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Customer Data Platforms: Use People Data to Transform the Future of Marketing Engagement [Kõva köide]

  • Formaat: Hardback, 240 pages, kõrgus x laius x paksus: 229x155x28 mm, kaal: 431 g
  • Ilmumisaeg: 25-Feb-2021
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119790115
  • ISBN-13: 9781119790112
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  • Formaat: Hardback, 240 pages, kõrgus x laius x paksus: 229x155x28 mm, kaal: 431 g
  • Ilmumisaeg: 25-Feb-2021
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119790115
  • ISBN-13: 9781119790112
Teised raamatud teemal:
"Never before has there been such a stark dichotomy in marketing: customers demand the type of deep personalization from brands that technology companies like Netflix and Amazon deliver, but they are increasingly leery of offering the type of personal data required to make it happen. Over the years companies have built byzantine "stacks" of various marketing and advertising technology to try and deliver the fabled "right person, right message, right time" experience to deliver on customer journeys, but have found themselves stuck with a hot mess of siloed systems, disconnected processes, and legacy technical debt. Riding in like a white knight, Customer Data Platforms have come to the fore, offering companies a seemingly plug-and-play way to capture, unify, activate and analyze customer data. CDPs are the hottest technology category for marketers today, a growing category with over 100 different companies, and a hot topic at industry events and in industry press. But are CDPs worthy of the hype? CustomerDriven takes a a deep dive into everything CDP and breaks down the fundamentals, including how to: -Understand the problems of managing customer data -Define the category and understand what CDPs do (and don't do) -Organize and harmonize customer data for use in marketing -Build a safe, compliant first party data asset your brand can use as fuel -Create a data-driven culture that puts customers at the center of everything you do -Understand how to leverage 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"--

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(10)
The Pizza Challenge
1(3)
The Perils of Personalization
4(1)
Rise of the Avoidant Customer
5(1)
The Disconnected Data Dilemma
6(1)
Crossing the Customer Data Chasm
7(1)
Customer Data Platform (CDP)
8(3)
Chapter 1 The Customer Data Conundrum
11(18)
Data Silos
11(3)
Known Data
14(1)
Customer Relationship Management (CRM)
15(1)
Customer Resolution
15(1)
Data Portability
16(1)
Unknown Data
16(3)
Cross-Device Identity Management (CDIM)
19(1)
Connecting the Known and Unknown
20(1)
Data Onboarding
21(1)
People Silos
22(2)
Customer-Driven Thinker: Kevin Mannion
24(2)
Summary: The Customer Data Problem
26(3)
Chapter 2 The Brief, Wondrous Life of Customer Data Management
29(18)
Customer Data on Cards and Tape?
29(2)
Direct Mail and Email: The Prototypes of Modern Marketing
31(1)
A Brief History of Customer Data Management
32(3)
Relational Databases
34(1)
The Rise of CRM and Marketing Automation
35(3)
Marketing Automation
36(1)
Improved User Interface (UI)
37(1)
The Multichannel Multiverse of the Thoroughly Modern Marketer
38(5)
The Growth of Digital
38(2)
Today's Landscape
40(1)
Today's Martech Frankenstack
41(2)
Customer-Driven Thinker: Scott Brinker
43(1)
Summary: The Brief, Wondrous Life of Customer Data Management
44(3)
Chapter 3 What Is a CDP, Anyway?
47(22)
Rise of the Customer Data Platform
47(5)
What Marketers Really Want from the CDP
51(1)
The Great RFP Adventure
52(2)
"We Want a Platform, Not a Product"
53(1)
Building a Platform Solution
54(1)
CDP Capabilities
54(4)
Data Collection
54(1)
Data Management
55(1)
Profile Unification
56(1)
Segmentation and Activation
56(1)
Insights/AI
57(1)
The Two (Actually Three) Types of CDPs
58(1)
A System of Insights
58(2)
System of Engagement
60(2)
The Third Type: Enterprise Holistic CDP
62(2)
Known and Unknown (CDMP) Data Must Be Unified
62(1)
A Business-User Friendly UI
62(1)
A Platform Ecosystem
63(1)
The Future Is Here
64(1)
Customer-Driven Thinker: David Raab
65(1)
Summary: What Is a CDP?
66(3)
Chapter 4 Organizing Customer Data
69(22)
Munging Data in the Midwest
69(2)
Elements of a Data Pipeline
71(1)
Data Management Steps
72(12)
1 Data Ingestion
72(2)
2 Data Harmonization
74(1)
Using an Information Model
75(1)
3 Identity Management
76(1)
Benefits of Identity Management
77(1)
Spectrum of Identity
78(1)
Identity Management in Practice
79(1)
4 Segmentation
79(3)
The Importance of Attributes
82(1)
5 Activation
83(1)
Getting It Done
84(1)
Different Spheres of Influence
84(2)
Customer-Driven Thinker: Brad Feinberg
86(2)
Summary: Organizing Customer Data
88(3)
Chapter 5 Build a First-Party Data Asset with Consent
91(16)
Privacy-First Is Customer-Driven
91(2)
Privacy Police: Browsers and Regulators
93(1)
Web Browsers and Standards Bodies
93(2)
Intelligent Tracking Prevention
94(1)
Enhanced Tracking Prevention and Brave
94(1)
Google's Chrome and AdID
94(1)
Government Regulators
95(1)
The Mistrustful Consumer
96(3)
How Can a Marketer Gain Trust?
98(1)
Attitudes Around the World
99(1)
The Privacy Paradox
100(2)
What Exactly Is the Privacy Paradox?
101(1)
How Do You Solve the Paradox?
101(1)
Four Privacy Tactics to Try
102(1)
Customer-Driven Thinker: Sebastian Baltruszewicz
103(1)
Summary: Build a First-Party Data Asset with Consent
104(3)
Chapter 6 Building a Customer-Driven Marketing Machine
107(24)
Know, Personalize, Engage, and Measure
107(1)
Know ("the Right Person ")
108(1)
Personalize ("the Right Message")
109(2)
Engage ("the Right Channel")
111(2)
Measure (and Optimize)
113(1)
Organizational Transformation
114(1)
The CDP Working Model
114(5)
Team
114(2)
Platform
116(1)
Use Cases
116(1)
Methodology
117(1)
Operating Model
118(1)
The People at the Center (the Center of Excellence Model)
119(4)
Marketing
120(1)
IT/CRM
121(1)
Analytics
122(1)
How the COE Works
123(1)
How to Get There from Here: A Working Maturity Model
124(4)
Channel Coordination Stages
126(1)
Engagement Maturity Stages
126(1)
Touchpoints: That Was Then
127(1)
Journeys: This Is Now
127(1)
Experiences: This Is the Future
128(1)
Summary: Build a Customer-Driven Marketing Machine
128(3)
Chapter 7 Adtech and the Data Management Platform
131(10)
The Magic Coffee Maker
131(1)
Background/Evolution of the DMP
132(1)
Five Sources of Value in DMP
133(1)
Advertising as Part of the Marketing Mix
134(1)
Role of Pseudonymous IDs in the Enterprise
135(1)
Advertising in "Walled Gardens" with First-Party Data
135(1)
End-to-end Journey Management: The CDMP
136(1)
Customer-Driven Thinker: Ron Amram
137(1)
Summary: Adtech and the Data Management Platform
138(3)
Chapter 8 Beyond Marketing
141(14)
The Expanding Role of Customer Data Across the Enterprise
141(5)
Service: Frontline Engagement with the Customer
144(2)
Commerce: The Storefront and the Nexus of Response
146(3)
Use of Commerce Data for Modeling and Scoring
147(2)
Sales: The B2B Context, and What That Means for Customer Data
149(2)
Sources of Truth
150(1)
Householding
150(1)
Targetable Attributes
151(1)
Marketing: The Brand Stewards, Revenue, and the Engagement Engine
151(1)
Customer-Driven Thinker: Kumar Subramanyam
152(1)
Summary: Beyond Marketing: Putting Sales, Service, and Commerce Data to Work
153(2)
Chapter 9 Machine Learning and Artificial Intelligence
155(20)
Once Upon a Time... in Silicon Valley
155(1)
Deep Learning and AI
156(3)
Back to the Hot Dogs
157(1)
Cast of Characters
157(2)
Customer-Driven Machine Learning and AI
159(1)
Data Science in Marketing
160(1)
Machine Learning Vs. Artificial Intelligence?
161(1)
What Does a Marketing Data Scientist Do?
161(1)
Customer Data and Experimental Design
161(1)
Customer Data, Machine Learning, and AI
162(3)
What Is a Model?
162(1)
Labeled Vs. Unlabeled Data
162(1)
Fitting a Model to Data
162(1)
Making Predictions
163(1)
Regression
163(1)
Classification
163(1)
Finding Structure
164(1)
Clustering
164(1)
Dimensionality Reduction
164(1)
Neural Networks
164(1)
Applying Machine Learning and AI in Marketing
165(4)
Machine-Learned Segmentation
165(2)
Machine-Learned Attribution
167(1)
Image Recognition and Natural Language Processing (NLP)
168(1)
Importance of Customer Data for AI
169(1)
AI/ML in the Organization: Data Science Teams
170(1)
Customer-Driven Thinker: Alysia Borsa
171(2)
Summary: Machine Learning and Artificial Intelligence
173(2)
Chapter 10 Orchestrating a Personalized Customer Journey
175(10)
The Rise of Context Marketing
175(2)
Prescriptive Journeys
177(1)
Predictive Journeys
178(2)
Real-Time Interaction Management (RTIM) Journeys
180(1)
Customer-Driven Thinker: Laura Lisowski Cox
181(2)
Summary: Orchestrating a Personalized Customer Journey
183(2)
Chapter 11 Connected Data for Analytics
185(16)
Customer Data for Marketing Analytics
185(3)
Analytical Capabilities
188(1)
Analytics Data Sources
188(1)
Beyond the Basics
189(1)
Key Types of Analytics
190(7)
Marketing/Email Analytics
190(1)
DMP Analytics
191(1)
Multitouch Attribution (MTA)
192(1)
Media Mix Modeling (MMM)
193(1)
Marketing Analytics Platforms
194(1)
Enterprise Analytics/BI
195(2)
Customer-Driven Thinker: Vinny Rinaldi
197(2)
Summary: Connected Data for Analytics
199(2)
Chapter 12 Summary and Looking Ahead
201(8)
Summary
201(3)
Looking Ahead
204(1)
Category Shake-Out!
205(1)
Aggregate-Level Data and "FLOCtimization"
206(1)
A Fresh Start for Multitouch Attribution
206(1)
AI Finally Takes Over
207(1)
The Future
208(1)
Further Reading 209(2)
Acknowledgments 211(2)
About the Authors 213(2)
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.