Glossary/Audience data/Lookalike audiences
What are lookalike audiences and how are they built?
How lookalike audiences are built, how to balance size and accuracy, and how publishers use them in advertising.
Written by Samhub with the help of AI·Updated 13 Oct 2026·3 min readA lookalike audience is a group of people who share traits with an existing “seed” audience, such as an advertiser’s customers, found by modeling patterns in data.
How lookalike modeling works
Choose a seed
A group you want more of, such as recent buyers.
Find patterns
What the seed has in common in behavior and traits.
Score everyone else
How closely each reader matches.
Pick the size
A tighter match gives fewer, more similar people.
Size vs accuracy
| Setting | Reach | Similarity |
|---|---|---|
| Narrow | Small | High |
| Broad | Large | Lower |
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How publishers use them
Publishers can expand a small, valuable segment, such as readers who stated an interest, into an audience large enough to sell. Lookalikes built on your own readers keep the data in your hands.
Addressability at Stampen Media after moving its reader data into one layer with Samhub.
Frequently asked questions
Big enough to show patterns, often at least a few thousand people.
No. Any platform with enough data can build them, including publishers.
Usually, since they model individual behavior. GDPR rules apply.
Compare its results with the seed and with a broad audience.
Related terms
Grouping readers so advertisers can reach them.
Targeting ads based on what readers have done before.
Data readers share with you on purpose.
Putting data to work in the tools that use it.
Audiences built from what people are likely to do.
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