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What you will learn in First-Party Data Activation

Modernize your marketing data platform

by Alina D. Magauova, Oskar Kennis, and David H. Joosten
Foreword by Murat Genc

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Chapter by chapter

Concepts explained in this book

• customer data platformA monolithic customer data platform is an integrated platform that provides customer-data collection, management, and activation capabilities within one product system.
• first-party dataData collected directly by an organization from its customers’ interactions with the organization across digital and physical touch points.
• data clean roomA neutral data clean room is an independently operated controlled environment that enables organizations to analyze combined data without exposing underlying raw records or restricting collaboration to one platform.
• entity resolutionThe process of reconciling and unifying records that refer to the same real-world entity, such as a person, household, business, or account.
• personalizationThe tailoring of content, recommendations, offers, or experiences to an individual’s characteristics or inferred preferences.
• privacy-enhancing technologyA technology or method designed to enable useful data processing while minimizing collection, exposure, or identifiability of personal information.
• differential privacyDifferential privacy is a mathematical privacy guarantee that limits how much the inclusion or exclusion of one individual can affect released statistical results.
• generative marketingA marketing-oriented system that uses generative AI to produce or adapt creative content and assets.
• data lakehouseA data management architecture that combines data-lake flexibility for diverse data with data-warehouse capabilities for structured management and analytics.
• generative modelingArtificial intelligence that generates new content, such as text, images, audio, video, or code, from learned patterns.
• brownfield environmentA brownfield approach modernizes, migrates, or extends an existing system while accommodating its legacy data, processes, infrastructure, and dependencies.
• greenfield environmentA greenfield approach builds a new system or data environment without constraints from an existing legacy implementation or infrastructure.
• federated learningA machine-learning approach in which models are trained across distributed devices or systems and only selected updates are shared with a coordinating server.
• parallel run patternAn implementation approach in which legacy and replacement systems operate simultaneously for comparison, validation, or continuity.
• first-party data strategyAn organizational transformation that changes how first-party data is collected, managed, measured, and activated to support marketing goals.
• source of truthA single source of truth is an authoritative and consistent data source from which other systems or views derive information.
• anonymizationThe transformation of data to remove or obscure identifying information so individuals cannot reasonably be identified.
• data activationData activation, also called marketing activation, is the process of applying audience data and insights to execute marketing actions, customer experiences, or delivery to downstream destinations.
• privacy-centric strategyA data strategy that treats privacy, consent, transparency, and user control as core requirements throughout the data lifecycle.
• experimentationExperimentation is the systematic use of experiments to compare marketing interventions and learn which produce better outcomes.
• privacySafeguards that prevent improper collection, use, disclosure, or exposure of personal information.
• pseudonymizationA technique that replaces direct identifiers with artificial identifiers while retaining the possibility of linking records through additional information.
• homomorphic encryptionEncryption that permits specified computations on ciphertexts whose decrypted results correspond to computations on the original plaintexts.
• external inputCustomer or market data collected by an external entity without a direct relationship between the customer and the organization using it.
• machine learningMachine learning is a computational approach in which models learn patterns from data to make predictions or decisions.
• customer journeyA coordinated sequence of interactions and decision steps that guides a prospect or customer toward a marketing objective over time.
• GrowthLoopGrowthLoop is a marketing audience activation tool that provides controlled self-service audience design and launching from connected data platforms.
• explicit user consentA person’s informed and affirmative permission for an organization to collect, process, or share their personal data.
• marketing data lakehouseAn enterprise data warehouse, data lake, or lakehouse environment where marketing datasets are stored and queried for analytics and activation.
• composable systemA composable architecture is a modular marketing technology architecture that centralizes data in a shared data platform and connects interchangeable activation and analytics systems to it.
• data aggregationThe combination of individual data records into summarized values that expose less detail about specific individuals.
• personalized product recommendationA product, upgrade, or offer suggestion tailored to an individual customer's inferred preferences or behavior.
• data minimization strategyThe practice of limiting personal-data collection and retention to what is necessary for specified purposes.
• data privacy regulationA legally enforceable rule governing the collection, use, sharing, and protection of personal data.
• probabilistic matchingProbabilistic matching is record linkage that estimates the likelihood that two records represent the same entity from similarities across their attributes or behaviors.
• secure multi-party computationA cryptographic technique that lets multiple parties compute jointly over their inputs without revealing those inputs to one another.
• data matchingThe process of identifying corresponding records or entities across data sources according to shared attributes.
• data foundation layerThe data foundation layer is the architectural stage where sourced data is ingested, structured, and refined for later analysis and activation.
• transparencyTransparency is the clear disclosure of what data is collected, how it is used, and why it is used.
• retentionThe maintenance of existing customer relationships through continued purchasing, renewal, or re-engagement.
• third-party cookieA browser cookie set by a domain other than the site the user is visiting, commonly enabling tracking across sites.
• deterministic matchingA record-linkage technique that matches entities when their identifiers or matching attributes satisfy an exact, predefined correspondence.
• customer churnThe percentage of customers who discontinue engagement or purchasing during a specified period.
• deterministic cascade matchingDeterministic cascade matching is a sequential rules-based linkage method that applies prioritized matching conditions until one succeeds or a base case is reached.
• audience targetingThe practice of selecting and addressing defined audience segments with marketing messages or offers.
• trustA customer's confidence that a brand will act reliably, honestly, and responsibly with respect to the customer relationship and data.
• interpretabilityThe degree to which a model’s outputs and the factors producing them can be understood by people.
• consent management platformSoftware that collects, records, communicates, and manages users’ consent choices for data processing purposes.
• operational dataOperational data is information generated by internal business systems that support routine processes and operations.
• self-serviceSelf-service is a capability that allows users to perform tasks independently without ongoing assistance from specialized technical teams.
• artificial intelligenceArtificial intelligence is the field of computing concerned with systems that perform tasks requiring capabilities associated with human intelligence.
• clusteringThe practice of dividing a customer or user population into groups with shared characteristics or behaviors for differentiated marketing.
• data modelA data model is a structured representation of entities, attributes, and relationships used to organize and work with data.
• walled gardenA walled garden is a restricted digital ecosystem controlled by a single platform that limits access by external parties while governing data sharing within the ecosystem.
• hash functionThe transformation of input data into a fixed-length value designed for integrity checking, lookup, or comparison without retaining the original representation.
• k-anonymityK-anonymity is a de-identification property requiring each record to be indistinguishable from at least k−1 other records on selected identifying attributes.
• retargetingRetargeting, also called remarketing, is a marketing technique that re-engages people who previously interacted with a brand, product, service, digital property, or campaign.
• return on investmentReturn on investment is the ratio of net gain from an investment to its cost, typically expressed as a percentage.
• change managementThe structured practice of preparing, supporting, and guiding people and organizations through changes in processes, technology, or ways of working.
• composite key matchingComposite key matching is a record-linkage method that matches records when multiple corresponding field values jointly agree.
• single field matchingSingle field matching is an exact record-linkage method that matches records when the same primary-key value appears in a shared field.
• sentiment analysisThe computational or analytical assessment of expressed attitudes and emotions in customer communications.
• advanced analyticsAdvanced analytics comprises analytical methods that use statistical, computational, or machine-learning techniques to reveal patterns and support decisions.
• customer journey analyticsAnalysis of customer interactions across touchpoints to understand journeys, identify friction, and improve conversion paths.
• optimizationThe systematic adjustment of decisions or assignments to improve desired outcomes under specified objectives and constraints.
• privacy-preserving machine learningAn artificial-intelligence system or method designed to perform learning or inference while limiting exposure of personal data.
• marketing technologySoftware and related services that support the collection, management, analysis, and activation of marketing data and interactions.
• event-driven dataDetailed records of individual user or customer interactions with marketing channels and digital properties.
• sourcing layerThe sourcing layer is the architectural stage where data is collected and consolidated from internal and external sources.
• continuous improvementA process in which outputs or results are fed back into subsequent decisions or actions to influence later outcomes.
• data cleaningThe process of detecting and correcting inaccurate, inconsistent, incomplete, or improperly formatted data.
• attribution modelingA model that assigns credit for a marketing outcome among contributing channels, touchpoints, or activities.
• device idA device identifier is a value associated with a computing device for recognizing that device across digital interactions.
• marketing data architectureAn organized structure of layers, systems, and processes for collecting, managing, activating, and distributing marketing data.
• lookalike audience targetingA lookalike audience is a group of prospective users selected because they share characteristics with an existing customer or source audience.
• securitySecurity is the protection of data and systems against unauthorized access, disclosure, alteration, or destruction.
• parallel validationParallel validation is the comparison of outputs from concurrent data environments to identify inconsistencies before decommissioning the older environment.
• second-party dataSecond-party data is another brand’s first-party data shared through a trusted collaboration between organizations with complementary customer interests.
• interoperabilityInteroperability is the ability of different systems or platforms to exchange, combine, and use data or functionality across their boundaries.
• AI agentA software system that uses artificial intelligence to perceive context, make decisions, and execute tasks toward goals with limited direct supervision.
• generative marketing platformA software platform that applies generative artificial intelligence to marketing audience, campaign, creative, targeting, activation, and measurement workflows.
• data fragmentationThe condition in which relevant customer information is split across sources or becomes incomplete because observations cannot be consistently linked.
• target audienceA group of customers defined by shared attributes, behaviors, or business value.
• customer loyaltyA customer’s durable preference and commitment to repeatedly choose a particular brand.
• incremental revenueThe additional revenue generated by an intervention group compared with a control group.
• marketing suppressionAdvertising suppression is the exclusion of specified customers or audiences from paid advertising campaigns.
• vendor lock-inDependence on a technology or vendor that makes replacing, migrating from, or interoperating with it difficult or costly.
• control groupA comparison group in an experiment that does not receive the tested intervention or receives a designated baseline condition.
• treatment groupThe experimentally assigned group that receives the marketing intervention being evaluated.
• customer contextThe relevant history, interactions, attributes, and circumstances surrounding a customer's request or relationship with an organization.
• causationCausation is a relationship in which an intervention or event produces a change in an outcome.
• privacy-by-designA design principle that incorporates privacy protections into systems, processes, and products from their inception rather than adding them afterward.
• audience activationThe delivery of defined marketing audiences to operational channels or destinations so they can be used in campaigns.
• AI-specific governanceAI governance is the policies, processes, and controls for directing and overseeing the development and use of artificial-intelligence systems.
• data destinationAn operational marketing system or channel that receives audience data for campaign execution or customer engagement.
• engagement and activation layerA marketing architecture layer that converts customer insights into personalized interactions and activation across channels.
• medallion architectureMedallion architecture is a layered data-organization pattern that progressively refines data through bronze, silver, and gold layers.
• build-and-buy strategyA technology sourcing pattern that combines internally built capabilities with purchased or managed external products.
• behavioral dataObservable actions performed by users when interacting with a digital product or service, such as visiting pages or purchasing items.
• incrementalityThe additional outcome attributable to an intervention beyond what would have occurred without it.
• marketing cloudAn integrated cloud-based software suite for managing marketing data, campaigns, channels, and customer interactions.
• in-house solutionA custom software or data solution developed and maintained internally by an organization for its own operational needs.
• multichannel modelA multichannel model is an analytical model that estimates how multiple marketing channels contribute to conversions, revenue, or other outcomes.
• data governanceData governance is the set of policies, roles, controls, and practices that manage data quality, access, security, and responsible use.
• marketing personaA modeled representation of a customer type defined by characteristic attributes, behaviors, needs, or attitudes.
• acquisitionThe marketing and sales process of attracting and converting new customers.
• unauthenticated principalAn anonymous visitor is a person who interacts with a website or brand without providing identifying information.
• consent management recordsConsent management records are logs of individuals’ opt-ins, opt-outs, and updates to their data-processing preferences.
• privacy-centric marketing architectureA privacy-centric marketing architecture is a marketing data architecture that embeds privacy protection, compliance, transparency, and consent across its stages.
• de-identification techniqueA de-identification technique alters or limits data so that individuals are less readily identifiable from it.
• non-personally identifiable informationNon-personally identifiable information is information that does not directly identify an individual but may become identifying when combined with other data.
• personal dataData that identifies, relates to, describes, or can reasonably be linked to an individual.
• re-identificationRe-identification is the process of linking supposedly de-identified data back to a specific individual.
• lifecycle-based email campaignAn email marketing program that sends messages according to a customer’s lifecycle stage, past behavior, or predicted need.
• customer profile unificationThe process of combining records from multiple data sources into a unified representation of a customer.
• data ingestionThe process of collecting and transferring data from source systems into a storage or processing environment.
• event collectionThe capture of user or system events from digital properties and other interaction sources, often as they occur.
• marketer as advisorA marketing role in which people guide and supervise largely autonomous AI-generated campaign development and execution.
• marketer as approverA marketing role in which people review, question, and approve AI-generated campaign recommendations and assets.
• marketer as creatorA marketing role in which people create campaign ideas, audiences, experiments, creative assets, and marketing operations.
• marketing workflowAn ordered set of processes and responsibilities for planning, creating, reviewing, launching, and evaluating marketing activities.
• decentralized identity systemA decentralized identity model that gives individuals control over their digital identity credentials and selective disclosure.
• data protection impact assessmentA formal assessment of processing activities that identifies and mitigates risks to individuals’ personal-data protection.
• responsible AIPractices for developing and operating artificial intelligence in ways that address fairness, accountability, transparency, safety, and societal impact.
• zero-knowledge proofA cryptographic proof that demonstrates the truth of a statement without revealing the underlying information beyond that fact.
• audience design capabilityA capability that lets marketers explore and define audiences by selecting attributes and inclusion or exclusion criteria from available data.
• liftThe measured improvement in an outcome attributable to an intervention relative to a comparison condition.
• accountabilityThe obligation of an organization to take responsibility for its data practices and demonstrate compliance with applicable commitments and requirements.
• human-in-the-loopA human-in-the-loop model is an AI system or workflow in which people participate in reviewing, approving, or directing model outputs.
• organizational readinessOrganizational readiness is an organization’s capability and willingness to adopt and sustain a planned change.
• reinforcement learning with human feedbackReinforcement learning from human feedback is a model-training technique that uses human evaluations to shape a system’s behavior.
• sub-processorA third party engaged by a data processor to process personal data on behalf of the processor or its controller.
• zero-party dataCustomer information that people intentionally and proactively provide to communicate their preferences, needs, intentions, or desires.
• customer churn predictionChurn prediction is the use of data and predictive models to estimate which customers are likely to stop engaging with a business.
• personalized customer experienceCommunication tailored to an individual recipient's characteristics, behavior, needs, or preferences.
• touchpointAn individual interaction between a customer and a brand across a marketing channel.
• cookieA first-party cookie is a web cookie created and controlled by the site owner for recognizing users or sessions within that owner's own interactions and infrastructure.
• device fingerprintDevice fingerprinting is a technique that combines device and software characteristics to create an identifier for a browsing device.
• progressive profilingProgressive profiling is a technique for collecting additional user attributes incrementally across interactions rather than requesting all information at once.
• AI-native productA product whose capabilities, workflows, and user experience are fundamentally designed around artificial intelligence rather than merely augmented with an AI feature.
• AI at every layerAn AI-native design principle in which artificial intelligence is incorporated across all product capabilities, including peripheral features.
• AI user designA product-design approach that supports collaborative iteration between human users and AI through natural interaction patterns.
• open data modelA data model that does not require organizational data to conform to a predetermined schema or set of fields.
• zero-copyA data-access pattern in which applications use data in its existing location without creating additional copies.
• match rateMatch rate is the proportion of submitted records or identifiers that are successfully linked to corresponding identities or audience records.
• persistent identifierA persistent identifier is a value retained across interactions to recognize the same user, device, or browsing context over time.
• paid media channelA marketing distribution channel in which an organization pays to deliver advertising or promotional messages to an audience.
• collaborative filteringA recommendation technique that infers a user's interests from patterns in the preferences or behavior of multiple users.
• content-based recommenderA recommendation technique that suggests items by comparing their attributes with the attributes of items a user has previously engaged with.
• contextual targetingAn advertising or content-targeting technique that selects audiences or messages from the context of viewed content rather than individual tracking profiles.
• privacy-preserving analyticsAnalytics that derive aggregate or useful insights while limiting disclosure of information about individual people.
• recommender systemA software system that predicts and presents items, content, or actions likely to be relevant to an individual.
• data vault modelingA data modeling methodology that represents business keys, relationships, and descriptive history through hubs, links, and satellites.
• dimensional modelingA data modeling technique that organizes analytical data into facts and descriptive dimensions for reporting and querying.
• entity-relationship modelA data modeling technique that represents entities, their attributes, and the relationships between them.
• silver layerA refined data-processing layer in a multilayer data architecture where raw data is cleansed, integrated, standardized, and validated for downstream use.
• cross-channel campaignA marketing campaign coordinated across multiple communication channels with shared audience or treatment logic.
• third-party enrichmentThird-party enrichment is the augmentation of an organization's records with data obtained from an external provider.
• CCPAA California privacy law granting consumers rights concerning the collection, use, sharing, and sale of their personal information.
• GDPRA European Union regulation governing the processing and protection of personal data.
• data siloA data silo is an isolated repository or system that restricts access to data and prevents effective sharing or integration with other systems.
• first-party trackingThe tracking of user activity with cookies controlled by the site or organization that directly interacts with the user.
• customer relationship managementCustomer relationship management is a set of processes and software systems for managing an organization’s interactions and relationships with customers.
• media distribution layerA final architectural stage that delivers marketing content, messages, and personalized experiences through customer-facing channels.
• bronze layerA bronze layer is the initial data-storage stage in a layered data architecture that retains source data in its original form with associated metadata.
• gold layerThe medallion-architecture layer that prepares data for direct operational use, such as marketing campaigns and advanced analytics.
• account-based marketingA marketing approach that treats selected business accounts as target markets and coordinates outreach to contacts within them.
• engagement-based targetingA targeting technique that selects audiences according to their observed interactions with marketing content or channels.
• value-driven designAn approach to designing data and marketing initiatives by prioritizing representative use cases according to their expected business value.
• data integrationData integration is the process of combining data from disparate sources into a coherent, accessible whole.
• confusion matrixA confusion matrix is a table that organizes the counts or shares of correct and incorrect outcomes for a classification system.
• false negativeA false negative is a classification outcome in which the system fails to identify two records as belonging to the same individual.
• false positiveA false positive is a classification outcome in which the system incorrectly identifies records from different individuals as belonging to the same individual.
• media mix modelA media mix model is a statistical model that estimates the contribution of marketing channels to aggregate outcomes from historical spending and performance data.

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