دانلود کتاب Marketing Analytics: A Practical Guide to Improving Consumer Insights Using Data Techniques

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Cover
Contents
Introduction
PART ONE How can marketing analytics help you?
1 Overview of statistics
Measures of central tendency
Measures of dispersion
The normal distribution
Confidence intervals
Relations among two variables: covariance and correlation
Probability and the sampling distribution
Conclusion
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2 Consumer behaviour and marketing strategy
Introduction
Consumer behaviour as the basis for marketing strategy
Overview of consumer behaviour
Overview of marketing strategy
Conclusion
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3 What is an insight?
Introduction
Insights tend not to be used by executives
Is this an insight?
So, what is an insight?
Ultimately, an insight is about action-ability
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PART TWO Dependent variable techniques
4 Modelling demand and elasticity
Introduction
Dependent equation type vs interrelationship type statistics
Deterministic vs probabilistic equations
Business case
Results applied to business case
Modelling elasticity
Technical notes
Highlight: Segmentation and elasticity modelling can maximize revenue in a retail/medical clinic chain: field test results
Abstract
The problem and some background
Description of the dataset
First: segmentation
Then: elasticity modelling
Last: test vs control
Discussion
Conclusion: why is elasticity modelling so rarely done?
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5 Polynomial distributed lags
What is PDL?
An example
Business case
Conclusion
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6 Using Poisson regression
When to use Poisson regression
Technical note
Business case
Conclusion
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7 Logistic regression and market basket analysis
Introduction
Conceptual notes
Business case
Results applied to the model
Lift charts
How deep to mail
Using the model – collinearity overview
Variable diagnostics
Highlight: Using logistic regression for market basket analysis
Abstract
What is a market basket?
How is it usually done?
Logistic regression
How to estimate/predict the market basket
An example
Conclusion
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8 Survival modelling and lifetime value
Introduction
Conceptual overview of survival analysis
Business case
More about survival analysis
Model output and interpretation
Note: The only way to do churn modelling
Conclusion
Highlight: Lifetime value: how predictive analysis is superior to descriptive analysis
Abstract
Descriptive analysis
Predictive analysis
An example
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9 Panel regression and same store sales
Introduction
What is panel regression?
Panel regression: details
Business case
Insights about marcom (direct mail, email and SMS)
Insights about time period (quarters)
Insights about cross-sections (counties)
Brief note on modelling same store sales
Conclusion
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10 Introduction to forecasting
Overview
Forecasting demand
Autocorrelation
Dummy variables and seasonality
Business case
Conclusion
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PART THREE Interrelationship techniques
11 Simultaneous equations
Introduction
What are simultaneous equations?
Why go to the trouble of using simultaneous equations?
Desirable properties of estimators
Business case
Conclusion
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12 Principal components and factor analysis
Interrelationship techniques
What is factor analysis?
What is PCA?
Similarities between PCA and factor analysis
Differences between PCA and factor analysis
Conclusion
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13 Segmentation overview
Introduction
Introduction to segmentation
What is segmentation? What is a segment?
Why segment? Strategic uses of segmentation
The four Ps of strategic marketing
Criteria for actionable segmentation
A priori or not?
Conceptual process
Highlight: Using segmentation to improve both strategy and predictive modelling
Introduction
Segmentation is a strategic, not an analytic, process
Why would segmentation improve predictive modelling accuracy?
Segmenting variables for model improvement
Example: churn modelling
Interpretation and insights
What if there was no segmentation?
Conclusion
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14 Tools of segmentation
Overview
Metrics of successful segmentation
General analytic techniques
Segmentation techniques summary
Business case
Analytics
Profile and output
Comments/details on individual segments
K-means compared to LCA
Highlight: Why go beyond RFM?
Abstract
What is RFM?
What is behavioural segmentation?
What does behavioural segmentation provide that RFM does not?
Conclusion
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PART FOUR Focus on media and loyalty
15 Modelling marcom value
Introduction
Value of marcom model
Business case
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16 Media mix modelling
Overview of MMM
Adstock models
Single equation and PDLs
Simultaneous equations
Business case
Conclusion
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17 Overview of loyalty
Introduction to loyalty
Is there a range or spectrum of loyalty?
What are the three Rs of loyalty?
Why design a programme with earn–burn measures?
Business case
A note on programme valuation
Conclusion
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18 Loyalty with SEM
Structural equation modelling (SEM)
Business case
Conclusion
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19 The customer loyalty journey
Introduction
Background
Segmentation
Elasticity modelling
Simultaneous equations
The experiences questionnaire
Conclusion
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PART FIVE More important topics for everyday marketing
20 Statistical testing
Everyone wants to test
Sample size equation: use the lift measure
A/B testing and full factorial differences
Business case
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21 Introduction to Big Data
Introduction
What is Big Data?
Is Big Data important?
What does it mean for analytics? For strategy?
So what?
Surviving the Big Data panic
Big Data analytics
Big Data – exotic algorithms
Conclusion
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Conclusion: The finale
References
Further reading
Index