P1 - Customer and Marketing analytics
Week 1...................................................................................................................................... 3
Lecture 1 ............................................................................................................................... 3
Classifying marketing research ......................................................................................... 6
Week 2...................................................................................................................................... 9
Preparation chapter 3 - Hair et al. (2014) ............................................................................. 9
Lecture 3 ............................................................................................................................. 11
Measurement & Marketing ............................................................................................ 11
Multiple item measurement........................................................................................... 14
Reliability & Validity ....................................................................................................... 15
Assessing Scale Reliability Internal Consistency Method (Cronbach’s α) ....................... 16
Assessing Scale Validity .................................................................................................. 17
Preparation - Dolan, Robert J. (1999). ................................................................................ 20
Lecture 4 ............................................................................................................................. 22
Part 1 – Factor Analysis .................................................................................................. 22
Part 2 - Perceptual maps ................................................................................................ 31
Week 3.................................................................................................................................... 42
Preparation chapter 4 - Hair et al. (2014) ........................................................................... 42
Lecture 5 ............................................................................................................................. 44
Primer on regression modeling ...................................................................................... 47
Simple linear regression ................................................................................................. 47
Forecasting ..................................................................................................................... 51
Multiple regression analysis ........................................................................................... 53
Multiple Regression Methods ........................................................................................ 56
Preparation – Zhao et al. (2010) Reconsidering Baron and Kenny: Myths and truths about
mediation analysis .............................................................................................................. 61
Lecture 6 ............................................................................................................................. 63
Moderation vs. mediation .............................................................................................. 63
Mediation ....................................................................................................................... 64
Serial mediation.............................................................................................................. 68
Moderation..................................................................................................................... 71
Week 4.................................................................................................................................... 77
Preparation chapter 6 – Hair et al. (2014) .......................................................................... 77
Lecture 7 & 8 ....................................................................................................................... 79
, Datamining ..................................................................................................................... 81
Logistic Regression ......................................................................................................... 87
Week 5.................................................................................................................................... 96
Lecture 9 & 10..................................................................................................................... 96
Conjoint analysis ............................................................................................................. 97
Rescaling regression coefficients .................................................................................. 108
Week 6.................................................................................................................................. 115
Guest Lecture – Philips ..................................................................................................... 115
Recap .................................................................................................................................... 118
How to study ..................................................................................................................... 118
Lecture 1: Introduction ..................................................................................................... 118
Lecture 3 ........................................................................................................................... 119
2
,Week 1
Lecture 1
• Continuous = Can be broken down into smaller parts (decimeters) etc.
• Discrete variables =Typically whole numbers, gaps between them where no other
values exist.
• Interval “true zero” = This means that the differences between values are consistent
and measurable, but the zero point does not indicate the absence of the quantity
being measured.
The marketing system
The task of marketing management the environment affecting marketing
3
, 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:
- Decreased uncertainty
- Increased likelihood of a correct decision
- Improved marketing performance and resulting higher profits
Fundamental distinction
Marketing Decision Problem Marketing Research Problem
- Asks what the decision-maker needs - Asks what information is needed
to do and how it can be best obtained
- Action oriented - Information oriented
- Focuses on the underlying causes
- Focuses on the symptoms
The iceberg principle =
suggests that in business, the visible
problems (like declining sales or unhappy
customers) are just the "tip of the
iceberg." The real, underlying issues
(such as poor product quality or
ineffective systems) are often hidden
beneath the surface.
To effectively solve problems, it's
important to look deeper and address
these root causes rather than just
focusing on the obvious symptoms.
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?
4
Week 1...................................................................................................................................... 3
Lecture 1 ............................................................................................................................... 3
Classifying marketing research ......................................................................................... 6
Week 2...................................................................................................................................... 9
Preparation chapter 3 - Hair et al. (2014) ............................................................................. 9
Lecture 3 ............................................................................................................................. 11
Measurement & Marketing ............................................................................................ 11
Multiple item measurement........................................................................................... 14
Reliability & Validity ....................................................................................................... 15
Assessing Scale Reliability Internal Consistency Method (Cronbach’s α) ....................... 16
Assessing Scale Validity .................................................................................................. 17
Preparation - Dolan, Robert J. (1999). ................................................................................ 20
Lecture 4 ............................................................................................................................. 22
Part 1 – Factor Analysis .................................................................................................. 22
Part 2 - Perceptual maps ................................................................................................ 31
Week 3.................................................................................................................................... 42
Preparation chapter 4 - Hair et al. (2014) ........................................................................... 42
Lecture 5 ............................................................................................................................. 44
Primer on regression modeling ...................................................................................... 47
Simple linear regression ................................................................................................. 47
Forecasting ..................................................................................................................... 51
Multiple regression analysis ........................................................................................... 53
Multiple Regression Methods ........................................................................................ 56
Preparation – Zhao et al. (2010) Reconsidering Baron and Kenny: Myths and truths about
mediation analysis .............................................................................................................. 61
Lecture 6 ............................................................................................................................. 63
Moderation vs. mediation .............................................................................................. 63
Mediation ....................................................................................................................... 64
Serial mediation.............................................................................................................. 68
Moderation..................................................................................................................... 71
Week 4.................................................................................................................................... 77
Preparation chapter 6 – Hair et al. (2014) .......................................................................... 77
Lecture 7 & 8 ....................................................................................................................... 79
, Datamining ..................................................................................................................... 81
Logistic Regression ......................................................................................................... 87
Week 5.................................................................................................................................... 96
Lecture 9 & 10..................................................................................................................... 96
Conjoint analysis ............................................................................................................. 97
Rescaling regression coefficients .................................................................................. 108
Week 6.................................................................................................................................. 115
Guest Lecture – Philips ..................................................................................................... 115
Recap .................................................................................................................................... 118
How to study ..................................................................................................................... 118
Lecture 1: Introduction ..................................................................................................... 118
Lecture 3 ........................................................................................................................... 119
2
,Week 1
Lecture 1
• Continuous = Can be broken down into smaller parts (decimeters) etc.
• Discrete variables =Typically whole numbers, gaps between them where no other
values exist.
• Interval “true zero” = This means that the differences between values are consistent
and measurable, but the zero point does not indicate the absence of the quantity
being measured.
The marketing system
The task of marketing management the environment affecting marketing
3
, 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:
- Decreased uncertainty
- Increased likelihood of a correct decision
- Improved marketing performance and resulting higher profits
Fundamental distinction
Marketing Decision Problem Marketing Research Problem
- Asks what the decision-maker needs - Asks what information is needed
to do and how it can be best obtained
- Action oriented - Information oriented
- Focuses on the underlying causes
- Focuses on the symptoms
The iceberg principle =
suggests that in business, the visible
problems (like declining sales or unhappy
customers) are just the "tip of the
iceberg." The real, underlying issues
(such as poor product quality or
ineffective systems) are often hidden
beneath the surface.
To effectively solve problems, it's
important to look deeper and address
these root causes rather than just
focusing on the obvious symptoms.
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?
4
