B2C Consumer Marketing Data Providers — Terminology, Explanations and Use Cases
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Aggregated/Anonymized Data
Summary: Data aggregated to non-identifiable groups or anonymized to prevent re-identification.
Use Cases:
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Publish city-level insights without PII risk
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Share cohort metrics with partners safely
Audience Cohorts
Summary: Groups defined by shared traits/events without exposing individual identities.
Use Cases:
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Run cohort-based ads in walled gardens
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Measure cohort lift vs. control groups
Behavioral & Transaction Data
Summary: Observed actions such as site/app events, SKU-level purchases, and card/receipt-level transactions.
Use Cases:
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Build high-intent audiences from recent shoppers
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Optimize media to products with rising demand
Consent & Preference Management
Summary: Processes and tooling to capture, store, and honor user consent and channel/content preferences.
Use Cases:
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Respect email/SMS opt-ins by purpose
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Sync consent state to downstream platforms
Consumer Identity Graph
Summary: Resolved map linking emails, phones, MAIDs, cookies, and addresses to a person/household.
Use Cases:
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De-duplicate profiles across channels
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Enable cross-device frequency capping and attribution
Data Enrichment (Append)
Summary: Adding third-party attributes (demographic, lifestyle, transaction) to first-party profiles.
Use Cases:
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Enhance CRM for lookalike seeds
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Complete missing demographics for personalization
Data Minimization
Summary: Collecting and using only the data necessary for a stated, lawful purpose.
Use Cases:
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Trim capture forms to essential fields
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Configure pipelines to drop unused columns/attributes
Data Onboarding (Offline-to-Online)
Summary: Translating offline identifiers (email, postal) into digital IDs using hashed matching.
Use Cases:
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Activate loyalty data in programmatic platforms
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Measure offline sales lift from digital ads
Data Provenance & Lineage
Summary: Traceability of data sources, transformations, and rights over time.
Use Cases:
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Audit vendor sources before procurement
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Prove license rights during compliance reviews
Data Quality (Coverage/Accuracy/Freshness)
Summary: Dimensions used to assess how complete, correct, and up-to-date a dataset is.
Use Cases:
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Score vendors by fill rate and timeliness
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Set SLAs for refresh cadence on key fields
Data Refresh Cadence & Decay
Summary: Planned update frequency and recognition that data value declines over time.
Use Cases:
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Refresh emails and addresses quarterly
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Expire stale affinities after inactivity windows
Demographic vs Psychographic
Summary: Demographic = who someone is (age, income, location); Psychographic = attitudes, interests, values.
Use Cases:
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Build audience mixes using life-stage + interests
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Test creative variants aligned to values-based segments
Deterministic vs Probabilistic Matching
Summary: Deterministic = exact identifier matches; Probabilistic = statistical matches on patterns/signals.
Use Cases:
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Use deterministic for CRM onboarding
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Use probabilistic to extend reach where IDs are sparse
Device IDs (MAIDs)
Summary: Mobile Advertising IDs (IDFA/GAID) used with consent for targeting and measurement.
Use Cases:
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Build app remarketing pools
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Measure app-to-CTV cross-device reach with consent
Geolocation Precision (GPS/Polygon/SDK)
Summary: Spatial accuracy levels and collection methods for location data.
Use Cases:
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Geo-fence store visits with polygon POIs
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Avoid centroid-only datasets for visit attribution
Hashed PII (HEM)
Summary: Cryptographic hash (often SHA-256) of PII used to enable privacy-preserving matching across systems.
Use Cases:
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Onboard CRM emails to media platforms via hash
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Join partner datasets without sharing raw PII
Householding
Summary: Grouping individuals/devices into a single household ID for deduplication and measurement.
Use Cases:
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Cap frequency at the household level in CTV
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Attribute sales to a household rather than a single device
Identity Resolution (Consumer)
Summary: Processes and models that unify identifiers to a stable person/household entity.
Use Cases:
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Create a ‘golden record’ per customer
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Normalize multiple emails/phones to one profile
Incrementality / Lift Testing
Summary: Experiments (e.g., geo holdouts, PSA ads) that estimate causal impact beyond baseline.
Use Cases:
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Validate partner audiences via lift studies
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Calibrate bid modifiers using measured lift
Lifestyle Affinities
Summary: Modeled interests and behaviors inferred from observed signals (e.g., purchases, content).
Use Cases:
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Target ‘outdoor enthusiasts’ across channels
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Suppress segments unlikely to convert for a category
Lookalike Modeling (B2C)
Summary: Finding new prospects similar to a high-value seed audience.
Use Cases:
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Scale acquisition from top LTV customers
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Expand reach while holding CPA targets
Loyalty & Receipt Data
Summary: Purchase data from loyalty programs and receipt-capture networks.
Use Cases:
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Target brand switchers by category buyers
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Attribute ad exposure to verified product purchases
Match Rate
Summary: Share of records successfully matched between datasets at the desired identifier level.
Use Cases:
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Benchmark onboarding partners by match performance
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Optimize hashing/salting to lift match rates
Panel vs. Census Data
Summary: Panel = sample with weights; Census = near-total coverage at event or ID level.
Use Cases:
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Use panels for attitudinal insights
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Use census feeds for granular measurement
Personas (Modeled)
Summary: Composite segments representing archetypal customers based on behaviors and attitudes.
Use Cases:
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Craft creative tailored to persona motivations
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Guide merchandising and content strategy
PETs (Privacy-Enhancing Technologies)
Summary: Techniques like differential privacy, k-anonymity, and secure multiparty compute to reduce privacy risk.
Use Cases:
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Release insights with noise injection
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Run partner analysis in a clean-room environment
PII (Personally Identifiable Information)
Summary: Data that can identify a person (e.g., name, email, phone); typically processed in hashed/encoded form for marketing.
Use Cases:
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Design flows to minimize usage of raw PII
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Segment access via least-privilege controls
Propensity Model
Summary: Predictive model estimating the likelihood of an outcome (buy, churn, subscribe).
Use Cases:
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Score customers for next-best-offer targeting
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Prioritize retention offers for high churn risk
RFM Segmentation
Summary: Recency, Frequency, Monetary value framework for customer value clustering.
Use Cases:
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Identify VIPs for loyalty perks
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Re-activate lapsed cohorts with tailored offers
Sensitive Categories (GDPR/CCPA)
Summary: Special-category or sensitive personal data requiring heightened protection/consent.
Use Cases:
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Exclude health/religion from marketing uses
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Segment pipelines to block sensitive fields
Suppression Lists
Summary: Lists of users to exclude from targeting (e.g., existing customers, opt-outs, recent purchasers).
Use Cases:
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Reduce wasted spend on recent buyers
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Honor do-not-sell/share requests in activation
Transparency & Compliance Flags
Summary: Labels indicating consent, opt-out, data provenance, sensitive status, and regional restrictions.
Use Cases:
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Filter activation to ‘consented’ records only
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Route EU data to EU-only processing/storage
Walled Garden & Clean Room Matching
Summary: Privacy-preserving matching inside platforms without exposing user-level data.
Use Cases:
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Measure conversions via on-platform clean rooms
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Activate cohorts without exporting raw PII
Zero-Party Data
Summary: Data explicitly and proactively shared by consumers (preferences, intents).
Use Cases:
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Power preference-based product recommendations
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Improve consented personalization and CX
