Customer question
“How can I use my customer data for advertising?”
What this solution does
Think of it as introducing your customer list to an ad platform without ever handing over the raw list — you send scrambled versions of emails or phone numbers, and the platform tells you which ones it recognises.
Specifically, it answers: How do we take the customer data we already own (CRM, loyalty, purchase history) and use it safely to target, suppress, or enrich advertising audiences?
How it works
Customer records are hashed into privacy-safe identifiers, matched against a platform's or identity provider's graph to find addressable records, assembled into an audience, then pushed to activation destinations like a DSP, paid social platform, or search engine.
Data requirements
- Customer identifiers (email, phone, mailing address, or a durable customer ID)
- Record freshness / last-updated timestamp
- Consent status per record
- Volume of records (affects statistical usefulness of resulting audience)
Discovery questions
Where does your customer data live today?
Determines integration complexity — a single CRM is simpler than data spread across POS, loyalty, and ecommerce systems.
What identifiers are available in the dataset?
Available identifiers directly affect whether records can be matched and activated through identity or advertising platforms.
How many records, and how often does the data change?
Small or rarely-refreshed lists have limited advertising value and may not clear platform minimum thresholds.
Which markets is this for?
Consent and privacy regulation (e.g. GDPR, CCPA, Australian Privacy Act) varies by market and shapes what's permissible.
Is the objective targeting, suppression, enrichment, or measurement?
Each objective uses the matched audience differently and may require different data fields or match confidence.
What consent and privacy controls currently apply to this data?
Onboarding data without proper consent basis creates real regulatory and reputational risk.
Build an experiment
Generate synthetic data
Data readiness
Run model / simulation
Results
Business ⟷ Technical interpretation
Of the totalRecords customer records generated, usableRecords were usable and matchedRecords matched to the destination platform (matchRate% match rate). This resulting audience is what would actually be available to target, suppress, or enrich against.
Shown as a template — the highlighted tokens are filled in once the model has been run.
What would you do next?
- Review which identifier types most improved match rate and prioritise capturing them
- Set a data refresh cadence appropriate to the audience's use case
- Confirm consent basis for onboarded records before activation
Solution & vendor landscape
Implementation approaches
LiveRamp
First Party Data / Identity · Data Collaboration / Clean Rooms
Experian Marketing Services
Audience / Data
TransUnion (TruAudience)
Audience / Data
InfoSum
Data Collaboration / Clean Rooms
AWS Clean Rooms
Data Collaboration / Clean Rooms