Synopses & Reviews
Mike Grigsby provides business analysts and marketers with the marketing science understanding and techniques they need to solve real-world marketing challenges, such as pulling a targeted list, segmenting data, testing campaign effectiveness, and forecasting demand.
Assuming no prior knowledge, Marketing Analytics introduces concepts relating to statistics, marketing strategy, and consumer behavior and then works through a series of problems by providing various data modeling options as solutions. By using this format of presenting a problem and multiple ways to solve it, this book both makes marketing science accessible to beginners and aids the more experienced practitioner in understanding the more complex aspects of data analytics to refine their skills and compete more effectively in the workplace.
Review
"Offers a truly accessible guide to the basics and practice of marketing analytics." Koen Pauwels, Professor of Marketing at Ozyegin University, Istanbul
Review
"This book gives a broad overview of marketing analytics to people who don't have any related background....[E]xamples are explained to give readers a clearer idea. I think the book is worth a read for anyone who wants to become a marketing analyst." and Honorary Professor at the University of Groningen
Review
"Marketing Analytics is a practical guidebook written in a conversational tone that makes complex theories easily understood. The author's experience in the industry combined with his inherent gift for explaining everything a successful marketing analyst needs to know makes this book a must-read." Yuan Fang - MSc (Marketing Analytics candidate)
Review
"Marketing Analytics is a must-read for analytics practitioners and marketing managers seeking a comprehensive overview of the most actionable techniques that virtually any organization can apply to gain immediate benefits.... Dr. Grigsby succinctly illustrates the concepts with real examples and provide references for analysts needing deeper guidance or theory. I wish Marketing Analytics was published 15 years ago - it would've saved me a lot of independent research!" Katy Richardson - Founder and Principal, 214 Creative
Review
"For those MBAs who barely passed their quantitative marketing and statistics classes without truly understanding the content, Marketing Analytics provides everything managers and executives need to know presented as a conversation with examples to boot! You'll definitely sound smarter in the boardroom after reading this book!" W. Dean Vogt, Jr. - Marketing Research and Analytics Practitioner
Review
"This is an excellent read for people in the industry who work in strategy and marketing. This 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 was 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
Synopsis
Who is most likely to buy and what is the best way to target them? Marketing Analytics enables marketers and business analysts to answer these questions by leveraging proven methodologies to measure and improve upon the effectiveness of marketing programs.
Marketing Analytics demonstrates how statistics, analytics and modeling can be put to optimal use to increase the effectiveness of every day marketing activities, from targeted list creation and data segmentation to testing campaign effectiveness and forecasting demand. The author explores many common marketing challenges and demonstrates how to apply different data models to arrive at viable solutions. Business cases and critical analysis are included to illustrate and reinforce key concepts throughout.
Beginners will benefit from clear, jargon-free explanations of methodologies relating to statistics, marketing strategy and consumer behaviour. More experienced practitioners will appreciate the more complex aspects of data analytics and data modeling, discovering new applications of various techniques in every day practice. Readers of Marketing Analytics will come away with a firm foundation in markets analytics and the tools they need to gain competitive edge and increase market share. Online supporting resources for this book include a bank of test questions as well as data sets relating to many of the chapters.
About the Author
Mike Grigsby has been involved in marketing science for over 25 years. He was marketing research director at Millward Brown and has held leadership positions at Hewlett-Packard and the Gap. With a wealth of practitioner experience at the forefront of marketing science and data analytics, he now heads up the strategic retail analysis practice at Targetbase. He is also known for his academic work, having written articles for academic and trade journals, and currently teaches at the University of Texas at Dallas. He is a regular speaker at trade conventions and seminars.
Table of Contents
Foreword
Preface
Introduction
Part One: Overview
01 A (little) statistical review
Measures of central tendency
Measures of dispersion
The normal distribution
Relations among two variables: covariance and correlation
Probability and the sampling distribution
Conclusion
Checklist: Youll be the smartest person in the room if you...
02 Brief principles of 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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Part Two Dependent variable techniques
03 Modelling dependent variable techniques (with one equation): what are the things that drive demand?
Introduction
Dependent equation type vs inter-relationship 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 data set
First: segmentation
Then: elasticity modelling
Last: test vs control
Discussion
Conclusion
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04 Who is most likely to buy and how do I target?
Introduction
Conceptual notes
Business case
Results applied to the model
Lift charts
Using the model - collinearity overview
Variable diagnostics
Highlight: Using logistic regression for market basket analysis
Abstract
What is a market basket?
Logistic regression
How to estimate/predict the market basket
Conclusion
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05 When are my customers most likely to buy?
Introduction
Conceptual overview of survival analysis
Business case
More about survival analysis
Model output and interpretation
Conclusion
Highlight: Lifetime value: how predictive analysis is superior to descriptive analysis
Abstract
Descriptive analysis
Predictive analysis
An example
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06 Modelling dependent variable techniques (with more than one equation)
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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Part Three Inter-relationship techniques
07 Modelling inter-relationship techniques: what does my (customer) market look like?
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
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08 Segmentation tools and techniques
Overview
Metrics of successful segmentation
General analytic techniques
Business case
Analytics
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
Segmentation techniques
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Part Four Other
09 Marketing Research
Introduction
How is survey data different than database data?
Missing value imputation
Combating respondent fatigue
A far too brief account of conjoint analysis
Structural equation modelling (SEM)
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10 Statistical testing: how do I know what works?
Everyone wants to test
Sample size equation: use the lift measure
A/B testing and full factorial differences
Business case
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Part Five Capstone
11 Capstone: focusing on digital analytics
Introduction
Modelling engagement
Business case
Model conception
How do I model multiple channels?
Conclusion
Part Six Conclusion
12 The Finale: What should you take away from this? Any other stories/soap box rants?
What things have I learned that Id like to pass on to you?
What other things should you take away from all this?
Glossary
Bibliography and further reading
Index