research-article

Is Combining Contextual and Behavioral Targeting Strategies Effective in Online Advertising?

Authors:

Ling XueAuthors Info & Claims

ACM Transactions on Management Information Systems (TMIS), Volume 7, Issue 1

Article No.: 1, Pages 1 - 20

https://doi.org/10.1145/2883816

Published: 26 February 2016 Publication History

Abstract

Online targeting has been increasingly used to deliver ads to consumers. But discovering how to target the most valuable web visitors and generate a high response rate is still a challenge for advertising intermediaries and advertisers. The purpose of this study is to examine how behavioral targeting (BT) impacts users’ responses to online ads and particularly whether BT works better in combination with contextual targeting (CT). Using a large, individual-level clickstream data set of an automobile advertising campaign from an Internet advertising intermediary, this study examines the impact of BT and CT strategies on users’ click behavior. The results show that (1) targeting a user with behavioral characteristics that are closely related to ads does not necessarily increase the click through rates (CTRs); whereas, targeting a user with behavioral characteristics that are loosely related to ads leads to a higher CTR, and (2) BT and CT work better in combination. Our study contributes to online advertising design literature and provides important managerial implications for advertising intermediaries and advertisers on targeting individual users.

References

[1]

J. Angwin. 2010. The Web's New Gold Mine: Your Secrets. The Wall Street Journal—What They Know. Retrieved from http://online.wsj.com/news/articles/SB10001424052748703940904575395073512989404/.

[2]

H. Beales. 2010. The Value of Behavioral Targeting. Network Advertising Initiative (NAI). Retrieved from http://www.turn.com/sites/default/files/wp-content/uploads/2010/06/Beales_NAI_Study.pdf.

[3]

M. Braun and W. Moe. 2013. Modeling the effects of multiple creatives and individual impression histories. Marketing Science 32, 5, 753--767.

Digital Library

[4]

D. Breznitz and V. Palermo. 2013. A strategic advantage with behavioral targeting? How can (and what) firms benefit from personal data-based online marketing strategies. In Proceedings of the 35th DRUID Celebration Conference.

[5]

M. C. Campbell. 1995. When attention-getting tactics elicit consumer inferences of manipulative intent: The importance of balancing benefits and investments. Journal of Consumer Psychology 4, 3, 225--254.

[6]

P. Chatterjee, D. Hoffman, and T. Novak. 2003. Modeling the clickstream: Implications for web-based advertising efforts. Marketing Science 22, 4, 520--541.

Digital Library

[7]

China Internet Network Information Center (CNNIC). 2014. Annual report of online shopping behavior analysis, Retrieved from http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/dzswbg/201301 at 2014/04/05.

[8]

C.-H. Cho and H. J. Cheon. 2004. Why do people avoid advertising on the Internet? Journal of Advertising 33, 4, 89--97.

[9]

Consumer Union. 2008. Consumer reports poll: Americans extremely concerned about Internet privacy. Retrieved from http://www.consumersunion.org/pub/core_telecom_and_utilities/006189.html.

[10]

J. Dubin and D. Rivers. 1989. Selection bias in linear regression, logit and probit models. Sociological Methods Research 18, 2--3, 360--390.

[11]

A. Edgcomb and F. Vahid. 2013. Accurate and efficient algorithms that adapt to privacy-enhanced video for improved assistive monitoring. ACM Transactions on Management Information Systems 4, 3, Article No. 14.

Digital Library

[12]

A. Farahat and M. C. Bailey. 2012. How effective is targeted advertising? In Proceedings of the 21st International Conference on the World Wide Web. ACM, 111--120.

Digital Library

[13]

A. Ghose, P. G. Ipeirotis, and B. Li. 2014. Examining the impact of ranking on consumer behavior and search engine revenue. Management Science 60, 7, 1632--1654.

Digital Library

[14]

A. Ghose and S. Yang. 2009. An empirical analysis of search engine advertising: Sponsored search in electronic markets. Management Science 55, 10, 1605--1623.

Digital Library

[15]

A. Goldfarb and C. Tucker. 2011a. Online display advertising: Targeting and obtrusiveness. Marketing Science 30, 3, 389--404.

Digital Library

[16]

A. Goldfarb and C. Tucker. 2011b. Privacy regulation and online advertising. Management Science 57, 1, 57--71.

Digital Library

[17]

A. Goldfarb and C. Tucker. 2011c. Search engine advertising: Channel substitution when pricing ads to context. Management Science 57, 3, 458--470.

Digital Library

[18]

IAB. 2010. Targeting Local Markets: An IAB Interactive Advertising Guide. Interactive Advertising Bureau. Retrieved from http://www.iab.net/media/file/IAB_Local_Targeting_Guide_0922_FINAL.pdf.

[19]

H. E. Krugman. 1983. Television program interest and commercial interruption: Are commercials on interesting programs less effective? Journal of Advertising Research 23, 21--23.

[20]

A. Lambrecht and C. Tucker. 2013. When does retargeting work? Information specificity in online advertising. Journal of Marketing Research 50, 5, 561--576.

[21]

H. Lebo. 2014. The 2008 digital future report: Surveying the digital future. USC Annenberg School Center for the Digital Future. Retrieved from http://www.digitalcenter.org/wp-content/uploads/2014/12/2014-Digital-Future-Report.pdf.

[22]

H. Li, S. M. Edward, and J. Lee. 2002. Measuring the intrusiveness of advertisements: Scale development and validation. Journal of Advertising 31, 37--47.

[23]

X. Ma, S. H. Kim, and S. S. Kim. 2014. Online gambling behavior: The impacts of cumulative outcomes, recent outcomes, and prior use. Information Systems Research 25, 3, 511--527.

Digital Library

[24]

G. Mandler. 1982. The structure of value: Accounting for taste. In Affect and Cognition: The 17th Annual Carnegie Symposium, M. S. Clark and S. T. Fisk (Eds.). Lawrence Erlbaum Associates, Hillsdale, NJ, 203--230.

[25]

P. Manchanda, J.-P. Dube, K. Y. Goh, and P. K. Chintagunta. 2006. The effect of banner advertising on Internet purchasing. Journal of Marketing Research XLIII, 98--108.

[26]

N. K. Malhotra, S. S. Kim, and Agarwal. 2004. Internet users’ information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information Systems Research 15, 4, 336--355.

Digital Library

[27]

G. Mathew and Z. Obradovic. 2013. Distributed privacy-preserving decision support system for highly imbalanced clinical data. ACM Transactions on Management Information Systems 4, 3, Article 12.

Digital Library

[28]

S. Mithas, N. Ramasubbu, M. S. Krishnan, and C. Fornell. 2007. Designing web sites for customer loyalty across business domains: A multilevel analysis. Journal of Management Information Systems 23, 3, 97--127.

Digital Library

[29]

R. S. Moore, C. A. Stammerjohan, and R. A. Coulter. 2005. Banner advertiser-web site congruity and color effects on attention and attitudes. Journal of Advertising 34, 2, 71--84.

[30]

J. Pancras and K. Sudhir. 2007. Optimal marketing strategies for a customer data intermediary. Journal of Marketing Research XLIV, 560--578.

[31]

S. Rodgers. 2003/2004. The effects of sponsor relevance on consumer reactions to Internet sponsorships. Journal of Advertising 32, 4, 67--76.

[32]

E. Smith. 2014. Targeted Display to Drive Online Ad Growth. Retrieved from http://www.netnewscheck.com/article/35766/targeted-display-to-drive-online-ad-growth.

[33]

C. E. Tucker. 2014. Social networks, personalized advertising, and privacy controls. Journal of Marketing Research 51, 5, 546--562.

[34]

J. Turow, J. King, C. J. Hoofnagle, A. Bleakley, and M. Hennessy. 2009. Americans reject tailored advertising and three activities that enable it. SSRN Working Paper. Retrieved from http://ssrn.com/abstract = 1478214.

[35]

I. Weber and A. Jaimes. 2011. Who uses web search for what: and how. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 15--24.

Digital Library

[36]

J. M. Woodridge. 2010. Econometric Analysis of Cross Section and Panel Data, 2nd ed. MIT Press Cambridge, Cambridge.

[37]

J. Yan, G. Wang, E. Zhang, Y. Jiang, and Z. Chen. 2009. How much can behavioral targeting help online advertising? In Proceedings of WWW 2009 MADRID. Retrieved from http://www2009.eprints.org.

Digital Library

[38]

K. Zhang and Z. Katona. 2012. Contextual advertising. Marketing Science 31, 6, 980--994.

Digital Library

[39]

X. Zhao. 2012. Service design of a customer data intermediary for competitive target promotions. Decision Support Systems 54, 1, 699--718.

Digital Library

[40]

X. Zhao and L. Xue. 2013. Competitive target advertising and consumer data sharing. Journal of Management Information Systems 29, 3, 189--222.

Cited By

Häglund EBjörklund J(2024)AI-Driven Contextual Advertising: Toward Relevant Messaging Without Personal DataJournal of Current Issues & Research in Advertising10.1080/10641734.2024.233493945:3(301-319)Online publication date: 29-Apr-2024
https://doi.org/10.1080/10641734.2024.2334939
Wang PJiang LYang J(2023)The Early Impact of GDPR Compliance on Display Advertising: The Case of an Ad PublisherJournal of Marketing Research10.1177/0022243723117184861:1(70-91)Online publication date: 23-Jun-2023
https://doi.org/10.1177/00222437231171848
Shi CHu PFan WQiu L(2023)Competitive peer influence on knowledge contribution behaviors in online Q&A communities: a social comparison perspectiveInternet Research10.1108/INTR-07-2022-0510Online publication date: 20-Jun-2023
https://doi.org/10.1108/INTR-07-2022-0510
Show More Cited By

Index Terms

Is Combining Contextual and Behavioral Targeting Strategies Effective in Online Advertising?
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Electronic commerce
        Online shopping

Recommendations

How effective is targeted advertising?
WWW '12: Proceedings of the 21st international conference on World Wide Web

Advertisers are demanding more accurate estimates of the impact of targeted advertisements, yet no study proposes an appropriate methodology to analyze the effectiveness of a targeted advertising campaign, and there is a dearth of empirical evidence on ...
How much can behavioral targeting help online advertising?
WWW '09: Proceedings of the 18th international conference on World wide web

Behavioral Targeting (BT) is a technique used by online advertisers to increase the effectiveness of their campaigns, and is playing an increasingly important role in the online advertising market. However, it is underexplored in academia when looking ...
An economic analysis of online advertising using behavioral targeting

Online publishers and advertisers have recently shown increasing interest in using targeted advertising online. Such targeting allows them to present users with advertisements that are a better match, based on their past browsing and search behavior and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 7, Issue 1

March 2016

61 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/2897823

Editor:
Alexander Tuzhilin
New York University, USA

Issue’s Table of Contents

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 February 2016

Accepted: 01 December 2015

Revised: 01 October 2015

Received: 01 October 2014

Published in TMIS Volume 7, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

National Science Fund of China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
1,331
Total Downloads

Downloads (Last 12 months)62
Downloads (Last 6 weeks)6

Reflects downloads up to 13 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Häglund EBjörklund J(2024)AI-Driven Contextual Advertising: Toward Relevant Messaging Without Personal DataJournal of Current Issues & Research in Advertising10.1080/10641734.2024.233493945:3(301-319)Online publication date: 29-Apr-2024
https://doi.org/10.1080/10641734.2024.2334939
Wang PJiang LYang J(2023)The Early Impact of GDPR Compliance on Display Advertising: The Case of an Ad PublisherJournal of Marketing Research10.1177/0022243723117184861:1(70-91)Online publication date: 23-Jun-2023
https://doi.org/10.1177/00222437231171848
Shi CHu PFan WQiu L(2023)Competitive peer influence on knowledge contribution behaviors in online Q&A communities: a social comparison perspectiveInternet Research10.1108/INTR-07-2022-0510Online publication date: 20-Jun-2023
https://doi.org/10.1108/INTR-07-2022-0510
Wang GTan GYuan YOoi KDwivedi Y(2022)Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysisTechnological Forecasting and Social Change10.1016/j.techfore.2021.121345175(121345)Online publication date: Feb-2022
https://doi.org/10.1016/j.techfore.2021.121345
Hao CYang L(2022)Platform advertising and targeted promotionElectronic Commerce Research and Applications10.1016/j.elerap.2022.10117855:COnline publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.elerap.2022.101178
Thomas TTuteja KChowdhury A(2021)Advergames Are More Persuasive Among Different Online AdvertisementsErgonomics for Improved Productivity10.1007/978-981-15-9054-2_12(105-117)Online publication date: 24-Mar-2021
https://doi.org/10.1007/978-981-15-9054-2_12
Kumar STan YWei L(2020)When to Play Your Advertisement? Optimal Insertion Policy of Behavioral AdvertisementInformation Systems Research10.1287/isre.2019.090431:2(589-606)Online publication date: 1-Jun-2020
https://dl.acm.org/doi/10.1287/isre.2019.0904
Vassio LGaretto MChiasserini CLeonardi E(2020)User Interaction with Online AdvertisementsACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/33771445:2(1-26)Online publication date: 5-Mar-2020
https://dl.acm.org/doi/10.1145/3377144
Varnali K(2019)Online behavioral advertising: An integrative reviewJournal of Marketing Communications10.1080/13527266.2019.163066427:1(93-114)Online publication date: 17-Jun-2019
https://doi.org/10.1080/13527266.2019.1630664
Lian SCha TXu Y(2019)Enhancing geotargeting with temporal targeting, behavioral targeting and promotion for comprehensive contextual targetingDecision Support Systems10.1016/j.dss.2018.12.004117(28-37)Online publication date: Feb-2019
https://doi.org/10.1016/j.dss.2018.12.004
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents