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

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