look like when implementing digital strategies and definitive new technologies, following
the health crisis after Covid-19 in the fashion industry.
Implications for Marketers
The first thing that a company in the fashion sector must identify is what its current target audience is
and how it is evolving after the boom in online shopping, since its investment in digital marketing will
depend on it and what kind of tools it should use to carry out viable strategies. Second, users want to
feel identified with the content generated by fashion companies as this will provoke positive opinions
from users influencing the purchase decision of new ones, to generate greater confidence in the face of
the current mistrust due to the pandemic. With all this, communication agencies will be able to identify
the digital marketing strategy to retain new users and give rise to a new way of totally two-way com-
munication.
Marketers should be able to analyze and forecast fashion trends before customers switch to competi-
tion, so that the fashion professional becomes a trendsetter and digital media becomes the best platform
for acquire and convert customers through the measurement and optimization of digital media in order
to satisfy customers to retain them loyal for life (Kamba and Rahman, 2017)
The results could be used to generate studies of new business models in the fashion industry after Co-
vid-19 and the implementation of new digital marketing strategies adopted by fashion brands. In this
way, experts will be able to better understand which way should be taken to develop digital marketing
strategies in their companies.
205

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Digital Fashion Industry: Digital ecosystem of fashion companies in which strategies focused on
online marketing are carried out.
Digital Marketing: Main communication, commercial and sales strategies promoted on the Internet.
Fashion Digital Experts: Experts in fashion strategies developed in the digital ecosystem.
Influencers: Opinion leaders in social networks with millions of followers.
Social Media: Communication channels to increase engagement between companies and users.
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Section 3
Case Studies: Advances in
Digital Marketing Strategies
Applied to Industries
This section presents case studies and future research lines for the digital marketing sector focused on
data analytics. The case studies encompass approaches to different business industries to, finally, show
the pros and cons of developing digital strategies according to the characteristics of each case.
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Chapter 12
How Digitalisation Is
Influencing Traditional Food
Restaurants in the Management
of Their Marketing Strategies
Juan-Gabriel Martínez-Navalón
ABSTRACT
There is no denying that digitalisation is a new revolution. At a time when technology is taking over all
aspects of society in an exponential way, not introducing it into the management of companies is a clear
mistake that could lead to their disappearance. But we cannot forget that the incorporation of technology
is a challenge for any type of company and sector. This study analyses the importance of digitalisation
in traditional restaurants and its influence on the management of marketing policies using case study
analysis. Therefore, the aim of this study is to show how digitalisation for order management in these
companies can also be applied to marketing policies. This is because, by monitoring and storing the
customer’s behaviour in the ordering process from start to finish, it is possible to know their preferences
and searches in a more concrete way. Such information enables the company to maximise its resources by
applying policies designed to meet customer needs, as well as to design targeted advertising to increase
the chances of success of the advertising campaign.
INTRODUCTION
More and more companies in the hospitality sector are opting to innovate in their order management
systems (Modica et al., 2020). Such technology implementation not only helps to increase productivity,
efficiency and control over the business’ stock, but also allows data to be collected when customers
place their orders (Line et al., 2020).
The systems applied for order management and data collection are diverse. The diversity is due to
the multitude of characteristics that the establishment may have when implementing digital order chain
DOI: 10.4018/978-1-7998-8003-5.ch012

How Digitalisation Is Influencing Traditional Food Restaurants in the Management
systems. It is influenced by the investment budget for the implementation of the technology, type of busi-
ness, location, capacity, income, etc. As well as more staff-focused ones, which are sometimes decisive,
especially in family-run establishments where staff turnover is low. In this case the characteristics to
take into account are the age, aversion to technology or the technological knowledge of the staff. It is
important to bear in mind that these systems were implemented by large multinationals, but nowadays
small establishments have also computerised their order management, in a very high proportion.
The digital revolution had already reached the hospitality sector at all levels in the second decade
of the 20th century. To a greater or lesser extent, we began to hear that the survival of establishments
no longer depended solely on prestige, quality or price, among others, but that technology was here to
stay and to be an almost indispensable element for the viability of any hotel establishment (Filimonau
& Naumova, 2020).
Implementation of software, cash drawers, PDAs (Personal Digital Assistant), digital letters, online
ordering systems, etc. They allowed greater control of the business and increased its profitability, which
in the future could determine viability, but was not entirely decisive. Everything changed when in early
2020 the Covid-19 global health crisis exploded (Belzunegui-Eraso and Erro-Garcés, 2020; Hou et al.,
2020; Oliver et al., 2020). In Spain, as in the rest of the countries in the world, the pandemic has forced
companies and people to change their living and working habits, since March 2020 a state of alarm
has been declared and most of the establishments have been temporarily closed (Pedauga et al., 2021;
Pérez-Calle et al., 2020). In the case of companies that could opt for teleworking, they have continued
to operate remotely. This change has had an economic impact on companies. Quarantines, restrictions
and new regulations have brought about a turning point in the digitisation of the hospitality sector.
Digitisation is no longer an alternative to be considered only to increase profitability and improve
management control. The pandemic has forced an implementation of digitization (Skulmowski & Rey,
2020). Health regulations and the collective fear of using physical letters, the closure of establishments
and their adaptation to take-away food or the reduction of capacity. It has made digitalisation absolutely
necessary for the survival of hospitality businesses (Schaffer et al., 2021).
The technological revolution has accelerated and has been a floater for many companies, which have
been clinging to it with great effort. In the case of large franchises, they have used this change to imple-
ment new, more disruptive systems that would have been criticised at other times but are now seen as
positive by the customer(Hobbs, 2021). This is the case of McDonals, which has eliminated the McAuto
order-taking staff and implemented an artificial intelligence system to take orders. It is also common
for most franchised fastfood outlets to have virtually eliminated checkout staff and replaced them with
digital ordering systems.
This technological implementation mainly in the order channel is also exploited by company man-
agers. The electronic management of customer orders provides a series of information that can be very
important for the marketing department. The information obtained will make it possible to gather relevant
information for marketing policies. Information such as target customer classification, payment method
used, order schedules, location from where the order is placed, hot spots on the menu, etc. (Prado-Román
& Nebreda, 2018).
This chapter will show how the technological revolution in the hospitality sector is allowing not
only to improve and optimise the ordering system in restaurants, but also it makes possible thanks to
the collection of information, to design better marketing strategies that are better adapted to the needs
of customers.
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It is important to note that this study provides an insight into the case of a traditional restaurant and
its digitalisation process, which has been considerably accelerated by the COVID-19 pandemic. But this
process is not new for pandemic reasons, as digitisation in the hospitality sector has been growing in
recent years. There are many studies that show how information can be obtained and how to analyse it
in order to make a profit from business management and marketing strategies (Saura, 2020).
This analysis of the case of the Rincón Manchego Restaurant is useful as an example when consider-
ing digitalisation in traditional restaurants with manual management habits and few digitalisation. This
analysis shows that this economic investment, which a priori can be a barrier for many small businesses,
is highly profitable when the whole system is managed efficiently.
Therefore, the study will is developed. First part is the literature review, where the importance of a
good order management and the collection of all the data generated in it will be discussed. This literature
review also analyses the importance of security in data collection. Subsequently, I proceed to explain
the methodology applied in this study, which will be developed in the case study method of the Rincón
Manchego Restaurant. Finally, in the conclusions section, the conclusions obtained in the study are de-
veloped, and different proposals are put forward in terms of managerial implications. The conclusions
section also discusses the limitations of the study and possible future lines of research. The future lines
of research proposed reflect the evolution of digitalisation within the hospitality sector and the need to
analyse all these changes.
DIGITISATION OF THE ORDER CHAIN
The digitisation in the hospitality industry of its order chain has not only made the work of the staff
easier, but also increases the quality of customer service. Various technological improvements can be
found around the order chain of an establishment. From software that manages orders and payment,
mobile apps that allow customers to order without the physical presence of company staff or computer
systems that manage the kitchen and provide instant orders (Kubicki et al., 2013).
Consumers no longer just want speed, good service and quality. Most consumers in the 21st century
are looking for a different, totally personalised dining experience. This desire is partly satiated by the
technological advances that restaurants possess (Martínez-Navalón et al., 2020).
An example of digital transformation are the strategies being carried out by wholesale companies in
the hospitality sector such as Mahou-San Miguel, Makro or Campofrío. These companies have plans to
support technology transformation in the hospitality sector. Both companies give training courses for
the implementation of technology, advise restaurants on their digitalisation, give courses on the use of
social networks, etc. This is because they are aware of the needs of their customers (the restaurants).
Failure to adapt to this digital revolution would lead to a drop in sales or, in a more extreme form, to the
disappearance of restaurants. This would have a direct impact on their suppliers.
The order management process begins with the customer searching for the restaurant in the different
mobile applications. Some of the most widely used are El Tenedor, Tripadvisor or GoogleMaps. Where
it is possible to find information about the establishments, their menus, prices, opening hours. These
changes in habits are causing the use of this type of technology associated with smartphones to force
restaurants to digitise to a certain extent. Not appearing in these search engines reduces the number of
new customers by almost half (Gelashvili et al., 2021).
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Once the customer has visualised the establishment and decides to try the food, he/she has two op-
tions. Eating on the premises or ordering take-away food. The latter option has increased considerably in
restaurants during and after the Covid-19 pandemic. This second point in the order chain is also mostly
handled by digitised systems. These manage bookings or orders, facilitating management and avoiding
possible human errors.
Once the customer is in the shop, another process within the order chain begins. In this case, the
visualisation of the products. Due to Covid-19 regulations, this visualisation is mostly done digitally
to avoid having an object that passes from customer to customer. Some establishments have chosen to
upload the menu on a generic mobile application for the hotel and catering industry, others have decided
to digitise the menu on their own website or on their own mobile application. What they all have in com-
mon is that the customer can access by means of a BIDI code (QR reader). The possibilities are wide
and varied due to the great technological advances that the establishments are using.
The third step in order management is the placing of the order or taking of the order. In this step,
differentiations must be made due to the characteristics of the establishment. In the case of fast food
restaurants with highly standardised products, with very little in-store processing, self-ordering systems
are often implemented. Brands such as McDonalds, Burger King, Carl’s Jr or Five Guys have opted for
this system where they provide the customer with an interactive screen with the entire menu and where
the customer can place their order.
It should be noted that this self-ordering system can also be carried out using mobile applications
that allow the same process to be carried out as on the interactive screen, but from our mobile phone.
This same application usually also allows the ordering of food at home, so that from a single system we
can manage the entire ordering process of an establishment.
In the case of restaurants with more elaborate menus, where the customer not only wants to eat, but
also wants to enjoy the moment by living a unique experience. An ordering system is usually chosen
where the customer-waiter relationship is not lost. The customer usually no longer has the option of
placing the order alone, but is advised by an employee. In this case, digitalisation is sought to help the
establishment in its management, but without losing contact and affinity with the customer. This process
is mostly carried out with a PDA on the table itself. Due to the great speed it provides, although there is
also the option of taking the order with a notebook and then entering it into the programme via a touch
screen monitor used by several waiters. The latter option is less widely used because of the potential for
errors that can be generated by duplicating the process, but some establishments use it because it is less
expensive to install a computer for several waiters than one PDA per waiter.
Once the order is placed, the order is distributed to the different processing stations in the restaurant.
The products ordered are produced there. With the advantage that failure due to miscommunication when
placing the order is practically non-existent.
The last process in the order chain is the final checkout and payment. A process that without digi-
tisation has two main problems. Possible errors in transcribing and adding up the amounts during the
preparation of the account and the delay in this process, which can sometimes be lengthy and complex.
It is proven that the digitisation of the order process increase the company’s profits. There are two
main reasons for this increase in profits. The first reason is the reduction in the time taken to carry out
the different processes, which increases staff productivity. By reducing process times, the restaurant
increases customer turnover per service. The second is that by having all order management digitised,
errors in the production of products practically disappear. There is no interference or misunderstandings
from the time the order is placed until it is processed.
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Taking all of the above into account, the implementation of digitalisation in the ordering system is
not only beneficial for the company, but necessary for its survival. The reduction of time, the improve-
ment in the rotation of tables, the reduction of errors, the control of stock, in short, the implementation
of digitalisation systems in the ordering system provides an increase in profits in hospitality businesses.
But there is more: by digitising all these processes, very relevant information is being generated for
the company. The digitisation of the ordering process allows the collection of data that is generated dur-
ing the ordering process. Data such as table turnover time, peak sales hours, types of products sold in
time slots, customer preferences, etc. This is very valuable data for the management of the company and
specifically we can use this data to design the digital marketing policies of hospitality establishments
(Ribeiro-Navarrete et al., 2021).
DATABASE GENERATION IN THE HOSPITALITY INDUSTRY
Nowadays, data-driven digital marketing strategies are widely used by companies to reach customers
more effectively (Jose Ramon Saura, 2020) It is a digital evolution of relationship marketing, but with
the same premises: listening and giving the customer what they want (Reyes-Menendez et al., 2019).
In this case, in order to choose the appropriate strategy or strategies, we must first know the charac-
teristics of our clients (Cross et al., 2009). In the hospitality sector, the customer-to-income ratio is much
higher than in other sectors, as the percentage of customers who enter the establishment and consume is
very high. This relationship allows us to analyse almost in its entirety what the customer demands and
how he demands it (Fernandes et al., 2021).
The content generated by customers via the order chain has been steadily increasing since the imple-
mentation of the first hospitality management software. This information gathering in the initial hospi-
tality sector has been joined by the generation of social media content, booking applications, payment
applications, etc. (Dossena & Mochi, 2020).
It is important to know how to sell the product the customer needs at the right price and the right
time (Kimes, 1999). To this end, it has become essential to develop forecasts to help guide managers,
which is why the collection, management and processing of data has become essential in the hospitality
sector. Software such as HOSTELCO, HOSTELTACTIL, BDP, LOYVERSE, etc, allow not only to see
purchase data, table service times, type of customer, invoicing per customer, best-selling products, etc,
not only in a numerical form, but also provide graphics and visual panels that improve interpretation
and allow information management in a quick way (Fernandes et al., 2021).
The type of restaurant and its characteristics must be analysed beforehand. This allows the data col-
lection to be adapted according to the type of customer, as the target customer changes (Wu et al., 2020).
Any adaptation will allow for different data collection, due to the variety of the service. For example,
in a fast food restaurant it collects navigation information via the digital display at the time of ordering,
or the duration of ordering. In the case of a restaurant of a standard category, the way the reservation is
made, the food consumed, the rotation time, etc. are measured (Medeiros & Salay, 2014).
This large amount of collected data is called “Big Data” (Mayer-Schönberger & Cukier, 2013). This
vast amount of information provides a powerful source of knowledge that is studied for patterns in
customer interactions (Lim et al., 2019). Such well processed information helps the company to decide
how to engage with its customers more effectively and to identify the most valuable customers. They can
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spend more time on them and thus generate higher cash flows. This has been demonstrated by several
studies that show that companies that use Big Data are more productive and profitable (Line et al., 2020).
Data collection and processing is seen as a new source of competitive advantage (Kunz et al., 2017).
For the collection and storage of data to obtain information comes to create significant value not only
for the company, but also for the global economy. It improves productivity and therefore the competi-
tiveness of the company, generating a substantial economic surplus. But it is important to clarify that
these data alone do not have a value on their own. Data must be associated with a commercial context
(Line et al., 2020). In this study where the importance of the data collected in the restaurant order chain
is analysed, the value generated can be perceived through a range of marketing strategies: advertising,
market research, merchandising, customer relations, resale, etc.
For example, the study conducted by (Li et al., 2018) demonstrates that data has value when it is
used to guide a digital social media advertising strategy (Saura et al., 2021). It allows to companies to
know customers’ preferences, food tastes, time of day when they order, etc. The data ensures that the
advertising message is correct according to the tastes and needs of the customers, as well as it provides
correct time of publication.
Such data can also be used to effectively manage companies’ integrated management systems, as well
as customer management platforms (Talón-Ballestero et al., 2018). Data allows us to allocate resources
in restaurants more efficiently, reducing costs. For example, if we have a sales history, we can prepare
more or less food, or reinforce the services with more staff for a better service. As well as knowing what
to offer to which customers. This will improve the economic performance of the restaurant (Fernandes
et al., 2021).
But data collection and processing should not be seen as something negative for the customer. Value
creation reciprocally affects the customer and the company. The interaction between company and cus-
tomer means that the company knows what the customer wants and can therefore offer it to him, while
the customer will more easily find what he wants (Ramaswamy & Ozcan, 2018).
But just because data collection is beneficial to the customer does not mean that the customer sees
it. It is important to bear in mind that it is necessary to know how to manage data collection, not only
from the point of view of data management for subsequent use, but also from the point of view of user
privacy when managing their data. (Saura et al., 2021). Such data collection may be viewed negatively
by the user and may affect product satisfaction. An order chain that asks for personal data, such as name,
telephone number, etc., can lead to rejection by the user. The company must study whether the data
obtained and the profitability that will be obtained from it, compensates for possible customer anger.
As more and more customers are becoming wary of their privacy, companies need to be clear about
what kind of information they want to collect. It is common for fully digitalised order chains to suffer
high abandonment rates at the time of registration and payment of the order (Saura, Ribeiro-Soriano, et
al., 2021). Mostly because they do not want to register by giving their details.
On the other hand, it may be the case that customers are not aware of such data collection, because
the design of the software architecture and digital designs used are often very well thought out, making
such data collection virtually unintentional. Therefore, it is important for companies to implement ethi-
cal and responsible strategies in terms of data collection for further processing (Pangrazio & Selwyn,
2019). This should be one of the priorities for companies.
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METHODOLOGY
The methodology used in this research work is the case method. This is a tool constantly used in social
science studies where the aim is to analyse a problem by means of a real case. This method develops a
high linkage between the literature studied and presented and the results of the study. Also,it provides
answers to such important questions as “why” and “how”, mostly harbouring a casual mechanism.
This methodology will attempt to verify the hypothesis on the relationship between the digitalisation
of the traditional restaurant order chain and the improvement of digital marketing strategies.
The choice of this methodology is motivated by the strength of this methodology for analysing
projects or management chains in an efficient way and answering the questions set by the researchers
in their initial objectives. This methodology allows for flexibility in the study, which allows for a more
personalised analysis of each case. (Rasnacis & Berzisa, 2017).
THE CASE OF THE RESTAURANT RINCÓN MANCHEGO
In this work, a methodological analysis of the case of the Restaurant Rincón Manchego has been car-
ried out. In order to further study the management of the digitalisation of the order chain and how this
helps the implementation of marketing strategies in restaurants that do not belong to franchises or large
hospitality chains.
The choice of Restaurant Rincón Manchego is motivated by the fact that it is a restaurant that has
been progressively implementing the digitalisation of its production chain since 2016 and that, together
with other resources for obtaining information, has enabled it to design marketing strategies that are
more effective.
Rincón Manchego is a restaurant located since 1970 in the historic town of Chinchilla de Montear-
agón in the province of Albacete. Currently (as of May 2021) it has 14 employees, is a small company
size (based on number of employees and total assets) and has been operating in the market for 52 years
without interruption. It specialises in traditional Manchegan food and has become a reference point at
provincial level both for its cuisine and for its careful Manchegan aesthetics. The building emulates an
old Castilian inn with a wooden balcony on the outside and rooms decorated with a multitude of histori-
cal elements from the last century that are indigenous to the area.
In 2015, the restaurant’s management decided to implement a fully digitalised ordering system to solve
some of its problems and to be able to implement new product lines demanded by some of its customers.
The main motivation came about because the establishment has a menu with some 220 items, most
of which are 100% prepared in the kitchen. Dishes such as Ajo de Mataero, Atascaburras, Migas Ruleras
or Patatas Bravas Rincón Manchego are highly elaborated and take a long time to serve. The aim was
also to manage table service more efficiently by trying to reduce the time taken for beverage service and
final payment service. It is the latter that generated the most complaints from customers, as the average
time to settle the bill and manage the collection was 10 minutes. Apart from other problems such as the
anxiety generated in the kitchen staff or the numerous mistakes in the ordering process in the kitchen, the
management also wanted to implement a new line of business. his new line was based on the production
of products to be consumed at home.
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Figure 1. Front of the restaurant Rincón Manchego

Implementation of Digitalisation in the Ordering

Process of the Rincón Manchego Restaurant
The digitisation process of the ordering process was not easy for the Rincón Manchego restaurant as it
had several problems to solve before the implementation. All their order management was practically
manual, except for the last one where the account was done with a cash register, the whole process was
done in a traditional way. The same waiter served the table from start to finish without any other waiter
entering his service. The order was written down in a notebook with a pen and sung loudly in the kitchen.
With the disadvantage of having several colleagues doing the same process and with a menu that had
numerous references.
The first process was to modify the entire service and kitchen management structure. To adapt it as
much as possible to the structure that the order management would have once it was digitised.
The first process was to modify the entire service and kitchen management structure. To adapt it as
much as possible to the structure that the order management would have once it was digitised. It was
decided to do the process in two steps so as not to create a collapse. First of all, we started working with
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tracing paper, where the waiter would no longer say the order by voice, but would leave a note. Kitchen
management changed. Before, each member of the kitchen had their own position and worked indepen-
dently, now there would be a person in charge who would call out all the orders in order of arrival. And
the entire kitchen team would work in synchronisation as the person in charge would be giving orders.
With the tracing process, time was saved as the waiters did not have to repeat the same order numer-
ous times. This change allowed them to dedicate this time to providing a better service and being able
to attend to more customers. But more importantly, the main problem for the digitisation of the ordering
process had been solved.
Once the fully digitalised order management system has been implemented in the restaurant. Further
changes had to be made in the restaurant’s personnel management, but this time in the dining room ser-
vice. In this case, the functions of several waiters had to be changed. This would change from the same
employee managing the table from start to finish, to having the beverage service and the cashiering
service performed by another employee.
In the digitalisation process, waiters were equipped with PDAs and printers were installed to print
out orders in the kitchen as well as in the preparation of drinks. From that moment on, the waiter takes
note of the order with the PDA and sends the information to three places automatically: to the central
computer, which is responsible for storing the data and what the diners are consuming; to the kitchen,
which sends all the kitchen references and to the beverage preparation section, which sends the bever-
ages. While the employee continues to wait on other tables, other wait staff serve drinks and the kitchen
staff prepare the dishes.
The modifications to personnel management and digitisation of the order process started to show
positive results from the first day it was installed. Errors due to problems in the communication chan-
nel between the dining room and the kitchen were almost completely eliminated. The average time to
charge customers was reduced to around 8 minutes per ticket. The control over the products sold and
collected is virtually complete, avoiding losses due to lost products. However, the most striking fact for
the restaurant’s management was that the turnover of customers in the lunch and dinner services increased
by almost 50%. By digitising the entire service, service downtime was almost completely eliminated,
which meant that more customers could be served as their time in the shop was considerably reduced.
The digitalisation of order management also opened up a new line of business. The installation of
order management software also made it easier to manage orders to be picked up at the establishment
and consumed at home. The high demand for this service could be met thanks to the digitisation de-
scribed above.
The implementation of the digitised ordering system not only improved the restaurant’s production
chain, but also substantially improved different areas of the family business. Being able to access impor-
tant information about the restaurant at the touch of a button made management tasks much easier. The
accounting and finance area reduced its working time as much of the company’s financial information
was collected and sorted on a daily basis using the management software. Office work was eliminated
as customer invoices were now done on the spot. The purchasing department knew at all times which
products had been sold and therefore facilitated warehouse control. In the case of marketing management
this changed drastically, having relevant information about, demands, times, products consumed, time
schedules, etc. It caused marketing strategies to change and adapt.
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Figure 2. Evolution of the digitised order chain.

Source: own elaboration based on (Prado-Román & Nebreda, 2018)
Digital Marketing Strategies in the restaurant Rincón Manchego
Improving the restaurant’s marketing strategies was not seen as one of the driving forces behind the
digitisation of the order chain. But when it was realised that the information collected through the man-
agement software was relevant to this issue, everything changed.
The digitalisation of the restaurant made it possible to obtain various data: Products sold, sales time
slots, associated products, means of payment, etc. This, together with the information that could be ob-
tained from social networks, booking channels, the establishment’s website and Google Business, was
a valuable source of value. But it was not until the end of 2019 that this data was used to manage the
restaurant’s marketing strategies.
Strategies became mostly digital. Advertising in the press, tourist magazines, travel guides, were
suppressed. Radio advertising was reduced to a minimum, only contracting advertising on very special
occasions. Contributions to associations, sports clubs, etc. were also reduced to a minimum. The strategies
were modified and implemented on the basis of the information gathered from the different data sources.
This data is also collected from online booking sites, e-WOM portals and the restaurant’s own
website. Data collection in the latter has increased since the start of the pandemic. Due to the need
to eliminate physical menus, it was decided to digitise the menu and the menu of the day and install
QR codes throughout the restaurant for customers to enter the website. Visits to the website increased
substantially compared to the previous year’s results. This will lead to a substantial improvement in the
natural positioning in Google.
Social media strategy is also supported by data-driven information. Publications are designed taking
into account the type of product sold in each time slot or day of the week. The data itself obtained hours
after from the publication shows how the publications trigger a desire to buy in customers. As part of
the same social media communication strategy, financial support is given at specific times to some of
the publications so that they can be more widely disseminated. Such support is focused by segmenting
the population, taking into account which population group we want to target. The media strategy has
220

also changed. Now they do not hire traditional advertisements to promote themselves, but participate in
a different way in various radio and television programmes.
The results of the digitisation of the order chain and its influence on digital marketing policies have
been very positive for this company. It is worth to point out that its adaptation to the pandemic and its
survival is thanks in part to the versatility that digitisation gives it. The restaurant’s staff has increased
by 20% since 2016. They have been able to increase the number of items on the menu without being
disruptive. Their communication policies are much more effective and more economical. The position-
ing on tourism websites, booking pages and google has improved substantially. The costs for losses in
the preparation of services have decreased as there is a history of sales.
The following table shows the advantages and disadvantages of digitalisation in the restaurant Rincón
Manchego:
Table 1. Main advantages and disadvantages of digitisation in Rincón Manchego
Advantages Disadvantages
Increase effectiveness Higher cost at the time of digitisation
Improving the quality of service Difficulty in changing and getting used to a new system
Low cost after digitisation Staff training
Competitive advantage over competitors Dependence on technology
Improving Marketing strategies
As we can see from the table, digitisation in the restaurant industry has brought numerous advantages,
among them the effectiveness of operating, reducing the time of passing information and improving
competitiveness in the local market.
CONCLUSION AND IMPLICATIONS
The implementation of digitisation systems in the order chain in the hospitality industry helps to improve
performance and increase the company’s chances of survival. This digitalisation generates a series of
information that, together with other sources such as social networks, booking portals, e-WOM portals,
websites, etc., allows traditional restaurants with a family business structure to carry out much more
focused marketing strategies with a greater chance of success.
Data collection allows to know what the customer wants, how they want it and when they want it.
Therefore, by means of specific advertisements it is possible to promote the consumption of a product
or a reminder to reserve a table for specific days.
Therefore it can be stated that the digitalisation of the order chain in traditional restaurants influences
the improvement of marketing strategies.
But not only in the improvement of marketing strategies is the advantage for this business. The collec-
tion and processing of data through the order chain of traditional food restaurants allows for improvements
in many other areas of business management. Knowing what the customer wants and when he wants
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it helps to better organise the restaurant’s resources. Important issues such as personnel management,
purchasing management or financial planning make the collection of information through the digitisa-
tion of the order chain one of the current points to be taken into account for a hospitality company. Not
to mention its influence on delivery management, helping to improve planning and management as well
as the location of potential customers.
There are many managerial implications that can be drawn from this study, which can be extrapolated
to other types of restaurants, with different types of cuisine or different customer approaches. Having
a greater impact on fast food lines of business. Where marketing strategies are crucial and where data
collection at the order line is easier to collect.
An establishment that knows the preferences of its customers can make personalised offers, as well
as offer them discount coupons if they have not shopped in the establishment for a long time. Another
important managerial implication is that if we know the sales history and what social casuistry happened
in those days, a replication of events can be provided. For example, if we look at the history, we can see
that at the end of each month, the consumption of more expensive dishes decreases at the end of the
week. Therefore, it would be possible to make an advertising campaign, where on the last Friday of each
month, special mention is made of the cheapest products to promote their sales even more.
As for the limitations of the study, it can be considered that the business is located in a non-franchised
region of Spain. This implies that the strategy of this business may have slight modifications depending
on the region or country where the facts are analysed. Another limitation is the non-existence of previ-
ous studies of this company, which means that at certain times part of the study may be poorly justified
with data from the company analysed.
Finally, the future lines of research that this study has led to are diverse and very interesting. First
of all, it would be better to re-analyse the company over a period of 1 or 2 years and see how it has
evolved in terms of digitisation, to see whether this digitisation is the result of a modernization strategy
or whether, on the contrary, digitisation was forced by the effects of the pandemic and the need to adapt
in order not to die.
Secondly, it would be good to analyse in the same way other family-run hospitality businesses in
different regions of Spain to know if digitalisation is being introduced in the same way, as well as to
apply such a study in restaurants with different characteristics, such as fast food, luxury restaurants, etc.
Finally, it would be very helpful to conduct interviews with both customers and staff to find out
their views on the advantages and disadvantages of digitisation in family-owned hospitality businesses.
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Digital Marketing: Digital marketing is the use of technologies to help marketing activities in order
to improve customer knowledge by matching their needs.
Digitalisation of the Restaurant: It is the implementation of technological systems for the manage-
ment of the company. Transforming its operations and processes in order to achieve a specific benefit.
This implies a redesign of the business model in order to make it more efficient.
Artificial intelligence: It is the combination of structured algorithms in order to create machines
that have human-like capabilities to make decisions.
Marketing strategies: It is the style and method used to create sales opportunities. Its purpose is
to place and position the products and services of a company in the most efficient way possible around
the market niche to be covered.
Big-Data: It is a process that analyzes and interprets large volumes of data, with or without structure.
This process is used so that the data stored can be used to make decisions.
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226
Chapter 13
Personal Brand Benefits
of Social Media Use
for Researchers:
A Case Study
Alberto Prado Román

Iria Paz-Gil
https://orcid.org/0000-0003-3696-5253
Miguel Prado Román

ABSTRACT
Social networks are a very relevant tool for businesses to connect efficiently with many users at the same
time. It means that in the second decade of the 21st century, companies have strengthened their strate-
gies to expand their influence. In the higher education context, social media can help develop teaching
strategies. Nevertheless, are they also relevant to expanding the professional capacity of researchers?
Given this, this research aims to determine whether they are relevant within the research field and how
they use them according to the researchers’ position and the professional objectives set.
ORGANIZATION BACKGROUND
In a volatile market like the current one, understanding users is fundamental to determining their pref-
erences to adjust brands’ services and products (Nieto, 2015). However, users’ behavior is affected by
objective, psychological (Gómez & Prado, 2014), or subjective factors, as the impact of the publication
of a news item on users (Fisher & Statman, 2000). Furthermore, this increases with the emergence of
DOI: 10.4018/978-1-7998-8003-5.ch013

Personal Brand Benefits of Social Media Use for Researchers
social networks in the 21st Century, which has had significant relevance in consumers’ behavior (Owyand
& Toll, 2007; O’Connor et al., 2008; De Moya & Jain, 2013; Pérez, 2017).
The constant evolution of the market has caused traditional communication channels to manage the
well-known web 2.0 (Martínez & Sánchez, 2015). The relevance and evolution of digital networks are
undisputed. They are considered meta-media both for their digital characteristics (navigation, search,
reading, interaction) and presenting different multimedia content (Jensen, 2013; Campos Freire, 2015a).
Digital development allows meta-media to keep its basis on traditional markets’ main characteristics
but making continuous evolutions implementing simple innovations for users (Manovich, 2005). Among
them, social networks have a relevant impact on the communication market (Manovich, 2008).
Social networks (arising from Web 2.0) are now powerful communication platforms: allow interac-
tion between millions of users, thus becoming relevant media ecosystems (Beer, 2008; Stenger 2009;
Campos Freire, 2015b). However, it is necessary to point out that social networks’ germane impact on the
communication sector relies on both tangible and intangible society’s dynamic exchanges (Allee, 2009).
Therefore, traditional media and meta-media have become a fundamental part of companies’ com-
munication strategies (Carpentier, 2016).
Due to it, organizations expand their interest in social networks regarding the evolution of their role
in communication channels (Barthel et al., 2015). Companies increasingly rely on mobile communica-
tion strategies to significantly impact users through social networks (Mitchell & Page, 2015). So much
so communication managers have increased since 2010 their strategies to improve their impact within
social networks (Lasorsa et al., 2011; Paulussen & Harder, 2014).
On the one hand, social networks have outstanding benefits to improve communication and interac-
tion within a vast number of users simultaneously. On the other, they also present significant challenges
requiring companies to make critical adjustments in their communication strategies (García, 2013). For
this reason, their efforts have to focus on presenting content that allows them to capture and maintain
their audience’s loyalty (Lee, 2015).
Besides, social networks have been used from the educational framework’s perspective, demonstrating
that their use in teaching improves students’ perception (Alonso & Muñoz de Luna, 2010). Therefore,
students can assimilate the technological and communicative skills for dynamic markets (De la Torre,
2009). Also, the networks favor students’ interaction, allowing the real-time sharing of information, which
facilitates teamwork (Gómez et al., 2012) and a more dynamic work environment (Imbernón et al., 2011).
Despite the research on using social networks in the communication sector, there is no evidence about
how they can help researchers generate professional agreements. It is in this scenario that the relevance
of this research lies. Accordingly to Saura (2021), some of the researchers’ challenges have to do with
extracting knowledge from the digital environment. Furthermore, knowing how to use social media for
boosting a professional career in the higher education field is still misunderstood. Therefore, the main
objective of this research is to determine the use and implications of social networks within the universi-
ties’ teaching and research staff. For it, the authors designed and applied a survey to identify the most
used social networks. Moreover, the respondents’ social media use, including achievements’ promotion
and professional management, like creating agreements, contracts, publications, or others.
This case study explores how does Spanish faculty use social media for a professional purpose. For
that, the structure of this chapter follows a case study one. In the first place, the organizational back-
ground sets the scope of the research, that is, to know if the researchers benefit from using social media
in the professional field. The next part frames the case in the previous literature. Following that, the
case description provides information about how the methodology is developed, containing a subpart
227

that analyzes the managerial and organizational concerns derived from this research. Then, the current
challenges related to the researcher’s personal brand are discussed. The chapter ends with the solutions
and recommendations based on the case study, followed by the references and the key terms and defini-
tions. In addition, the chapter is completed with the teaching notes files, which contain questions and
answers, epilogue and lessons learned, and additional resources for deepening the topic.
SETTING THE STAGE
Traditionally, communication strategies had a triple perspective: press, television, and radio. However,
over the years, it has become more hermetic in the face of social demands (Jerez et al., 2000). Users
cannot participate in the official media contents (Humphreys, 1996), causing a disconnection feeling.
The technological evolution of recent years and the birth and development of the internet have led to
a new technological and social framework, causing the constant emergence of new digital relationship
platforms (Martínez & Sánchez, 2015).
From a group perspective, social networks are highly relevant in collective action (Della Porta et
al., 2009; Carty, 2010), allowing creating relevant events in minutes (Reinghold, 2004; Sádaba, 2012).
So it is essential to maximize existing resources, as is the case of digital platforms (Puricelli, 2005).
Therefore, social networks have caused an evolution in the communication paradigm (Rovira, 2012).
In this scenario, social networks have become a communication channel with exponential transmission
speed, allowing reaching large groups of users that traditional channels do not achieve (Candia, 2014).
As a result, social networks have become new communication models in which it has been necessary
to deepen these networks’ functioning (Boyd & Ellison, 2007) and the methodologies applied within
social networks (Carrington et al., 2005). Nature and relationships that take place between users have
also been deepened (Hargittai, 2007). Since their creation, they have been used to analyze political
movements, both from the perspectives of theoretical studies and significant case studies (Tilly, 2005;
Tilly & Wood, 2014). However, many of the digital group communications within social media are not
supported by society’s collective values but by users’ desire for transgression (Lasén & Martínez, 2008).
Therefore, it is essential to identify users’ interacting within social network motivations (Gangadhar-
batla, 2008), these interactions’ impact on their behavior (Christakis & Fowler, 2009), and the possible
results derived from their use (Kim et al., 2014).
If it is necessary to highlight some social networks, Facebook and Twitter are the two most used from
the digital market’s research perspective (Montero, 2018).
The studies that focus on Facebook aim to identify users’ type (Sun et al., 2009), its possibilities,
and its influence on users (Kirkpatrick, 2010). Besides, other approaches highlight the use of the social
network from the journalistic sector’s perspective in different countries (Noguera, 2010; García de Torres
et al., 2011; Bakshy et al., 2012; González & Ramos, 2013; Mitchell & Page, 2015).
The research on Twitter analyzes users’ comments on events, being this analysis a new methodol-
ogy (Bruns & Burgess, 2011), or its role as a news communication channel (Hermida, 2013; Larsson &
Hallvard, 2015; Arrabal & De Aguilera, 2016).
If the focus is on the digital business scenario, the arrival of new technologies has forced companies
to look for new horizons in the market to ensure their sustainability (Aras & Crowther, 2010; Miron et
al., 2011; Millar et al., 2012). Companies must ensure that users validate their behaviors to guarantee
their future activity (Schau & Gilly, 2003; Ritter, 2009; Celaya, 2008). Therefore, a change of business
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paradigm is needed since, in the 1980s, intangible assets represented 35% of companies (Kendrick,
1994), while in the 21st Century, they represented 70% (Daum, 2002).
Based on the above, it has been determined that companies do not only use social networks as platforms
for recreational content but to efficiently manage businesses to ensure customers a complete experience
(Luque & Castañeda, 2007). Previous has allowed service companies to ensure an evolution in their
services’ quality compared to users (Martínez et al., 2013). In fact, within the hotel sector, the leading
chains use Facebook as a tool to determine the behavior of users (Beltrán et al., 2017).
It is worth mentioning that social networks play a fundamental role within the organizational sphere.
They allow expanding and improving employees’ skills (Dyer & Nobeoka, 2000) since the level of learning
cannot be solely focused on an isolated organizational framework such as the company (Argote, 1999).
Therefore, companies need to consider the relationships between them and their environments (Uzzi &
Lancaster, 2003), thus increasing their learning level and productivity (Baum et al., 2000).
Furthermore, the digital environment allows the company to create communication flows. It means
reinforcing their contacts’ networks (providing a greater transference and combination of knowledge),
thus improving their results (Ingran & Roberts, 2000). Moreover, creating managerial networks with
other companies allows them to streamline their respective selection processes and performance (Col-
lins & Clark, 2003).
Therefore, both companies and users must manage social networks efficiently in such a dynamic
market, because it allows collaborative work and the exchange of information regardless of where they
are (Fliaster & Spiess, 2008), as well as identifying new challenges and their corresponding solutions
(Kijkuit & Van den Ende, 2007).
The added value of this study case relies on exploring how the researchers use social media to get
professional opportunities. Moreover, this research shows which social media are best indicated for
getting different opportunities depending on the type of activity (scientific publications, conferences,
teaching, and mobility, or contacts network), or on the academic category (professor, associate professor,
assistant professor, or others; see tables 4 to 11).
CASE DESCRIPTION
This study case analyzes the faculty of Spain’s Universities. Despite the relevance of social media in
the communication sector is clear, there is not enough scientific evidence on whether social media can
facilitate professional agreements within the faculty.
To present, analyze, and discuss this case study the researchers use the academic staff’s USA names,
followed by the Spanish ones (table 1).
The collection data method is a survey to test the study’s validity and determine the possible relevance
of social networks in Spanish Universities’ faculty’s employment development. The questionnaire was
distributed online following the snowball sampling technique, a non-probabilistic sampling technique
where the first subjects selected share the questionnaire among referrals (Baltar & Brunet, 2012) (avail-
able at https://www.encuestafacil.com/respweb/cuestionarios.aspx?EID=2721351&MT=X).
The data collection process takes place between February and March 2021. The months chosen to
launch the survey and collect data are justified because the academic year is in the middle of its develop-
ment. This scenario allows the faculty to focus its efforts on other tasks in place of the strictly academic
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Table 1. Equivalences in academic staff in Spain, USA, and UK (adapted from Morales, 2016)
Spain USA UK
Catedrático de Universidad
Professor (full) Professor
Catedrático de Escuela Universitaria
Profesor Titular de Universidad
Associate Professor (tenured) Senior Lecturer
Profesor Titular de Escuela Universitaria
Profesor Contratado Doctor Associate Professor (tenured) Lecturer (permanent position)
Profesor Titular de Universidad Interino
Profesor Titular de Escuela Universitaria Associate Professor (tenure track) Lecturer (fixed term contract)
Interino
Profesor Contratado Doctor Interino Associate Professor (tenure track) Lecturer (fixed term contract)
Profesor Ayudante Doctor Assistant Professor Lecturer (fixed term contract)
Profesor Visitante Visiting Professor Visiting
Profesor Asociado Adjunct Professor Associate Lecturer
ones, such as the use and updating of their social networks. The survey circulates among researchers
from the different universities that carry out their functions in Spain.
The questionnaire begins with a control-question. This question allows differentiating the academic staff
who use social networks, the study’s target, from the rest of the respondents. Upon the control-question
follows a block of questions addressed to know the respondents’ use of social media. The questions aim
to reveal how does faculty use social media and the benefits they obtain. In other words, if faculty fol-
low a researcher’s brand strategy. Finally, the survey ends with a block of demographic questions. The
demographic questions mainly focus on identifying the faculty respondents’ job profiles to determine
the relevance of social networks’ use according to their university position.
The sample consists of 313 surveys, of which 108 are considered valid for the research. The selected
sample consists of both men (51%) and women (49%), predominantly respondents whose age range is
between 30 and 64 years (87% of the sample). The sample provides a complete overview of social media’s
importance for faculty. It covers almost 50% of both researchers with permanent job status at university
(tenured positions, 51%) and researchers with non-permanent job status (non-tenure positions, 49%).
The predominant faculty groups are Associate Professor, tenured (Titular de Universidad) (22%), and
Associate Professor, tenured (Profesor Contratado Doctor) (20%).
Finally, after collecting the data, the actual relevance of social networks for university faculty is
analyzed, identifying different relevance levels according to their university’s working position.
Management and Organizational Concerns
After completing the data collection process, starts a process of analysis of the results obtained in the
study of social media’s relevance in the development of faculty activity.
The results reflect a great use of social media as relevant tools in developing and disseminating their
research (more than 73% of faculty have researcher profiles on social media). However, the use of personal
web pages is scarce. A priori was considered a relevant media, but only 15% of the respondent faculty
own a web page. This result indicates that the researchers do not consider helpful having a website,
despite it being a way to centralize the researchers’ studies and build a researcher brand. It may be due
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not to the consideration of difficult to develop mechanisms but to disseminating the researchers’ studies
through social media more efficiently. A researcher who wishes to use his/her website to spread his/her
research and professional activities will have to make a great effort in its diffusion. For this, the researcher
must communicate the contacts the constant work updates using links that he/she needs to upload to
the social media in which he/she has a researcher profile. By performing this task, the researcher might
find that he/she could perform the same broadcast tasks without constantly updating his/her web page.
Thus, the researcher may understand that he/she would not be focusing his/her efforts properly. On the
contrary, using social media could probably reach better his/her research dissemination objectives. This
reason would justify the scarce use of own websites, elements of centralization and dissemination of
publications, works, and other research activities, and the high use of social media for these purposes.
Regarding the use of social media by researchers, two social networks stand out above the rest
(table 2), ResearchGate (74.23%) and Linkedln (69.07%). The use of LinkedIn by a high percentage
of the surveyed faculty may be due to its nature. This social network was born to put professionals and
companies in contact, seeking to generate synergies and job opportunities between them. Thus, one of
the great benefits of using LinkedIn is generating a contact network among professionals. Neverthe-
less, this benefit is due to its purpose, so, understandably, the most used network to develop different
scientific activities is ResearchGate. This social network, unlike Linkedln, has a researcher profile that
better fits the development of the scientific activities. For all this, the high use of both social networks
is comprehensible. They are complementary and provide benefits to the researcher that allow him/her
to develop and disseminate his/her research activity and generate new research opportunities. Besides,
the use of the Publons network is not too high (38.14%) among researchers, but this may be due to the
academic nature of the network itself, compared to the research nature of ResearchGate. These results
give an insight into that, even though the different social media are relevant according to the advantages
derived from its own nature, for the academic staff, this becomes essential. They can complement their
use with networks that allow generating a high network of contacts.
Table 2. Identification of social networks in which researchers have a research profile (own elaboration)
Social Network Research Profile

ResearchGate 74,23%
Linkedln 69,07%
Publons 38,14%
Twitter 15,46%
Facebook 9,28%
Instagram 8,25%
Tuenti 1,03%
Other 8,25%
Regarding the relevance of social media for the development of researchers’ scientific activity, the
results reveal that researchers mostly understand that social media is critical to ensure the successful
development of their scientific work (table 3). Thus, almost half of the respondents (49.48%) under-
stand that social media is fundamental both for the development and dissemination of their research,
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and 22.68% understand its relevance. However, they do not stand out so much. Only a tiny percentage
(7.22%) do not understand the essential role of social media. It is important to note that social media’s
relevance not only helps in the dissemination of the faculty’s professional activities. It also allows con-
tacting other researchers to improve their research techniques, develop data collection techniques, or
establish future collaborations.
Table 3. The relevance of social media to the development of researchers’ professional activities (own
elaboration)
Social media use assessment (from 1, the less important, to 5,

Researchers´ Assessment Percentage
the most)
1 7,22%
2 20,62%
3 22,68%
4 24,74%
5 24,74%
Next, to analyze the benefits of using social media for the faculty, the study divides the sample ac-
cording to their respective universities’ relative positions. The approximated equivalences among Spain,
the USA, and the United Kingdom faculty are available in table 1. The professional activities rely on four
main areas: scientific publication opportunities, holding conferences and similar, teaching and mobility
opportunities, and contacts networks growth.
University Professors (Catedráticos de Universidad) and University School Professors (Catedráticos
de Escuela Universitaria) (table 4) obtain a significant benefit in the form of publications in scientific
journals and books. However, not through all social media, since to obtain these benefits, they mainly
use ResearchGate (40%), although they also use Linkedln (20% publication of journals, 30% publication
of books), and Publons (30%). Regarding holding conferences and teaching and mobility opportunities,
they opt for Linkedln (40% holding congresses, 40% teaching courses, seminars, masters, or other). These
are very consistent results due to the social media’s professional and researcher nature (LinkedIn and
ReseachGate, respectively). Finally, to increase the contacts’ network, both national and international,
University Professors and University School Professors use LinkedIn (30% and 40%, respectively). This
social network can increase their national and international contacts, both professional or more investigative.
Associate Professors (tenured) (Titular de Universidad and Titular de Escuela Universitaria) (table 5)
obtain a significant benefit in the form of scientific publications in journals and books. These benefits
come from ResearchGate (39% publications in journals, 32% publications in books) and Linkedln (28%
publish in journals, 21% publish in books). Regarding holding conferences and teaching and mobil-
ity opportunities, they opt for Linkedln (21%). These are very consistent results due to social media’s
professional and researcher nature (LinkedIn and ResearchGate, respectively). Finally, to increase the
contacts’ network, both national and international, Associate Professor (tenured) (Titular de Universidad
and Titular de Escuela Universitaria) use ResearchGate (50% and 46% respectively) and Linkedln (46%
and 36% respectively). This social network can increase their national and international contacts, both
professional or more investigative.
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Table 4. Analysis of the relevance of social media in the development of professional activities of uni-
versity professors and university school professors (Catedráticos de Universidad and Catedráticos de
Escuela Universitaria) (own elaboration)
Professional activity ResearchGate Linkedln Publons Twitter Facebook Instagram Tuenti

Journal publications 40% 20% 30% 20%
Book publications 40% 30% 30% 10%
Holding conferences 10% 40% 10% 20% 20% 10%
Holding seminars 10% 20% 20% 10% 10%
Teaching courses, seminars,
20% 40% 10% 10%
master’s degree, or others
Improving the contacts
30% 30% 20% 10% 10%
network nationwide
30% 40% 10% 10%
network worldwide
Table 5. Analysis of the relevance of social media in the development of professional activities of associ-
ate professor (tenured) (Titular de Universidad and Titular de Escuela Universitaria) (own elaboration)

Journal publications 39% 28% 18% 7% 11% 3%
Book publications 32% 21% 14% 7% 7% 3%
Holding conferences 7% 21% 3% 14% 14% 3%
Holding seminars 21% 11% 11%
21% 14% 11% 3%
11% 14% 3% 25% 50%
network nationwide
11% 11% 7% 25% 46%
network worldwide
Associate Professors (tenured) (Profesor Contratado Doctor) (table 6) obtain a significant benefit in
the form of scientific publications in journals and books. These benefits come from using ResearchGate
(52% publications in journals, 40% publications in books) and Linkedln (28% publish in journals, 32%
publish in books). Regarding holding conferences and teaching and mobility opportunities, they opt for
Linkedln (20% holding conferences, 28% holding courses, seminars, masters, or others). They also use
LinkedIn to generate opportunities to teach courses, seminars, masters, or other (40%). These results
continue to be consistent due to these social networks’ professional or researcher nature (LinkedIn and
ResearchGate, respectively). Finally, to increase the contacts’ network, both national and international,
these faculty group uses ResearchGate (44%) and Linkedln (48% and 44% respectively). Thus, they can
increase their network of national and international contacts, both professional or more investigative.
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Table 6. Analysis of the relevance of social media in the development of professional activities of as-
sociate professor (tenured) (profesor contratado doctor) (own elaboration)

Journal publications 52% 28% 14% 8% 4%
Book publications 40% 32% 14% 4%
Holding conferences 16% 20% 8% 4% 4%
Holding seminars 16% 28% 12% 8%
12% 40% 12% 8% 4%
44% 48% 4% 14% 4%
network nationwide
44% 44% 4% 4% 4%
network worldwide
Associate Professors (tenure track) (Profesor Titular de Universidad Interino and Profesor Titular de
Escuela Universitaria Interino) (table 7) obtain a significant benefit in the form of scientific publications
in journals and books. These benefits come from using LinkedIn (43% publication of journals, 71%
publication of books). Regarding holding conferences and teaching and mobility opportunities, they opt
for Linkedln (71% holding congresses, 57% holding courses, seminars, masters, or others). They also
use LinkedIn to generate opportunities to teach courses, seminars, masters, or other (57%). The results
show that social media has an outstanding professional character for this faculty group since they do
not opt for social media of a more investigative nature, such as ResearchGate. Finally, to increase the
contacts’ network, both national and international, they seek to expand it more in the profession than
in the researcher field. They mostly choose to use LinkedIn (71% network of national contacts, 57%
network of international contacts).
ate professor (tenure track) (Profesor Titular de Universidad Interino and Profesor Titular de Escuela
Universitaria Interino) (own elaboration)

Journal publications 43% 14% 28%
Book publications 71% 14%
Holding conferences 71%
Holding seminars 57%
57%
71% 14% 14%
network nationwide
57% 14% 28%
network worldwide
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Associate Professors (tenure track) (Profesor Contratado Doctor Interino) (table 8) obtain a significant
benefit in the form of scientific publications in journals and books. These benefits come from LinkedIn
(85% publication of journals, 38% publication of books). Although unlike the Associate Professor (tenure
track) (Profesor Titular de Universidad Interino and Profesor Titular de Escuela Universitaria Interino),
they choose to use ResearchGate to improve the management of their publications in scientific journals
and books (31%). Regarding holding conferences and teaching and mobility opportunities, they opt for
LinkedIn (23% holding conferences, 69% holding courses, seminars, masters, and others). They also
use LinkedIn to generate opportunities to teach courses, seminars, masters, or others (85%). The results
show that social media has an outstanding professional character for this faculty group since they do not
opt for social media of a more investigative nature, such as ResearchGate, to hold or give conferences,
courses, seminars, masters, or similar. Finally, to increase the contacts’ network, both national and inter-
national, they seek to expand it more in the profession than in the researcher field. They mostly choose
to use LinkedIn (92% network of national contacts, 92% international contacts network). They choose
to use ResearchGate to increase their national (15%) and international (23%) contacts network. In this
way, they can increase their national and international contacts, both professional and more investigative.
ate professor (tenure track) (profesor contratado doctor interino) (own elaboration)

Journal publications 31% 85% 15%
Book publications 31% 38% 15%
Holding conferences 23% 8%
Holding seminars 69% 8%
85%
15% 92%
network nationwide
23% 92%
network worldwide
Assistant Professors (Profesor Ayudante Doctor) (table 9) obtain a significant benefit in the form
of scientific publications in journals and books. These benefits come mainly from using ResearchGate
(38% journal publication, 38% book publication). Regarding holding conferences and teaching and
mobility opportunities, they opt for Linkedln (25% holding congresses, 13% holding courses, seminars,
masters, or others) and ResearchGate (25% holding conferences, 25% holding courses, seminars, masters,
or others). They also use LinkedIn (25%) and ResearchGate (25%) to generate opportunities to teach
courses, seminars, masters, or others. These results continue to be consistent due to these social net-
works’ professional or researcher nature (LinkedIn and ResearchGate, respectively). Finally, to increase
the contacts’ network, both national and international, this faculty group seeks to expand their contacts
both at a professional and research level. In generating a national contacts network, they mainly use
LinkedIn (38%) and ResearchGate (25%). In generating an international one, most use ResearchGate
(38%) and Linkedln (25%).
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Table 9. Analysis of the relevance of social media in the development of professional activities of as-
sistant professor (profesor ayudante doctor) (own elaboration)

Journal publications 38%
Book publications 38%
Holding conferences 25% 25% 13% 13%
Holding seminars 25% 13%
25% 25% 13% 13%
25% 38% 13% 13%
network nationwide
38% 25% 13% 13%
network worldwide
Visiting faculty (Profesor Visitante) (table 10) obtain a significant benefit in the form of scientific
publications in journals and books. These benefits come from ResearchGate (33% publication of jour-
nals, 27% publication of books). It also stands the use of Publons to publish their articles in journals
(27%) and Linkedln for the publication of their articles in scientific books (20%). Regarding holding
conferences and teaching and mobility opportunities, they opt for Linkedln and Instagram (20% on both
social media). They also focus on LinkedIn (20%) to generate opportunities to teach courses, seminars,
masters, or others. The results obtained continue to be consistent due to the social media’s professional or
researcher nature (LinkedIn and ResearchGate, respectively). Finally, to increase the contacts’ network,
both national and international, this faculty group seeks to expand their contacts both at a professional
and research level. In generating a national contacts network, they usually choose ResearchGate (33%)
and LinkedIn (20%). In generating an international one, they use ResearchGate (33%) and Linkedln
(20%). Through these networks, they seek to increase their national and international contacts, both
professional and researcher.
Table 10. Analysis of the relevance of social media in the development of professional activities of visit-
ing faculty (profesor visitante) (own elaboration)

Journal publications 33% 13% 27% 7%
Book publications 27% 20% 7% 13% 13%
Holding conferences 13% 20% 13% 20%
Holding seminars 13% 20% 13% 20%
13% 20% 13% 13%
33% 20% 13% 13%
network nationwide
33% 20% 13% 13% 7%
network worldwide
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Adjunct Professors (Profesor Asociado) (table 11) show inferior results in obtaining benefits through
scientific publications in journals and books. It is barely a slight benefit in publishing articles in books
using LinkedIn (9%). Regarding holding conferences and teaching and mobility opportunities, even
though the results point in the same line in terms of their relevance, LinkedIn (9%) is the chosen social
media. They also use LinkedIn to generate opportunities to teach courses, seminars, masters, or others
(9%). Due to the Adjunct Professor’s labor nature, it is understandable that the social network most
used is LinkedIn since it is a professional network. However, to increase the contacts’ network, both
national and international, this faculty group seeks to expand it both professionally and in research. For
that reason, they choose to use LinkedIn (13% network of national contacts, 9% network of international
contacts) and ResearchGate (9% network of national contacts, 9% network of international contacts).
Table 11. Analysis of the relevance of social media in the development of professional activities of adjunct
professor (Profesor Asociado) (own elaboration)

Journal publications
Book publications 9%
Holding conferences 9%
Holding seminars 9%
9%
9% 13%
network nationwide
9% 9% 9%
network worldwide
These results show the professional impact of professional social media use in researching activities.
However, the relevance of each network varies among different faculty groups.
CURRENT CHALLENGES FACING THE ORGANIZATION
In the past, companies based their communication strategies on the traditional media, although in recent
years, society criticized the media’s secrecy (Jerez et al., 2000). In response to this need, this scenario
is where the new digital platforms emerge (Martínez & Sánchez, 2015). Social media becomes neces-
sary to make multiple communications to thousands of users simultaneously (Beer, 2008; Stenger 2009;
Campos Freire, 2015b), which requires companies to formulate dynamic communications to capture
their interest (Allee, 2009).
Thus, companies design mobile communication strategies through social networks (Mitchell & Page,
2015) to increase user interest (Lasorsa et al., 2011; Paulussen & Harder, 2014). Besides, they also use
them to improve their organizational vision within them by expanding their employees’ knowledge and
relationships (Dyer & Nobeoka, 2000) and the relationships between them and their respective environ-
ments (Uzzi & Lancaster, 2003).
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The results of this study case are identified through the surveys conducted according to the different
categories’ preferences for the segments analyzed. It is noteworthy the participation of the permanent
faculty, which represent 66.67% of the sample. Among them, University Professors (Catedráticos de
Universidad) and University School Professors (Catedráticos de Escuela Universitaria) are the 7.94%,
Associate Professor (tenured) (Titular de Universidad and Titular de Escuela Universitaria) and Associ-
ate Professor (tenure track) (Profesor Titular de Universidad Interino and Profesor Titular de Escuela
Universitaria Interino) are the 28.57%, and Associate Professor (tenured) (Profesor Contratado Doctor)
and Associate Professor (tenure track) (Profesor Contratado Doctor Interino) are the 30.16%. There is
also significant participation of Visiting professors’ faculty group, with 11.90% of the sample.
According to this study case’s main objective, if the Spanish faculty uses social media for profes-
sional purposes, the results provide important insights. Thus, social networks are relevant to build a
researcher’s personal brand. Specifically, this use improves their vision and teachers (Dyer & Nobeoka,
2000) and increases their relations with the educational and business environment (Uzzi & Lancaster,
2003). However, although Facebook, Twitter, Instagram, or Tuenti are the main social networks in soci-
ety, the use of none of them reaches 10%. In contrast, social networks such as ResearchGate (74.23%),
Linkedln (69.07%), ResearcherID (41.24%), and Publons (38.14%) are the most used from the research
perspective. So social networks significantly relate to the researcher’s profile.
The results are widely distributed among the respondents to determine social networks’ relevance
in developing the researchers’ professional activities. Among them, 49.48% of the faculty consider that
having a researcher profile on social networks has a significant impact on their professional activities. In
comparison, 22.68% consider that it influences mediumly. Only 7.22% consider that it has no relevance
to generate new professional activities.
After proving the relevance of social networks on the researchers’ activity, the next step consists of
verifying if the respondent faculty has their website. The initial expectation was that the vast majority
has a website. However, the results show the opposite since only 15.79% of researchers have websites
to support their research.
Since the creation of social networks, just as companies use them to attract users’ attention to the
activities they develop (Lasorsa et al., 2011; Paulussen & Harder, 2014), researchers do. The faculty
benefit from social media to build their personal brand and getting opportunities to increase their capacity
to publish articles and other activities, such as holding congresses and seminars and organizing courses,
seminars, or other scientific duties.
In developing the researcher’s personal brand, the survey includes questions about four main areas:
scientific publications, participation in conferences and similar, teaching and mobility opportunities,
and contacts network growth.
Regarding the publications in scientific journals, faculty uses, in general, and in this order, Research-
Gate, LinkedIn, and Publons. Nevertheless, in the case of book publications, they prefer LinkedIn,
ResearchGate, and Publons.
In terms of holding conferences, faculty uses LinkedIn first and then reinforces Twitter and Instagram
communication. When holding seminars, they also base their primary strategy on LinkedIn and focus
then on ResearchGate and Instagram.
Concerning the teaching and mobility opportunities, faculty focuses on LinkedIn, then ReserachGate,
and finally, other social networks more generalists like Twitter and Facebook.
Finally, social media use to increase the contacts network both in national and international areas
partially differs. In both cases, faculty use more LinkedIn, followed by ResearchGate. However, it is the
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third place where there are differences. In the national field, Publons is the most used network, while
for the international is Twitter.
SOLUTIONS AND RECOMMENDATIONS
This research has several implications. On the theoretical side, the study defines how the faculty can
use social media to build a researcher’s personal brand. Given the scarcity of previous research on this
issue, this is an important contribution to help professional researcher’s growth. This study also identifies
which social media contributes best to getting different professional opportunities (scientific publications,
conferences, teaching and mobility, and contacts network). On the practical side, researchers can use this
study to determine which social media could better contribute to developing the professional activities
they need to perform for their academic growth. Furthermore, they can use this research to identify the
social network they should focus on to build a researcher’s personal brand. Finally, scientific institutions,
(i.e. universities), can use these findings to provide better conditions to their faculty, like publishing their
achievements, announcing their activities, or boosting their online presence, among others, to help not
only the researcher’s development but also the institution’s one.
According to the results, social networks have nothing to do with age or the position of researchers
within the university. It depends on the strategy set by each of the researchers to achieve each of the
previously set objectives.
This study case shows that it is highly recommendable that the faculty have a profile in social media.
Depending on the stage and their interests, they should focus on the sectorial networks (ResearchGate,
Publons). It is also essential to develop a researcher brand in LinkedIn to nurture the contacts network.
All these activities will boost with the support of the communication on more generalistic social media.
In the scientific field, researchers must develop several facets for their professional growth. From
management-related activities (i.e., academic positions) to teaching ones (i.e., giving lessons), research
dissemination (i.e., conferences), and the research itself (i.e., exploring phenomena). In a hybrid society
that shifts more to the digital environment, increased by global events like pandemic (Paz-Gil et al.,
2021), researchers, like organizations, must have a presence on the Internet and take care of their per-
sonal brand. For that, social media are the most conductive platforms. Since privacy issues generated in
the digital ecosystem should not be overlooked (Saura et al., 2021), one fundamental recommendation
when developing a researcher’s personal brand is establishing a clear distinction between the public and
private profiles. Thus, preserving our privacy to minimize privacy loss.
The main theoretical and practical contributions of this research are that (i) it states the social media
that the Spanish researcher uses the most, (ii) it shows the different activity areas that the different social
media contribute the best for achieving professional opportunities, and (iii) the study clear that different
academic categories use different social media.
During the research preparation, the main drawback is that the sample focuses mainly on researchers
from Spanish universities. Along these lines, future research lines are intended to expand the sample of
research to collect results from researchers from other countries. In this way, the next phase is to compare
online dissemination strategies according to the researchers’ nationality and teaching areas.
239

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Faculty: The whole academic staff in the university. They usually develop academic activities, like
teaching, or organizing courses or seminars, and researching ones, like developing scientific publica-
tions or holding conferences.
LinkedIn: LinkedIn is a social network addressed to put in contact professionals, both companies and
employees. It gives job and career opportunities and has almost 700 million members all over the world.
Meta-Media: This term embraces the new communication meanings concerning the form, the con-
tents, and their relations, in the development of new communications media and technologies.
Personal Brand: This concept refers to one person’s fundamental characteristics that make him/her
different and attractive to others. It is a common term in working contexts and implies the added value
of the worker.
Publons: Publons is an academic, social network. Its focus is mainly on scientific publications. It
has over 200.000 researchers and 25.000 scientific journals.
ResearchGate: ResearchGate is a scientific, social network. It hosts more than 19 million researchers
worldwide of all the scientific fields and over 130 million scientific publications.
Social Media: Social media are online communication platforms in which their members also create
content. They are based on Web 2.0 and social influence and interaction.
245
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Chapter 14
Digital Customer Journey in
the Luxury Hotel Experience:
A Case Study Approach
Jonathan Gomez Punzon

ABSTRACT
Digitalization is one of the biggest changes in our fast-moving world. There are a lot of digital innovations
in many different industries, which are also affecting the hospitality industry. Taking into consideration
that travel and tourism is all about the combination between a wide range of experiences and on-site
benefits, the implementation of the digitalization in this industry is completely necessary. As a part of
the travel and tourism industry, the luxury hospitality segment is continuing to grow more than what
was expected, reaching figures of big spending and revenue regarding luxury hospitality bookings, and
it is even expected to grow more than 60% by 2026. Luxury hospitality brands have a lot of upcoming
opportunities to generate discussions through digital tools and innovations, not only about bookings,
but also covering the whole customer journey.
INTRODUCTION
As a part of the travel and tourism industry, the luxury hospitality segment is continuing to grow more
than what it was expected, reaching figures of big spending and revenue regarding luxury hospitality
bookings, and it is even expected to grow more than 60% by 2026. Luxury hospitality brands have a lot
of upcoming opportunities to generate discussions through digital tools and innovations, not only about
bookings, but also covering the whole customer journey.
We cannot forget how sustainable luxury tourism and hospitality are also linked, improving the already
mentioned figures of growing. When sustainability is linked to technology in luxury travel, it explains
why consumers might react positively when learning that a luxury hotel is committed to sustainability.
Amatulli, C., De Angelis, M., & Stoppani, A. (2021).
DOI: 10.4018/978-1-7998-8003-5.ch014

Digital Customer Journey in the Luxury Hotel Experience
It means that luxury hospitality brands can turn into a huge scalability of digital interactions providing
maximization of the service with decreasing the cost for all the parts involved in the process.
These procedures accelerate changes in the industry from the current perspectives which were be-
ing stablished as for example cooperation or coopetition between luxury hotel groups and online travel
agencies or online tour operators and intermediaries, undermining the buying power and the relevance
of these intermediaries, avoiding their participation of the intermediaries which are currently taking a
big piece of the cake through big commissions during the booking or selling process.
The digital transformation has completely affected the luxury hospitality industry and the challenge
that this segment of the hospitality business has to face, is about how skilled will be this industry to
transform their current on-site customer service procedures on providing high-level of quality, adapting
them to the digital requests of the luxury hospitality client.
To embrace the new digital impulse initiated by the customers of the tourism and hospitality indus-
try, it will be mandatory for the luxury hospitality brands to match these expectations on the digital
interactions, which would be switched on increasing brands incomes, Hence, luxury hotel companies
are moving to maximize the quality and the content of the digital journey.
Wealth travelers and wealth clients of luxury hospitality brands are always looking for immediate
rewards and immediate sense of service quality, so their expectations during the digital interaction with
the hospitality brand, are moving forward when it comes to perceiving that hospitality premium experi-
ence, no matter the moment of the customer journey the client is feeling: Inspiration, planning, booking,
experiencing, or sharing.
Clients are willing to maintain the same rewarding perception all along the customer journey, and
the client experience of the digital connections between the clients and the luxury hospitality brands,
must be smoothly integrated, providing a seamless service experience.
Some researchers indicate that that optimism and technology innovativeness positively influence luxury
hotel customer perceived value, and the customer perceived value when using technology innovations in
the luxury hotel experience, positively influences customer satisfaction and customer purchase intention.
Pham, L., Williamson, S., Lane, P., Limbu, Y., Nguyen, P. T. H., & Coomer, T. (2020).
Luxury hospitality brands are keen to keep themselves competitive within this industry through the
digital channels.
Due to the inception of the democratization of technology, the luxury hotel brands are moving to
implement a forward-thinking strategy on how they should approach to their digital clients of the luxury
hospitality.
Since many other actors of the travel industry adopted the use of technology prior than luxury hos-
pitality brands, this segment needs to take control of the client’s conversations flow, in order to benefit
their loyal clients making it easier for them to perceive the same benefits in values historically perceived
from these brands.
When we are talking about clients, we can mention basically families Ann couples of affluent travel-
ers and it is important to consider how the adoption of the technology among the younger generations
are coming as novelty to take into consideration.
Some researchers provide findings highlighting a significant gap in how luxury hotel managers were
managing the technological experiences of children under 12 years of age and that further incorporation
of various touchpoints is needed to improve the management of the service design. Lahouel, B. B., &
Montargot, N. (2020). We also have to take into consideration that no matter the group of age we are
trying to reach through the technological interactions during the luxury hospitality experience, some
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guests in clients they can provide fake reviews and comments to try to damage the reputation of the
company for any given reason I see it happens in many different industries nowadays. Saura, J. R., &
Punzon, J. G. (2020).
To do so, it is necessary to leverage an updated user generated content, becoming influential to cre-
ate a digital community of clients looking for having immediate mobile conversations and interactions.
LITERATURE REVIEW
Due to the COVID-19, the digital interactions became a day life task for luxury hotels all around the
world, but there are some segments that are still struggling on moving the customers habits into a digital
perspective. In this literature review we will have a look in today’s growing importance of digital service
luxury hotel companies.
Having a look on how researchers discuss about the technology implications through the different
stages of the customer journey on the travel, tourism, and luxury hospitality industry, the literature review
showcases interesting insights on how the technology reshaped the hospitality industry.
This article aims to define how the luxury hotel brands are using the technology to improve their
services, but we realize an existing lack of research on how the luxury hospitality implemented the
technology all along the different customer journey stages.
There are few researchers who attempted to analyze how these technologies affect the customer
journey interaction.
Prior to the interaction, we must take into consideration that the luxury tourists’ decisions are taken
based on concrete factors which reshapes their decision-making process.
Since the origins of the hospitality industry, the business of luxury hospitality has been based on
personal interactions, moving now into a digital communication scenario, providing much more non-
human-controlled factors, creating a base of reference on how technology is taking over the change of
paradigm of luxury hotel brands and customer interactions.
In the next figure, we can check the different kind of factors which historically have influenced the
traveler’s decisions, and how these factors welcome some more current hidden factors we do not perceive
but are created through the technological communication.
Based on the previous figure, the customer journey service is apparently being reshaped and built by
technological factors which have been incorporated in the decision-making process. Hence, the luxury
hospitality companies must consider how to target their clients to provide bi-directional customer-hotel
conversations which are becoming personalized and based on a better user experience. Zsarnoczky, M.
(2018).
When both clients and luxury hotel brands realized this new scenario of digital conversations, and
how these factors are modifying the decision-making process, hence, the customer journey communica-
tions, it is necessary to question how important would be the fact of being ever connected also during
the hotel stay, wondering if the hotel experience stage would require these connections, (prior to this
sanitary emergency we are facing), Loureiro, A. (2017).
Some researchers consider that it is true the importance of technology and high-tech, state-of-the-art
tools in facilitating the co-creation and delivery of experiences in the context of luxury hospitality. How-
ever, it also emphasises that the high-touch dimension is the core of hospitality in luxury and premium
hotels and should remain the primary driver of this segment.
248

Figure 1. From past factors of traveler’s decision, to current factors of traveller’s decisions. Source:
Zsarnoczky, M. (2018)
Luxury hotels will have to fine-tune and tailor their services and provide the right mix of high-tech
and high touch, depending on the micro-niche segments to which they cater. Bharwani, S., & Mathews,
D. (2021).
Based on this new communication dimension, the luxury hospitality companies are looking for ap-
proaching to the luxury guests to motivate them to become hospitality services consumers through a
digital conversation based on engagement and branded content strategies.
These strategies target the luxury hospitality clients, and based on the results of the interactions, guests
are welcoming and considering as a positive strategy from the luxury hospitality brands, to develop a
digital brand conversation and a digital service which makes a significant effect on the perception of
the luxurious and quality-based service during the digital customer service. Quarm, R. S., Adoli, H. L.,
& Zadid, A. I. (2020).
Prior to the improvement of these digital engagement-based conversations, it is already proved that
technologies applied during the booking stage of the customer journey, strengthen the overall bookings
experience satisfaction with hospitality brands. Huang, C. D., Goo, J., Nam, K., & Yoo, C. W. (2017).
We also have to consider that online guest service quality contributes to customer satisfaction, then
influences their brand loyalty. Additionally, customer satisfaction when using technology, acts as a partial
mediation on the relationships between the luxury hotel website service quality and their guests. VO, N.
T., Chovancová, M., & Tri, H. T. (2020).
Having discussed that some new technological factors of travel decision made that hospitality com-
panies initiates online engaging conversations with potential brand clients, looking for generating more
bookings, it is demonstrated that hospitality companies are using the technology to increase their revenues
in order to protect their market share against intermediaries or disruptive business models.
Therefore, it makes sense to maintain the customer service through technological channels, as a nec-
essary strategy to guarantee the client engagement. Alrawadieh, Z., Alrawadieh, Z., & Cetin, G. (2020).
However not only the luxury hotel chains are willing to adopt the technology becoming early adopt-
ers on digital conversations with their clients, since it is a global trend of the industry to overcome the
barriers triggered by the worldwide sanitary emergency, which moved these customer service journey
into the digital world.
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As an important part of luxury hotels to keep relevant within their clients and guests, technology can
be also used in a different way. Electronic word-of-mouth on social networking sites is one of the most
effective marketing resources in the lodging industry. The interaction between the tourists in the hotel
through the technology must be ethical and respectful at each moment, Saura, J.R., Palacios-Marqués, D.
& Iturricha-Fernández, A. (2021), as it should be in the luxury hospitality before and after the pandemic,
Ribeiro-Navarrete, S., Saura, J. R., & Palacios-Marqués, D. (2021).
Furthermore, the usage of eWOM on social networking sites not only helps potential guests select
suitable luxury hotels but also allows luxury hoteliers to better cater to this growing market segment.
Lee, H., Min, J., & Yuan, J. (2021).
The lockdown accelerated the adoption of technology all over the industry, and luxury hospitality
companies had to quickly adapt their technological systems to stay relevant all along the customer jour-
ney, in order to maintain their brands on top of mind of the clients.
These changes to adopt the digital technology to improve the customer service, enriches the conversa-
tions between the hotel and the client, and provide added value for the customers. This adoption made
companies embraced new technologies as many other tourism companies due to the pandemic crisis, in
order to be ready for the moment when travelers and clients they can go back to experience the luxury
hotel services onsite. Ivan, I. (2020).
As a part of these digital conversations, it is important to consider the efficiency of the application
of the technology on the booking systems. It has no sense to provide a great customer experience dur-
ing the inspiration of engagement stage of the digital conversation with the hospitality brand, and not
provide it during the booking process, once the clients chose a concrete luxury hospitality company to
experience their Holidays, Busulwa, R., Evans, N., Oh, A., & Kang, M. (2020).
Once in the hotel, when the guests are experiencing the customer service, the use of technologies to
enhance the self-service and contactless interactions is becoming necessary. But we have to consider
that we are talking about luxury hospitality and the traditional personal physical customer services is
not going to be replaced due to the excellence provided by these luxury hospitality brands.
In fact, most of luxury hospitality clients, perceive more benefits on the personal interactions rather
than on self-service digital technologies interaction.
Nevertheless, we can sustain that luxury clients of luxury hospitality brands are moving to gradually
accept that the quality-based service can be ruled and managed by technology, with a greater preference
for smartphone-based technologies . Liu, C., & Hung, K. (2020).
Furthermore, during the onsite hotel experience stage within the luxury hospitality customer journey,
we find different technological implications to make easier the stay of the hotel guests. Now technology
is being used to make easier the interaction with the room facilities, and the use of smartphones is once
again making easier the features of the technology application into the guest room. Torres, A. M. (2018).
These rising perceived effects of the digitalization on the luxury hospitality companies, can be alleged
from several perspectives, but we must highlight the positive and innovative strategies that could be
implemented to fill the customers’ expectations through the digital application in the luxury hospitality
industry. Gamage, T. (2021).
Some scholars argue that hospitality industry has not to focus only on the technology itself but on
how it can be used for the benefit of the luxury hospitality clients only during the mentioned onsite stage
of the hospitality experience.
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Therefore, there are researchers suggesting that the era of engage and build branded conversations with
potential clients through digital channels is over, and it is time for facing upcoming technologies as the
use of blockchain for hospitality industry rather than the current situation. Önder, I., & Gunter, U. (2020).
As a conclusion of the literature review, we could reveal that scholars showcase how different tech-
nology applications are becoming useful without any doubt. Regarding the hospitality luxury compa-
nies, their popularity and the early adoption of customer journey service technologies is flourishing
and undoubtedly changing the digital client’s behavior. We can undertake that combination of different
technologies can maintain the customer journey interaction and the perception of service quality to be
spread to inspire clients to choose a concrete luxury hospitality brand.
Finally, we must say that both technologies for planning and booking your stay, as well as technologies
to make their own side experience seamless and rewarding, allow digital guests to share the experience
through the use of these mentioned technologies. Dorcic, J., Komsic, J., & Markovic, S. (2019).
METHODOLOGY
The Case Studies
The case study is one of the many different ways of researching best practices in the travel and tour-
ism industry. In this context, the case study that we are analyzing, will allow us to focus on the current
phenomena of the digital customer journey interactions in the luxury hospitality.
Through the analysis of the case study, we will be able to stablish certain evidence through targeting
the procedures followed by the luxury hospitality companies in achieving their objectives. In this chapter,
we have adopted the explanatory case studies methodology, aim to answer the luxury hospitality and
technology relation and phenomena, within the contexts of real-life situations.
Advantages of selecting exploratory case study procedure include analysis within the context this
topic (tech and luxury hotel), integration of qualitative perceptions, and the ability to capture events of
real-life situations so that the fact can be explored in larger levels of complexity. The exploratory case
studies do have specific weaknesses that may involve lack of rigor, challenges linked with analysis and
truly little basis for generalizations of findings and conclusions.
The exploratory case study must contemplate taking into consideration the different perspectives
brought to this analysis, and the different findings which will be concluded by the company.
Exploratory case study methodology allows investigators to conduct an in-depth analysis within some
specific context as the luxury hotel case study and the technology interaction.
First, in this article we did research real case studies and experiences, learning from recently performed
in-depth luxury hotel case studies in the luxury hospitality industry. Rather than examining these hotel
case study in general, the chapter explores each particular situation through various hotel chains, and it
undertakes the study through variety of situations in order to reveal multiple facets of the application of
the technologies in the luxury hospitality.
In these exploratory case studies, a real-time event is explored within its naturally following frame-
work, with the understanding that each situation and perspective will create a difference.
Even though being one of the most frequently used research methods in academic research, the
researchers have not yet developed consensus on preparing and completion of case studies due to the
singularity of the luxury hospitality industry and the common subjectivity around this industry, even
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considering how technology is evolving just daily. We are going to analyze 5 case studies on how the
technology reshaped the interactions and the quality of services.
RESULTS ANALYSIS
Luxury hospitality companies are confronting the challenge of being prepared and up to date on provid-
ing technological channels to spread their quality standards through digital interactions. When talking
about customer engagement, Mandarin Oriental is working on personalizing the customer engagement,
through a new and relaunched website.
Guest centric strategy is a must for this hotel chain, and they have designed a new website to maintain
the conversation with their clients during the prohibition of travelling all around the world. The website
development is also accompanied with the development of a whole portfolio of digital tools to interact
with the clients, promising that the enabled digital conversations will change the onsite experience of
the client, once at the hotel, not only when interact with the hotel staff but also on the quality of service
perceived.
These digital tools such as online check-in, or in advance room selection, will enable staff to prepare
everything with a devoted service but with a contactless interaction, and it means that Mandarin Orien-
tal is focused on relationship-building from the early stages of the customer journey, as the inspiration
stage, looking for building trust from the client side, delivering a better service once the onsite interac-
tion happens.
Mandarin Oriental is also wondering how the better use of data will deliver a major growth opportunity
for this hotel chain, which have gathered huge amounts of relevant data but have not yet converted this
into an enhanced customer experience. Luxury 5 Star Hotels & Resorts Worldwide | Mandarin Oriental
Hotel Group. (2020, 4 september).
In this regard, world-famous Peninsula Hotels luxury hospitality brand, have been working on drafting
a digital strategy which matches the client’s expectations. Peninsula clients expect a digital experience
aligned with the high-quality level of the brand.
Peninsula brand strategists realized that today, luxury hospitality brands must move from being a
hospitality brand focused on delivering a world class service, to become a digital luxury hospitality
brand delivering an online world-class service.
The company wants to provide a kind of luxurious digital experience from the very first interaction
of the client with the company technologies. Therefore, Peninsula Hotels stablished a 30 people team
to provide digital personalized attention, using social media accounts from a business perspective side,
rather than a branding portfolio.
Now, Peninsula Hotels increased the guest digital conversations prior to the onsite hotel experience,
due to the capacity of the technology adopted and the human resources implemented on this task, being
able to sell and upsell more luxury services in advance.
Due to the decision of enhancing their digital conversation skills, the brand moved to a better current
position in services in social media, climbing almost 30 positions in the international ranking of luxury
hospitality brands in social media.
This achievement has been perceived by clients, who perceived that the digital conversations were
more realistic and personalized, and it was made just creating the in-house team of digital customer ex-
perience, reducing development and implementation times as well as costs, inspiring the digital journeys
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of the potential Peninsula Hotels clients, pushing up the conversions from inspiring digital interactions
to Peninsula hotel bookings. The Peninsula Luxury Hotels Group. (2020).
But sometimes, to be able to digitally engage with the clients due to the technological innovations
implemented by the hotel chain is not enough. Some luxury hospitality chains are willing to provide
this service during the customer journey, focusing on concrete segments, based for example on age
segmentation.
This is the case of how Raffles Hotel, which is a world landmark on luxury hospitality traditionally
seen as a hotel chain for senior generations of clients, placed the target on younger generations due to
the technology implementations. Raffles was looking to attract a wider younger segment of clients, but
they need to reshape the message of the brand and move it from traditional communication channels
into digital channels.
They drafted a complete digital strategy based on the motivations of the young affluent segment of
clients, stablishing different connecting dots with them. For example, Raffles wanted to get the most of
their event’s venues and decided to create a strategy to connect with young couples who wanted to get
married soon in style and with distinguished and guaranteed exclusivity. Therefore, Raffles created an
international digital campaign combined with a digital event, producing an annual wedding branded-
content workshop.
The expected results were expanded, and the hotel chain gained more than 17000 new contacts of
potential clients younger than 30 years old. In very few weeks, adapting the message for this group of
interest, the overall digital strategy enlarged the company average engagement within the segment, book-
ing more than two times the expected weddings in two months’ time; an achievement never matched
before. Raffles Magazine - Luxury Hotels & Resorts - Raffles Hotels. (2020, july).
Sometimes, the interaction is already achieved, not as the previous case, and it is all about to refresh
the inspiration messages. AMAN Hotels is a luxury hospitality brand world perceived as the designers of
the hideaway luxury hospitality and is currently benchmarked by many other luxury hospitality brands.
But this brand in less than five years house redesigned the overall way of interacting with their clients
through their websites. AMAN wanted to generate the best of luxury hospitality websites design to drive
new messages on customer relationships of connection with the client, from the very first inspiration
moment until the check-out moment in the digital ‘scenario.
A new digital technical platform was created incorporating a highly tailor-made online booking
system. These improvements were followed by new interactive tools as chatbots, and a new up-to-date
content to be used during the digital engagement with clients in the social media conversations. As a
result, AMAN created a new portfolio of revamped websites where building growing organic traffic and
increasing direct booking through rather than through OTA websites.
To support the interaction with any user, this brand also designed their websites in a responsive way
in order to allow any digital consumer to interact with AMAN no matter the technology that the client
is using. In this regard, this brand matched the expectations of the clients who wanted to have direct
conversations learning new inspiring messages, achieving the brand objective, since brand loyal clients
and members of the brand membership program used these new developments. AMAN closed the digital
circle creating a new smartphone app, which allows much more immediate contact and service delivery
with the clients.
The app allows conversations to become much more direct and richer for both the guests and the
hotel brand. The purpose of the app is to be perceived as a digital concierge in the pocket of the client,
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enhancing the on-site experience, providing exclusive information, or featuring tools for enriching their
stay, as the digital key or the facility of book hotel services in advance from the smartphone.
The digital ecosystem of tools and technologies created by AMAN, stablished more authenticity to
stand out and connect through digital stories; The data gained during these digital interactions, confirmed
that luxury clients are currently looking for inspiring messages when planning luxury stays, even though
pandemic did not hurt these inspiring expectations, but increasing them. In general AMAN affluent
guests welcome the inspiration since it reflects the uniqueness of the luxury brand.
From the perspective of the client, to be able to connect with the AMAN at any moment of the cus-
tomer journey is perceived a real luxurious and high-level service and proves how this luxury hospitality
brand takes seriously the customer service journey in the digital universe. Aman Hotels & Resorts - World
Hotel Destinations Across to Globe –. (2020, 15 april).
Moving into big players: How the biggest hotel chain anticipated this trend and unveiled the digital
interactions on customer service with their clients? Marriott Hotels & Resorts is a good example on how
a luxury hotel brand engages with their customers through stories transmitted through digital channels.
This brand decided to focus on branded content creating a series of international city guides linked
with their most luxurious properties and brands. This content was able to catch the attraction from the
wealthiest clients, inspiring them to book room nights in their hotels, as an entry door to the different
expediencies displayed on these guides. Marriott also paid attention to another stage of the customer
journey which is the sharing stage:
The brand pushes clients to be protagonists of their own user generated content, sharing their experi-
ences in social media showcasing these experiences through their channels in order to inspire more brand
clients. Marriot digital strategies make easier to place the brand into the guest digital perception, which
is necessary to engage and connect with digital tourists. More and more platforms are coming up and
more and more tools are appearing to connect with travelers and share different experiences inspiring
future hospitality luxury guests.
Hence, Marriott launched long time ago their social media accounts, their own hotel property chatbots,
or a wide range of dedicated websites, covering the whole digital interactions with their clients, so their
efforts are dedicated to increase the interaction, since the tools and channels are mostly updated and useful.
Some new interactive tools that Marriot is beginning to use are for example that luxury hospitality
clients can also book the Marriott services though a personalized online booking process, which does
not need the intermediation services, becoming now direct so that clients can directly check savings and
benefits directly from the brand.
Another relevant disruptive technology adopted by Marriot is based on segmentation. Not only the age
segmentation Raffles implemented, but also segmentation by regional markets. In this regard Marriott
innovated launching a program, combining direct chat with online payment using the functionalities of
the Chinese leading company, WeChat, combining payment and chat facilities.
This service implementation allowed the brand to cover the whole customer journey, being present
on the digital conversation from early stages until the last interaction after the hotel experience.
This technology made accessible for Chinese travelers to directly discuss with the property and with
the guest experience professionals from their smartphones, being able to digitally pay once at the hotel
or in advance. Hotels & Resorts | Marriott Bonvoy. (2020, 7 september).
All the previously benchmarked technologies mean that the digital customer journey of the clients of
the luxury hospitality brands are becoming much more seamless from the very first moment the digital
connection begins.
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Most of luxury hospitality brands are willing to create one-stop solution for all the different stages
of the customer journey. In fact, the digital technologies allow hotel brands to also make much more
further connections based on real interactions from the digital side.
Most of these brands are also using the digital platforms to inspire their loyal clients allowing them
to buy the different goods of the brand anyone cannot buy directly in the property, as bathrooms sets,
bed linen sets, or hotel artwork, etc, which is only possible thanks to the digital customer journey.
CONCLUSION
Digital technologies are useful to provide content an interaction with the brand as well as looking for
digital feasibility, and more and more the experience is becoming easier for all kinds of digital users.
The digital strategies are currently facing how to increase direct bookings in the luxury hospitality
websites. It is interesting to see how digitalization is helping to the luxury hotel brands to overcome the
situation of hospitality industry lockdown. Baum, T., & Hai, N. T. T. (2020).
The current situation strongly suggests that technology integration within the hospitality brands and
the client’s adoption must be focused on making easier the future hospitality operations.
During the last year we have pumped the digital hospitality technologies to provide a better and closer
service through a customer journey more dedicated to reminding good moments of the clients in the past.
Some recent investigations revealed that luxury hospitality chains are facing a great technology adop-
tion to maintain the customer journey quality with the clients after the pandemic and during the next
couple of years. Gursoy, D., & Chi, C. G. (2020).
We have seen how different hotel chains put into practice different strategies on how to tackle day
distance and separations of the clients due to the current restrictions we are suffering, but all of them
genuinely believe that the digital discussions and the enhancing of the customer service through digital
technologies undoubtedly drives into more revenues for these hospitality brands.
We cannot forget the hotels must track and measure the impact in the global interactions they have
in order to control the conversations at each moment Saura, J.R. (2020). We also have to consider we
are talking about luxury travelers and luxury clients who are usually affluent people, and they are very
keen on their own privacy and data. Hence these luxury hotels they must see create a privacy policy
guaranteeing the confidentiality of the conversations between the luxury hotel and the clients at each
stage, Saura, J. R., Ribeiro-Soriano, D., & Palacios-Marqués, D. (2021).
Some practical simplifications are directly aligned with the analysis of this article. For example, that
thanks to the technology, the hotels are increasing their bookings certainly. It is a practical implication
that as much developed is the digital interaction with the client, more possibilities the hotel chain has
to gain more revenue.
The enhancement of the hospitality technologies boosts the digital customer journey from the very
first moment that any potential guest is looking for a luxury hospitality brand, making it easier for these
clients to find the adequate brand and to connect with their choice.
The use of technology can track the practical implication of the new increasing cost of guarantying
the social distance during the onsite hotel experience; there are several concerns on how to create the
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necessary adjustments to guarantee distances without reducing the customer journey quality since the
hospitality industry has been based on human relationships and social interaction.
It would be interesting to analyze the implications of the distance and displacement effect due to the
sanitary restrictions and how this technology will help in the short term.
It is interesting to mention the different practical implications that the technology can provide to the
luxury hotel industry. First at all, we have to consider the technology made hotel clients to meet remotely
during this pandemic we have suffered, allowing them to create conversations through social media or
through different technological communication channels.
This implication means that the flow of information between both players is solid and continuous,
achieving a tangible practical implication improving the customer relations and experience.
Another practical implication is the possibility of improving the experience during the hotel stay due
to the technology. Once at the hotels, and taking into consideration the high-level of the hotels, the guests
can have immediate access to the hotel services and facilities through the app of the hotel, or through
the webapp designed to get direct connection with the luxury hotel staff.
Theorical Implications
Clearly mentioned in the introduction of the research, there is a lack of research on how the technology
positively or negatively affects the luxury hospitality brands during the interactions with the clients
along the customer journey.
A lot of research can be done if researchers will assess the growing interest on focusing on this topic,
moving from the overall hospitality research to the luxury hospitality research on how technology can
reshape and improve the digital guest experience.
This chapter include novelty topics on how the interactions between the hotel guests and the luxury
hotels adding new implications on how the technology can recover the guest relations after this pandemic
stage.
One of the most interesting novelty, is to consider on researching on how the new relationship between
the luxury hotel guest and the luxury hotel is . After this lockdown in pandemic. There is an unexplored
scenario on the relationship between clients in luxury hotels, it is important to mention that the technol-
ogy can facilitate the new interactions.
There are obvious research limitations and future research opportunities. The future of the revenue
figures and the digital client connections with the luxury hospitality brands is not scientifically proven,
showing an interesting limitation to research about, since it will depend on how effective the interaction
between these brands and the luxury hospitality clients will be.
The fast increase of many technology solutions which is happening from the last decade in the
hospitality industry, is changing how consumers will decide on the relationship model with the luxury
hospitality brands.
Hence, there is a lot of opportunities for future research, based in concepts as the information requests
during the customer journey and how to target that flow of information from the brands to the clients.
Another future research should be focused on understanding how positively or negatively affect the
overall presence of the brand during the whole customer journey of the client.
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An additional future research should stress the current necessity on providing data for future fea-
sibilities of the technological interaction between the brands and the customers overdone the current
bookings or conversations.
Another topic to research about could be focused on the utility of tracking the use of technology from
the information site to the data site when talking about luxury hospitality brands, in order to continue
enhancing the clients experience during the whole customer journey but based on existing data analysis
in this case.
An alternative potential future research should be focused on how these websites will showcase and
will provide tailor made branded information and where are the limits to make the brand image more
personalized within the client profile.
Therefore, more research can be produced to check how the customer journey conversations based on
digital promotions could become relevant to push potential clients to interact with the hospitality brand,
and how it will affect in terms of revenue for the hotel brand.
A behavioral research is also welcome due to the current situation on how the luxury hospitality brands
are maintaining the client’s feeling of trust, after this pandemic situation, and how the digital platforms
and digital strategies of interaction between the luxury hospitality brands and the luxury hospitality
clients could enable the building of trust, along the different digital touchpoints.
Regarding trust, it also will be interesting to analyze how the digital technologies of the luxury hos-
pitality brands will help to the clients to feel safe on testing health and sanitary safety in any concrete
hotel, tracking and tracing security checkpoints prior to the experience in the hotel though digital tools,
being updated on potential changes or urgent communications between hotel and client.
Regarding safety measures, yet it has not been researched on how the customer journey into digital
platforms can help to spread the voice of the contactless technologies provided by luxury hotel brands,
in particular the contactless way to interact with the existing onsite luxurious service but controlling
new contactless aspects of the customer journey.
This potential research should welcome factors as the automation during the process, or the usability
of the virtual and augmented reality as well as updated health safety requirements on demand. Since CO-
VID-19 transformed our travel motivations, finally, another future research could be conveyed to highlight
and discuss how to pandemic transformed the motivations of the clients of the luxury hospitality brands.
This transformation can be analyzed not only from the perspective of the length of the stay, but also
about the motivation of the stay, being interesting to research on how the new messages based on health
and safety provided through digital channels are attracting new motivations of the clients to travel to these
hotels, as the remote work, or looking for an added value on protection, providing feasible guarantee of
safety when luxury guests travel to luxury hotels.
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Affluent Guest: Guests with annual incomes that should be considered a very rich person.
Chatbot: A computer program designed to simulate conversation with human users, especially over
the internet. Hospitality is the computer program designed to stablish interactions between the luxury
hotel and the hotel guests.
Customer Journey: Eat describes the path of a guest of any hotel from the very first moment that
they get information about the hotel until they leave the hotel after their experience.
Digitalization: At the early technological services times, the conversion of text, pictures, or sound
into a digital form that can be processed by a computer. Currently adding conversion of physical services
for example.
Guest Service: Is the mix of tangible and non-tangible services offered from a hotel to a guest, from
the social interactions to the physical interactions including the products consumed and experienced by
the guests in the hotel.
Luxury Hotel: This is a hotel with high level service in terms of the quality of the products and the
services offered.
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Chapter 15
Approach to Social Media
Marketing Strategies in
Different World Regions:
A Descriptive Study
Luis Matosas-López
https://orcid.org/0000-0001-7313-0146
Roberto Baelo Alvarez

https://orcid.org/0000-0003-1003-6739
University of León, Spain
ABSTRACT
The present study, using a sample of university organizations from different world regions, aims to
provide an overview of social media marketing strategies used in different geographical locations. For
this purpose, the authors conducted a descriptive study of the communication patterns implemented
by university institutions in four regions: Africa and the Middle East, North America, Latin America,
and Europe. The study, which adopts a comparative format, contrasts the findings obtained in each of
the aforementioned regions, highlighting the existence of both similarities and differences in the social
media marketing strategies of the organizations observed. In line with previous research, the authors
took Twitter as the social media platform to be monitored.
INTRODUCTION
The development of Information and Communication Technologies (ICTs), in general, and the emergence
of the Internet, in particular, has caused numerous social changes in the past decades (Cabero Almenara
et al., 2007; Feng et al., 2019; García-Jiménez et al., 2013; García-Ruiz et al., 2015; García Galera et
al., 2017; Gómez-García et al., 2020; Richter et al., 2011).
DOI: 10.4018/978-1-7998-8003-5.ch015

Approach to Social Media Marketing Strategies in Different World Regions
Access to the Internet among the citizenry has resulted in an increase in the use of social network
services. By 2020, the number of Internet users worldwide reached 4.54 billion, being the average pen-
etration of social media users 49% (Kemp, 2020). This average penetration in global terms obviously
varies between countries. Thus, for example, the percentage in Ghana is 20%, in Egypt 41%, in Germany
45%, in Colombia 69%, in the US 70% and in South Korea 87% (Kemp, 2020).
The impact of these platforms over time is undeniable, and their integration into our daily life is a
consummate reality (Piscitelli, 2010). The success of social media is caused by different factors, but
among them stand out aspects such as the dynamism of the content, its collaborative utility, its intuitive
use, its easy access and its interactive nature (Castaño et al., 2015).
Nevertheless, what is a social network service? Castañeda Quintero (2010) generically defines these
platforms as those telematics tools organized around user profiles, personal or professional, that pursue
the objective of connecting people with common interests.
The literature review by Almansa, Fonseca and Castillo (2013) differentiates three major topics of
research in this field: (a) user representation and generation of links between users (Junco, 2012; McAn-
drew and Jeong, 2012); (b) structuring of the network around interests and motivations (Backstrom et
al., 2006; Liu et al., 2006); and (c) privacy and risks of the media (Calvete et al., 2010; McBride, 2011).
The information stored on these platforms provides a vast record of thoughts and behaviors of in-
dividuals of all types and social conditions. In recent years, users’ messages in social network services
have been used around the world to explore social, economic, and cultural realities of various kinds.
Examples of this are the analysis of the ideological polarization of political parties (Urman, 2020), the
prediction of economic fluctuations in stock exchange markets (Li et al., 2016), the dissemination of
hate messages after terrorist attacks (Bruns and Hanusch, 2017), the identification of environmental
problems in certain areas (Chen et al., 2015) and the momentum of social activism movements (Matsilele
and Ruhanya, 2020).
The widespread nature of the social media phenomena has transformed the way we communicate
and interact with our environment to the point of making it necessary to create a descriptive term for
the typical user of these platforms.
The academic community has recovered the term “prosumer”, a term created during the 1970s
(McLuhan & Nevitt, 1972; Toffler, 1980) to define the typical user of this media. Born from the union
of the ideas of producer and consumer, “prosumer” serves to describe those individuals capable of con-
suming what they themselves produce. More recently, other authors have created from this concept the
term “media prosumer” to refer to the user accustomed to the social media setting.
Sandoval Romero and Aguaded Gómez (2012), for example, describe the “media prosumer” as the
subject able to take a leading role in the media, producing and consuming information to generate a
culture of participation and interactivity. Sánchez Carrero and Contreras Pulido (2012), for instance,
define the “media prosumer” as the user who actively assumes the role of the communication channel,
becoming a recommender and opinion generator on a variety of topics.
However, the particularities and motivations of this “media prosumer” require an in-depth analysis.
For this purpose, one of the approaches commonly employed by researchers is the so-called Uses and
Gratifications Theory (U&G). Even though this approach has been applied in numerous studies on the
use of social media in recent years (Chen, 2011; García-Ruiz et al., 2018; Matosas López, 2018; Raacke
& Bonds-Raacke, 2008; Smock et al., 2011), U&G theory had been used previously to describe how
audiences interact with other mass media such as radio, press or television (Katz et al., 1973, 1974;
Ruggiero, 2000).
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The conceptual framework defined by this theory explores how mass media is used to meet the needs
of the individual. In accordance with Rubin (1994), U&G theory is based on five pillars: (1) the selec-
tion and use of the media have a goal; (2) the subject is the one who takes the initiative by selecting the
media in order to satisfy an existing need; (3) the subject’s behavior is conditioned by different social
factors; (4) there are different media alternatives that compete with each other in terms of selection, use
and needs satisfaction; and (5) the subject has a position of empowerment in the media.
The nature of the social media phenomena fits perfectly with the assumptions established by this
theory. Users, having other possibilities, freely choose these platforms; they access them to obtain a
reward; their communication is conditioned by social aspects; and they enjoy a position of privilege on
the platform. The potential of social network services to propagate information to large audiences, as
happens with other mass media (press, radio, or television), makes the U&G theory particularly suitable
for contextualizing research in this field.
Chapter Objective and Structure
This chapter, using as a theoretical framework that of the U&G theory described above, aims to explore,
from an international perspective, the social media digital marketing strategies carried out in different
geographical locations around the world.
For this purpose, the authors conduct a descriptive study of communication patterns implemented
in university organizations of four regions: Africa and the Middle East, North America, Latin America,
and Europe.
The study adopts a comparative format, addressing the similarities and differences in the social media
marketing strategies of the organizations subject to observation.
The chapter structure is organized as follow. Firstly, the chapter presents a Theoretical Background
section in which the authors address, on the one hand, the importance of social media at university
organizations and, on the other, the relevance of these techniques as marketing and communication
tool in these institutions. Secondly, the Methodology is presented detailing all the information required
within the subsections of: Sample Design, Data Extraction and Cleaning, and Data Analysis. Next, the
authors show the main findings obtained in the Results section. To conclude, in the chapter’s final part,
the traditional sections of Discussion and Conclusions are presented, the last one including specifically
subsections of Professional and Managerial Implications and Limitations and Future Research.
THEORETICAL BACKGROUND
Social Media at University Organizations
Within the university context, U&G theory is also used in numerous studies. In this regard, most au-
thors who adopt U&G theory in their research examine the use patterns of these technologies among
university students. Examples of this are the studies by Durán and Guerra (2015), García-Ruiz, Tirado
and Hernando (2018), Florenthal (2015), and Doval-Avendaño et al. (2018).
Durán and Guerra (2015) explore the addictive behavior of students from different degree programs
in the use of the extinct social network Tuenti. García-Ruiz, Tirado and Hernando (2018) examine
the benefits that students find in the intensive use of Facebook, Instagram and YouTube. Likewise,
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Florenthal (2015) discusses the motivations and barriers that business students identify for the use of
LinkedIn. Finally, Doval-Avendaño et al. (2018) explore the reflections of a group of communication
students after remaining deprived of access to their digital devices, and therefore their social profiles,
over a 24 hour-period.
Another topic of study in the university context is the application of these technologies as a sup-
port for teaching. Notable in this regard are studies by Kabilan et al. (2010), Cabero and Marín (2014),
Santoveña-Casal and Bernal-Bravo (2019), and Matosas-López and Romero-Luis (2019).
Kabilan et al. (2010) examine the possibilities of using Facebook as a learning environment in English
language teaching. Similarly, the study developed by Cabero and Marín (2014) explores the instruction
potential of different social networks (Twitter, Facebook, LinkedIn or Hi5) for collaborative purposes.
The study conducted by Santoveña-Casal and Bernal-Bravo (2019) examines the use of Twitter as a mo-
tivational element in autonomous learning processes. Finally, the study by Matosas-López and Romero-
Luis (2019), with Marketing students, explores the correlations between usage patterns on Facebook,
Twitter and Instagram, and how students perceive the usefulness of certain digital learning resources.
Social Media in University Organizations as a

Marketing and Communication Tool
University organizations, like any other type of organization, set their goals in the context in which they
operate, to meet the needs of their target audiences. Although the target audience of any university,
within its social purpose, consists of a wide range of entities (government agencies, companies, social
agents, etc.), students in their broadest sense occupy a leading position. This category includes current
students, future students, and alumni.
With regard to the student community, then, universities design and implement recruitment and loyalty
plans in the same way that any company would do. Recruitment plans aim to reach as many subjects
as possible, and loyalty plans build links with current students and with those who will become alumni
after their graduation.
Within this context, examples of actions that could be part of recruitment plans are open days, par-
ticipation in educational fairs or advertising in the press, radio, or television at the local level. As far as
loyalty plans are concerned, we can point to discounts on subsidiary educational services, tuition funding,
or the creation of postgraduate programs linked to student’s undergraduate training.
These recruitment and loyalty plans are a good example of the type of actions that are framed in the
marketing strategies of any university organization, whether public or private. However, all the forego-
ing actions are contextualized in an offline setting. In addition to this offline setting, there is also a wide
range of potential actions within the digital sphere. Banners on educational portals, as a recruitment
mechanism; or newsletters, as a loyalty tool, are just two examples. In this area of marketing strategies
in digital environments, social network services are particularly relevant.
In recent years, these platforms have been used intensively by university organizations around the
world. Nevertheless, different authors claim that there is still wide margin for improvement in exploit-
ing these technologies as part of university marketing strategies. Casanoves Boix et al. (2018) point out
that universities should invest in a greater and more professionalized presence on social media, in order
to enhance their branding strategies. In the same line, Guzmán Duque et al. (2012) underline that these
technologies should help universities to consolidate their corporate identity and to develop promotional
and recruitment campaigns in the territories in which they operate.
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Although social media marketing strategies still have a wide margin for improvement, numerous
works have addressed this issue in recent years. Table 1 shows some of the studies conducted over the
past ten years.
Laaser et al. (2012) use semi-structured interviews with management experts to analyze the use given
to social network services such as Facebook, Twitter, LinkedIn and Google+. Concerning the use of
these platforms, the authors reveal the existence of management problems, lack of strategic vision, and
the need to define efficiency and reach indicators.
Kuzma and Wright (2013) study the role of Facebook, Twitter, LinkedIn and MySpace as a catalyst
in the transformation of marketing strategies in the university context. According to these researchers,
when such technologies are properly integrated into the organization’s strategy, they not only create
added value for the institution’s audience and stakeholders, but also act as a recruitment instrument.
The study by Valerio Ureña et al. (2014), focusing on Facebook, examines the engagement between
institution and target audience on this platform. The authors identify that time of publication impacts
effectiveness in terms of likes, comments, and shared content; showing that the most successful publica-
tions occur outside the workday and usual office hours.
Bulbulia and Wassermann’s research (2015) discusses the possibilities of using Twitter as a channel
of interaction with the student community. In this work, the authors emphasize the possibilities of using
social network services in the university context, and that the communication potential inherent to these
technologies is not always exploited.
Túñez López et al. (2015) analyze the usefulness of Facebook and Twitter as digital communication
tools in a sample of universities. In addition to showing that most publications were concentrated in the
early and central part of the week, the authors point out that the content is fundamentally composed of
images and links.
Similarly, Laudano et al. (2016) examine university libraries’ use of the social network Twitter. Their
findings reveal that although libraries use this platform to disseminate information about collections,
services, or to promote activities, their use is generally diffuse and poorly planned from the strategic
point of view.
The study conducted by López-Pérez and Olvera-Lobo (2016) explores the use of social networks
for the dissemination of research results at the public university. The authors note that around 40% of
the institutions examined use their corporate Facebook and Twitter accounts in the propagation of this
type of content.
Puertas Hidalgo and Carpio Jiménez (2016) examine universities’ use of Facebook, Twitter and
Instagram platforms from a strategic perspective. The authors point out that the engagement generated
throughout these social network services help the organization in achieving its strategic objectives.
Cabrera and Camarero’s work (2016) analyses the communication channels used by universities for
the dissemination of science and technology events. Among other findings, shows that 80% of students
use Facebook, even above the university website, to be informed of their faculty events.
Peruta and Shields (2017) study how Facebook can improve engagement between university organiza-
tions and stakeholders. The authors demonstrate that aspects such as type of publication or publication
frequency can contribute to improving both engagement with the audience and dissemination of the
organization’s content.
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Table 1. Studies on the use of social media as a marketing and communication tool in the university context
Region where the study was

Author/s Platform considered
conducted
Laaser et al. (2012) Facebook, Twitter, LinkedIn, and Google+ Latin America
Kuzma and Wright (2013) Facebook, Twitter, LinkedIn and MySpace Asia, Africa, and Europe
Valerio Ureña et al. (2014) Facebook Latin America
Bulbulia and Aquarius (2015) Twitter Africa
Tuñez López et al. (2015) Facebook and Twitter Europe and Latin America
Laudano et al. (2016) Twitter Latin America
López-Pérez and Olvera-Lobo (2016) Facebook and Twitter Europe
Puertas Hidalgo and Carpio Jiménez (2016) Facebook, Twitter, Instagram, and Google+ Latin America
Cabrera and Waiter (2016) Facebook Latin America
Peruta and Shields (2017) Facebook North America
Bodunde et al. (2017) Facebook and Twitter Africa
Kimmons et al. (2017) Twitter North America
Quintana Pujalte et al. (2018) Twitter Europe
Wu et al. (2019) Facebook Asia
Matosas-Lopez and Romero-Ania (2020) Twitter Europe
Likewise, Bodunde et al. (2017) compare the internal and external communication strategies of uni-
versity organizations and banking companies. After exploring the uses given to Facebook and Twitter,
among other marketing instruments, the authors emphasize that transparency and dialogue are key in
the success of any organization, regardless of its nature.
Kimmons et al. (2017), analyzing a sample of 5.7 million Twitter messages from higher education
institutions, underline that although social media has improved the reach of these organizations, their
current reach is limited. The authors reveal that most of the messages from these institutions are one-
way, lacked any feeling and focused on a very small variety of topics.
The work of Quintana Pujalte et al. (2018) explores the use of social media accounts to respond to
situations of institutional crisis. The study reveals how the Twitter social profile can be used in such
circumstances to redirect the flow of corporate communication, either to the official university website,
or to press releases.
Wu et al. (2019) analyze the comments that publications from a sample of university institutions were
able to generate on Facebook. The authors reveal that messages using a friendly tone receive a higher
volume of comments than those using a direct, rigid tone.
Finally, the study by Matosas-López and Romero-Ania (2020) explores the variables that allow more
efficient management of university organizations on Twitter. The authors reveal that the use of links,
hashtags, messages in the early morning or publications on gender equality issues contribute to increase
audience interaction with the institution.
All these works consider platforms such as Facebook, Twitter, Instagram, LinkedIn, Google+ or
MySpace. However, these last two have gradually lost importance in favor of the former, just as did other
platforms considered in previous investigations (Cabero-Almenara and Marín-Díaz, 2014; Durán and
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Guerra, 2015; Espuny Vidal et al., 2011; Gómez-Aguilar et al., 2012; Laaser et al., 2012), for instance
Hi5, MySpace, Tuenti, or Xing.
In addition, as can be seen in the third column of Table 1, the foregoing studies also show the vari-
ety of geographical locations considered in these investigations. Argentina, Ecuador, the United States,
Spain, China, England, and Nigeria are just a few examples of countries whose university organizations
have been the subject of study. In this regard, although Latin America and Europe are the regions where
most studies have been carried out, we also find studies carried out in Africa, North America, and Asia.
Main Focus of the Chapter
The present study, using a sample of university organizations from different regions around the world, aims
to provide an overview of the social media marketing strategies used in different geographical locations.
For this purpose, the authors conduct a descriptive study of communication patterns implemented in
university institutions from four regions: Africa and the Middle East, North America, Latin America,
and Europe.
The study, which adopts a comparative format, contrasts the findings obtained in each of the afore-
mentioned regions, highlighting the existence of both similarities and differences in the social media
marketing strategies of the organizations observed.
In line with previous research (Guzmán Duque et al., 2013; Kimmons et al., 2017; Laudano et al.,
2016; Matosas-López and Romero-Ania, 2020), the authors took Twitter as the social media platform
to be monitored.
The relevance of social media marketing strategies is beyond question. However, despite this fact,
relevant evidence on the way to improve these strategies is still necessary. In this sense, the literature
review carried out by Saura (2021) have recently underline the need to generate insights on these tech-
niques in order to improve the strategies implemented in this field.
The originality and main value of this study lies in the analysis of social media marketing strategies
from an international approach and perspective. This research reveal the similarities and divergences
in social media marketing strategies in different geographical locations. Addressing that the general
paradigms followed on the planning of these strategies, probably require reformulation when they have
to be adapted to the specific realities of the geographical locations where they are implemented.
METHODOLOGY
Sample Design
The selection of sampling elements took into account two of the most recognized rankings for assess-
ing the activity of university organizations worldwide: the Webometrics list (Marciniak, 2013) and the
Academic Ranking of World Universities (ARWU), also known as the Shanghai ranking (Túñez López
et al., 2015).
The Webometrics ranking was taken as the starting point in the selection of sampling elements. This
ranking, developed by the Cybermetry Laboratory of the Higher Council for Scientific Research (CSIC)
in Spain, not only measures the web presence and visibility of higher education institutions worldwide
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but also offers a categorization by geographical region that satisfies the comparative objective of the
study (assessment criteria shown in Table 2).
Table 2. Webometrics ranking assessment criteria
Assessment criteria Description Criteria weight

Size (nº of pages) of the institution’s main web domain. It
Presence includes all the subdomains that share the same main web 5%
domain.
Nº of external networks (subnets) linking to the institution’s
Visibility 50%
web pages (normalized and then average value).
Transparency or openness Nº of citations from top 210 authors. 10%
Nº of papers amongst the top 10% most cited in one of the 26
Excellence or scholarship 35%
disciplines of the full database.
Total 100%
Source: Webometrics
The information extracted from the Webometrics ranking was then screened by the authors using the
ARWU global ranking. This ranking of universities, designed by a group of experts from the Shanghai
Jiao Tong University, lists the 1000 most reputable universities worldwide (assessment criteria shown
in Table 3).
Table 3. ARWU ranking assessment criteria
Assessment criteria Description Criteria weight

Alumni of an institution winning Nobel Prizes and Fields
Quality of education 10%
Medals.
Faculty quality I Staff of an institution winning Nobel Prizes and Fields Medals. 20%
Faculty quality I Highly Cited Researchers. 20%
Research output I Papers published in Nature and Science. 20%
Papers indexed in Science Citation Index-Expanded and Social
Research output I 20%
Science Citation Index.
Per capita performance Per capita academic performance of an institution. 10%
Total 100%
Source: ARWU
Researchers began with universities in the top 50 of the Webometrics rankings in each region (Africa
and the Middle East, North America, Latin America, and Europe). After that, the authors checked whether
these institutions appeared also in the ARWU global ranking. After this verification, we took the first
ten universities in each region that met the following two criteria: (1) among the top 50 of their regions
in the Webometrics list, and (2) among the top 1000 in the world according to the ARWU ranking. The
ten universities selected for each of the four regions examined are presented in Table 4.
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For future investigations (after this work) only organizations that use English or Spanish to interact
with their audiences were selected. This allows researchers to analyze text publications in the future. For
the same reason, Asia, where university organizations tend to use the local language in their publications,
was also eliminated for study purposes.
Data Extraction and Cleaning
Once the sampling elements were selected, researchers extracted from the Twitter platform all content
published during 2019 by the official accounts of the forty institutions. Following the procedure of
previous studies (Alkadri et al., 2015; Quintana Pujalte et al., 2018), the data were extracted through
Twitter’s API using the service provider Twitonomy.
This process allowed the authors to gather a total of 137,463 publications. Of these messages, 98,169
were tweets originally created and published by the university, 26,483 were retweets from the account
to third-party publications, and 12,811 were replies from the organization when mentioned by another
user of its audience.
The compiled dataset was stored for cleaning, extracting a total of thirty-two indicators organized
into five categories: (a) Publication volumes, (b) Publication components, (c) Publications by day of the
week, (d) Publications by time slot, and (e) Followership (see Table 5).
From the thirty-two indicators extracted, those corresponding to categories (a), (b), (c), and (d)
served to examine social media marketing strategies in the regions analyzed in the study. Likewise, the
eight indicators in category (e) served to obtain a first impression of the success of these strategies in
each region.
Data Analysis
The authors, in line with similar research (Balan, 2017; Guzmán Duque et al., 2013; Matosas-Lopez,
2020; Valerio Ureña et al., 2014) in this field of study, applied descriptive analysis to the information
collected.
The publications extracted for the selected sampling elements in each region were analyzed in an
aggregated manner for the different areas (Africa and the Middle East, North America, Latin America,
and Europe). All the analyses were conducted using the statistical software IBM SPSS in its version 26.
RESULTS
The results of the descriptive exploration of the 137,463 publications analyzed are presented following
the categorization in Table 5: (a) Publication volumes, (b) Publication components, (c) Publications by
day of the week, (d) Publications by time slot, and (e) Followership.
Within each category, findings are presented in a disaggregated manner for each of the four regions
under observation: Africa and the Middle East, North America, Latin America, and Europe.
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Table 4. Universities by region and country
Region / University Country

Africa and the Middle East
American University of Beirut Lebanon
King Abdullah University of Science and Technology Saudi Arabia
Stellenbosch University South Africa
University of Cape Town South Africa
University of Ibadan Nigeria
University of Johannesburg South Africa
University of KwaZulu-Natal South Africa
University of Pretoria South Africa
University of South Africa South Africa
Wits University South Africa
North America
Columbia University USA
Cornell University USA
Harvard University USA
Johns Hopkins University USA
Massachusetts Institute of Technology (MIT) USA
Stanford University USA
University of California, Berkeley USA
University of Michigan USA
University of Pennsylvania USA
University of Washington USA
Latin America
Catholic University Chile
University of Buenos Aires Argentina
University of Chile Chile
Federal University of Minas Gerais Brazil
Federal University of Rio de Janeiro Brazil
Federal University of Rio Grande del Sur Brazil
Federal University of Santa Catarina Brazil
National Autonomous University of Mexico Mexico
University of São Paulo Brazil
State University of Campinas Brazil
Europe
Cambridge University England
ETH Zurich (Swiss Federal Institute of Technology Zurich) Switzerland
Imperial College England
King’s College London England
Oxford University England
The University of Edinburgh Scotland
The University of Manchester England
University College of London England
University of Copenhagen Denmark
Utrecht University Netherlands
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Table 5. Indicators extracted from the dataset
Number of
Category Indicator name
indicators
(a) Publication volumes 6 Tweets, Retweets, Replies, Daily Tweets, Daily Retweets, Daily Replies
(b) Publication Components 3 Mentions by post, Links by post, Hashtags by post
Post on Monday, Post on Tuesday, Post on Wednesday, Post on Thursday, Post
(c) Publications by day of the week 7
on Friday, Post on Saturday, Post on Sunday
Post 8:00 a.m. - 10:00 a.m., Post 11:00 a.m. - 13:00 p.m., Post 14:00 p.m. -
(d) Publications by time slot 8 16:00 p.m., Post 17:00 p.m. - 19:00 p.m., Post 20:00 p.m. - 22:00 p.m., Post
23:00 p.m. - 1:00 a.m., Post 2:00 a.m. - 4:00 a.m., Post 5:00 a.m. - 7:00 a.m.
Followers, Ratio followers / following, Tweets Retweeted, % of Tweets
retweeted over total posts, Times each Tweet retweeted is retweeted, Tweets
(e) Followership 8
marked as favorite, % of Tweets marked as favorite over total posts, Times each
Tweet marked as favorite is marked as favorite
Total 32
Publication Volumes
With regard to the publication volumes, results in Table 6 show that universities in North America and
Latin America carry out more intense activity than institutions in Africa and the Middle East or Europe.
Such a situation can be seen in the indicators of Tweets and Retweets.
Conversely, the response indicator presents values that invite reflection. The regions of North America
and Latin America, the most active in terms of Tweets and Retweets, are the ones that show the lowest
average of Replies. This fact points to the existence of one-way messages in these locations.
Table 6. General publication volumes
Tweets Retweets Replies

Region M SD M SD M SD
Africa and Middle East 1655.50 1041.02 537.10 613.68 285.20 394.67
North America 2300.60 855.95 791.30 473.78 150.00 208.18
Latin America 2311.70 1119.97 381.80 458.06 222.20 330.36
Europe 1372.00 1070.01 323.70 207.72 333.30 513.42
Total sample 1909.95 1069.65 508.45 480.28 247.67 369.88
The exploration of this same information in daily terms corroborates what is shown in Table 6. The
universities analyzed in North America and Latin America have the lowest daily response ratios, with
averages of 0.41 and 0.60, respectively. By contrast, the institutions in Africa and the Middle East or
Europe show average response rates of 0.78, in the first case, and 0.91, in the second.
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Table 7. Daily publication volumes
Daily tweets Daily retweets Daily replies

North America 6.28 2.33 2.16 1.29 0.41 0.57
Latin America 6.31 3.059 1.04 1.25 0.60 0.90
Europe 3.74 2.92 0.88 0.56 0.91 1.40
Total sample 5.21 2.92 1.39 1.31 0.67 1.01
Publication Components
The information in Table 8 shows the degree to which the most characteristic components of Twitter
publications are employed in each region.
As far as mentions are concerned, European universities make the most intensive use of this function
with 0.86 mentions per publication. Additionally, links and hashtags reach their highest use levels in the
case of Latin American universities, with averages of 0.73 and 0.71, respectively.
Table 8. Publication components
Mentions by post Links by post Hashtags by post

North America 0.48 0.24 0.54 0.20 0.34 0.23
Latin America 0.45 0.37 0.73 0.19 0.71 0.92
Europe 0.86 0.46 0.46 0.16 0.64 0.38
Total sample 0.53 0.39 0.53 0.22 0.58 0.64
These findings are in line with the results in Publication volumes’ heading, at least as far as the Euro-
pean institutions are concerned. The organizations analyzed in Europe present the highest values in the
response indicators, both in general terms and on a daily basis, a fact which indicates the existence of a
dialogue between institution and target audience. In this regard, the mentions function, where European
universities stand out, is also a mechanism of direct interaction between brand and audience.
Publications by Day of the Week
Table 9 shows, again, the intensity level in posting activity, in this case presenting the information by
day of the week.
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In the four regions, a clear cut between workdays and weekend days can be appreciated. This fact
is reflected even in the institutions of Africa and the Middle East, areas where Muslim countries take,
generally, their rest-day on Friday. Such would be the case in the organizations of Lebanon, Saudi Arabia
and Nigeria listed in the sample. However, despite the above, there are no significant differences in this
region, probably due to the international dimension of the institutions under observation.
Table 9. Average number of posts by day of the week
Post on Post on Post on Post on Post on Post on Post on

Region
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Africa and Middle
294.60 286.60 311.10 318.50 287.50 104.30 52.90
East
North America 341.00 385.60 393.90 404.50 388.7 221.90 165.00
Latin America 422.40 443.30 444.40 421.10 414.60 93.20 72.70
Europe 218.00 240.00 249.40 297.60 235.00 75.40 56.60
Total sample 319.00 338.87 349.70 360.42 331.45 123.70 86.80
Regarding workdays, although it is true that no big variations between days were detected, there is
a slight increase in the activity around the central part of the week (Wednesday and Thursday). This
increase can be observed in all four regions under study.
Publications by Time Slot
Concerning the time of publication, Table 10 indicates similar patterns of action in all zones. In the four
regions, the bulk of the activity is concentrated in the morning and afternoon slots, from 8 a.m. to 4 p.m.
Table 10. Average posts by time slot
Post 8:00 Post 11:00 Post 14:00 Post 17:00 Post 20:00 Post 23:00 Post 2:00 Post 5:00
Region a.m. - a.m. - p.m. - p.m. - p.m. - p.m. - a.m. - 4:00 a.m. - 7:00
10:00 a.m. 13:00 p.m. 16:00 p.m. 19:00 p.m. 22:00 p.m. 1:00 a.m. a.m. a.m.
Africa and Middle East 513.60 459.10 254.00 125.80 25.20 5.00 21.20 251.60
North America 506.40 610.40 533.70 365.10 169.80 24.20 1.60 89.40
Latin America 444.30 691.20 560.90 354.30 142.90 13.50 0.30 104.30
Europe 447.20 373.30 331.40 110.00 19.80 1.50 0.90 87.90
Total sample 477.87 533.50 420.00 238.80 89.42 11.05 6.00 133.30
On the opposite side, what we call peak-off hours, the activity drops substantially in the 8 p.m. to 10
p.m. time slot, and falls down drastically between 11 p.m. and 4 a.m. This situation is also homogeneous
for all the locations examined.
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The exception to the aforementioned homogeneity in peak hours and peak-off hours is detected in
the time slot from 5 a.m. to 7 a.m. This period indicates that activity seems to start earlier in the regions
of Africa and the Middle East and Latin America.
Followership
The fifth category of indicators includes those elements that can serve as a sign of the success obtained
by the different social media marketing strategies in the regions analyzed. Table 11 provides an over-
view of the size of the organizations’ target audiences in each zone. In this regard, universities in North
America and Latin America seem to have significantly bigger target audiences than those observed for
Africa and the Middle East or Europe. This fact can be seen not only in the average number of followers
per account but also in the average ratio of followers / following per organization in each region.
Table 11. Followers per organization
Region Average followers Average ratio followers / following

Africa and the Middle East 120,198.40 358.14
North America 412,585.70 661.50
Latin America 393,387.50 989.06
Europe 151,049.80 187.50
Total sample 269,305.35 549.05
Tables 12 and 13 present the recognition obtained by the publications of each institution in each
region, on average. Table 12 displays acknowledgment obtained in terms of retweets, while Table 13
shows recognition achieved in terms of favorites.
The three retweet indicators are higher in North America and Latin America. In this point, perhaps
the average percentage of Tweets that are retweeted over the total number of posts made by the university
deserves special mention in Latin America. In this case, on average, 69.24% of posts are shared using
the retweet function by members of the organization’s audience.
Table 12. Retweets obtained per organization
Average % of Tweets retweeted Average nº of times each Tweet

Region Average Tweets retweeted
over total posts retweeted is retweeted
Africa and Middle East 776.80 55.05 97.64
North America 1313.80 57.29 158.90
Latin America 1535.20 69.24 158.20
Europe 765.00 51.57 112.10
Total sample 1097.70 58.29 526.84
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Regarding the favorite indicators, once again, the recognition achieved by the universities analyzed
in North America and Latin America stands out from the rest. Here, as it happens with the equivalent
indicator in Table 12, it is worth mentioning the acknowledgment obtained by publications in Latin
America. In this case, publications from the organizations examined in the region, on average, were
marked as favorites in 82.61% of cases.
Table 13. Favorites obtained per organization
Average nº of times each Tweet

Average Tweets marked as Average % of Tweets marked as
Region marked as favorite is marked as
Favorite favorite over total posts
favorite
Africa and Middle East 950.80 66.63 249.80
North America 1465.40 63.70 584.97
Latin America 1830.60 82.61 422.45
Europe 955.00 61.45 273.60
Total sample 1300.45 68.60 1530.82
DISCUSSION
The importance of social network services in digital marketing strategies is undeniable. This fact is
confirmed by the literature review on branding and marketing strategies in social media carried out by
Cuevas-Molano et al. (2019). These authors examined articles indexed in the Web of Science (WOS)
database over the previous fourteen years and underlined not only the existence of a mature and consoli-
dated field of study, but also the relevance of a topic that concerns academics and professionals alike.
The dynamic and changing nature of these technologies forces brands and organizations to periodi-
cally analyze the data generated by these platforms, in order to extract the knowledge hidden therein.
The millions of interactions that happen daily on social network services, between organizations and
users, generate a huge volume of information that can be analyzed (Phillips et al., 2017).
In addition, social media marketing strategies are not homogeneous and universal, but depend on the
market or sector, the target audience of the organization or the geographical area in which the actions
are implemented (Matosas-Lopez, 2020). Thus, for example, a social media strategy can be efficient in
one particular market and useless in a different sector. Similarly, a certain strategy can be appropriate
in one geographical location and inadequate in another.
Different authors underline that social media marketing strategies, and media discourses, must be
redefined and customized in accordance with the specific demands of markets or sectors, target audi-
ences and geographical areas.
In this sense, Saura et al. (2021) emphasizes that organizations must personalize their publications
based on the needs of their audiences. Scheffert (2011), for example, indicates that due to the digital
revolution, marketing paradigms have drastically changed demanding higher leves sophistication and
personalization in the relationship between audience and organization. Park et al. (2011) address that,
in the management of social media strategies, factors such as the interaction or the customization have
a direct influence on brand loyalty indicators. Likewise, Tong and Chan (2020) underline that, in the
275

digital era, market-oriented approches are essential in the relationship with our target audience in social
network services.
In addition, some studies suggest that these platforms require professionalized management systems
and that their management cannot be left to non-specialized professionals (Casanoves Boix et al., 2018;
Laaser et al., 2012). Other authors claim that organizations manage these technologies without a de-
fined strategic vision (Laudano et al., 2016). In the same line, there are also studies which indicate that
organizations should not settle for using their accounts to build their institutional image, but must also
protect their reputation (Gureeva, 2018). Certain authors even assert that properly managed social media
can become a powerful recruitment tool (Guzmán Duque et al., 2013).
The findings in this study provide academics and professionals with a good overview of social media
marketing strategies that can be effective in different geographical locations around the world.
Publication Volumes
In terms of publication volumes, while strategies in North America and Latin America display the high-
est volume of Tweets and Retweets, the regions of Africa and the Middle East and Europe stand out for
their response ratios.
These findings corroborate the results of Chen (2011) in his research on uses and perks on Twit-
ter. Study in which the author points out that a high volume of posts acts as a motivating element that
encourages the subject to interact with other users. Nevertheless, the low response rate in the American
regions indicates more unidirectionality in the social media marketing strategies employed in these areas.
Publication Components
As far as publication components are concerned, European strategies emphasize the use of mentions,
while Latin American universities accentuate the use of links and hashtags.
Authors such as Túñez López et al. (2015) and Guzmán Duque et al. (2013) have stressed the im-
portance of links and hashtags in social media strategies. Examining the role of social networks as
communication channels, they highlight the potential of these elements in facilitating promotion and
projection of the organization in front of its target audiences.
However, Latin America’s prioritization of link and hashtag use in preference to mentions’ (traditional
indicator of dialogue), seems to corroborate the previously mentioned unidirectionality of their social
media marketing strategies.
Publications by Day of the Week
Day of publication, unlike the previous aspects of publication volume and publication components,
does not show variations between regions. In all cases, the strategies applied concentrate the activity on
workdays, in general, and on Wednesdays and Thursdays, in particular.
These results are in line with studies by Túñez López et al. (2015) and Valerio Ureña et al. (2014),
where the authors emphasized the importance of publication frequency in the central part of the week.
Nonetheless, these findings contradict the research of Hanifawati et al. (2019) on brand management in
Facebook, where no significant differences were observed regarding day of publication, within workdays.
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Publications by Time Slot
Concerning time of publication, just as with day of publication, there is also homogeneity between areas.
In the four regions under study, most of the activity is concentrated in the morning and afternoon time
slots, more specifically between 8 a.m. and 4 p.m.
These results are aligned with the findings of Hanifawati et al. (2019) and Valerio Ureña et al. (2014)
mentioned above. These studies underline that strategies with high publication frequencies in the first
part of the day tend to be positively perceived by the organization’s target audience.
Followership
Finally, regarding audience response, the results show that in North America and Latin America not only
did brands achieve greater followership on the platform, but also more user proactivity than in Africa
and the Middle East or in Europe.
This fact confirms what has been stated by previous studies on marketing and communication-related
strategies in Latin American university institutions (Cabrera and Camarero, 2016; Guzmán Duque et al.,
2012; Laudano et al., 2016; Puertas Hidalgo and Carpio Jiménez, 2016). These studies always stress the
importance and weight of social network services in institutional strategies.
CONCLUSION
Certain aspects of social media marketing strategies are universal and are managed similarly in different
regions worldwide. Examples from our study were days of the week and times of publications. However,
we can also identify aspects where important differences were detected, depending on the geographical
area analyzed.
In this regard, it is worth mentioning that publication volumes were notably higher in North America
and Latin America than in the locations of Africa and the Middle East, and Europe. Such a situation of
heterogeneity in social media marketing strategies can also be seen in the publication components. While
in Latin America the use of links and hashtags was prevalent, organizations in Europe put emphasis on
the use of mentions. These nuances were also detected in the followership achieved by organizations,
more intensive in North America and Latin America than in Africa and the Middle East, or in Europe.
The originality and main value of the present research lies in the international approach and perspec-
tive applied by the authors. The findings obtained in the present study lead us to reflect on differences
in social media marketing strategies in different regions worldwide.
While strategies in social network services should be based generally on dialogue and interaction with
the organization’s target audience, this point can be more or less critical depending on the geographical
region in which the brand operates. While Europe confers great importance on the organization’s inter-
action with its audience (proof of this is the intensive use of replies and mentions), both North America
and Latin America tolerate a greater degree of one-way communication. This is illustrated in that fol-
lowership does not appear to be affected despite the lower degree of dialogue observed, according to
the indicators analyzed during the study.
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All the foregoing, then, leads to the conclusion that the paradigms governing the definition of social
media marketing strategies at the global level, a priori universal, probably must be reformulated to fit
on the particular realities of the regions where these strategies are applied.
Professional and Managerial Implications
In light of the findings obtained, the next professional-managerial implications can be pointed out for
the design and implementation of social media marketing strategies.
In first place, the acceptance of our audience to a certain bulk of daily publications may differ con-
siderably depending on the geographical location. Secondly, the volume of mentions, links, and hashtags
per publication may have better or worse acceptance, depending on the region in which our organization
operates. Consequently, each organization must identify the adequate management patterns in accordance
whit its geographical location and its audience.
In conclusion, it can be said that management approaches in social media marketing strategies are
influenced, among other variables, by the geographical area of the organization and its target audience.
This situation forces marketing managers, in general, and social media managers and community manag-
ers, in particular, to develop a significant effort to adapt and customize their actions.
Finally, several limitations and prospects for future research can be addressed. Firstly, the investigation
covers Africa and Middle East, North America, Latin America, and Europe; however, no geographical
location is considered in Asia. Unfortunately, the platform monitored (has it happens whit other social
network services) has a scarce presence in many of the territories of the Asian continent, which makes
it impossible to carry out a comparative analysis under identical conditions. Future research should look
for social media platforms with a minimal coverage in the Asia region, in order to expand this compara-
tive analysis from an, even more, international perspective, if possible.
In second place, this research examines exclusively university organizations, ignoring other sorts
of organizations. Therefore, future research should take into consideration, for instance, the analysis of
social media marketing strategies in business organizations. This type of study would allow to expand
the knowledge generated in the present study, revealing to what extent the findings obtained here can
be extrapolated to other forms of organizations and target audiences.
Despite these limitations, the present study, in the authors’ opinion, offers, academics and profession-
als, knowledge of relevance, addressing in addition new opportunities for future research.
ACKNOWLEDGMENT
This research received no specific grant from any funding agency in the public, commercial, or not-for-
profit sectors.
278

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Followership: Intentional practice of someone in a subordinate role to enhance the synergetic inter-
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Hashtag: Word or phrase, preceded by a sign (#), used on social network services to identify digital
content on a specific topic.
Media Prosumer: Term created to define the typical user of social network services.
Prosumer: Term created to define the typical user of mass media.
Target Audience: Group of subjects most likely to want a product or service, and therefore, the group
of people who follow the organization.
Uses and Gratifications Theory (U&G): Conceptual framework used to describe how audiences
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About the Contributors
Jose Ramon Saura research has focused on the theoretical and practical insights of various aspects
of User Generated Content (UGC), with a specific focus around three major research approaches applied
to business and marketing: data mining, knowledge discovery and information sciences. International
Ph.D. in Business Economics at the Rey Juan Carlos University, London South Bank University (LSBU)
and Harvard University (RCC). He is a Marketing Lecturer of the Department of Business Economics
at the Rey Juan Carlos University, lecturing at the Faculty of Social Science and Law of Madrid. He has
published articles focused on areas such as Business, Management, Computer Sciences, Multidisciplinary
or Information Sciences, among others. His research has appeared in leading international journals such
as the Journal of Business Research, International Journal of Information Management, Journal of Busi-
ness & Industrial Marketing, Review of Managerial Sciences, Journal of Innovation and Knowledge,
Journal of Mobile Communication, IJERPH or IEEE Access. Jose Ramon Saura serves on editorial and
reviewer boards of a number of business and information sciences journals.
***
Roberto Baelo Alvarez is a Senior Lecturer/Associate Professor (Department of General and Specific
Didactics and Educational Theory) and the current Vice-Rector/Vice-President/Pro-Vice-Chancellor
for Internationalisation at the University of León. Ph.D. in Psychology and Educational Sciences. He
is leading the inter-university research educational group “Edutools”. His teaching and research activi-
ties are linked to promoting methodological innovations through the integration in the education of the
Information and Communication Technologies (ICT). He has participated in a large number of research
projects financed by national and international governmental entities. Besides, he has also taken part in
several projects of educational innovation. All this activity has been reflected in his publications, related
to methodological innovations, teacher training programs, the integration of ICT in education, as well as
with the counseling, guidance, quality, and equity in education, just as attention to diversity. He is part
of the scientific committee of several national and international journals and he has taken part in presti-
gious conferences and workshops. He participates in numerous editorial boards and is a usual reviewer
for renowned national and international journals. Moreover, the National Committee that evaluates the
investigative activity of Spanish researchers, which belongs to the Spanish Ministry of Education, has
evaluated positively his research and knowledge transfer activities.
Belem Barbosa received her PhD in Business and Management Studies – Specialization in Marketing
and Strategy from the University of Porto, Portugal. She is Assistant Professor at the School of Economics

and Management of the University of Porto. She is full researcher at GOVCOPP, the Research Unit of
Governance, Competitiveness, and Public Policies, and Invited Researcher at cef.up Center for Econom-
ics and Finance at the University of Porto. Her research interests lie primarily in the area of consumer
behavior, including word-of-mouth communication, internet marketing and sustainability marketing.
Margaret Bruce is Director of the Brandmovers Institute for Digital and Creative Economy and
Professor of Digital Strategy at London South Bank University. Her previous roles include Deputy Vice-
Chancellor of Derby University, leading research, innovation and enterprise, and Pro Vice-Chancellor
of UCLan, leading research and innovation. She has also worked as a research professor at Manchester
University, where she produced eight books and over 200 papers relating to design and innovation man-
agement, fashion marketing and branding. Professor Bruce has received a number of honorary profes-
sorships in recognition of her academic contribution.
Neslihan Cavlak is a senior lecturer at Tekirdag Namik Kemal University, department of Tourism
and Hotel Management. She holds a PhD degree on Marketing. She has an experience of 15 years in
lecturing. She is the author of numerous articles specifically dealing with marketing issues. She has
given speeches at leading marketing conferences.
Ruziye Cop is a professor of marketing at Bolu İzzet Baysal University, Faculty of Economics and
Administrative Sciences, Bolu-Turkey. She holds a PhD degree on Marketing. She is the author of nu-
merous articles specifically dealing with marketing issues. She has given speeches at leading marketing
conferences.
Eloísa Díaz Garrido has a PhD in Economic and Business from the Rey Juan Carlos University (Ma-
drid- Spain). She has been a professor at the Faculty of Legal and Social Sciences of the Rey Juan Carlos
University (Madrid) since 1998. She teaches subjects related to Production and Operations Management
and Business Management in different Undergraduate and Postgraduate Degrees. His line of research
focuses on the strategy of operations in both industrial and service companies, environmental manage-
ment and the management of service companies and service processes in manufacturing companies,
as well as the Servitization process as a strategic decision of industrial companies, generating value by
incorporating services into products. She is a member of the URJC’s GES2ME group of excellence in
Service Science, Management and Engineering. She has participated as a researcher in Research Projects
and Contracts of the National Plan granted by the Ministry of Science and Innovation and Projects of the
Community of Madrid, focused on topics consistent with her research lines. She has presented differ-
ent communications and papers at national and international conferences. She is the author of different
publications in national and international journals and books since 2000.
Vera Gelashvili is an Assistant Professor of the Business Economics Department at King Juan Carlos
University. She was graduated in Economics and Business Administration and she was doctorate with
honours at the Complutense University of Madrid in June 2017. Previously she has completed a Master
of Research in Business Management, Marketing, and Accounting in Business Administration Faculty,
Complutense University of Madrid. She is the author of several papers and book chapters and presented
her research in the National and International Congress and Workshops. Member of the research group
INIFCO-UCM.
335
Vasco Paço Gomes earned his Master in Marketing from the University of Aveiro. His main research
interest management control and performance indicators.
Charles Graham is an associate professor in Marketing at London South Bank University Business
School and an adjunct research fellow at the Ehrenberg-Bass Institute, University of South Australia. His
research interests include long-term behavioural brand loyalty, competitive market structure modelling
and effective brand portfolio management. Before becoming an academic, Charles spent nearly 15 years
marketing national and international grocery and foodservice brands.
Rocío López Muniesa is a PhD candidate in fashion marketing at Rey Juan Carlos University.
Ammarah Marjan is a doctoral researcher and associate lecturer in Marketing at London South
Bank University Business School. Her research focuses on online loyalty programmes and involves
the scientific replication and extension of empirical norms to the management of loyalty relationships
in the customer base. Prior to her career in academia, Ammarah worked as a trade marketer at Nestlé
S.A., where she designed strategies to increase the sales and visibility of Nestlé brands in key local and
international accounts.
Rubén Martínez-Alonso is a Junior Researcher in the Economics and Business Department at the
University of Almería, Spain. He collaborates with the Family Business Chair at this University. His
primary research interests include family firm research focusing on emotional, innovative, management,
and finance aspects. He has published his latest research findings in international journals such as Review
of Managerial Science, Business Research Quarterly BRQ, European Journal of Innovation Management,
International Journal of Entrepreneurship and Small Business and, Management Research: Journal of
the Iberoamerican Academy of Management.
María J. Martínez-Romero is an Assistant Professor of Accounting and Finance in the Economics

and Business Department at the University of Almería, Spain. She collaborates with the Family Busi-
ness Chair at this University. Her primary research interests include family firm research focusing on
emotional, innovative, finance and corporate governance aspects. She has published her latest research
findings in international journals such as Small Business Economics, Review of Managerial Science,
Business Research Quarterly BRQ, European Journal of Innovation Management, Spanish Accounting
Review, Canadian Journal of Administrative Science, International Journal of Entrepreneurship and
Small Business and, Management Research: Journal of the Iberoamerican Academy of Management.
Luis Matosas-López earned a PhD in Economics, Master in Marketing, and Graduate in Business
Administration, all at Rey Juan Carlos University. Luis Matosas López has made more than twenty highly
relevant publications, all of them related to the use of ICTs and their implications in the management
of different types of organizations. These publications include articles published in journals indexed in
JCR-Wos and SJR-Scopus (Q1 to Q3) as well as different book chapters in publishers such as McGraw
Hill, Tirant Lo Blanch, Pirámide, or Tecnos, among others. His teaching focuses on the development of
ICT skills applied to management. Applications such as Excel or Access, CRM (Customer Relationship
Management) and ERP (Enterprise Resources Planning) solutions are the central core of their courses.
336
Before starting his career as teacher and researcher, he worked for several years at Vodafone Spain and
France Telecom (Orange) in the Marketing and Business Intelligence departments, respectively.
Juan Mejía-Trejo received his PhD in Management Sciences from the Instituto Politécnico Nacional
in 2010. He is former Manager of Teléfonos de México (1987-2008). In 2010 he was hired as Research
Professor in Management Innovation at the Universidad de Guadalajara and since 2015 is currently the
Management Sciences Coordinator. Since 2010 is a member of the National Researchers System of the
National Science and Technology Council (CONACYT). He writes and presents widely on innovation
issues, publishing articles in SSRN, Researchgate, Academia.edu, Google Academics and several important
portals and other book titles in Alfaomega and Patria editorials. His ORCID is 0000-0003-0558-1943.
Andrew Mitchell has over 25 years of global digital engagement experience with some of the world’s
leading marketers, and has worked on digital promotions for a diverse portfolio of global clients. His
areas of expertise span multiple industries, including consumer goods, entertainment, retail, travel and
financial services. Andrew earned his doctorate for studies in identifying and developing digital technol-
ogy that can be deployed and measured across various digital and social media channels. Dr Mitchell
founded Brandmovers in 2003 and has since expanded across multiple continents and industries. He is
a Chartered Marketer and a graduate of Manchester University and the Harvard Business School.
Zaila Oliveira received her PhD in Management and Business – Specialization in Marketing and
Strategy from the University of Porto, Portugal. She teaches undergraduate and postgraduate courses
at Christus University Center-UNICHRISTUS and Fametro University Center-UNIFAMETRO. Her
research interests include marketing, tourism, entrepreneurship, and social responsibility.
Paula Adriana Padilla researches digital marketing and the use of digital technology in the interac-
tion with customers. She is specialized in Marketing of Services & Business. Paula González-Padilla
held positions of project manager and made consultancy at a number of other private companies.
Alberto Prado Román is a professor at the Rey Juan Carlos University in Madrid (Spain), in the
Department of Business Economics, within the area of Marketing and Market Research. PhD. in Busi-
ness Economics. Master’s degree in Marketing. Participation in prestigious projects developed by the
Royal Academy of Economic and Financial Sciences, or by the Spanish Agency for International Co-
operation, among others. Author of several articles published in prestigious journals, such as Revista de
Administração de Empresas or Revista Europea de Dirección y Economía de la Empresa, and publishers,
such as Springer. Member of the organising committee of national and international conferences of the
AEDEM academy. He has taught in specialization and master courses, such as the Specialist Course in
Sports Management or the University Master’s Degree in Executive Management.
Miguel Prado Román is a professor at the Rey Juan Carlos University in Madrid (Spain), in the
Department of Business Economics, within the area of Accounting and Finance. PhD. in Business
Economics. Master’s degree in Auditing and Higher Accounting. Participation in prestigious projects
developed by the Royal Academy of Economic and Financial Sciences, or by the Spanish Agency for
International Cooperation, among others. Author of several articles published in prestigious journals, such
as Revista de Administração de Empresas or Revista Europea de Dirección y Economía de la Empresa,
337
and publishers, such as Springer. Member of the organising committee of national and international
conferences of the AEDEM academy. He has taught on specialisation and master’s courses, such as a
specialist course in Sports Management, University Master’s Degree in Senior Management or University
Master’s Degree in Auditing and Higher Accounting.
Beatriz Rodríguez Herráez is a professor of Marketing, Faculty of Economics and Business Ad-
ministration at the University Rey Juan Carlos, Spain. She obtained a doctor’s degree for the University
Complutense of Madrid, doctoral thesis realized with a scholarship granted by the General Secretary of
Tourism. She is the main investigator of the group EcoTuG (Economic, Tourism and gestion). Has pub-
lished more than 30 publications on tourism, marketing and communication in magazines and national
and international books. Nowadays it centres her investigation on the commercial communication and
the corporate social responsibility.
Daniel Rodríguez-Rodríguez is a recent graduate student in the Business Administration and Man-
agement Degree at the University of Almería.
Sandrina Francisca Teixeira is a lecture at Porto Accounting and Business School/ Polytechnic
Institute of Porto. She holds a PhD in Communication and Advertisement. At the moment, she is the
director of the Master in Digital Marketing. She coordinates the CEOS Marketing and Digital Com-
munication Strategies research line and is the Head of CEOS.PP - Centre for Organizational and Social
Studies of Polyethnic of Porto. Her main research field is marketing and digital marketing. The main
research projects are Behaviour of the Digital Consumers; Digital marketing; Accountability and Com-
munication in Local Government. She has published in national and international journals. In addition,
she is the project coordinator of TACLARO: Transparency, Accountability and Communication in
local public administration. She participated in several projects and has participated in national and
international conferences and, she also has scientific collaborations as a peer reviewer in national and
international journals and conferences.
Carlota Zori, as a Business Consultant in the CRM area, is responsible of the Deployment team
of CRM Dynamics in EMEA region. She worked also as Key Account Manager and she has been the
responsible of international expansion department on a company of health and beauty sector.
338
339
Index
A consumer behaviour 33, 41, 68-69, 72

Consumer-Centric Marketing 33
academic staff 229-231, 245 Consumer-to-Consumer (C2C) 98
Affluent Guest 260 content analysis 104, 106, 118, 120-122
Andalusia 172-173, 178, 183-184, 190 conversion rate 124, 128-131, 133-134, 137-139, 143
artificial intelligence 3, 13, 88, 90, 93, 96, 159, 212, COVID-19 44-47, 114, 119, 145-146, 156, 158-160,
225, 260 164, 167-170, 192-193, 198-202, 204-207, 212-
214, 222-224, 244, 248, 257-258
B Cross selling 157
customer 11-12, 17-18, 21-23, 25-27, 29, 32, 35, 40-
big data 1-4, 6, 8-33, 48, 57, 68-71, 73, 76-77, 81-82, 41, 51-69, 71-77, 82, 89-90, 92-93, 96-97, 100,
89, 92-96, 98, 114, 117, 145-146, 151, 154-157, 127-131, 134, 142, 145-157, 159, 165-167, 189,
194, 211, 215-216, 223-224, 280, 282, 285 193-195, 202, 204, 211-216, 219, 221-225, 246-
big data analytics 1-4, 6, 8-16, 26, 28-29, 31-32, 89, 257, 259-260, 284
94-95, 285 customer journey 32, 73, 90, 130, 246-257, 260
Big-Data 225 customers 2-3, 9, 17-18, 20, 23-27, 31-37, 40-44, 48,
bounce rate 123-124, 127-130, 133, 137-138, 143 50, 52-53, 56-61, 63-70, 73, 76, 78, 82, 84, 86-92,
business analytics 1, 18, 29-31, 58, 96 118, 124, 127, 129, 131, 139-140, 143, 145, 147-
Business-to-Business (B2B) 98 155, 157, 160, 166-167, 172, 174, 176, 180-181,
Business-to-Consumer (B2C) 98 183, 190-191, 193, 195, 201-202, 205, 211-213,
215-217, 219-220, 222, 229, 247-248, 250, 254,
C 257-258, 260, 284
cyberactivism 99-100, 120, 122
case study 13, 32, 47, 53, 74, 80-81, 95, 101, 104-106,
114-115, 118, 120-122, 149, 156, 185, 211, 213, D
224, 226-229, 243, 246, 251
channel marketing 51, 53, 55, 57, 60, 68, 77 data-driven marketing 16, 33, 56
Chatbot 260 Delphi method 192-193, 195-197, 204-205, 207, 209
Chinese companies 80, 82 design 14, 21, 26, 29, 48, 75, 107, 114-115, 120, 122,
click-through rate 123, 128-130, 133, 135-136, 138- 140, 142, 147, 152, 155, 158, 161, 164-169, 176,
139, 143 186, 193, 198, 208, 211-212, 215-217, 224, 237,
communication 3, 10, 12, 14, 18, 20-23, 27, 29, 35- 247, 253, 259, 263-264, 267, 278
36, 42-44, 47, 50, 57, 61, 65-66, 70, 73, 77-78, digital activism 99-100, 103, 117, 120
84, 89, 102, 106, 109, 111, 115-122, 156, 172- digital age 13, 19, 24, 26, 33, 60, 97, 99, 193-194,
175, 184, 189-193, 195-198, 200-205, 207, 209, 202, 204, 207
219-221, 224, 226-229, 237-240, 242-243, 245, digital business models 80-81, 193, 204
248-249, 253, 256, 258, 261-267, 276-277, 279, digital ecosystem 81, 93, 98, 115, 209, 222, 239, 254
281, 283-285 Digital Fashion Industry 192, 209
communication strategy 172-173, 190, 220 digital marketing 1-3, 6, 9-10, 12-14, 16-18, 22, 27-29,

Index
31-37, 40-50, 53, 56-57, 64, 66, 71, 74-75, 77, 80- hospitality 27, 30-31, 74, 142, 157, 185, 206, 211-213,
81, 83, 86-88, 92-93, 95, 103, 120, 123-124, 127, 215, 217, 221-223, 246-260
135-136, 142, 144-146, 155, 157-159, 161-171, hotel brands 246-248, 255, 257, 260
185, 189, 192-198, 200-209, 215, 217, 220-221, hotels 88, 119, 142, 224, 246, 248-250, 252-260
224-225, 244, 259, 263, 275, 283
digital marketing model innovation 158-159, 161, I
165, 169-170
Digital Marketing Tools (DMT) 164-168, 171 Inditex 145-147, 149, 152, 155-156
digital media 16, 22, 43, 91, 115, 172-174, 178, 183, influencers 29, 198, 209
190, 205 Instagram 21, 99-100, 103-104, 118, 178, 181, 201,
Digitalisation of the Restaurant 220, 225 236, 238, 261, 263-266, 279
digitalization 13, 16-17, 26-27, 46, 48, 81-82, 224, international digital marketing strategies 80-81, 88
246, 250, 255, 260 international expansion 80-81, 86-88, 93, 98
DMMI 158-159, 161-165, 167-169, 171 international marketing 47, 73, 81-82, 86, 92, 94, 96, 98
Internet of Things 17, 29, 157, 194
E
L
e-commerce 3, 9, 19, 56, 73, 80, 83-84, 86-90, 93, 95-
98, 145, 147, 149, 152, 154, 157, 197, 206-207 LinkedIn 21, 24, 131, 180, 226, 231-239, 242, 245,
education 20, 24, 46, 49, 71, 73, 117, 159-160, 170, 264-266, 280
226-227, 266-267, 279-282 literature review 1-2, 4, 9, 13-14, 16, 18, 30, 34-37,
efficiency 3, 17-18, 20, 22-24, 26, 53, 61, 63, 67-68, 40, 42, 44, 47-49, 71, 81-82, 95, 98, 131, 134,
128, 132, 139, 165, 184, 187-188, 211, 250, 258, 159, 165, 189-190, 203, 208, 213, 223, 248, 251,
265, 282 262, 267, 275, 283
Empirical Generalisation 77 loyalty programmes 51-57, 61, 65, 67-71, 74, 77
engagement 13-14, 35, 51, 57, 66-69, 75, 82, 91, 96, 100, luxury 75, 87, 197, 207, 222, 246-260, 282
102, 111, 166, 168, 187, 192, 204, 209, 223, 249- luxury hotel 246-251, 254-257, 260
250, 252-253, 259, 261, 265, 279, 281, 284-285
M
F
Management 3-4, 10-14, 18, 20, 24, 26-32, 36-38,
Facebook 17, 21, 24, 26, 75, 101-104, 106, 116, 118, 42, 46-47, 49-50, 53-54, 58, 66-67, 69-77, 81,
121, 143, 151, 156, 178, 180, 186-187, 201, 228- 85, 88-90, 94-98, 119-121, 134, 140-142, 146-
229, 238, 240-244, 261, 263-266, 276, 279-285 147, 154, 156-157, 173, 175, 178-179, 185-188,
faculty 227, 229-239, 243, 245, 265 190-191, 195-196, 206-209, 211-225, 227, 230,
Family Firm Heterogeneity 172, 174, 184, 190 235, 240-244, 247, 257-261, 265-266, 275-276,
Family Managers 172, 191 278-279, 282-285
Family Owners 191 marketing 1-4, 6, 8-61, 64-78, 80-89, 92-98, 103,
Fashion Digital Experts 209 111-114, 116-117, 119-120, 123-125, 127, 135-
firm performance 12, 28, 30, 32, 94, 174, 183-184, 136, 138, 140-146, 148-149, 155-159, 161-171,
186-187, 191, 241 183-187, 189-190, 192-209, 211-213, 215-217,
Followership 269, 274, 277, 285 219-225, 244, 250, 258-261, 263-267, 269, 274-
278, 281-285
G Marketing Analytics 1, 3-4, 6, 8-10, 14-15, 32
marketing digital 34, 204, 207-209
Guest Service 249, 260 marketing strategy 14, 17, 33-34, 41-42, 44-45, 50,
61, 66, 77-78, 92, 94, 96, 98, 125, 140, 146, 155,
H 170, 193, 195, 200, 202, 205, 207-208
media prosumer 262, 285
hashtag 100, 102-104, 106, 111, 115-118, 121-122, meta-media 227, 242, 245
276, 285 Mexico 158-160, 162, 164-165, 167-170, 241, 280
340
Index
mixed-method 58-59, 123-124 165, 168

Search Engine Results Pages (SERPs) 144
N social media 9, 13-14, 17-21, 23-24, 26, 29-32, 37,
40, 43, 47-50, 65-66, 72, 74-75, 77, 94-96, 99-
Negative Binomial Distribution (NBD) 78 101, 103-104, 111-122, 139, 142, 165-166, 168,
183-190, 192, 194, 197, 200-201, 204, 206-209,
O 215-216, 220, 222-224, 226-239, 243-246, 252-
254, 256, 259-267, 269, 274-280, 282-284
Omnichannel retailing 157 social media activism 99, 118-119
organizational website 173, 175-179, 183-184, 191 social movements 99-102, 104, 111-115, 117-122, 241
social network services 261-265, 275-278, 282, 285
P social networks 2-3, 17, 41, 47, 49, 99-104, 106, 111-
114, 121, 131, 151, 173-174, 176-177, 179-183,
pay per click 40, 123, 125 189, 194, 201, 208-209, 213, 220-221, 224,
penetration 56, 59, 78, 89, 91, 262 226-231, 233, 235, 237-241, 264-265, 276, 279,
personal brand 226, 228, 238-239, 245 281-282
post-Covid 158-159, 162, 164-168 Standardization vs. Adaptation 81, 98
prosumer 262, 285 Startup company 171
Publons 226, 231-232, 236, 238-239, 245 strategies 1-4, 6, 9-10, 12-14, 16-18, 20, 22-27, 29, 33-
pull strategy 52-54, 57, 60, 66-67, 78 35, 37, 40, 44-45, 49, 51-52, 54, 60-61, 65-66, 68,
purchase frequency 53, 56, 62-64, 78 71, 80-81, 88, 92-93, 97, 103-104, 111-112, 114,
push strategy 54, 60-61, 78 124, 126, 153, 157-159, 162, 164-169, 171-174,
179, 183-186, 192-198, 200-201, 204-206, 208-
R 209, 211-213, 215-217, 219-222, 225-228, 237,
239, 249-250, 254-255, 257-258, 261, 263-267,
relationship marketing 52, 57, 72, 74-78, 145, 148, 269, 274-279, 281, 285
157, 215 Structural Equation Modeling 158, 162, 169
ResarchGate 226
research 1, 4, 9-16, 19, 21, 23, 25, 27-32, 36-39, 41,
44-49, 53, 57-60, 63, 65-66, 69, 71-76, 81-82,
T
92, 95, 97-107, 109, 111-116, 118-120, 122-124, target audience 35-36, 43, 150, 198, 205, 264-265,
126, 130-131, 137-142, 148, 156, 160, 162-163, 272, 275-278, 285
165, 167, 169, 173-174, 176, 179, 183-190, 192- technology 2, 6, 9-10, 17-20, 22-23, 25-26, 29, 38, 48,
198, 200-205, 207-209, 213, 216-217, 222-224, 50, 57, 61, 77, 83, 89-90, 93, 98, 100, 116, 120,
226-232, 235-240, 242, 244, 248, 251, 256-259, 140-141, 146, 154, 156, 160, 167, 170-171, 194,
261-263, 265, 267, 269, 276-278, 281, 283-284 203, 205, 208, 211-213, 223, 241, 246-260, 265
ResearchGate 231-239, 245 traditional marketing 15, 17, 32, 34-50, 194-195, 204
restaurant 46-47, 211, 213-225 Twitter 17, 21, 26, 99-104, 106, 109, 111, 114-122, 151,
return on investment 4, 124, 126, 128-131, 134-135, 156, 178, 181, 201, 228, 238-244, 261, 264-267,
137-140, 143, 167, 200 269, 272, 276, 279-284
return rate 124, 128-129, 131, 134, 136-139, 144
returns 143, 145-148, 152-153, 155-156, 189, 241
U
S University 1, 16, 34, 49, 51, 70, 75-76, 80, 99, 117,
120, 123, 140, 145, 149, 160, 170, 172, 186, 192,
sales 12, 21-22, 25-26, 35-37, 43, 47, 50-51, 53-56, 208-209, 211, 224, 226, 230, 232-233, 238-239,
58-60, 62-63, 66-67, 69, 74, 89-91, 142, 145-150, 241-243, 245-246, 261, 263-269, 274, 277-279,
152-157, 166-168, 184, 199-202, 204, 207, 209, 281-282
213, 215-216, 220-223, 225 user-generated content 2, 73, 99-101, 103-104, 116,
search engine ads 123 118-119, 122, 243
search engine marketing 123-124, 138, 140, 142-144, Uses and Gratifications Theory 262, 285
341
Index
W Z
web 2.0 94, 226-227, 241, 243, 245, 282 Zara 145-156
342

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What is the book about?

What is the book about?

What topics are covered in the book?

What topics are covered in the book?

Advanced Digital

Jose Ramon Saura

A volume in the Advances in Marketing, Customer

Names: Saura, José Ramón, 1992- editor.

British Cataloguing in Publication Data

For electronic access to this publication, please contact: eresources@igi-global.com.

The Advances in Marketing, Customer Relationship Management, and E-Services (AMCRMES)

Blockchain Technology and Applications for Digital Marketing

Handbook of Research on Future Policies and Strategies for Nation Branding

Developing Successful Global Strategies for Marketing Luxury Brands

Insights, Innovation, and Analytics for Optimal Customer Engagement

New Techniques for Brand Management in the Healthcare Sector

Impact of ICTs on Event Management and Marketing

701 East Chocolate Avenue, Hershey, PA 17033, USA

Compilation of References................................................................................................................ 286

About the Contributors..................................................................................................................... 334

Compilation of References................................................................................................................ 286

About the Contributors..................................................................................................................... 334

As mentioned above, appropriate management and development of data-centric digital marketing

• Exploring the use of the main data-centric digital marketing techniques

DIGITAL MARKETING DATA-BASED STRATEGIES:

ORGANIZATION OF THIS BOOK

Jose Ramon Saura

The Importance of Big Data Analytics and

Table 1. Terms used in the databases

Database Searched terms Fields

Table 2. Number of article classifications by results

Database Number of results Number of relevant results - 2º

Figure 1. The SLR process

Table 3. Results of the SLR

Authors Journal Category

ANALYSIS OF THE RESULTS

Table 4. Key terms definition

Key terms Definition

Table 5. Classification of terms in big data analytics and marketing analytics.

Big Data Analytics Marketing Analytics

Table 6. Categorization of big data analytics terms

Techniques Systems Technology

Table 7. Categorization of marketing analytics terms

Software Metrics/KPIs Strategies Concepts

Limitations and Future Research

THE ROLE OF BIG DATA IN DIGITAL ENVIRONMENT

EXAMPLES OF BUSINESSES USING BIG DATA IN DIGITAL ENVIRONMENT

SOLUTIONS AND RECOMMENDATIONS

FUTURE RESEARCH DIRECTIONS

KEY TERMS AND DEFINITIONS

Figure 2. Traditional marketing

Table 1. Digital marketing definition: systematic literature review

Author Year Definition

DIGITAL MARKETING VS. TRADITIONAL MARKETING

Main Differences between Traditional and Digital Marketing

Main Similarities between Traditional and Digital Marketing

DISCUSSION, CONCLUSION AND IMPLICATIONS

KEY TERMS AND DEFINITIONS

• it would be relatively cheap to set up, using new digital technologies

The NBD Model of Repeat Buying

For electronic access to this publication, please contact: eresources@igi-global.com.