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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
Make personalized marketing a reality with this practical guide to predictive analytics Predictive Marketing is a predictive analytics primer for organizations large and small, offering practical tips and actionable strategies for implementing more personalized marketing immediately. The marketing paradigm is changing, and this book provides a blueprint for navigating the transition from creative- to data-driven marketing, from one-size-fits-all to one-on-one, and from marketing campaigns to real-time customer experiences. You'll learn how to use machine-learning technologies to improve customer acquisition and customer growth, and how to identify and re-engage at-risk or lapsed customers by implementing an easy, automated approach to predictive analytics. Much more than just theory and testament to the power of personalized marketing, this book focuses on action, helping you understand and actually begin using this revolutionary approach to the customer experience. Predictive analytics can finally make personalized marketing a reality. For the first time, predictive marketing is accessible to all marketers, not just those at large corporations -- in fact, many smaller organizations are leapfrogging their larger counterparts with innovative programs. This book shows you how to bring predictive analytics to your organization, with actionable guidance that get you started today. * Implement predictive marketing at any size organization * Deliver a more personalized marketing experience * Automate predictive analytics with machine learning technology * Base marketing decisions on concrete data rather than unproven ideas Marketers have long been talking about delivering personalized experiences across channels. All marketers want to deliver happiness, but most still employ a one-size-fits-all approach. Predictive Marketing provides the information and insight you need to lift your organization out of the campaign rut and into the rarefied atmosphere of a truly personalized customer experience.
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Seitenzahl: 396
Veröffentlichungsjahr: 2015
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Table of Contents
Title Page
Copyright
Dedication
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
Index
End User License Agreement
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Guide
Cover
Table of Contents
Introduction
Part 1: A Complete Predictive Marketing Primer
Begin Reading
List of Illustrations
Chapter 1: Big Data and Predictive Analytics Are Now Easily Accessible to All Marketers
Figure 1.1 The Predictive Marketing Revolution
Figure 1.2 From a Product to a Customer Orientation
Figure 1.3 Customers Are Key to Market Value
Figure 1.4 Ten Examples of Predictive Marketing
Figure 1.5 The Predictive Marketing Process
Chapter 2: An Easy Primer to Predictive Analytics for Marketers
Figure 2.1 The Principle of Clustering
Figure 2.2 Training Propensity Models
Figure 2.3 An Example of a Direct Mail Response Model
Figure 2.4 Products Typically Bought Together
Figure 2.5 Overview of the Predictive Analytics Process
Chapter 3: Get to Know Your Customers First: Build Complete Customer Profiles
Figure 3.1 Design Principles for Data Collection
Figure 3.2 Three Steps to Customer Data Collection
Figure 3.3 The Anatomy of a Purchase
Figure 3.4 The Data Preparation Process
Figure 3.5 Example of a Customer Profile
Chapter 4: Managing Your Customers as a Portfolio to Improve Your Valuation
Figure 4.1 Sample Life Cycle Marketing Strategies
Figure 4.2 The Pool Cycle Management Framework
Chapter 5: Play One: Optimize Your Marketing Spending Using Customer Data
Figure 5.1 Growth from New and Existing Customers
Figure 5.2 How Many New Customers Do You Need?
Figure 5.3 Benchmarking Your Growth
Figure 5.4 Engineering the Marketing Funnel
Figure 5.5 Marketing Funnel by Life Cycle
Figure 5.6 Marketing Spending Optimization Example
Figure 5.7 The Customer Value Path
Figure 5.8 Acquisition Sources Worksheet
Figure 5.9 Spending Based on Value and Risk
Figure 5.10 Lifetime Value by Acquisition Category
Figure 5.11 Last-Touch versus Multitouch Attribution
Chapter 6: Play Two: Predict Customer Personas and Make Marketing Relevant Again
Figure 6.1 Example of Product-Based Clusters
Figure 6.2 Example of Brand-Based Clusters
Figure 6.3 Example of Behavior-Based Clusters
Chapter 7: Play Three: Predict the Customer Journey for Life Cycle Marketing
Figure 7.1 The Customer Value Journey
Figure 7.2 Overview of Life Cycle Marketing Strategies
Chapter 8: Play Four: Predict Customer Value and Value-Based Marketing
Figure 8.1 Value-Based Marketing Strategies
Figure 8.2 Value Transition and Definition of Value Segments
Figure 8.3 Example of Value Transition Framework
Figure 8.4 Net Loss/Gain from Acquisition, Reactivation, and Lapsed Segments
Chapter 9: Play Five: Predict Likelihood to Buy or Engage to Rank Customers
Figure 9.1 Percentage Customers Acquired with Discounts
Figure 9.2 Considered versus Quick Decisions
Figure 9.3 Lead Scoring Methods for Business Marketers
Figure 9.4 Open and Click Rates of Different Email Segments
Figure 9.5 How to Balance Short- and Long-Term Email Revenues
Figure 9.6 When to Reduce Email Frequency
Figure 9.7 Reducing Opt-Outs with Equal Margin Dollars
Chapter 11: Play Seven: Launch Predictive Programs to Convert More Customers
Figure 11.1 Facebook Look-Alike Targeting
Chapter 12: Play Eight: Launch Predictive Programs to Grow Customer Value
Figure 12.1 The Complete Customer Funnel
Chapter 13: Play Nine: Launch Predictive Programs to Retain More Customers
Figure 13.1 Business Models
Figure 13.2 Example of GolfGear Churn Rate Details
Figure 13.3 Declining Customer Count, but Increasing Aov
Figure 13.4 Decline in Low-Value, Not High-Value Customers
Figure 13.5 Overview of Churn Management Programs
Chapter 14: An Easy-to-Use Checklist of Predictive Marketing Capabilities
Figure 14.1 Checklist of Predictive Marketing Capabilities
Chapter 15: An Overview of Predictive (and Related) Marketing Technology
Figure 15.1 Predictive Marketing Technologies Overview
Figure 15.2 Convergence of DMP and Predictive Marketing
Predictive Marketing
Easy Ways Every Marketer Can Use Customer Analytics and Big Data
Ömer Artun, PhD
Dominique Levin
This book is printed on acid-free paper.
Copyright © 2015 by AgilOne. All rights reserved
Published by John Wiley & Sons, Inc., Hoboken, New Jersey
Published simultaneously in Canada
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Library of Congress Cataloging-in-Publication Data:
Artun, Omer, 1969–
Predictive marketing : easy ways every marketer can use customer analytics and big data / Omer Artun, Dominique Levin.
pages cm
Includes index.
ISBN 978-1-119-03736-1 (hardback)
ISBN 978-1-119-03732-3 (ePDF)
ISBN 978-1-119-03733-0 (ePub)
1. Marketing. I. Levin, Dominique, 1971– II. Title.
HF5415.A7458 2015
658.8—dc23
2015013473
Cover image: Wiley
Cover design: Abstract Shoppers © Maciej Noskwoski/GettyImages
Dedicated to
My darling wife Dr. Burcak Artun for always believing in me
Ömer Artun
My husband Eilam Levin without whom it would not be worthwhile
Dominique Levin
Introduction: Who Should Read this Book
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.
About This Book
We are passionate about improving the quality of marketing and about arming marketers with the knowledge and tools they need to make marketing relevant again. We hope that the chapters that follow give marketers the vocabulary and the inspiration to start to understand and use big data and machine learning–powered marketing. We believe this will lead to a win-win for customers, businesses, and marketers. Customers will have more relevant and meaningful experiences, businesses will be able to build more profitable customer relationships, and marketers will gain visibility and respect within their organizations. We look forward to continuing the dialogue on our website www.predictivemarketingbook.com, the “Predictive Marketing Book” LinkedIn group (https://www.linkedin.com/groups?gid=8292127), or via twitter.com/agilone.
This book is divided in three main parts. The first part, “A Complete Predictive Marketing Primer,” introduces many of the foundational elements in predictive marketing, including what is happening under the hood of predictive marketing software, how data science and predictive analytics work, and what are fundamentals behind the customer lifetime value concept. The second part of the book, “Nine Easy Plays to Get Started with Predictive Marketing,” is a playbook with concrete strategies to get you started with predictive marketing. The last part of the book, “How to Become a True Predictive Marketing Ninja,” gives an overview of predictive marketing technologies, some career advice for marketers, and looks at privacy and the future of predictive marketing. Many of the chapters can be read as stand-alone essays, so use the executive summary below to jump to the chapters that are most relevant to you.
What Is in This Book
Chapter 1: Big Data and Predictive Analytics Are Now Easily Accessible to All Marketers
Predictive marketing is a new way of thinking about customer relationships, powered by new technologies in big data and machine learning, which we collectively call predictive analytics. Marketers better pay attention to predictive analytics. Applying predictive analytics is the biggest game-changing opportunity since the Internet went mainstream almost 20 years ago. Although some large brands have been using pieces of predictive marketing for many years now, we are still in the early stages of adoption, and this is the right time to get started. The adoption of predictive marketing is accelerating among companies large and small because: (a) customers are demanding more meaningful relationships with brands, (b) early adopters show that predictive marketing delivers enormous value, and (c) new technologies are available to make predictive marketing easy.
Chapter 2: An Easy Primer to Predictive Analytics for Marketers
Many marketers want to at least understand what is happening in the predictive analytics black box, to more confidently apply these models or to be able to communicate with data scientists. After reading this chapter marketers will have a good understanding of the entire predictive analytics process. There are three types of predictive analytics models that marketers should know about: unsupervised learning, supervised learning, and reinforcement learning. Many marketers don't realize that 80 percent of the work associated with predicting future customer behavior is going towards collecting and cleaning customer data. This data janitor work is not glamorous but essential: without accurate and complete customer data, there can be no meaningful customer analytics.
Chapter 3: Get to Know Your Customers First: Build Complete Customer Profiles
Building complete and accurate customer profiles is no easy task, but it has a lot of value. If yours is like most companies, customer data is all over the place, full of errors and duplicates and not accessible to everyday marketers. Fortunately, predictive technology, including fuzzy matching, can help—at least some—to clean up your data mess and to connect online and offline data to resolve customer identities across the digital and physical divide. Just getting all customer data in one place has enormous value, and making customer profiles accessible to customer-facing personnel throughout the organization is a great first step to start to deliver better experiences to each and every customer.
Chapter 4: Managing Your Customers as a Portfolio to Improve Your Valuation
It is our strong belief that the best way for any business to optimize enterprise value is to optimize the customer lifetime value of each and every customer. Customers are the unit of value for any company and therefore customer lifetime value is the most important metric in marketing. If you maximize the lifetime value, or profitability, of each and every customer, you also maximize the profitability and valuation of your company as a whole. The best way to optimize lifetime value for all customers is to manage your customers as if they were a stock portfolio. You take different actions and send different messages for customers who are brand-new than for those who have been doing business with you for a while. You will need to adjust your thinking and budget for unprofitable, medium-value, and high-value customers.
Chapter 5: Play One: Optimize Your Marketing Spending Using Customer Data
When asked to allocate marketing budgets, most marketers immediately think about acquisition spending and about allocating budget to the best performing channels and products. However, the predictive marketing way to allocate spending is based on allocating dollars to the right people, rather than to the right products or channels. Most companies are focused on acquisition, whereas they could achieve growth more cost-effectively by focusing more of their time and budget on retention and reactivation of customers. Marketers should learn to allocate budgets based on their goals to acquire, retain, and reactivate customers and to find products and channels that deliver the highest value customers.
Chapter 6: Play Two: Predict Customer Personas and Make Marketing Relevant Again
We will look at the predictive technique of clustering and how it is different from classical customer segmentation. Clustering is a powerful tool in order to discover personas or communities in your customer base. Specifically, in this chapter we look at product-based, brand-based, and behavior-based clusters as examples. Clustering can be used to gain insight into differences in customers' needs, behaviors, demographics, attitudes, and preferences regarding marketing interactions, products, and service usage. Using these clusters, you can also start to differentiate and optimize both marketing actions and product strategy for different groups of customers.
Chapter 7: Play Three: Predict the Customer Journey for Life Cycle Marketing
In this chapter we look at the customer life cycle in more detail, from acquisition, to growth, and to retention and see how your engagement strategy should evolve with each and every customer during the life cycle. The basic principle of optimizing customer lifetime value is the same for all stages of the life cycle and can be summarized in three words: give to get. Customers are much more likely to buy from you if they trust you. The best way to gain trust is to deliver an experience of value. So to get customer value, give customer value.
Chapter 8: Play Four: Predict Customer Value and Value-Based Marketing
Not all customers have equal lifetime value. Any business will have high-value customers, medium-value customers, and low lifetime value customers. There is an opportunity to create enterprise value by crafting marketing strategies that are differentiated based on the value of the customer. This practice to segment and target by customer lifetime value is called value-based marketing. Spend more money to appreciate and retain high-value customers. Upsell to medium-value customers in order to migrate these customers to higher value segments. Finally, reduce your costs to service low-value or unprofitable customers.
Chapter 9: Play Five: Predict Likelihood to Buy or Engage to Rank Customers
Likelihood to buy models is what most people think about when you use the word predictive analytics. With these models you can predict the likelihood of a certain type of future behavior of a customer. In this chapter we look at programs based on likelihood to buy predictions spanning both consumer and business marketing. We see how in business marketing predictive lead scoring or customer scoring can optimize the time of your sales and customer success teams. We also show you how consumer marketers can optimize their discount strategy and the frequency of their emails based on propensity models.
Chapter 10: Play Six: Predict Individual Recommendations for Each Customer
Another popular predictive technique is personalized recommendations. In this chapter we provide marketers a primer on recommendations and we teach you about different types of recommendations. We explore recommendations made at the time of purchase versus those made as a follow-up to a purchase, and recommendations that are tied to specific products versus those that are tied to specific customer profiles. We also discuss what can go wrong when making personalized recommendations, and we highlight the need for merchandising rules, omni-channel orchestration, and giving customers control when making personal recommendations.
Chapter 11: Play Seven: Launch Predictive Programs to Convert More Customers
In this chapter we cover three specific predictive marketing strategies that can help you acquire more, and better, customers: using personas to design better acquisition campaigns, using remarketing to increase conversion and using look alike targeting. When it comes to remarketing, you should be able to differentiate between customers who are likely to come back, and send them a simple reminder, versus those who are unlikely to come back and may need an additional incentive. This is true for abandoned cart, browse, and search campaigns. Using lookalike targeting features of Facebook and other advertising platforms, you can find more customers who look just like your existing customers, for example, new customers just like your best customers.
Chapter 12: Play Eight: Launch Predictive Programs to Grow Customer Value
The secret to retaining a customer is to start trying to keep the customer the day you acquire her. The initial transaction is just the beginning of a long relationship that needs to be nurtured and developed. Engagement with customers should not stop when you convert a prospect into a buyer. In this chapter we cover a number of specific predictive marketing strategies to help grow customer value: postpurchase campaigns, replenishment campaigns, repeat purchase programs, new product introductions, and customer appreciation campaigns. We will also discuss loyalty programs and omni-channel marketing in the age of predictive analytics.
Chapter 13: Play Nine: Launch Predictive Programs to Retain More Customers
We recommend you focus on dollar value retention. If you don't, you could be retaining customers, but losing money anyway. Also, when measuring customer retention it is important to realize that not all churn is created equal. Losing an unprofitable customer is not nearly as bad as losing one of your best customers. Also, it is a lot easier, cheaper, and more effective to try and prevent a customer from leaving than it is to reactivate that customer after she has already stopped shopping with you. In this chapter we look at different churn management programs, from untargeted, applying equally to all your customers, to targeted, and we will cover proactive retention management and customer reactivation campaigns.
Chapter 14: An Easy-to-Use Checklist of Predictive Marketing Capabilities
In order to use the predictive marketing techniques discussed in this book you need to acquire both a predictive marketing mind-set as well as certain predictive marketing technical capabilities. You need to evolve your thinking from being focused on campaigns, channels, and one-size-fits-all marketing to being focused on individual customers and their context. From a technology point of view you need to acquire basic capabilities in the areas of customer data integration, predictive intelligence, and campaign automation.
Chapter 15: An Overview of Predictive (and Related) Marketing Technology
We live in an exciting and somewhat confusing time. A large number of new marketing technologies are becoming available every year. In this chapter, we will give you a high-level overview of the various types of commercially available technologies and describe what it would take to build a predictive marketing solution in-house from the ground up.
Chapter 16: Career Advice for Aspiring Predictive Marketers
There is a huge career opportunity that comes from being an early adopter of new methodologies and technologies, predictive marketing and predictive analytics included. If you are uncomfortable with numbers and math, and fearful of getting started with predictive marketing, there are a couple of things you should know: business understanding trumps math, asking the right questions goes a long way, the best marketers blend the art and science of marketing, and there is a lot you can learn from others.
Chapter 17: Privacy and the Difference Between Delightful and Invasive
In general, consumers are willing to share preference information in exchange for apparent benefits, such as convenience, from using personalized products and services. When it comes to personalization, there are different types of customer information that can be used and consumers may feel different about one type of information over the other. Use common sense when considering whether a marketing campaign is delightful or creepy and consider the context of the situation. This chapter will provide some guidelines for dealing with customer data that will engender trust.
Chapter 18: The Future of Predictive Marketing
Predictive analytics will continue to find new applications inside and beyond marketing. Not only will more algorithms become available, but real-time customer insights will start to shape our physical world, including the store of the future. There are huge benefits for customers, companies, and marketers alike to get started with predictive marketing sooner rather than later. Sooner or later your customers and competitors will force you to adopt a predictive marketing mind-set, so you might as well be an early adopter and derive a huge competitive advantage.
About the Authors
Omer Artun
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.
When I joined McKinsey & Co. in 1999, I was able to test drive some of this data scientific approach in a few studies. My first project was to help a large technology company improve sales coverage, scientifically matching the sales team with the customers based on customer needs, sales team's skill, and experience. The CEO was impressed with the results on paper, but was unable to operationalize the results in real life, in a repeatable way. This is what I call the last mile problem of analytics. I realized that this is a big problem to solve. Analytics is an important enabler in improving commercial efficiency, but can only create value if it becomes part of the day-to-day execution workflow. I saw this theme repeat over and over again in many areas of business, pricing, supply chain, marketing, and sales. Most McKinsey projects I have been part of ended up on a slide deck which had all the right answers but very rarely created any real value. Equipped with McKinsey training, I joined one of my clients, Micro Warehouse as VP of Marketing, in 2002, with the goal to bring data science to everyday operations. I was lucky to be empowered by the CEO Jerry York and President Kirby Myers. Jerry was the most analytically driven person I ever knew in business, still to this day. He was previously CFO of IBM during Gerstner years, and CFO of Chrysler before that. He encouraged me to use data science to help him run the business better.
I knew I had to architect my approach in a way that married data science with execution to solve the last mile problem. I had two important recruits, Dr. Michel Nahon, a brilliant Yale-trained applied mathematician who helped me with machine-learning algorithms, and the hacker extraordinaire Glen Demeraski, who helped me with everything database and application related. I created approaches and systems that used data to more efficiently allocate resources, reduce marketing costs, and uncover new revenue sources. We had significant impact on marketing efficiency, pricing, and discounting patterns as well as salesforce effectiveness. In early 2003 we had real-time systems alerting purchase, pricing, and customer acquisition patterns of the sales team compared to moving averages to take immediate action by the sales leadership. After Micro Warehouse, from 2004 to 2006, I joined Best Buy as Senior Director of Business-to-Business marketing of its newly founded Best Buy for Business division. Best Buy at the time also struggled with the same exact last mile problem, lots of internal resources, tools, many high-flying consultants talking about customer segmentation, and analytics, but when you walked into a store, none of that had any impact at the customer level. This is the true test of analytics; does it impact the customers in a positive way that they can experience it? If not, then you have the wrong setup. Making progress at Best Buy was much more difficult, which I will touch on in Chapter 1.
While working at Micro Warehouse and Best Buy, I was also a regular guest lecturer at Columbia University and NYU Stern MBA programs Relationship Marketing and Pricing courses that Dr. Hitendra Wadhwa taught. I also became an Adjunct Professor at NYU Stern for Spring 2006, teaching the MBA level Relationship Marketing program. During this period, talking to students, doing market research, talking to colleagues at different companies, I postulated that data-driven predictive marketing would become the new paradigm for the next 10 years. The value of predictive marketing was already clear to me, but its importance has accelerated due to digital transformation of commerce, increase in customer touch-points, and exponential increase in the size, variety, and velocity of data (which is now popularly called “big data”).
If you ask me what is the one important thing I learned from Dr. Cooper, I would say that it is breaking the problem down to its core and solving it at a fundamental level. He always said the idea behind the solution to any problem has to be clean and very simple. This is how I thought about the marketer's problem. Marketing was easy in the days of the old corner store. People knew our name, our likes and dislikes, and treated us on a one-to-one basis. Marketers lost touch with their customers in the era of one-size-fits-all mass optimization. Customers became survey responders and focus group participants; it was all about products and channels. However, the need for customer-centric marketing has always been there, it just wasn't practical and cost effective to practice. Digital transformation including web, email, mobile, social, location technologies combined with technologies to store, process, and extract information has significantly changed what is practical and cost effective.
Predictive marketing is the approach that restores that personal touch by bringing that human sensibility into our digital and offline lives, by focusing on the consumers individually to understand what they did and what they will do next. Predictive analytics, based on machine-learning algorithms, offers enormous leverage to marketers trying to make sense of these actions. Rather than replacing human decision making, machine learning and complex algorithms could help people amplify their intelligence and deal with problems on a much larger scale, something like giving a bulldozer to people used to digging with a shovel.
I saw the opportunity to solve a problem that a growing number of companies were struggling with, and I decided to disrupt the status quo and solve this problem. In 2006, I founded AgilOne, to bring the power of big data and predictive analytics to everyday marketers with an easy-to-use, yet powerful, cloud-based software platform.
AgilOne was initially bootstrapped for the first 5 years, then backed by top tier VC firms including Sequoia Capital, Mayfield Fund, Tenaya Capital, and Next World Capital. We are helping more than 150 brands in retail, B2B, Internet, media, publishing, and education deliver relevant experiences across channels. Through complete and accurate customer profiles, predictive insights, and built-in life cycle marketing campaigns, marketers boost customer loyalty and increase customer lifetime value.
In my spare time, I claim to be an accomplished potter of 28 years, having studied at Rhode Island School of Design under Lawrence Bush during my years at Brown. A native of Turkey, I now live in Los Gatos with my wife Burcak and two daughters, Ayse and Leyla. As I write this introduction, my daughter Ayse, who is a freshman at Castilleja School in Palo Alto, is reading an article about predictive marketing for her math class, which shows how predictive marketing will become mainstream for the next generation.
Dominique Levin
I credit my education, a combination of engineering school, design school, and business school for my left-brain–right-brain approach to marketing: I have a master's of science (Cum Laude) in industrial design engineering from Delft University in The Netherlands and a master's of business administration (with Distinction) from Harvard University. I recommend all marketers to marry human creativity with technology learning in order to deliver value to customers. Over the past 20 years I have run marketing at companies large and small, on four different continents, targeting businesses and consumers. Above all, I was an early convert to the importance of customer data.
In 1994 I took my first marketing job: a summer internship in Cusco, Peru. I drove around in a pickup truck to visit local farmers and tally how many would join a local cooperative to process fruits into marmalades and liquors. For my next job, at Philips Consumer Electronics, I was asked to find a way to sell more electronics to girls and women. I mingled with teenagers at local high schools to collect data. Philips launched a product called KidCom, an electronic organizer for girls, and proto-typed TeenCom, a two-way paging device for teenagers. My boss on this project was Tony Fadell, who later became the father of the iPod and iPhone, and who went on to found NEST. In 1997, I relocated to Tokyo, Japan, to work for Nippon Telegraph and Telephone (NTT). All employees at NTT, whether in product or finance, worked one weekend in the company store to meet and serve customers. I recommend such “meet the customer” program to any company as no numbers can totally replace meeting customers face to face.
In 2000, I moved to Silicon Valley and ran marketing for my first big data company, LogLogic—later acquired by TIBCO Software. For the first time I had access to lots of customer data in digital form. Log files are like the digital video cameras of the Internet. At LogLogic we used this log data to monitor security, but it also opened my eyes to the possibilities of using similar data to better understand and serve customers.
I went on to work for several other technology companies, including Fundly and Totango, focusing on building highly data-driven marketing organizations. Fundly helps non-profits use social media to raise money. We used data to automate the process from self-service sign-up to fundraising success. Totango offered a predictive marketing solution that monitors customer behavior to identify both promising and struggling customers. In both cases data and predictions helped to accelerate customer acquisition and increase customer lifetime value, while lowering the cost of sales.
I met Omer in my role as CMO at Agilone, where I got to work with thousands of marketers just like you to figure out how they can best use customer data to delight customers. Omer and I are united in our data-driven and customer-centric approach to marketing. Data and humanistic experiences go hand-in-hand. Our passion for customers has led us to this book.
In my spare time, I love to travel with my husband and three children and experience people, places, and cultures around the world. I play ice hockey to blow off steam and was once a member of the Dutch national team. I love to work with entrepreneurs and help them make their dreams a reality.
Acknowledgments
This book was significantly enhanced by the efforts of Anne Puyt, Barbara Von Euw, Rinat Shimshi, Dhruv Bhargava, Carrie Koy, Joe Mancini, Angela Sanfilippo, Hac Phan, and Francis Brero, who not only work tirelessly every day to help companies be successful with predictive marketing, but who also went above and beyond the call of duty to add their experiences, examples, and wisdom to the manuscript.
We also want to thank visionary CEOs and CMOs who were early adopters of the predictive marketing approach, specifically John Seabreeze, VP Marketing at Billy Casper Golf; Joe McDonald, SVP Sales and Marketing of Stargas, Eoin Comerford, CEO of Moosejaw; Levent Cakiroglu, CEO of Arcelik; Ersin Akarlilar, CEO of Mavi; Adam Shaffer, EVP Marketing of TigerDirect.
Additionally, Omer's personal success, the success of AgilOne, and the concepts in this book would not have become a reality without the help from Bonnie Bartoli, Peter Godfrey, and his “adopted sons and daughter” Ozer Unat, Dhruv Bhargava, Oyku Akca, Anselme LeVan, Louis Lecat, Ryan Willette, and Francis Brero.
We would also like to thank our families:
Omer would also very much like to thank his wife Dr. Burcak Artun, always believing and encouraging him for challenging the status quo and being patient with his busy schedule.
Dominique thanks her husband, Eilam, and children Liv, Yanai, and Milo, for their encouragement during the writing process. Similarly, she would like to thank her AgilOne marketing superstars, Chris Field, Johnson Kang, Kessawan Lelanaphaparn, and Angela Sanfilippo for being so independent and professional so she could focus on the book at times.
Part 1
A Complete Predictive Marketing Primer
Chapter 1Big Data and Predictive Analytics Are Now Easily Accessible to All Marketers
Predictive marketing is the evolution of relationship marketing defined and practiced by many direct marketers in the last few decades. Predictive marketing is not a technology, but an approach or a philosophy. Predictive marketing uses predictive analytics as a way to deliver more relevant and meaningful customer experiences, at all customer touch points, throughout the customer life cycle, boosting customer loyalty and revenues.
The rise of predictive marketing is fueled by three factors: (1) customers are demanding a more personal, integrated approach as they interact with marketing and sales through many channels, (2) early adopters show that predictive marketing delivers enormous value, and (3) new technologies are available to capture new and existing sources of customer data, to recognize patterns, and to make it easier than ever to use customer data at the intersection of the physical and digital worlds.
Predictive analytics is a set of tools and algorithms used to make predictive marketing possible. It is an umbrella term that covers a variety of mathematical and statistical techniques to recognize patterns in data or make predictions about the future. When applied to marketing, predictive analytics can predict future customer behavior, classify customers into clusters among other use cases. Other terms you might hear in the media to describe this process include machine learning, pattern recognition, artificial intelligence, and data mining. Predictive analytics and machine learning are used interchangeably in this book.
Predictive marketing is fundamentally changing both business and consumer marketing across the customer life cycle. It is transforming the focus from products and channels to a focus on the customer. Predictive analytics is used to improve strategies to acquire new customers, to grow customer lifetime value, and to retain more customers over time.
Innovative, technology driven companies like Netflix and Amazon have been using predictive analytics for years, and so have others like many in the telecommunications, financial services, and gaming industries, such as Harrah's Entertainment. The row of movies and TV shows “you might like” that appear when you curl up on the couch and turn on Netflix is a driving force of the company's success. It's all made possible by the translation of customer data with smart analytics. In fact, “75% of what people watch [on Netflix] is from some sort of recommendation,” Netflix's Research Director Xavier Amatriain wrote on the company's tech blog in 2012.
Amazon has been using predictive analytics to drive success since the very beginning of the company. Recommendations that appear under a product you are thinking of adding into your cart is part of what makes Amazon such an e-commerce powerhouse today. The company has stated publicly that 35 percent of its sales comes from recommendations made by their predictive engines. That would equate to $26 billion of revenue in 2013. The company is using predictive analytics in many other ways too, such as predicting which email newsletter to send you, or to nudge you at the right times to reorder an item.
In the gaming industry predictive models can set budgets and calendars for the casino's gamblers, calculating their predicted lifetime value in the process. If a gambler wagers less than usual because they may have skipped a monthly visit, the casino can intervene with a letter or phone call offering a free meal, a show ticket, or gaming comps. Without this type of customer analytics, casino operators might not notice what could be a slight, almost imperceptible change in customer behavior that might portend future problems with that patron. For example, if a long-time customer decides to cash in all their player card points, perhaps it's because they are dissatisfied with their last experience at the casino property. Predictive analytics can quickly spot these trends and alert casino management to the issue so that they can approach the individual to find out if there is a problem. This kind of personalization can go a long way in appeasing a disgruntled customer, which might be the difference between retaining or losing them as a customer.
Harrah's Entertainment's Total Rewards, which was rolled out as Total Gold in 1997 and renamed Total Rewards a year later, is heralded by many as the gold standard of customer-relationship programs and is powered heavily by predictive analytics algorithms. The company's belief in its loyalty program grew so strong that it cut its traditional ad spending from 2008 and 2009 more than 50%. The company spent $106 million on measured media in 2008; for the first half of last year it spent $52 million and in this year's first half $20 million. (Source: http://adage.com/article/news/harrah-s-loyalty-program-industry-s-gold-standard/139424/.)
Although some large brands have been using predictive analytics for many years now, it is not too late for other brands, large and small. In fact, predictive marketing is only now finding widespread adoption in medium and small organizations. A good example of a company that has achieved significant success with predictive marketing is Mavi, a high-fashion clothing manufacturer and retailer based in Istanbul, Turkey. Mavi is known for its organic denim favored by celebrities and supermodels. Mavi operates over 350 multinational stores and sales channels in the United States, Canada, Australia, Turkey, and 10 European countries.
Mavi started with a single predictive marketing campaign six years ago. When Mavi first got started, each department, including marketing and IT, used its own set of marketing reports and customer data, including key performance indicators. This led to cumbersome cross-referencing and impeded important decision making. Like many companies, the Mavi marketing team initially did not have access to customer data without relying on IT resources. This was the first problem that the team tackled. Mavi deployed a modern, cloud-based predictive marketing solution in 2009. This allowed the company to consolidate, cleanse, and de-dupe their customer data on a daily basis. They were then ready to start using data in hyperpersonalized campaigns.
One of the first predictive marketing programs that Mavi tested was a program around specific buying personas. Mavi used predictive analytics to find groups of people with distinct product preferences. In predictive lingo these are called product-based clusters
