{"product_id":"customer-data-platforms-9781119790112","title":"Customer Data Platforms","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMaster 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eIntroduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Pizza Challenge 1\u003c\/p\u003e \u003cp\u003eThe Perils of Personalization 4\u003c\/p\u003e \u003cp\u003eRise of the Avoidant Customer 5\u003c\/p\u003e \u003cp\u003eThe Disconnected Data Dilemma 6\u003c\/p\u003e \u003cp\u003eCrossing the Customer Data Chasm 7\u003c\/p\u003e \u003cp\u003eCustomer Data Platform (CDP) 8\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 The Customer Data Conundrum 11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Silos 11\u003c\/p\u003e \u003cp\u003eKnown Data 14\u003c\/p\u003e \u003cp\u003eCustomer Relationship Management (CRM) 15\u003c\/p\u003e \u003cp\u003eCustomer Resolution 15\u003c\/p\u003e \u003cp\u003eData Portability 16\u003c\/p\u003e \u003cp\u003eUnknown Data 16\u003c\/p\u003e \u003cp\u003eCross-Device Identity Management (CDIM) 19\u003c\/p\u003e \u003cp\u003eConnecting the Known and Unknown 20\u003c\/p\u003e \u003cp\u003eData Onboarding 21\u003c\/p\u003e \u003cp\u003ePeople Silos 22\u003c\/p\u003e \u003cp\u003eCustomer-Driven Thinker: Kevin Mannion 24\u003c\/p\u003e \u003cp\u003eSummary: The Customer Data Problem 26\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 The Brief, Wondrous Life of Customer Data Management 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCustomer Data on Cards and Tape? 29\u003c\/p\u003e \u003cp\u003eDirect Mail and Email: The Prototypes of Modern Marketing 31\u003c\/p\u003e \u003cp\u003eA Brief History of Customer Data Management 32\u003c\/p\u003e \u003cp\u003eRelational Databases 34\u003c\/p\u003e \u003cp\u003eThe Rise of CRM and Marketing Automation 35\u003c\/p\u003e \u003cp\u003eMarketing Automation 36\u003c\/p\u003e \u003cp\u003eImproved User Interface (UI) 37\u003c\/p\u003e \u003cp\u003eThe Multichannel Multiverse of the Thoroughly Modern Marketer 38\u003c\/p\u003e \u003cp\u003eThe Growth of Digital 38\u003c\/p\u003e \u003cp\u003eToday’s Landscape 40\u003c\/p\u003e \u003cp\u003eToday’s Martech Frankenstack 41\u003c\/p\u003e \u003cp\u003eCustomer-Driven Thinker: Scott Brinker 43\u003c\/p\u003e \u003cp\u003eSummary: The Brief, Wondrous Life of Customer Data Management 44\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 What is a CDP, Anyway? 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRise of the Customer Data Platform 47\u003c\/p\u003e \u003cp\u003eWhat Marketers Really Want from the CDP 51\u003c\/p\u003e \u003cp\u003eThe Great RFP Adventure 52\u003c\/p\u003e \u003cp\u003e“We Want a Platform, Not a Product” 53\u003c\/p\u003e \u003cp\u003eBuilding a Platform Solution 54\u003c\/p\u003e \u003cp\u003eCDP Capabilities 54\u003c\/p\u003e \u003cp\u003eData Collection 54\u003c\/p\u003e \u003cp\u003eData Management 55\u003c\/p\u003e \u003cp\u003eProfile Unification 56\u003c\/p\u003e \u003cp\u003eSegmentation and Activation 56\u003c\/p\u003e \u003cp\u003eInsights\/AI 57\u003c\/p\u003e \u003cp\u003eThe Two (Actually Three) Types of CDPs 58\u003c\/p\u003e \u003cp\u003eA System of Insights 58\u003c\/p\u003e \u003cp\u003eSystem of Engagement 60\u003c\/p\u003e \u003cp\u003eThe Third Type: Enterprise Holistic CDP 62\u003c\/p\u003e \u003cp\u003eKnown and Unknown (CDMP) Data Must Be Unified 62\u003c\/p\u003e \u003cp\u003eA Business-User Friendly UI 62\u003c\/p\u003e \u003cp\u003eA Platform Ecosystem 63\u003c\/p\u003e \u003cp\u003eThe Future is Here 64\u003c\/p\u003e \u003cp\u003eCustomer-Driven Thinker: David Raab 65\u003c\/p\u003e \u003cp\u003eSummary: What is a CDP? 66\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Organizing Customer Data 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMunging Data in the Midwest 69\u003c\/p\u003e \u003cp\u003eElements of a Data Pipeline 71\u003c\/p\u003e \u003cp\u003eData Management Steps 72\u003c\/p\u003e \u003cp\u003e1 Data Ingestion 72\u003c\/p\u003e \u003cp\u003e2 Data Harmonization 74\u003c\/p\u003e \u003cp\u003eUsing an Information Model 75\u003c\/p\u003e \u003cp\u003e3 Identity Management 76\u003c\/p\u003e \u003cp\u003eBenefits of Identity Management 77\u003c\/p\u003e \u003cp\u003eSpectrum of Identity 78\u003c\/p\u003e \u003cp\u003eIdentity Management in Practice 79\u003c\/p\u003e \u003cp\u003e4 Segmentation 79\u003c\/p\u003e \u003cp\u003eThe Importance of Attributes 82\u003c\/p\u003e \u003cp\u003e5 Activation 83\u003c\/p\u003e \u003cp\u003eGetting It Done 84\u003c\/p\u003e \u003cp\u003eDifferent Spheres of Influence 84\u003c\/p\u003e \u003cp\u003eCustomer-Driven Thinker: Brad Feinberg 86\u003c\/p\u003e \u003cp\u003eSummary: Organizing Customer Data 88\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Build a First-Party Data Asset with Consent 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePrivacy-First is Customer-Driven 91\u003c\/p\u003e \u003cp\u003ePrivacy Police: Browsers and Regulators 93\u003c\/p\u003e \u003cp\u003eWeb Browsers and Standards Bodies 93\u003c\/p\u003e \u003cp\u003eIntelligent Tracking Prevention 94\u003c\/p\u003e \u003cp\u003eEnhanced Tracking Prevention and Brave 94\u003c\/p\u003e \u003cp\u003eGoogle’s Chrome and AdID 94\u003c\/p\u003e \u003cp\u003eGovernment Regulators 95\u003c\/p\u003e \u003cp\u003eThe Mistrustful Consumer 96\u003c\/p\u003e \u003cp\u003eHow Can a Marketer Gain Trust? 98\u003c\/p\u003e \u003cp\u003eAttitudes Around the World 99\u003c\/p\u003e \u003cp\u003eThe Privacy Paradox 100\u003c\/p\u003e \u003cp\u003eWhat Exactly is the Privacy Paradox? 101\u003c\/p\u003e \u003cp\u003eHow Do You Solve the Paradox? 101\u003c\/p\u003e \u003cp\u003eFour Privacy Tactics to Try 102\u003c\/p\u003e \u003cp\u003eCustomer-Driven Thinker: Sebastian Baltruszewicz 103\u003c\/p\u003e \u003cp\u003eSummary: Build a First-Party Data Asset with Consent 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Building a Customer-Driven Marketing Machine 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eKnow, Personalize, Engage, and Measure 107\u003c\/p\u003e \u003cp\u003eKnow (“the Right Person”) 108\u003c\/p\u003e \u003cp\u003ePersonalize (“the Right Message”) 109\u003c\/p\u003e \u003cp\u003eEngage (“the Right Channel”) 111\u003c\/p\u003e \u003cp\u003eMeasure (and Optimize) 113\u003c\/p\u003e \u003cp\u003eOrganizational Transformation 114\u003c\/p\u003e \u003cp\u003eThe CDP Working Model 114\u003c\/p\u003e \u003cp\u003eTeam 114\u003c\/p\u003e \u003cp\u003ePlatform 116\u003c\/p\u003e \u003cp\u003eUse Cases 116\u003c\/p\u003e \u003cp\u003eMethodology 117\u003c\/p\u003e \u003cp\u003eOperating Model 118\u003c\/p\u003e \u003cp\u003eThe People at the Center (the Center of Excellence Model) 119\u003c\/p\u003e \u003cp\u003eMarketing 120\u003c\/p\u003e \u003cp\u003eIT\/CRM 121\u003c\/p\u003e \u003cp\u003eAnalytics 122\u003c\/p\u003e \u003cp\u003eHow the COE Works 123\u003c\/p\u003e \u003cp\u003eHow to Get There from Here: A Working Maturity Model 124\u003c\/p\u003e \u003cp\u003eChannel Coordination Stages 126\u003c\/p\u003e \u003cp\u003eEngagement Maturity Stages 126\u003c\/p\u003e \u003cp\u003eTouchpoints: That Was Then 127\u003c\/p\u003e \u003cp\u003eJourneys: This is Now 127\u003c\/p\u003e \u003cp\u003eExperiences: This is the Future 128\u003c\/p\u003e \u003cp\u003eSummary: Build a Customer-Driven Marketing Machine 128\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Adtech and the Data Management Platform 131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Magic Coffee Maker 131\u003c\/p\u003e \u003cp\u003eBackground\/Evolution of the DMP 132\u003c\/p\u003e \u003cp\u003eFive Sources of Value in DMP 133\u003c\/p\u003e \u003cp\u003eAdvertising as Part of the Marketing Mix 134\u003c\/p\u003e \u003cp\u003eRole of Pseudonymous IDs in the Enterprise 135\u003c\/p\u003e \u003cp\u003eAdvertising in “Walled Gardens” with First-Party Data 135\u003c\/p\u003e \u003cp\u003eEnd-to-end Journey Management: The CDMP 136\u003c\/p\u003e \u003cp\u003eCustomer-Driven Thinker: Ron Amram 137\u003c\/p\u003e \u003cp\u003eSummary: Adtech and the Data Management Platform 138\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Beyond Marketing 141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Expanding Role of Customer Data Across the Enterprise 141\u003c\/p\u003e \u003cp\u003eService: Frontline Engagement with the Customer 144\u003c\/p\u003e \u003cp\u003eCommerce: The Storefront and the Nexus of Response 146\u003c\/p\u003e \u003cp\u003eUse of Commerce Data for Modeling and Scoring 147\u003c\/p\u003e \u003cp\u003eSales: The B2B Context, and What That Means for Customer Data 149\u003c\/p\u003e \u003cp\u003eSources of Truth 150\u003c\/p\u003e \u003cp\u003eHouseholding 150\u003c\/p\u003e \u003cp\u003eTargetable Attributes 151\u003c\/p\u003e \u003cp\u003eMarketing: The Brand Stewards, Revenue, and the Engagement Engine 151\u003c\/p\u003e \u003cp\u003eCustomer-Driven Thinker: Kumar Subramanyam 152\u003c\/p\u003e \u003cp\u003eSummary: Beyond Marketing: Putting Sales, Service, and Commerce Data to Work 153\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Machine Learning and Artificial Intelligence 155\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOnce Upon a Time . . . in Silicon Valley 155\u003c\/p\u003e \u003cp\u003eDeep Learning and AI 156\u003c\/p\u003e \u003cp\u003eBack to the Hot Dogs 157\u003c\/p\u003e \u003cp\u003eCast of Characters 157\u003c\/p\u003e \u003cp\u003eCustomer-Driven Machine Learning and AI 159\u003c\/p\u003e \u003cp\u003eData Science in Marketing 160\u003c\/p\u003e \u003cp\u003eMachine Learning Vs. Artificial Intelligence? 161\u003c\/p\u003e \u003cp\u003eWhat Does a Marketing Data Scientist Do? 161\u003c\/p\u003e \u003cp\u003eCustomer Data and Experimental Design 161\u003c\/p\u003e \u003cp\u003eCustomer Data, Machine Learning, and AI 162\u003c\/p\u003e \u003cp\u003eWhat is a Model? 162\u003c\/p\u003e \u003cp\u003eLabeled Vs. Unlabeled Data 162\u003c\/p\u003e \u003cp\u003eFitting a Model to Data 162\u003c\/p\u003e \u003cp\u003eMaking Predictions 163\u003c\/p\u003e \u003cp\u003eRegression 163\u003c\/p\u003e \u003cp\u003eClassification 163\u003c\/p\u003e \u003cp\u003eFinding Structure 164\u003c\/p\u003e \u003cp\u003eClustering 164\u003c\/p\u003e \u003cp\u003eDimensionality Reduction 164\u003c\/p\u003e \u003cp\u003eNeural Networks 164\u003c\/p\u003e \u003cp\u003eApplying Machine Learning and AI in Marketing 165\u003c\/p\u003e \u003cp\u003eMachine-Learned Segmentation 165\u003c\/p\u003e \u003cp\u003eMachine-Learned Attribution 167\u003c\/p\u003e \u003cp\u003eImage Recognition and Natural Language Processing (NLP) 168\u003c\/p\u003e \u003cp\u003eImportance of Customer Data for AI 169\u003c\/p\u003e \u003cp\u003eAI\/ML in the Organization: Data Science Teams 170\u003c\/p\u003e \u003cp\u003eCustomer-Driven Thinker: Alysia Borsa 171\u003c\/p\u003e \u003cp\u003eSummary: Machine Learning and Artificial Intelligence 173\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Orchestrating a Personalized Customer Journey 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Rise of Context Marketing 175\u003c\/p\u003e \u003cp\u003ePrescriptive Journeys 177\u003c\/p\u003e \u003cp\u003ePredictive Journeys 178\u003c\/p\u003e \u003cp\u003eReal-Time Interaction Management (RTIM) Journeys 180\u003c\/p\u003e \u003cp\u003eCustomer-Driven Thinker: Laura Lisowski Cox 181\u003c\/p\u003e \u003cp\u003eSummary: Orchestrating a Personalized Customer Journey 183\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Connected Data for Analytics 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCustomer Data for Marketing Analytics 185\u003c\/p\u003e \u003cp\u003eAnalytical Capabilities 188\u003c\/p\u003e \u003cp\u003eAnalytics Data Sources 188\u003c\/p\u003e \u003cp\u003eBeyond the Basics 189\u003c\/p\u003e \u003cp\u003eKey Types of Analytics 190\u003c\/p\u003e \u003cp\u003eMarketing\/Email Analytics 190\u003c\/p\u003e \u003cp\u003eDMP Analytics 191\u003c\/p\u003e \u003cp\u003eMultitouch Attribution (MTA) 192\u003c\/p\u003e \u003cp\u003eMedia Mix Modeling (MMM) 193\u003c\/p\u003e \u003cp\u003eMarketing Analytics Platforms 194\u003c\/p\u003e \u003cp\u003eEnterprise Analytics\/BI 195\u003c\/p\u003e \u003cp\u003eCustomer-Driven Thinker: Vinny Rinaldi 197\u003c\/p\u003e \u003cp\u003eSummary: Connected Data for Analytics 199\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Summary and Looking Ahead 201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 201\u003c\/p\u003e \u003cp\u003eLooking Ahead 204\u003c\/p\u003e \u003cp\u003eCategory Shake-Out! 205\u003c\/p\u003e \u003cp\u003eAggregate-Level Data and “FLOCtimization” 206\u003c\/p\u003e \u003cp\u003eA Fresh Start for Multitouch Attribution 206\u003c\/p\u003e \u003cp\u003eAI Finally Takes Over 207\u003c\/p\u003e \u003cp\u003eThe Future 208\u003c\/p\u003e \u003cp\u003eFurther Reading 209\u003c\/p\u003e \u003cp\u003eAcknowledgments 211\u003c\/p\u003e \u003cp\u003eAbout the Authors 213\u003c\/p\u003e \u003cp\u003eIndex 215\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866416034135,"sku":"9781119790112","price":17.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119790112.jpg?v=1722278536","url":"https:\/\/bookcurl.com\/products\/customer-data-platforms-9781119790112","provider":"Book Curl","version":"1.0","type":"link"}