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Table of Contents
Lecture 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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