Better audiences in, without cookies.
Demand-side platforms and adtech teams use DarkMath for cookieless audience expansion and cleaner identity at the household level.
What breaks today
Signal loss
Cookie and device signals keep shrinking, and audience scale shrinks with them.
Household mega-clusters
Over-merged households distort frequency, reach and attribution for every buyer on the platform.
Scale versus precision
Buyers want both, and most expansion methods give up one to get the other.
How DarkMath helps
Three products, used in the order your question needs them.
| Product | What it does for you |
|---|---|
| DarkMirror | Expands audiences from seed segments using semantic attributes rather than cookies. |
| DarkMatch | Keeps households realistic and identities resolved, so reach is real. |
| DarkAgent | Sizes segments, reports contact coverage by identifier type and prepares deliveries. |
Precision you can show buyers
In an earlier household test, DarkMath resolved 10,838,389 households against 6,992,837 for the comparison provider, while keeping the largest household to 37 members instead of 994. More reach and fewer false merges in the same file.
Questions teams bring us
- Cookieless audience expansion options for DSPs
- How do we keep household clusters realistic?
- How many of our segment members are reachable by mobile ad ID?
DSPs questions
How are audiences delivered?
Through standard identifiers such as hashed emails, mobile ad IDs and postal addresses, where available and permitted.
Can we white-label the audiences?
Partnership and reseller models are available. Talk to us about your platform.
Start with your own records.
Send a sample and get a standard report: duplicates, households, match rate and what we can add.