Customer Data Platforms Use People Data to Transform the Future of Marketing Engagement

Master the hottest technology around to drive marketing success 

Marketers are faced with a stark and 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 companies a way to capture, unify, activate, and analyze customer data. CDPs are the hottest marketing technology around today, but are they worthy of the hype? Customer Data Platforms takes a deep dive into everything CDP so you can learn how to steer your firm toward the future of personalization. 

Over the years, many of us have built byzantine ?stacks? of various marketing and advertising technology in an attempt to deliver the fabled ?right person, right message, right time? experience. This can lead to siloed systems, disconnected processes, and legacy technical debt. CDPs offer a way to simplify the stack and deliver a balanced and engaging customer experience. Customer Data Platforms breaks down the fundamentals, including how to: 

• Understand the problems of managing customer data
• Understand what CDPs are and what they do (and don't do)
• Organize and harmonize customer data for use in marketing
• Build a safe, compliant first-party data asset that your brand can use as fuel
• Create a data-driven culture that puts customers at the center of everything you do
• Understand how to use AI and machine learning to drive the future of personalization
• Orchestrate modern customer journeys that react to customers in real-time
• Power analytics with customer data to get closer to true attribution

In this book, you?ll discover how to build 1:1 engagement that scales at the speed of today?s customers. 

Introduction 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

MARTIN KIHN is SVP, Strategy, Marketing Cloud at Salesforce. Previously, he spent 5 years as a leading Gartner analyst covering marketing, advertising, and data.

CHRIS O'HARA is Vice President, Global Product Marketing at Salesforce for the Data & Identity Group, covering all things data-driven marketing and customer experience.