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

337 Pages·2022·18.893 MB·English

by  Mike Grigsby

i
PRAISE FOR MARKETING ANALYTICS,
SECOND EDITION
‘This is an excellent read for people in the industry who work in strategy
and marketing. It is one of the first books that I have read that covers the
entire spectrum from demand, segmentation, targeting, and how results
can be calculated. In an age where marketing is becoming more and more
sophisticated, this book provides the tools and the mathematics behind
the facts. Marketing Analytics is written with a scientific voice, but is
very readable, with the science wrapped into everyday activities, based
on a character we can all relate to, that are derived from these formulas,
ultimately driving ROI.’
Elizabeth Johnson, CEO, PathFormance
‘Grigsby’s book is the right blend of theory applied to the real-world large-
scale data problems of marketing. It’s exactly the book I wish I’d had when
I started out in this field.’
Jeff Weiner, Senior Director, Analytics, One10
‘An insightful, practical book for analytics marketing practitioners. It
both entertains and serves as a handbook for marketing analytics. With
easy-to-follow examples, Grigsby paints a clear picture of how to execute
data analytics and its role in the larger marketing and organizational
goals.’
Craig Armstrong, Director, Strategic Business Analysis, Targetbase
‘In Marketing Analytics, Mike Grigsby takes passionate marketing strate-
gists on a practical, real-life journey for solving common marketing
challenges. By combining the concepts and knowledge areas of statistics,
marketing strategy and consumer behaviour, Grigsby recommends scientific
and innovative solutions to common marketing problems in the current
business environment. Every chapter is an interesting journey for the reader.
What I like most about the book is its simplicity and how it applies to real
work-related situations in which almost all of us have been involved while
ii
practising marketing of any sort. I also like how the author talks about
tangible measurements of strategic recommended marketing solutions as
well as how they add value to companies’ strategic endeavours. I highly
recommend reading this book as it adds a completely new dimension to
marketing science.’
Kristina Domazetoska, Project Manager and Implementation Consultant at
Insala – Talent Development and Mentoring Solutions
‘Marketing Analytics, second edition is a must-read for students and budding
analytics professionals. The book illustrates concepts in statistics and
marketing with real-world examples and provides solutions without getting
too technical. It begins with basic statistical concepts required in the field of
marketing analytics, then illustrates the application of these concepts to real-
world business problems. It also touches upon concepts of big data analytics
and, most importantly, what really IS an insight. This book is extremely
conversational and entertaining to read and I’ve found myself reaching for
it on multiple occasions when I’ve encountered various marketing-analytics-
related problems, during both my student and professional life.’
Akshay Kher, Analytics Practitioner
iii
Marketing Analytics
A practical guide to improving consumer insights
using data techniques
THIRD EDITION
Mike Grigsby
iv
Publisher’s note
Every possible effort has been made to ensure that the information contained in this book is
accurate at the time of going to press, and the publishers and author cannot accept
responsibility for any errors or omissions, however caused. No responsibility for loss or
damage occasioned to any person acting, or refraining from action, as a result of the material
in this publication can be accepted by the editor, the publisher or the author.
First published in Great Britain and the United States in 2015 by Kogan Page Limited
Second edition published in 2018
Third edition published in 2023
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as
permitted under the Copyright, Designs and Patents Act 1988, this publication may only be
reproduced, stored or transmitted, in any form or by any means, with the prior permission in
writing of the publishers, or in the case of reprographic reproduction in accordance with the terms
and licences issued by the CLA. Enquiries concerning reproduction outside these terms should be
sent to the publishers at the undermentioned addresses:
2nd Floor, 45 Gee Street 8 W 38th Street, Suite 902 4737/23 Ansari Road
London New York, NY 10018 Daryaganj
EC1V 3RS USA New Delhi 110002
United Kingdom India
www.koganpage.com
Kogan Page books are printed on paper from sustainable forests.
© Mike Grigsby, 2015, 2018, 2023
The right of Mike Grigsby to be identified as the author of this work has been asserted by him in
accordance with the Copyright, Designs and Patents Act 1988.
ISBNs
Hardback 978 1 3986 0821 4
Paperback 978 1 3986 0819 1
Ebook 978 1 3986 0820 7
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library.
Library of Congress Cataloging-in-Publication Data
Names: Grigsby, Mike, author.
Title: Marketing analytics: a practical guide to improving consumer
insights using data techniques / Mike Grigsby.
Description: Third edition. | London, United Kingdom; New York, NY:
KoganPage, 2023. | Includes bibliographical references and index.
Identifiers: LCCN 2022043394 (print) | LCCN 2022043395 (ebook) | ISBN
9781398608191 (paperback) | ISBN 9781398608214 (hardback) | ISBN
9781398608207 (ebook)
Subjects: LCSH: Marketing research. | Marketing.
Classification: LCC HF5415.2 .G754 2023 (print) | LCC HF5415.2 (ebook) |
DDC 658.8/3–dc23/eng/20220920
LC record available at https://lccn.loc.gov/2022043394
LC ebook record available at https://lccn.loc.gov/2022043395
Typeset by Integra Software Services, Pondicherry
Print production managed by Jellyfish
Printed and bound by CPI Group (UK) Ltd, Croydon, CR0 4YY
v
CONTENTS
Introduction 1
PART ONE
How can marketing analytics help you?
7
1 Overview of statistics 9
Measures of central tendency 9
Measures of dispersion 11
The normal distribution 14
Confidence intervals 15
Relations among two variables: covariance and correlation 16
Probability and the sampling distribution 18
Conclusion 18
Checklist: You’ll be the smartest person in the room if you… 19
2 Consumer behaviour and marketing strategy 20
Introduction 20
Consumer behaviour as the basis for marketing strategy 21
Overview of consumer behaviour 22
Overview of marketing strategy 24
Conclusion 27
Checklist: You’ll be the smartest person in the room if you... 28
3 What is an insight? 29
Introduction 29
Insights tend not to be used by executives 29
Is this an insight? 30
So, what is an insight? 31
Ultimately, an insight is about action-ability 32
Checklist: You’ll be the smartest person in the room if you... 34
vi cONTENTs
PART TWO
Dependent variable techniques
35
4 Modelling demand and elasticity 37
Introduction 37
Dependent equation type vs interrelationship type statistics 38
Deterministic vs probabilistic equations 38
Business case 38
Results applied to business case 44
Modelling elasticity 44
Technical notes 47
Highlight: Segmentation and elasticity modelling can maximize
revenue in a retail/medical clinic chain: field test results 51
Abstract 51
The problem and some background 52
Description of the dataset 53
First: segmentation 53
Then: elasticity modelling 55
Last: test vs control 61
Discussion 61
Conclusion: why is elasticity modelling so rarely done? 62
Checklist: You’ll be the smartest person in the room if you… 63
5 Polynomial distributed lags 64
What is PDL? 64
An example 66
Business case 68
Conclusion 71
Checklist: You’ll be the smartest person in the room if you… 72
6 Using Poisson regression 73
When to use Poisson regression 73
Technical note 74
Business case 75
Conclusion 77
Checklist: You’ll be the smartest person in the room if you… 78
cONTENTs vii
7 Logistic regression and market basket analysis 79
Introduction 79
Conceptual notes 80
Business case 81
Results applied to the model 82
Lift charts 85
How deep to mail 86
Using the model – collinearity overview 90
Variable diagnostics 94
Highlight: Using logistic regression for market basket analysis 96
Abstract 96
What is a market basket? 96
How is it usually done? 96
Logistic regression 97
How to estimate/predict the market basket 97
An example 98
Conclusion 100
Checklist: You’ll be the smartest person in the room if you… 101
8 Survival modelling and lifetime value 102
Introduction 102
Conceptual overview of survival analysis 103
Business case 104
More about survival analysis 106
Model output and interpretation 108
Note: The only way to do churn modelling 112
Conclusion 114
Highlight: Lifetime value: how predictive analysis is superior to
descriptive analysis 115
Abstract 115
Descriptive analysis 115
Predictive analysis 116
An example 118
Checklist: You’ll be the smartest person in the room if you… 122
viii cONTENTs
9 Panel regression and same store sales 123
Introduction 123
What is panel regression? 126
Panel regression: details 126
Business case 128
Insights about marcom (direct mail, email and SMS) 128
Insights about time period (quarters) 129
Insights about cross-sections (counties) 130
Brief note on modelling same store sales 130
Conclusion 131
Checklist: You’ll be the smartest person in the room if you… 132
10 Introduction to forecasting 133
Overview 133
Forecasting demand 134
Autocorrelation 134
Dummy variables and seasonality 135
Business case 137
Conclusion 141
Checklist: You’ll be the smartest person in the room if you… 142
PART THREE
Interrelationship techniques
143
11 Simultaneous equations 145
Introduction 145
What are simultaneous equations? 146
Why go to the trouble of using simultaneous equations? 147
Desirable properties of estimators 148
Business case 151
Conclusion 154
Checklist: You’ll be the smartest person in the room if you… 155
12 Principal components and factor analysis 156
Interrelationship techniques 156
What is factor analysis? 157
What is PCA? 159
Similarities between PCA and factor analysis 159
cONTENTs ix
Differences between PCA and factor analysis 159
Conclusion 160
Checklist: You’ll be the smartest person in the room if you… 161
13 Segmentation overview 162
Introduction 162
Introduction to segmentation 163
What is segmentation? What is a segment? 163
Why segment? Strategic uses of segmentation 164
The four Ps of strategic marketing 166
Criteria for actionable segmentation 168
A priori or not? 168
Conceptual process 169
Highlight: Using segmentation to improve both strategy and predictive
modelling 175
Introduction 175
Segmentation is a strategic, not an analytic, process 175
Why would segmentation improve predictive modelling
accuracy? 176
Segmenting variables for model improvement 177
Example: churn modelling 177
Interpretation and insights 179
What if there was no segmentation? 181
Conclusion 182
Checklist: You’ll be the smartest person in the room if you… 183
14 Tools of segmentation 184
Overview 184
Metrics of successful segmentation 185
General analytic techniques 185
Segmentation techniques summary 193
Business case 194
Analytics 197
Profile and output 201
Comments/details on individual segments 203
K-means compared to LCA 210
Highlight: Why go beyond RFM? 214
Abstract 214
What is RFM? 214