Artificial Intelligence Marketing and Predicting Consumer Choice: An Overview of Tools and Techniques

394 Pages·2017·6.49 MB·English

by  Dr Steven Struhl

Praise for Artificial Intelligence
Marketing and Predicting Consumer
Choice
‘Full of hard-won practical wisdom, this is a comprehensive guide to
navigating the complexity of market forecasting. Foregoing the hyperbole
that so often characterizes discussions of artificial intelligence, Dr Struhl
thoroughly explains a wide range of methods, where their difficulties lie
and how to get the best insights from each.’ Peter Goldstein, Software
Engineer, Google
‘Dr Struhl’s new book is a rare jewel among marketing science tomes –
informative, easy to understand and, dare I say, even entertaining. Dr
Struhl surveys several major analytic techniques in plain English,
introducing the novice to foundational concepts while at the same time
reminding the seasoned analyst of best practices often forgotten, all while
sprinkling his wry humour like a spoonful of sugar to help the medicine go
down. For techniques already familiar, it’s an enjoyable refresher; for
techniques unfamiliar, an excellent introduction. A valuable resource for
beginner and expert alike.’ Dr Richard McCullough, President, Macro
Consulting Inc
‘This book covers lucidly a number of research methodologies that
commonly support very important new product development and
marketing strategy decisions. Dr Struhl should be commended for making
the materials accessible to a wide range of audiences by emphasizing the
practicality, appropriateness, and pros and cons of the various
methodologies.’ Jehoshua Eliashberg, Sebastian S Kresge Professor of
Marketing, and Professor of Operations, Information and Decisions, The
Wharton School
‘Dr Struhl has written another highly informative book. It offers an easy-to-
understand way of thinking about how to best use data to answer bigger
marketing questions. His explanations are clear and relatable, making this
book an invaluable tool for anyone involved in commercial decision
making, especially marketers and researchers.’ Katie Szelc, Manager,
Customer Insights, Global Business Insights, Johnson & Johnson Medical
Devices
‘An excellent all-in-one primer for today’s marketer and researcher. This is
clear, to the point and a comprehensive guide to this complex field.’ Louis A
Tucci PhD, Associate Professor of Marketing, The College of New Jersey
‘Dr Struhl does an excellent job of explaining the strengths and weaknesses
of methods of predicting consumer behaviour. This book is thoughtful,
well-written, and also a practical book for marketers, marketing
researchers and business consultants. If you help organizations make
decisions, the best decision you can make right now is to read this book.’
David F Harris, author of The Complete Guide to Writing Questionnaires:
How to get better information for better decisions
‘Artificial Intelligence Marketing and Predicting Consumer Choice clearly
explains the tools that drive sophisticated market research. I heartily
recommend this book for anyone looking for greater insight and success
with cutting-edge techniques.’ Robert Kaminsky, President, MedSpan
Research
‘For researchers who want to tackle the complex tasks of predicting
consumer choices and creating market simulations, this book is a great
one-stop reference. In easy-to-read style with plenty of useful examples,
the author covers conventional multivariate data analysis techniques
(conjoint, discrete choice, CHAID, regression) as well as the latest ones
(Hierarchical Bayesian analysis). The book also includes many key concepts
and definitions useful for any quantitative researcher, such as statistical
significance, sampling, and more.’ Kathryn Korostoff, Lead Instructor and
Founder, Research Rockstar LLC
‘This book covers an extensive set of methods for predicting consumer
choices, including conjoint analysis, discrete choice modelling, neural
networks, classification trees, Bayesian methods, and so much more. Dr
Struhl has written a genuinely practical guide to predictive analytics that is
so easy, it reads like a bedtime reader. Having been a practitioner in this
area for over 20 years, I found this book to not only be informative for a
pertinent 21st century topic, but also a fun read.’ Don Meyer, Client
Director, Analytics, AC Nielsen
‘I’ve been working with Dr Struhl for the past two and a half years and am
truly impressed by his expertise. His book covers a truly expansive range of
methods for predicting consumer choices. These include neural networks,
ensembles, Bayesian Networks and classification trees. He also talks about
how more established methods such as conjoint analysis and discrete
choice modelling have benefited from machine learning methods. Here you
will find the best applications of each approach, with plenty of examples
from real life, showing what works and what does not. The online,
downloadable simulator programs are incredibly impressive and show you
the amazing things that can be done in the realm of predictions.’ Sung Lee,
President, The Research Associates
‘This book covers a truly expansive range of methods for predicting
consumer choices. Some of these include conjoint analysis, discrete choice
modelling, neural networks, classification trees, and Bayesian networks.
With practical tips and examples, and a welcome use of humour, this is a
clear, easy-to-read and definitive guide for experts and novices alike.’ Paul
Nisbet PhD, President, One Research
‘Steven Struhl’s decades of experience as an analytics guru in real-world
marketing applications shine through in this highly readable guide to
modern analyses and models of consumer choices. In engaging and
entertaining style, brimming with practical examples, without abstruse
theory or sales hype, his book is a down-to-earth and much-needed guide
that clears up the mysteries of these methods.’ Dave Lyon, Principal,
Aurora Market Modeling
‘Dr Struhl has done it again! He’s taken a cutting-edge topic and grounded
it in very accessible prose, using real-life situations so that marketing and
market research practitioners can immediately act upon Artificial
Intelligence and its ability to predict consumer choice. I strongly
recommend this book for anyone wanting to better understand how to use
the growing presence of Artificial Intelligence and machine learning in our
day-to-day responsibilities.’ Darrin Helsel, Past Research Chair, American
Marketing Association, Portland
‘Dr Struhl’s latest book, Artificial Intelligence Marketing and Predicting
Consumer Choice, provides concrete and easy-to-understand information
about a set of analyses that can be intimidating for researchers and clients
alike. His examples are clear and applicable to the concepts being
discussed and provide excellent insights into how these valuable analytical
techniques can be used to answer real-life business questions. In addition,
Dr Struhl also writes with a sense of humour which helps to make readers
comfortable and the material even more readily understandable. This book
is an excellent resource for both market research suppliers and clients!’
Julie Worwa MBA, Research and Marketing Consultant
‘Steven Struhl has a gift for taking complex methodological issues and
explaining them in accessible, meaningful language. His writing is thought-
provoking and entertaining. Artificial Intelligence Marketing and Predicting
Consumer Choice tackles many of the contemporary challenges of turning
an abundance of data into crucial insights. It is a must-read for anyone who
is exploring the use of artificial intelligence methods to inform marketing
tactics and strategies.’ Larry Durkin, Senior Consultant, MSP Analytics
Artificial Intelligence Marketing
and Predicting Consumer Choice
An overview of tools and techniques
Steven Struhl
CONTENTS
Cover
Title Page
Copyright
Contents
List of Figures
List of Tables
Preface
01 Who should read this book and why?
What we cover in this book
What can you expect in this book?
Data versus information
What is important?
The methods we will be discussing
Implicit views of people and biases
One way of comparing these methods
Sense and sensibility with predictions
Where we will not be going
Summary of key points
02 Getting the project going
At the beginning
Know who you are talking about or talking to
What is the most you can expect from each method?
How do you judge the result?
What is significant?
On to correlations
How do I plan to evaluate the results?
Know what sensible goals might look like
Summary of key points
03 Conjoint, discrete choice and other trade-offs: let’s do an
experiment
The reasons we need these methods
The basic thinking behind the experimentally
designed methods
What the methods ask – and get
What is a designed experiment?
The great measurement power of experiments
Getting more from experiments: HB to the rescue
A brief talk about origins
Applications in brief
Summary of key points
04 Creating the best, newest thing: discrete choice modelling
Key features
Thinking through and setting up the problem
How many people you need
Utility and share
Market simulations
Making more than one choice: allocating purchases
Using the simulator program in the online resources
Rounding out the picture
Summary of key points
05 Conjoint analysis and its uses
Thinking in conjoint versus thinking in choices
Conjoint analysis for single-product optimization
Using the single product simulator in the online
resources
Conjoint remains an excellent method for messages
Conjoint analysis for the best service delivery
Using the message optimization simulator in the
online resources
Conjoint analysis and interactions
Variants of conjoint analysis
Summary of key points
06 Predictive models: via classifications that grow on trees
Classification trees: understanding an amazing
analytical method
Seeing how trees work, step by step
Strong, yet weak
A case study: let’s take a cruise
CHAID and CART (and CRT, C&RT, QUEST, J48 and
others)
Summary: applications and cautions
07 Remarkable predictive models with Bayes Nets
What are Bayes Nets and how do they compare with
other methods?
Let’s make a deal
Our first example: Bayes Nets linking survey
questions and behaviour
Bayes Nets confirm a theoretical model, mostly
What is important to buyers of children’s apparel
Summary and conclusions
08 Putting it together: what to use when
The tasks the methods do
Thinking about thinking
Bibliography
Index
Backcover
List of Figures
FIGURE 1.1 One way to categorize the methods we will discuss
FIGURE 2.1 Getting the frame right is critical
FIGURE 2.2 Sample percentages’ errors at different sample
sizes
FIGURE 2.3 Not everything falls into a straight line
FIGURE 2.4 Correct classification table – 63 per cent correct
overall
FIGURE 3.1 Rating the features of a floor-standing wine cooler
FIGURE 3.2 A sample marketplace scenario for discrete choice
modelling
FIGURE 3.3 A profile of a service for conjoint analysis
FIGURE 3.4 A simple simulator for one product
FIGURE 3.5 A survey task for maximum difference scaling
(MaxDiff)
FIGURE 3.6 A survey task for a Q-Sort/Case 5
FIGURE 3.7 A small and wrong way to measure and a larger
correct way
FIGURE 3.8 Elements of a designed experiment