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Levin Dominique. Predictive Marketing
Introduction: Who Should Read this Book
About This Book
What Is in This Book
About the Authors
Acknowledgments
Part 1. A Complete Predictive Marketing Primer
Chapter 1. Big Data and Predictive Analytics Are Now Easily Accessible to All Marketers
The Predictive Marketing Revolution
The Power of Customer Equity
Predictive Marketing Use Cases
Predictive Marketing Adoption Is Accelerating
What Do You Need for Predictive Marketing?
Chapter 2. An Easy Primer to Predictive Analytics for Marketers
What Is Predictive Analytics?
Unsupervised Learning: Clustering Models
Supervised Learning: Propensity Models
Reinforcement Learning and Collaborative Filtering
The Predictive Analytics Process
Chapter 3. Get to Know Your Customers First: Build Complete Customer Profiles
How Much Data to Collect
What Type of Data to Collect
Preparing Your Data for Analysis
Working with IT on Data Integration
One Hundred Questions to Ask Your Data
Chapter 4. Managing Your Customers as a Portfolio to Improve Your Valuation
What Is Customer Lifetime Value?
Increase Customer Lifetime Value for One Customer
Increase Customer Lifetime Value for All Customers
Part 2. Nine Easy Plays to Get Started with Predictive Marketing
Chapter 5. Play One: Optimize Your Marketing Spending Using Customer Data
Invest in Acquisition, Retention, and Reactivation
Differentiate Spending Based on Customer Value
Find Products That Bring High-Value Customers
Find Channels That Bring High-Value Customers
The Case for Last-Touch Attribution
Chapter 6. Play Two: Predict Customer Personas and Make Marketing Relevant Again
Types of Clusters
Using Clusters to Improve Customer Acquisition
Things to Watch Out for When Using Clusters
Clusters in Action
Chapter 7. Play Three: Predict the Customer Journey for Life Cycle Marketing
The Customer Value Journey
Life Cycle Marketing Strategies
Chapter 8. Play Four: Predict Customer Value and Value-Based Marketing
Value-Based Marketing
Chapter 9. Play Five: Predict Likelihood to Buy or Engage to Rank Customers
Likelihood to Buy Predictions
Likelihood to Engage Models
Chapter 10. Play Six: Predict Individual Recommendations for Each Customer
Choosing the Right Customer or Segment
Understanding Customer Context
Content – What to Recommend
Beyond Recommendations
Chapter 11. Play Seven: Launch Predictive Programs to Convert More Customers
Predictive Remarketing Campaigns
Using Look-Alike Targeting
Chapter 12. Play Eight: Launch Predictive Programs to Grow Customer Value
The Secret to Growing Customer Value
Predictive Post-Purchase Programs
Customer Appreciation Campaigns
Chapter 13. Play Nine: Launch Predictive Programs to Retain More Customers
Understanding Your Retention Rate
The Concept of Negative Churn
Understanding Your Business Model
Not All Churn Is Created Equal
Churn Management Programs
Proactive Retention Management
Customer Reactivation Campaigns
Part 3. How to Become a True Predictive Marketing Ninja
Chapter 14. An Easy-to-Use Checklist of Predictive Marketing Capabilities
Organizational Capabilities for Predictive Marketing
Technical Capabilities for Predictive Marketing
Questions to Ask Predictive Marketing Vendors
Chapter 15. An Overview of Predictive (and Related) Marketing Technology
Do-It-Yourself Predictive Marketing
Outsourcing to Marketing Service Providers
Campaign Management and Marketing Cloud Options
Other Tools You May Have Heard About
Which Solution Is Right for Me?
Whatever You Do – Get Started
Chapter 16. Career Advice for Aspiring Predictive Marketers
Business Understanding Trumps Math
Ask the Right Questions
Blend the Art and Science of Marketing
Learn from Others
Chapter 17. Privacy and the Difference Between Delightful and Invasive
Types of Personal Information
Avoid Invasive Situations
Give Customers Control
Hard Boundaries and Government Legislation
Chapter 18. The Future of Predictive Marketing
Advanced Predictive Analytics Models
Think Like a Predictive Marketer
Appendix. Overview of Customer Data Types
Purchases and Transactions
Web and Online Behavior
Email Behavior
Household and Account Grouping
Location
Call Center Interactions, Meetings, and Social Interactions
Returns, Complaints, and Reviews
Gender
U.S. Census Data
Vertical and Size
Other Customer Data Points
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This book is for everyday marketers who want to learn what predictive marketing is all about, as well as for those marketers who are ready to use predictive marketing in their organizations. Whether you are just getting started with your research, or have already begun to implement predictive marketing, you will find many practical tips in this book.
We share what marketers at companies large and small should know about predictive marketing. We show you how to achieve the same large returns as early adopters such as Harrah's Entertainment, Amazon, and Netflix. We also give you a practical guidebook to help you get started with this new way of marketing. And above all, we share stories from companies small and large, from retail to publishing, to software to manufacturing. All of these marketers have achieved revolutionary returns, and so can you.
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I am a scientist by training; I am an entrepreneur at heart, driven by curiosity of knowledge and challenging status quo. In elementary school, I saw the opportunity to make a profit collecting fruit from mulberry trees from our school backyard and selling it on the street, enlisting my schoolmates to help me run this small business. With some prodding from my engineer parents, I followed in my older brother's footsteps to enter a PhD program in physics at Brown University, studying under Leon Cooper at The Institute for Brain and Neural Systems. Dr. Cooper has received the Nobel Prize in Physics for his work on superconductivity and later decided that the next big problem to solve was in neuroscience, decoding how we learn and adapt. He is a pioneer in learning theory since the early 70s, using both experimental neuroscience as a base as well as statistical techniques for understanding and creating learning systems, now popularly called machine learning. I worked on both biological mechanisms that underlie learning and memory storage as well as construction of artificial neural networks, networks that can learn, associate, and reproduce such higher level cognitive acts as abstraction, computation, and language acquisition. Although these tasks are carried out easily by humans, they have not been easy to embody as conventional computer program.
As I was getting close to graduating from the PhD program at Brown University around 1998, I noticed that the business world was mostly running on simple spreadsheets, and I wanted to apply a data science and machine-learning approach to business. This goal led me to work for McKinsey & Co., the premier strategy consulting firm that helps large companies formulate strategies based on a fact-based problem solving approach.
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