Case StudiesMedia Practice
08Major Retail & Ad Network · Clean Room · BYOM
Clean Room Architecture & Bring Your Own Model (BYOM) Framework for Privacy-Safe Measurement
DataInc.ai architected a privacy-preserving clean room data collaboration layer and BYOM framework for a major retail and advertising network — enabling advertisers to run proprietary attribution and measurement models against first-party retail signals without compromising data sovereignty.
Clean RoomBYOMAMCPrivacy-SafeFirst-Party DataRetail Media
01
Situation · Solution · Outcomes
Situation
→Advertisers couldn't trust black-box attribution — brands needed to validate campaign performance against their own internal models, but the platform's closed measurement system prevented custom analysis
→Privacy constraints blocked data sharing — traditional data collaboration approaches required moving sensitive first-party data outside controlled environments, creating legal and reputational risk
→No model portability — sophisticated advertisers with proprietary MMM, MTA, or attribution models had no way to bring those models to the data — they were forced to use platform-native measurement only
Solution
→Clean room data layer — architected a privacy-preserving environment where advertiser and retailer first-party data can be joined and queried without either party exposing raw records
→BYOM execution framework — built a secure model execution sandbox enabling advertisers to bring proprietary Python, R, or SQL-based models and run them against matched retail signals
→Standardized output API — designed a structured results layer that returns model outputs in consistent formats, enabling downstream integration with advertiser BI, MMM, and planning tools
Outcomes Delivered
→Custom model deployment enabled — advertisers can now run their own attribution, MMM, and lift models against first-party retail data for the first time
→Privacy compliance maintained — zero raw data movement; all computation occurs within the clean room boundary with differential privacy protections
→Measurement trust restored — brands validate and triangulate platform measurement against their own models, dramatically increasing confidence in reported ROAS
02
Before & After
Before — Closed Platform Measurement
Black-box attribution only — brands forced to use platform-native measurement with no ability to validate or customize
No model portability — proprietary MMM and MTA models had no access to retail first-party signals
Raw data movement required — any custom analysis required extracting data, creating privacy and legal risk
Single source of truth problem — platform measured its own performance with no independent verification possible
Advertiser measurement fragmentation — retail, social, and search attribution lived in separate silos with no reconciliation
After — Open BYOM Clean Room Architecture
Custom model execution in-environment — bring any model to the data; no data needs to leave the clean room
Full model portability — proprietary MMM, MTA, and attribution models run against first-party retail signals
Privacy by design — differential privacy and aggregation thresholds ensure no individual-level exposure
Independent measurement validation — advertisers verify platform attribution against their own models
Unified output layer — results from all models flow into consistent formats for BI and planning integration
03
Solution Architecture
End-to-End Data Flow
Advertiser Data
First-Party CRM — Customer & purchase history
Media Signals — Campaign exposure data
Proprietary Models — MMM, MTA, attribution
Clean Room Layer
Identity Resolution — Privacy-safe matching
Access Controls — Row-level permissions
Computation Engine — In-environment queries
Retail Signals
Purchase Transactions — SKU-level conversion
Audience Segments — Behavioral clusters
Inventory Data — Product availability
BYOM Outputs
Attribution Results — Channel credit splits
Lift Estimates — Incremental ROAS
Audience Insights — Conversion profiles
Advertiser → Clean Room → Retail Signals → Outputs
04
Platform Capabilities
Identity Resolution
Privacy-safe customer matching across advertiser CRM and retailer transaction data without raw ID exchange
Model Sandbox
Secure execution environment supporting Python, R, and SQL-based advertiser models on matched datasets
Differential Privacy
Mathematical privacy guarantees on all query outputs — prevents reconstruction of individual records
Output API
Standardized results format enabling downstream integration with advertiser MMM, BI, and planning systems
05
Results & Impact
0
Raw data records exchanged — all computation within clean room boundary
3x
Increase in advertiser model coverage vs. platform-native measurement only
100%
Privacy compliance maintained across all advertiser and retailer data joins
$2.3M
Estimated annual value from prevented measurement blind spots and budget misallocation
Advertisers can run proprietary attribution and MMM models against retail first-party data for the first time
Privacy compliance maintained across all data collaboration — zero raw record exposure to either party
Platform measurement trust significantly improved as brands validate ROAS against independent models
Retail media revenue grew as advertiser confidence in measurement quality increased investment commitment
Standardized output layer enables multi-touch measurement across retail, social, and search in a single workflow
Next Steps
DataInc.ai is expanding the BYOM framework to support additional model types and integrating the clean room output layer with advertiser planning platforms.
01Model Type Expansion — Add support for ML-based lift models and Bayesian MMM execution within the clean room
02Cross-Network Attribution — Extend the clean room to enable privacy-safe joins with additional media networks
03Real-Time Signals — Incorporate near-real-time transaction data for faster measurement feedback cycles
04Planning Integration — Connect clean room outputs directly to media buying and budget planning systems
About DataInc.ai
DataInc.ai is the marketing data reliability platform built for enterprise teams with $5M+ in annual media spend. We monitor measurement pipelines across connectors, mapping, taxonomy, observability, and alerting — eliminating data risk before it impacts decisions.
Proprietary & Confidential · Media Practice · 2025