book

First-Party Data Activation

Name: First-Party Data Activation
ISBN: 9781633436367

by David Joosten, Oscar Kennis, Alina Darkhovsky

May 2025

Beginner to intermediate

264 pages

8h 43m

English

Manning Publications

Audiobook available

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First-Party Data Activation Modernize your marketing data platform
copyright
contents
dedication
foreword
preface
acknowledgments
about this book
about the authors
about the cover illustration

Part 1 Marketing foundations of first-party data
1 First-party data in the digital marketing space
1.1 The evolving data landscape1.2 Why first-party data matters more now1.3 Understanding the data ecosystem1.3.1 Zero-party data1.3.2 First-party data1.3.3 Second-party data1.3.4 Third-party data1.3.5 From zero to third: Strategic recap and key takeaways1.4 Many faces of first-party data1.4.1 Behavioral data1.4.2 Psychographic data1.4.3 Transactional data1.4.4 Engagement data1.4.5 Contextual data1.4.6 Technographic data1.4.7 Beyond these six faces of first-party data1.5 Challenges of first-party data1.5.1 Unifying diverse data streams1.5.2 Privacy-centric approach1.6 Opportunities of first-party data1.6.1 Acquisition, cross-sell and upsell, retention and churn winback1.6.2 Generative AI and machine learning
2 Privacy-centric marketing
2.1 Understanding data privacy today2.2 Foundations of user privacy2.2.1 Transparency and consent in first-party data2.2.2 The value and ethics of first-party data collection2.3 Privacy by layer: Integrating privacy throughout the marketing data journey2.3.1 Sourcing layer2.3.2 Data foundation layer2.3.3 Insights and intelligence layer2.3.4 Engagement and activation layer2.3.5 Media distribution layer2.4 Evolving technologies and frameworks
3 Marketing first-party data: Crawl, walk, run
3.1 Phase 1: Remove barriers3.1.1 Self-serve audience capabilities for marketers3.1.2 Standardized opt-outs and suppressions3.1.3 First automated cross-channel audience3.2 Phase 2: Experiment and measure3.2.1 Automate experiments consistently3.2.2 Measure marketing performance on any metric3.3 Phase 3: Use predictive models3.3.1 Incorporate predictive models3.3.2 Adopt onboarding, retention, and cross-sell journeys3.3.3 Try multivariate splits3.3.4 Use generative AI to accelerate ideation
4 Choosing your first-party data solution
4.1 Why marketing technology is necessary4.2 Why choose a composable customer data platform4.2.1 Why consider a composable CDP?4.2.2 How does a composable CDP work?4.3 Comparison of major solution categories4.3.1 Composable CDPs4.3.2 Marketing clouds and engagement platforms4.3.3 Monolithic CDPs4.3.4 In-house builds4.4 Core capabilities and considerations4.4.1 Time-to-value4.4.2 Marketing execution speed4.4.3 Audience portability (cross-channel campaigns)4.4.4 Data trust and reliability4.4.5 Artificial intelligence4.4.6 Security and compliance4.4.7 Standardized measurement frameworks4.4.8 Cost4.4.9 Real-time capabilities4.4.10 Identity resolution4.4.11 Technology stack agility and lock-in
Part 2 First-party data building blocks
5 Modernizing the marketing data platform: Data lakehouses and composable CDPs
5.1 Understanding the data lakehouse5.1.1 Combining flexibility and performance5.1.2 A single place for every customer touchpoint5.1.3 Ensuring consistency and reliability5.1.4 Fitting into your existing marketing ecosystem5.2 Composable CDP5.2.1 A more flexible approach to customer data5.2.2 How composable CDPs evolved5.2.3 The composable CDP advantage5.2.4 Tying it back to the lakehouse5.2.5 Streamlined and future-ready5.3 Architectural blueprint for a marketing data platform5.3.1 Medallion architecture overview5.3.2 Bronze layer5.3.3 Silver layer5.3.4 Gold layer5.3.5 Bringing it all together5.4 Key considerations for implementing a marketing data lakehouse5.4.1 Laying the groundwork: Scope, estimates, and planning5.4.2 Designing a resilient architecture for marketing data5.4.3 Implementation and testing in real environments5.4.4 Ensuring long-term success and governance5.4.5 Putting it all together: Deliverables and reference architectures5.4.6 Visualizing your project and measuring ROI
6 Identity resolution and enrichment: Building a complete customer profile
6.1 Getting started with identity resolution6.1.1 Foundational identity resolution strategies6.1.2 Identity resolution for company records6.1.3 Marketing technology solutions6.1.4 Risk mitigation approaches6.2 Anonymous-to-known identity resolution6.2.1 Third-party cookie tracking6.2.2 First-party cookie tracking by third parties6.2.3 Full first-party tracking6.3 Third-party enrichment of identity resolution6.3.1 How third-party enrichment works6.3.2 Identity stitching6.3.3 Contact expansion6.3.4 Profile enrichment6.3.5 Purchasing third-party data services
7 First-party data activation: Igniting the marketing engine
7.1 Understanding data activation: From insights to effect7.2 Precision targeting with paid media7.3 Nurturing relationships through personalized communication7.4 Empowering sales teams with data through CRM and sales outreach7.5 From reactive to proactive with customer engagement and support data7.6 Use case with GrowthLoop7.6.1 Problem and effect7.6.2 Selecting engagement channels7.6.3 Breaking down the pipeline7.6.4 Empowering marketers with GrowthLoop7.7 Proving the effect of data activation7.7.1 The three essential categories of marketing measurement for data activation7.7.2 Essential category 1: Campaign counts and rates (tracking basic performance)7.7.3 Essential category 2: Experiments (proving causal effect)7.7.4 Essential category 3: Multichannel models (guiding investment decisions)7.7.5 Data collection for effective data activation measurement7.7.6 Turning measurement into actionable targeting: The measurement–activation feedback loop7.7.7 Visualization and reporting
Part 3 New and upcoming technology opportunities
8 Data clean rooms
8.1 Introduction to clean rooms8.1.1 Data clean rooms of walled gardens8.1.2 Data clean rooms of AdTech providers as intermediaries8.2 Clean rooms for matching and activation8.2.1 Example: Brand and publisher collaboration8.3 Clean rooms for compliance and fraud prevention applications8.3.1 Suppressing fraudulent customers8.3.2 Supporting anti-money laundering (AML) compliance8.4 Clean rooms for measurement8.5 The future of clean rooms
9 Upcoming: Generative AI for marketing
9.1 Introducing generative marketing9.1.1 Why generative marketing matters9.1.2 Compound marketing9.2 How to use generative marketing today9.2.1 Functional examples of generative marketing9.2.2 Marketing campaign example9.3 Forming your generative marketing strategy9.3.1 Your data strategy9.3.2 Your technology strategy9.3.3 User-design considerations9.3.4 Managing organizational risks9.3.5 Designing new team workflows and roles
appendix A Integrating GrowthLoop with Klaviyo
A.1 PrerequisitesA.2 Step-by-step guideA.3 Next steps and best practicesA.4 Troubleshooting tips
appendix B Creating customer journeys in GrowthLoop

Content preview from First-Party Data Activation

9 Upcoming: Generative AI for marketing

This chapter covers

What generative marketing is and why it matters
Examples of current use cases in generative marketing applied to first-party data
Risks of generative marketing with governance recommendations
A preview of future capabilities

AI will take increasingly more jobs as its sophistication and capabilities increase, from computer use to agents, and the only antidote for people is to skill up on AI.—Christopher Penn, Co-Founder and Chief Data Scientist at TrustInsights.ai

Major changes in marketing with first-party data are underway, thanks to the rise of new AI capabilities. The marketing teams that harness these new capabilities in generative AI for marketing stand to gain a strong ...

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First-Party Data Activation

by David Joosten, Oscar Kennis, Alina Darkhovsky