SBM-NMIMS: COURSE TEACHING PLAN

Assurance of Learning AOL Specific

Course Code
Course Title Marketing Research
Course Instructor(s) Arun Sharma and Somnath Roy
Credit Value 3 Credits (100 Marks)
Program & Trimester FTMBA II, Trimester: IV
Pre-requisite
CLOs Mapped PLOs
Get a knowledge of marketing issues and the
CLO 1) appropriate qualitative or quantitative research PLO 2b
techniques to address those.
Learning Objectives
To appreciate the usage of features in analytical
CLO 2) PLO 2b
tools for solutions of business problems
To apply analytical techniques in different aspects
CLO 3) PLO 2a
of marketing research

Connected CLOs
Plan and undertake qualitative or quantitative Market
1) Research and demonstrate the ability to appropriately CLO 1
Learning Outcomes analyze data to resolve marketing issues.
(Must be connected to Identify and understand conditions of applicability of the
Learning Objectives) 2) methods and validity of the results, and their impact on CLO 2
the conclusions of the analysis
Appreciation of the scientific approach to different aspects
3) CLO 3
of marketing research to analyze information
The objective of the course is to familiarize students with several types of managerial
problems as well as data sources and techniques, commonly employed in making
effective marketing decisions. This course provides you with the skills and tools
needed to understand and evaluate marketing research. Marketing research involves
developing research questions, collecting data, analysing it and drawing inference,
with a view to making better marketing decisions. The course would involve
formulating critical managerial problems, developing relevant hypotheses, analyzing
Course Description data and, most importantly, drawing inferences for actionable results.

In this course we will employ a combination of readings, cases and statistical

software-based exercises. The course emphasizes applications and marketing
decision making. Cases will help in understanding the applications of
marketing research problems. The course plan outlines selected cases and the
important readings that are required for the course. The textbook will serve
as background and general readings that will help in better understanding the

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 topics discussed in class. Computer-based exercises will provide "hands-on"
experience with marketing research analyses

Specific 3 1.5 AOL CLO 1 CLO 2 CLO 3

assessment Credit Credit Instruments
methods (*)
Case analysis and
15
discussion
Group Project 25 Rubric ✓
Assignment/Quiz 20
Final (End-term) Embedded
Evaluation Pattern 40 ✓ ✓
examination Questions
Total 100 50

*AOL Assessment Instruments:

• Embedded Questions: Quiz, Class Test, Midterm Examination, Final Examination
• Rubrics: Case & Article Discussion, Individual Assignment, Group Projects & Viva’s,
Case Problem analysis, Oral and written communication presentations, Role Play,
Group Presentation, Group Project etc.

Chapter detail/ Details of pedagogy

Topics / Subtopics Article Reference / adopted for class
Sessions
Learning Outcome if provided session wise Case Studies engagement - Class
Exercises etc.
Marketing problems and research tools
Discussion with
- Nature of marketing problems examples and Q&A
- Research solutions to the marketing “Backward Marketing
1 Research”, Harvard LO: get a broad
problems
Business Review. overview of the key
- Need of research in marketing
aspects of Marketing
- Marketing research tools Research

Research Design and process Discussion with

examples and Q&A
- Research design
- Types of research designs: exploratory, “Note on Marketing LO: role of research
2
descriptive and causal Research”, Stanford. design in the market
- Steps of research process research activity,
types of business
questions addressed

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 Chapter detail/ Details of pedagogy
Topics / Subtopics Article Reference / adopted for class
Sessions
Learning Outcome if provided session wise Case Studies engagement - Class
Exercises etc.
Data types and collection techniques Discussion with
examples and Q&A
- Types of data
- Scales of measurements LO: Select appropriate
Ch 8 [NM]; Page
3 - Data collection and field work survey methodologies
numbers: 256-267
- Data preparation based on specific
business problem
- Sampling
being researched

Causal Research: Experimentation in Marketing

- Concept of causality
Caselet and Data sets
- Between and within subject design
to be shared
- A/B testing Ch 7 [NM]; Page
4 - Hypothesis testing numbers: 222-231 LO: concept of
- Testing the significance causality, application
- Non-parametric tests of Analysis of Variance
- Analysis of Variance

Advanced Marketing Experiments

Discussion with
- Controlling confounds and examples and Q&A
randomization Why Businesses Don’t
5 Experiment, Harvard LO: different aspects
- Internal and external validity
Business Review. of validity, evaluation
- Complex Designs of experimental
- Measuring interaction and main effect designs

Caselet and Data sets

Boost your marketing to be shared
Case discussion: Measuring ROI of search ads ROI with experimental
6
design, Harvard LO: practical
Business Review. application of
experimental design
Descriptive Design: Survey research
Discussion with
- Descriptive Research design
examples and Q&A
- Questionnaire design Ch 6 [NM]; Page
7 - Conducting marketing surveys numbers: 182-192 LO: considerations
- Net Promoter Score involved in
- Validity and Reliability implementing surveys

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 Chapter detail/ Details of pedagogy
Topics / Subtopics Article Reference / adopted for class
Sessions
Learning Outcome if provided session wise Case Studies engagement - Class
Exercises etc.
Qualitative Research in Marketing: Exploratory
Design
Discussion with
- Qualitative vs. quantitative methods
examples and Q&A
- Conducting qualitative research & Ch 5 [NM]; Page
8 Interpreting results numbers: 132-137 LO: Understand the
- Ethnography & Netnography various facets of
- Projective techniques qualitative research
- Other qualitative methods

Doing marketing segmentation - I Discussion with

examples and Q&A
- How to divide customers into different
segments? Ch 20 [NM]; Page LO: appreciate the
9 - Clustering techniques numbers: 624-638 theoretical
- Hierarchical & Non-Hierarchical clusters underpinnings of
Cluster Analysis
- K-means clusters, Two stage clusters

Doing marketing segmentation - II Doing cases on SPSS

Recommended
Software: Data sets to
- Cluster analysis Reading:
be shared
10 - Running cluster analysis
Ch 20 [NM]; Page
- Interpreting the results LO: Applying tools to
numbers: 639- 647
- Applications of cluster analysis resolve cluster
analysis problems
Doing cases on SPSS
Market Positioning: Perceptual Maps Software: Data sets to
be shared
- Analyzing consumer perceptions Ch 21 [NM]; Page
11 - Market Positioning LO: concepts and
numbers: 650-660
scope of
- Preparing Perceptual Maps
multidimensional
scaling of perception
data
Market Positioning: MDS

- Collecting data for preparing perceptual Doing cases on SPSS

maps Software: Data sets to
- MDS (Multidimensional Scaling) Ch 21 [NM]; Page be shared
12
- Running and interpreting the MDS numbers: 650-660
LO: Applying tools to
results
resolve MDS problems
- More applications in marketing

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 Chapter detail/ Details of pedagogy
Topics / Subtopics Article Reference / adopted for class
Sessions
Learning Outcome if provided session wise Case Studies engagement - Class
Exercises etc.
Doing cases on SPSS
Factor Analysis in Marketing I Software: Data sets to
be shared
- Factor Analysis and marketing problems Ch 19 [NM]; Page
13 - EFA and CFA numbers: 598-605 LO: theoretical
- Applications in marketing aspects of factor
analysis

Factor Analysis in Marketing II

Doing cases on SPSS
- Running a Factor Analysis Software: Data sets to
- Interpreting the results be shared
Ch 19 [NM]; Page
14 - KMO test of sampling adequacy numbers: 605-610
LO: Applying tools to
- Bartlett’s test of sphericity
resolve factor analysis
- Scree plots problems

Predictive analysis I Doing cases on SPSS

Software: Data sets to
- Predictive models types be shared
- Regression-based modelling Ch 17 [NM]; Page
15 - Simple regression numbers: 533-550 LO: Applying tools to
- Multiple regression resolve regression
type problems

Predictive analysis II
Doing cases on SPSS
- Classification problems
Software: Data sets to
- Logistic regression Ch 18 [NM]; Page be shared
16 - Logistic regression models numbers: 584-591
- Predictions using logistic Regression LO: Applying tools to
Models resolve LR problems

Lectures by Guest Faculty (Industry Experts): Industry Student Interaction

17 – 18
Group assignment: discussion and presentations
19 – 20

Reading List Text Book:

and
References Marketing Research: An applied orientation – Naresh Malhotra [NM] 7th Edition

Reference Books:

Multivariate Data Analysis - Hair, Black, Babin & Anderson

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 Essentials of Marketing Research - V Kumar, D A Aaker, G S Day
Basic Marketing Research - Gilbert A Churchill

Prepared by Faculty Team Area & Program chairpersons

Faculty Chair AOL Approved by Dean SBM

Approved by Associate Deans

Instruction for Use of Intellectual Property of NMIMS SBM (By Order, NMIMS SBM)

Course Outlines are strictly for private and restricted circulation among the concerned Faculty
Members and the Students of this Programme. They are permitted to use the contents for study
and research purpose only. No part of this Course outline can be copied, reproduced, shared
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person responsible for violating this Instruction shall be liable for appropriate disciplinary action
initiated by SBM.

Disclaimer

While care has been taken in compiling this Course outline, The School of Business Management
of SVKM’s NMIMS University shall not be held liable in any manner to any person for any mistake
and / or omission in the contents of the Course outline.

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