Description
Book SynopsisMaster the hottest technology around to drive marketing success Marketers are faced with astarkand challenging dilemma: customers demand deep personalization, but they are increasingly leery of offering the type of personal data required to make it happen. As a solution to this problem,Customer Data Platforms have come to the fore, offering companiesaway to capture, unify, activate,and analyze customer data. CDPs are the hottestmarketingtechnologyaroundtoday,but arethey worthyof the hype?Customer Data Platformstakes a deep dive into everything CDPso you can learn how to steer your firm toward the future of personalization. Over the years,many of ushave built byzantine stacks of various marketing and advertising technologyin an attemptto deliver the fabled right person,right message, right time experience.This can lead tosiloed systems, disconnected processes, and legacy technical debt.CDPs offer a way tosimplify the stack and delivera balanced and engaging customer experience.Customer Data Platformsbreaks down the fundamentals, including how to: Understand the problems of managing customer dataUnderstand what CDPs are and what they do (and don't do)Organize and harmonize customer data for use in marketingBuild a safe, compliant first-party data asset that your brand can use as fuelCreate a data-driven culture that puts customers at the center of everything you doUnderstand how to use AI and machine learning to drive the future of personalizationOrchestrate modern customer journeys that react to customers in real-timePower analytics with customer data to get closer to true attribution Inthisbook, you'll discover how to build 1:1 engagement that scales at the speed of today's customers.
Table of ContentsIntroduction 1
The Pizza Challenge 1
The Perils of Personalization 4
Rise of the Avoidant Customer 5
The Disconnected Data Dilemma 6
Crossing the Customer Data Chasm 7
Customer Data Platform (CDP) 8
Chapter 1 The Customer Data Conundrum 11
Data Silos 11
Known Data 14
Customer Relationship Management (CRM) 15
Customer Resolution 15
Data Portability 16
Unknown Data 16
Cross-Device Identity Management (CDIM) 19
Connecting the Known and Unknown 20
Data Onboarding 21
People Silos 22
Customer-Driven Thinker: Kevin Mannion 24
Summary: The Customer Data Problem 26
Chapter 2 The Brief, Wondrous Life of Customer Data Management 29
Customer Data on Cards and Tape? 29
Direct Mail and Email: The Prototypes of Modern Marketing 31
A Brief History of Customer Data Management 32
Relational Databases 34
The Rise of CRM and Marketing Automation 35
Marketing Automation 36
Improved User Interface (UI) 37
The Multichannel Multiverse of the Thoroughly Modern Marketer 38
The Growth of Digital 38
Today’s Landscape 40
Today’s Martech Frankenstack 41
Customer-Driven Thinker: Scott Brinker 43
Summary: The Brief, Wondrous Life of Customer Data Management 44
Chapter 3 What is a CDP, Anyway? 47
Rise of the Customer Data Platform 47
What Marketers Really Want from the CDP 51
The Great RFP Adventure 52
“We Want a Platform, Not a Product” 53
Building a Platform Solution 54
CDP Capabilities 54
Data Collection 54
Data Management 55
Profile Unification 56
Segmentation and Activation 56
Insights/AI 57
The Two (Actually Three) Types of CDPs 58
A System of Insights 58
System of Engagement 60
The Third Type: Enterprise Holistic CDP 62
Known and Unknown (CDMP) Data Must Be Unified 62
A Business-User Friendly UI 62
A Platform Ecosystem 63
The Future is Here 64
Customer-Driven Thinker: David Raab 65
Summary: What is a CDP? 66
Chapter 4 Organizing Customer Data 69
Munging Data in the Midwest 69
Elements of a Data Pipeline 71
Data Management Steps 72
1 Data Ingestion 72
2 Data Harmonization 74
Using an Information Model 75
3 Identity Management 76
Benefits of Identity Management 77
Spectrum of Identity 78
Identity Management in Practice 79
4 Segmentation 79
The Importance of Attributes 82
5 Activation 83
Getting It Done 84
Different Spheres of Influence 84
Customer-Driven Thinker: Brad Feinberg 86
Summary: Organizing Customer Data 88
Chapter 5 Build a First-Party Data Asset with Consent 91
Privacy-First is Customer-Driven 91
Privacy Police: Browsers and Regulators 93
Web Browsers and Standards Bodies 93
Intelligent Tracking Prevention 94
Enhanced Tracking Prevention and Brave 94
Google’s Chrome and AdID 94
Government Regulators 95
The Mistrustful Consumer 96
How Can a Marketer Gain Trust? 98
Attitudes Around the World 99
The Privacy Paradox 100
What Exactly is the Privacy Paradox? 101
How Do You Solve the Paradox? 101
Four Privacy Tactics to Try 102
Customer-Driven Thinker: Sebastian Baltruszewicz 103
Summary: Build a First-Party Data Asset with Consent 104
Chapter 6 Building a Customer-Driven Marketing Machine 107
Know, Personalize, Engage, and Measure 107
Know (“the Right Person”) 108
Personalize (“the Right Message”) 109
Engage (“the Right Channel”) 111
Measure (and Optimize) 113
Organizational Transformation 114
The CDP Working Model 114
Team 114
Platform 116
Use Cases 116
Methodology 117
Operating Model 118
The People at the Center (the Center of Excellence Model) 119
Marketing 120
IT/CRM 121
Analytics 122
How the COE Works 123
How to Get There from Here: A Working Maturity Model 124
Channel Coordination Stages 126
Engagement Maturity Stages 126
Touchpoints: That Was Then 127
Journeys: This is Now 127
Experiences: This is the Future 128
Summary: Build a Customer-Driven Marketing Machine 128
Chapter 7 Adtech and the Data Management Platform 131
The Magic Coffee Maker 131
Background/Evolution of the DMP 132
Five Sources of Value in DMP 133
Advertising as Part of the Marketing Mix 134
Role of Pseudonymous IDs in the Enterprise 135
Advertising in “Walled Gardens” with First-Party Data 135
End-to-end Journey Management: The CDMP 136
Customer-Driven Thinker: Ron Amram 137
Summary: Adtech and the Data Management Platform 138
Chapter 8 Beyond Marketing 141
The Expanding Role of Customer Data Across the Enterprise 141
Service: Frontline Engagement with the Customer 144
Commerce: The Storefront and the Nexus of Response 146
Use of Commerce Data for Modeling and Scoring 147
Sales: The B2B Context, and What That Means for Customer Data 149
Sources of Truth 150
Householding 150
Targetable Attributes 151
Marketing: The Brand Stewards, Revenue, and the Engagement Engine 151
Customer-Driven Thinker: Kumar Subramanyam 152
Summary: Beyond Marketing: Putting Sales, Service, and Commerce Data to Work 153
Chapter 9 Machine Learning and Artificial Intelligence 155
Once Upon a Time . . . in Silicon Valley 155
Deep Learning and AI 156
Back to the Hot Dogs 157
Cast of Characters 157
Customer-Driven Machine Learning and AI 159
Data Science in Marketing 160
Machine Learning Vs. Artificial Intelligence? 161
What Does a Marketing Data Scientist Do? 161
Customer Data and Experimental Design 161
Customer Data, Machine Learning, and AI 162
What is a Model? 162
Labeled Vs. Unlabeled Data 162
Fitting a Model to Data 162
Making Predictions 163
Regression 163
Classification 163
Finding Structure 164
Clustering 164
Dimensionality Reduction 164
Neural Networks 164
Applying Machine Learning and AI in Marketing 165
Machine-Learned Segmentation 165
Machine-Learned Attribution 167
Image Recognition and Natural Language Processing (NLP) 168
Importance of Customer Data for AI 169
AI/ML in the Organization: Data Science Teams 170
Customer-Driven Thinker: Alysia Borsa 171
Summary: Machine Learning and Artificial Intelligence 173
Chapter 10 Orchestrating a Personalized Customer Journey 175
The Rise of Context Marketing 175
Prescriptive Journeys 177
Predictive Journeys 178
Real-Time Interaction Management (RTIM) Journeys 180
Customer-Driven Thinker: Laura Lisowski Cox 181
Summary: Orchestrating a Personalized Customer Journey 183
Chapter 11 Connected Data for Analytics 185
Customer Data for Marketing Analytics 185
Analytical Capabilities 188
Analytics Data Sources 188
Beyond the Basics 189
Key Types of Analytics 190
Marketing/Email Analytics 190
DMP Analytics 191
Multitouch Attribution (MTA) 192
Media Mix Modeling (MMM) 193
Marketing Analytics Platforms 194
Enterprise Analytics/BI 195
Customer-Driven Thinker: Vinny Rinaldi 197
Summary: Connected Data for Analytics 199
Chapter 12 Summary and Looking Ahead 201
Summary 201
Looking Ahead 204
Category Shake-Out! 205
Aggregate-Level Data and “FLOCtimization” 206
A Fresh Start for Multitouch Attribution 206
AI Finally Takes Over 207
The Future 208
Further Reading 209
Acknowledgments 211
About the Authors 213
Index 215
