Marketing Analytics A Practical Guide to Improving Consumer Insights Using Data Techniques 3rd Edition by Mike Grigsby ISBN 9781398608214 1398608211

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Product details:
ISBN 10: 1398608211
ISBN 13: 9781398608214
Author: Mike Grigsby

Marketing Analytics A Practical Guide to Improving Consumer Insights Using Data Techniques 3rd Edition Table of contents:

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

What things have I learnt that I’d like to pass on to you?

Anecdote #1

References

Further reading

Index

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Tags: Mike Grigsby, Marketing Analytics, Improving Consumer, Data Techniques