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Summary Customer and Marketing Analytics (E_MKT_CMA) lecture notes + video tutorials
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This is a summary of all the lectures for the course CMA at the VU
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Customer Marketing & Analytics E_MKT_CMATable of Contents
Week 1 2
Lecture 1: Introduction 2
Lecture 2: Basic Statistical Analysis 4
Video tutorial #1 12
Week 2 18
Lecture 3: Measurement and scaling: reliability, validity, dimensionality 18
Lecture 4: Creating perceptual maps using factor analysis. 27
Video tutorial #2 32
Week 3 34
Lecture 5: Market response models (multiple regression analysis) 34
Lecture 6: Mediation & Moderation 40
Video tutorial #3 41
Week 4 42
Lecture 7 & 8: Predicting Customer Response using Logistic Regression 42
Video tutorial #4 49
Week 5 50
Lecture 9 & 10 50
Video tutorial #5 53
1
,Week 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.
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.
A good problem definition is super-duper important!
2
,Classifying marketing research:
• Type of data
o Quantitative
▪ Focus on
numbers,
amendable to
statistical
analytics.
o Qualitative
▪ Not concerned
with numbers.
• Research design
o Exploratory
▪ 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
o Descriptive
▪ 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.
o Causal
▪ 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.
▪ Makes use of experiments:
• Laboratory experiments
• Field experiments.
• Data source
o Secondary
3
, ▪Data previously collected for purposes other than the research at hand.
▪Internal sources:
• Accounting records
• Customer transaction databases
• Clickstream data
• Operating records
• Previous market research studies.
▪ External sources:
• Market and industry research publishers
• Trade associations
• Government agencies.
▪ Syndicated research – large-scale marketing research that is undertaken
by a research firm to be sold, often on a subscription basis, to a number
of clients.
o Primary
▪ Data collected specifically to answer the question(s) posed by the
current research objectives.
• Types:
o Demographic / socioeconomic / lifestyle characteristics
o Attitudes / opinions
o Awareness / knowledge
o Motivation
o Intentions and behavior
• Collecting primary data:
o Communication: questioning respondents to secure the
desired information (via surveys, focus groups etc.)
o Observation: the situation of interest is watched and the
relevant facts, actions, or behaviours are recorded.
Consumer panel = a consumer panel is a panel of households or individuals whose
purchases are monitored on a continuous or ongoing basis.
Lecture 2: Basic Statistical Analysis
Basic Data Analysis
• Screen dataset: investigate quality of data.
o Errors, missing values, inconsistencies.
• Explore and analyze the data.
o Describe and summarize data: a complete run-down analysis of all the
variables in your dataset one-at-a-time (univariate statistics).
o Inferential analysis: learning about ‘’the world’’ (univariate statistics).
o Differential analysis (bivariate statistics).
o Associative analysis (bivariate statistics).
Screening the dataset
• Check for missing data.
o In long surveys, participants accidentally or deliberately miss out questions.
4
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