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Table of ContentsLecture 1: Introduction....................................................................................................................2
Lecture 2: Basic Statistics.................................................................................................................4
Lecture 3: Measurement and Scaling: Reliabilty, Validity, Dimensionality (Factor Analysis).............8
Lecture 4: Factor Analysis and Perceptual Maps............................................................................13
Lecture 5: Market Response Models (Multiple Regression Analysis)..............................................15
Lecture 6: Mediation and Moderation...........................................................................................19
Lecture 7 & 8: Predicting Consumer Response Using Logistic Regression........................................22
Lecture 9 & 10: Understanding Individual Customer Preferences Using Conjoint Analysis..............27
Lecture 11: Course Wrap-Up..........................................................................................................31
1
, Lecture 1: Introduction
Why do Firms do Research in Marketing?
Marketers use the “right” principle ‘to do’ Marketing
o Get the right products to the right people at the right place at the right time at the right price
using the right promotion techniques
To be “right” in marketing: need for decision making information that reduces uncertainty to aid in
smarter managerial decision making
The marketing system
The task of marketing management The environment affecting marketing
Marketing research
Planning, collection, and analysis of data relevant to marketing decision
making and the communication of the results of this analysis to
management.
It can be micro-level (individual) or macro-level (market) in nature
The value of marketing research:
o Decreased uncertainty
o Increased likelihood of a correct decision
o Improved marketing performance and resulting higher profits
o Stronger customer relationships
Process of identifying the problem and problem definition
There is a fundamental distinction between the marketing decision problem and the marketing research
problem
Marketing decision problem
Asks what the decision-maker needs to do
It’s action oriented
Focuses on the symptoms
Marketing research problem
Asks what information is needed and how it can best be
obtained
Information oriented
Focuses on the underlying causes
Sample Decision Problems
What should we do to increase our store traffic?
How can we reduce consumer complaints about our product?
Which product line extension should we invest in?
Should we reposition our brand with an emphasis on raising prices?
From Decision Problem to Research Problem
Olympics Committee:
o Decision problem: What logo design should we use for the Olympics in London?
o Research problem: How much do people like the different proposed
logos?
2
,Classifying Marketing Research
By type of data
o Quantitative research
o Qualitative research
By research design
o Exploratory research
o Descriptive research
o Causal research
By data source
o Secondary data
Syndicated research
o Primary data
>> By type of data
> Qualitative: not concerned with numbers.
Good for:
Mapping the customer’s overall range of behaviour and attitudes
Pinpointing the motivations behind people’s behaviour
Stimulating new and creative ideas
Providing a forum for fresh creative thinking
> Quantitative: Focus on numbers, amendable to statistical analysis.
Good for:
Profiling detailed usage and behaviour
Highlighting variations between different sub ‐groups
Precisely measuring consumer preferences for different products and services
Measuring the exact priorities consumers attach to different product features
>>> By research design
> Exploratory research: Research in which the major emphasis is on gaining ideas and insights
Purposes:
Increase familiarity with problem
Clarify concepts
Develop specific hypotheses
Approaches:
Literature survey
Experience/key informant survey
Case studies
Focus groups
qualitative
> Descriptive research: Often guided by an initial hypothesis
Purposes:
Describe the characteristics of certain groups
Estimate the proportion of people in a specified population who behave in a certain way
Examine associations between two or more variables
Make specific predictions
quantitative
> Causal research: Research in which the major emphasis is on determining a cause ‐and ‐effect relationship
Descriptive research reveals associations between variables
Causal research reveals associations between changes in variables
3
, Makes use of experiments:
Laboratory experiments
Field experiments
>> By data source
> Secondary data: Data previously collected for purposes other than the research at hand
Internal sources:
Accounting records (e.g., sales invoices, marketing expenditures)
Customer transaction databases
Clickstream data
Operating records (e.g., warranty cards, customer complaint services)
Previous market research studies
External sources:
Market and industry research publishers (e.g., Datamonitor, Euromoniter EIU, Forrester, Mintel)
Trade associations
Government agencies
> Syndicated research (panel/scanner data): Large‐scale marketing research that is undertaken by a research
firm to be sold, often on a subscription basis, to a number of clients (consumer panel data/scanner purchase
data)
Consumer panel: panel of households or individuals whose purchases are monitored on a continuous or
ongoing basis.
> Primary data: Data collected specifically to answer the question(s) posed by the current
research objectives
Types of primary data:
Demographic / Socioeconomic / Lifestyle characteristics
Attitudes / Opinions
Awareness / Knowledge
Motivation
Intentions and behaviour
Collecting primary data:
Communication: Questioning respondents to secure the desired information (via surveys, focus
groups etc.)
Observation: The situation of interest is watched and the relevant facts, actions, or behaviours
recorded
Lecture 2: Basic Statistics
Basic data analysis
1. Screen dataset: Investigate quality of data
> Errors, missing values, inconsistencies
2. Explore and analyze the data*:
> Describe and summarize data: A complete run-down analysis of all the variables in your dataset
one-at-a-time (univariate statistics)
> Inferential analysis: Learning about “the world” (univariate statistics)
> Differential analysis (bivariate statistics)
> Associative analysis (bivariate statistics)
Univariate analysis bivariate analysis multivariate analysis
4
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