MARC details
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09896nam a22002417a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20220704163005.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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220704b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781119790112 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
658.8340285 |
| Item number |
KIH |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Kihn, Martin |
| 245 ## - TITLE STATEMENT |
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| Title |
Customer data platforms: use people data to transform the future of marketing engagement |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc. |
John Wiley & Sons, Inc. |
| Place of publication, distribution, etc. |
New Jersey |
| Date of publication, distribution, etc. |
2021 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xii, 227 p. |
| 365 ## - TRADE PRICE |
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| Price type code |
USD |
| Price amount |
24.95 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
TABLE OF CONTENTS<br/>Introduction 1<br/><br/>The Pizza Challenge 1<br/><br/>The Perils of Personalization 4<br/><br/>Rise of the Avoidant Customer 5<br/><br/>The Disconnected Data Dilemma 6<br/><br/>Crossing the Customer Data Chasm 7<br/><br/>Customer Data Platform (CDP) 8<br/><br/>Chapter 1 The Customer Data Conundrum 11<br/><br/>Data Silos 11<br/><br/>Known Data 14<br/><br/>Customer Relationship Management (CRM) 15<br/><br/>Customer Resolution 15<br/><br/>Data Portability 16<br/><br/>Unknown Data 16<br/><br/>Cross-Device Identity Management (CDIM) 19<br/><br/>Connecting the Known and Unknown 20<br/><br/>Data Onboarding 21<br/><br/>People Silos 22<br/><br/>Customer-Driven Thinker: Kevin Mannion 24<br/><br/>Summary: The Customer Data Problem 26<br/><br/>Chapter 2 The Brief, Wondrous Life of Customer Data Management 29<br/><br/>Customer Data on Cards and Tape? 29<br/><br/>Direct Mail and Email: The Prototypes of Modern Marketing 31<br/><br/>A Brief History of Customer Data Management 32<br/><br/>Relational Databases 34<br/><br/>The Rise of CRM and Marketing Automation 35<br/><br/>Marketing Automation 36<br/><br/>Improved User Interface (UI) 37<br/><br/>The Multichannel Multiverse of the Thoroughly Modern Marketer 38<br/><br/>The Growth of Digital 38<br/><br/>Today’s Landscape 40<br/><br/>Today’s Martech Frankenstack 41<br/><br/>Customer-Driven Thinker: Scott Brinker 43<br/><br/>Summary: The Brief, Wondrous Life of Customer Data Management 44<br/><br/>Chapter 3 What is a CDP, Anyway? 47<br/><br/>Rise of the Customer Data Platform 47<br/><br/>What Marketers Really Want from the CDP 51<br/><br/>The Great RFP Adventure 52<br/><br/>“We Want a Platform, Not a Product” 53<br/><br/>Building a Platform Solution 54<br/><br/>CDP Capabilities 54<br/><br/>Data Collection 54<br/><br/>Data Management 55<br/><br/>Profile Unification 56<br/><br/>Segmentation and Activation 56<br/><br/>Insights/AI 57<br/><br/>The Two (Actually Three) Types of CDPs 58<br/><br/>A System of Insights 58<br/><br/>System of Engagement 60<br/><br/>The Third Type: Enterprise Holistic CDP 62<br/><br/>Known and Unknown (CDMP) Data Must Be Unified 62<br/><br/>A Business-User Friendly UI 62<br/><br/>A Platform Ecosystem 63<br/><br/>The Future is Here 64<br/><br/>Customer-Driven Thinker: David Raab 65<br/><br/>Summary: What is a CDP? 66<br/><br/>Chapter 4 Organizing Customer Data 69<br/><br/>Munging Data in the Midwest 69<br/><br/>Elements of a Data Pipeline 71<br/><br/>Data Management Steps 72<br/><br/>1 Data Ingestion 72<br/><br/>2 Data Harmonization 74<br/><br/>Using an Information Model 75<br/><br/>3 Identity Management 76<br/><br/>Benefits of Identity Management 77<br/><br/>Spectrum of Identity 78<br/><br/>Identity Management in Practice 79<br/><br/>4 Segmentation 79<br/><br/>The Importance of Attributes 82<br/><br/>5 Activation 83<br/><br/>Getting It Done 84<br/><br/>Different Spheres of Influence 84<br/><br/>Customer-Driven Thinker: Brad Feinberg 86<br/><br/>Summary: Organizing Customer Data 88<br/><br/>Chapter 5 Build a First-Party Data Asset with Consent 91<br/><br/>Privacy-First is Customer-Driven 91<br/><br/>Privacy Police: Browsers and Regulators 93<br/><br/>Web Browsers and Standards Bodies 93<br/><br/>Intelligent Tracking Prevention 94<br/><br/>Enhanced Tracking Prevention and Brave 94<br/><br/>Google’s Chrome and AdID 94<br/><br/>Government Regulators 95<br/><br/>The Mistrustful Consumer 96<br/><br/>How Can a Marketer Gain Trust? 98<br/><br/>Attitudes Around the World 99<br/><br/>The Privacy Paradox 100<br/><br/>What Exactly is the Privacy Paradox? 101<br/><br/>How Do You Solve the Paradox? 101<br/><br/>Four Privacy Tactics to Try 102<br/><br/>Customer-Driven Thinker: Sebastian Baltruszewicz 103<br/><br/>Summary: Build a First-Party Data Asset with Consent 104<br/><br/>Chapter 6 Building a Customer-Driven Marketing Machine 107<br/><br/>Know, Personalize, Engage, and Measure 107<br/><br/>Know (“the Right Person”) 108<br/><br/>Personalize (“the Right Message”) 109<br/><br/>Engage (“the Right Channel”) 111<br/><br/>Measure (and Optimize) 113<br/><br/>Organizational Transformation 114<br/><br/>The CDP Working Model 114<br/><br/>Team 114<br/><br/>Platform 116<br/><br/>Use Cases 116<br/><br/>Methodology 117<br/><br/>Operating Model 118<br/><br/>The People at the Center (the Center of Excellence Model) 119<br/><br/>Marketing 120<br/><br/>IT/CRM 121<br/><br/>Analytics 122<br/><br/>How the COE Works 123<br/><br/>How to Get There from Here: A Working Maturity Model 124<br/><br/>Channel Coordination Stages 126<br/><br/>Engagement Maturity Stages 126<br/><br/>Touchpoints: That Was Then 127<br/><br/>Journeys: This is Now 127<br/><br/>Experiences: This is the Future 128<br/><br/>Summary: Build a Customer-Driven Marketing Machine 128<br/><br/>Chapter 7 Adtech and the Data Management Platform 131<br/><br/>The Magic Coffee Maker 131<br/><br/>Background/Evolution of the DMP 132<br/><br/>Five Sources of Value in DMP 133<br/><br/>Advertising as Part of the Marketing Mix 134<br/><br/>Role of Pseudonymous IDs in the Enterprise 135<br/><br/>Advertising in “Walled Gardens” with First-Party Data 135<br/><br/>End-to-end Journey Management: The CDMP 136<br/><br/>Customer-Driven Thinker: Ron Amram 137<br/><br/>Summary: Adtech and the Data Management Platform 138<br/><br/>Chapter 8 Beyond Marketing 141<br/><br/>The Expanding Role of Customer Data Across the Enterprise 141<br/><br/>Service: Frontline Engagement with the Customer 144<br/><br/>Commerce: The Storefront and the Nexus of Response 146<br/><br/>Use of Commerce Data for Modeling and Scoring 147<br/><br/>Sales: The B2B Context, and What That Means for Customer Data 149<br/><br/>Sources of Truth 150<br/><br/>Householding 150<br/><br/>Targetable Attributes 151<br/><br/>Marketing: The Brand Stewards, Revenue, and the Engagement Engine 151<br/><br/>Customer-Driven Thinker: Kumar Subramanyam 152<br/><br/>Summary: Beyond Marketing: Putting Sales, Service, and Commerce Data to Work 153<br/><br/>Chapter 9 Machine Learning and Artificial Intelligence 155<br/><br/>Once Upon a Time . . . in Silicon Valley 155<br/><br/>Deep Learning and AI 156<br/><br/>Back to the Hot Dogs 157<br/><br/>Cast of Characters 157<br/><br/>Customer-Driven Machine Learning and AI 159<br/><br/>Data Science in Marketing 160<br/><br/>Machine Learning Vs. Artificial Intelligence? 161<br/><br/>What Does a Marketing Data Scientist Do? 161<br/><br/>Customer Data and Experimental Design 161<br/><br/>Customer Data, Machine Learning, and AI 162<br/><br/>What is a Model? 162<br/><br/>Labeled Vs. Unlabeled Data 162<br/><br/>Fitting a Model to Data 162<br/><br/>Making Predictions 163<br/><br/>Regression 163<br/><br/>Classification 163<br/><br/>Finding Structure 164<br/><br/>Clustering 164<br/><br/>Dimensionality Reduction 164<br/><br/>Neural Networks 164<br/><br/>Applying Machine Learning and AI in Marketing 165<br/><br/>Machine-Learned Segmentation 165<br/><br/>Machine-Learned Attribution 167<br/><br/>Image Recognition and Natural Language Processing (NLP) 168<br/><br/>Importance of Customer Data for AI 169<br/><br/>AI/ML in the Organization: Data Science Teams 170<br/><br/>Customer-Driven Thinker: Alysia Borsa 171<br/><br/>Summary: Machine Learning and Artificial Intelligence 173<br/><br/>Chapter 10 Orchestrating a Personalized Customer Journey 175<br/><br/>The Rise of Context Marketing 175<br/><br/>Prescriptive Journeys 177<br/><br/>Predictive Journeys 178<br/><br/>Real-Time Interaction Management (RTIM) Journeys 180<br/><br/>Customer-Driven Thinker: Laura Lisowski Cox 181<br/><br/>Summary: Orchestrating a Personalized Customer Journey 183<br/><br/>Chapter 11 Connected Data for Analytics 185<br/><br/>Customer Data for Marketing Analytics 185<br/><br/>Analytical Capabilities 188<br/><br/>Analytics Data Sources 188<br/><br/>Beyond the Basics 189<br/><br/>Key Types of Analytics 190<br/><br/>Marketing/Email Analytics 190<br/><br/>DMP Analytics 191<br/><br/>Multitouch Attribution (MTA) 192<br/><br/>Media Mix Modeling (MMM) 193<br/><br/>Marketing Analytics Platforms 194<br/><br/>Enterprise Analytics/BI 195<br/><br/>Customer-Driven Thinker: Vinny Rinaldi 197<br/><br/>Summary: Connected Data for Analytics 199<br/><br/>Chapter 12 Summary and Looking Ahead 201<br/><br/>Summary 201<br/><br/>Looking Ahead 204<br/><br/>Category Shake-Out! 205<br/><br/>Aggregate-Level Data and “FLOCtimization” 206<br/><br/>A Fresh Start for Multitouch Attribution 206<br/><br/>AI Finally Takes Over 207<br/><br/>The Future 208<br/><br/>Further Reading 209<br/><br/>Acknowledgments 211<br/><br/>About the Authors 213<br/><br/>Index 215 |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
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. <br/><br/>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: <br/><br/>Understand the problems of managing customer data<br/>Understand what CDPs are and what they do (and don't do)<br/>Organize and harmonize customer data for use in marketing<br/>Build a safe, compliant first-party data asset that your brand can use as fuel<br/>Create a data-driven culture that puts customers at the center of everything you do<br/>Understand how to use AI and machine learning to drive the future of personalization<br/>Orchestrate modern customer journeys that react to customers in real-time<br/>Power analytics with customer data to get closer to true attribution<br/>In this book, you’ll discover how to build 1:1 engagement that scales at the speed of today’s customers. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Marketing--Data processing |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Privacy, Right of |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Customer relations--Data processing |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Data protection |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
O'Hara, Chris |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Book |