Media companies and their brand partners are sitting on complementary audience data that, together, could drive significantly better targeting and measurement through a data clean room. But sharing that data directly is a legal, contractual, and reputational risk. The result is walled gardens, fragmented insights, and audience strategies built on assumptions instead of evidence.
Direct data sharing with brand or publisher partners exposes PII, violates privacy regulations, and breaches contractual data governance obligations - so it simply does not happen.
Without a secure collaboration layer, publishers and brands cannot measure how their audiences actually overlap - leaving targeting and reach decisions based on estimates rather than verified data. Our guide to audience overlap analysis shows how publishers verify it with advertising partners, without sharing raw data.
Connecting media exposure to real consumer outcomes - customer lifetime value, purchase frequency, category affinity - requires data that neither side can share under conventional terms.
Without probabilistic extension built on verified overlap signals, audience activation relies on broad segments that underperform and take weeks to assemble manually.
Audience Intelligence Platform creates a secure, privacy-preserving environment where publisher and brand data can be analysed together – without either party ever seeing the other’s raw records. The insights are real. The exposure is zero.
A Databricks-native data clean room is provisioned within your existing data environment - no data leaves your infrastructure. Partner clean room data is brought in under governed access controls.
First-party data from both sides is pseudonymised and encrypted before any analysis runs - ensuring raw records are never exposed, even within the shared computation layer.
Overlap analysis reveals the size and composition of shared audiences across demographics, content preferences, purchase behaviour, and engagement patterns - without surfacing individual records.
Brand transaction data is analysed against publisher audience segments to calculate CLV, purchase frequency, and category affinity for verified audience cohorts - giving both sides signals they could not access alone.
Verified overlap signals are used to build seed audiences, which are then extended probabilistically to reach lookalike audiences at scale - without requiring additional raw data access.
Learn how publishers build the first-party segments behind this in our privacy-safe first-party data activation approach.
The Audience Intelligence Platform is a Databricks-native data collaboration platform that enables secure multi-party audience analysis, customer lifetime value (CLV) measurement and privacy-safe activation, without raw data exposure at any stage of the workflow.
A fully managed, Databricks-native data clean room that enables multi-party audience analysis using clean room data without raw data exposure – compliant with GDPR, CCPA, and enterprise data governance standards.
Verified overlap analysis across publisher and brand audiences – by demographics, content affinity, purchase behaviour, and engagement – without surfacing individual-level records.
Brand transaction data analysed against publisher segments to calculate customer lifetime value, purchase frequency, and category signals for verified audience cohorts.
Seed audiences built from verified overlap signals and extended probabilistically to reach scale – improving targeting precision without requiring additional raw data from either party.
Audience segments exported to DSPs, CTV platforms, and social channels in privacy-compliant formats – no PII exposed at any point in the activation pipeline.
Built on Databricks Delta Sharing and Unity Catalog – enterprise-grade access controls, audit logs, and data lineage throughout the entire collaboration and activation workflow.
When publishers and brand partners can analyse audience data together – without legal or contractual risk – targeting improves, measurement becomes credible, and both sides capture value that was previously inaccessible.
Better-Performing Campaigns
Audiences built on verified overlap and probabilistic extension outperform broad segment targeting - improving campaign ROI for both publishers and brand partners.
Revenue from New Partnership Models
Publishers can offer audience intelligence as a premium product - creating new revenue streams from data collaboration that do not require raw data transfer. See how a global media and events company built a privacy-safe view of its audience.
Credible Cross-Platform Measurement
CLV and purchase frequency signals give both sides a shared, evidence-based view of campaign impact - replacing post-campaign estimates with verified outcomes. See how we handle media measurement and attribution.
Reduced Legal and Compliance Exposure
All collaboration happens within a privacy-safe, governed environment - eliminating the data sharing risk that previously made publisher-brand audience collaboration impractical.
Enable audience data collaboration with brand partners without exposing raw data or violating governance obligations - surfacing insights that were previously off-limits
Offer audience intelligence as a premium product to brand partners. Back every pitch with verified overlap data instead of estimated reach figures. See also SalesNav AI for pitch preparation.
Move beyond survey-based audience measurement. Analyse real overlap and affinity signals from verified partner data - within a fully governed clean room environment.
Data collaboration that is GDPR and CCPA compliant by design. No raw data leaves your environment. Full audit trail across every analysis and activation step.
Audience Intelligence Platform is built on Databricks Delta Sharing and Unity Catalog, and integrates with identity resolution, activation, and measurement partners to deliver end-to-end audience collaboration within your existing technology landscape. For the practitioner view, read our perspective on building privacy-safe audience intelligence.
Explore insights on data clean rooms, privacy-safe audience collaboration, and publisher-brand data partnerships for media companies.
A data clean room is a secure computation environment where two parties can run joint analyses on their combined data without either side seeing the other’s raw records. Our Databricks-native implementation ensures all data is pseudonymised and encrypted before any analysis runs – and no individual-level data is ever surfaced in any output.
Any publisher or brand partner that agrees to the governance framework. Common use cases include publisher-advertiser audience overlap analysis, co-op measurement between media companies, and retailer-publisher data collaboration for commerce media activation.
No. The clean room complements your existing identity and audience management infrastructure. It adds a secure collaboration layer on top – enabling analyses and activations that your current setup cannot support without raw data exposure.
In a clean room, you retain full control of your data throughout — it never leaves your Databricks environment. Third-party marketplaces require you to send data to an external platform. Clean rooms enable the same collaborative insights with zero raw data transfer and full compliance accountability on your side.
Yes. Privacy-safe audience segments generated from clean room analysis can be exported directly to DSPs, CTV platforms, and social channels for activation – in compliant formats that contain no PII.
Partner onboarding typically takes 2–3 weeks, covering governance agreement, data pseudonymisation setup, and Delta Sharing configuration. The analysis environment itself can be provisioned within the same timeframe.
Snowflake Data Clean Rooms run natively inside Snowflake, and LiveRamp is a separate platform with its own environment. Audience Intelligence Platform is built natively on Databricks Delta Sharing and Unity Catalog, so if your data already lives in Databricks there is no new platform to provision and no data to move.
Yes. In a clean room, a publisher and a partner can analyse overlap on pseudonymised, encrypted data, build seed audiences from verified overlap, extend them probabilistically and export privacy-safe segments to DSPs and CTV platforms. Neither side sees the other’s raw records.
Yes. Overlap analysis shows the size and composition of shared audiences across demographics, content affinity, purchase behaviour and engagement patterns, without surfacing individual records.
The platform builds seed audiences from verified overlap between your first-party data and a brand partner’s data, then extends them probabilistically, so targeting does not rely on third-party cookies. For the wider strategy, see our approach to first-party data activation.
Audience segments built in the clean room are exported to DSPs, CTV platforms and social channels in privacy-compliant formats, with no PII exposed at any point in the activation pipeline.
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