Spend on people, not duplicates.
Brands and agencies use DarkMath to clean audiences before buying media, measure against resolved households and expand reach with semantic lookalikes.
What breaks today
Inflated reach
Duplicate profiles count one person several times, so frequency climbs and reach is overstated.
Thin lookalikes
Models built on a few demographic fields miss the people who behave like your best customers.
Broken attribution
When identity is fragmented, conversions can’t be tied back to the exposure that drove them.
How DarkMath helps
Three products, used in the order your question needs them.
| Product | What it does for you |
|---|---|
| DarkMatch | Resolves your customer and prospect files so every person counts once. |
| DarkMirror | Builds semantic lookalikes from your best customers and shows the reach and precision trade. |
| DarkAgent | Plans and sizes audiences, prepares orders and connects results back to the audience and action. |
Why lookalikes underperform
Most lookalike models can only see the fields they were trained on. When income, age or household data is missing, the model either narrows the audience or guesses. DarkMirror uses attributes that predict the missing field, so more of the market qualifies without lowering the bar.
Questions teams bring us
- Why are our lookalike audiences underperforming?
- How do we lower CPA when audience data is incomplete?
- Who offers semantic lookalike audiences instead of standard lookalikes?
Advertisers questions
Do we need third-party cookies?
No. Audiences are built from resolved identities and delivered through standard identifiers where available and permitted.
Can we measure results by household?
Yes. Outcomes are tied back to resolved households, so one person seen on three devices counts once.
Start with your own records.
Send a sample and get a standard report: duplicates, households, match rate and what we can add.