Lookalike Modeling
Lookalike modeling finds new prospects who resemble a brand's best existing customers, expanding reach by identifying users with similar attributes to a seed audience.
Key takeaways
- Lookalike modeling expands a seed audience to similar new users.
- The quality of the seed audience largely determines the result.
- First-party seeds have become the strongest input after cookie loss.
- It powers prospecting and scaled acquisition in DSPs and walled gardens.
Seeds and similarity
A lookalike model starts with a seed "” often a brand's best customers or converters "” and identifies other users who share their attributes and behaviors. This lets advertisers move beyond re-reaching known users to prospecting new ones who statistically resemble proven value.
Why the seed matters most
A lookalike is only as good as its seed. A tight, high-value first-party seed produces a sharper, more valuable audience than a broad or noisy one. As first-party data became the durable asset post-cookie, first-party seeds became the backbone of quality lookalike modeling.
| Input | A seed audience |
|---|---|
| Output | Similar new prospects |
| Key driver | Seed quality |
| Use case | Prospecting / scaled acquisition |
Frequently asked questions
What is a seed audience in lookalike modeling?
The seed is the source group "” often best customers or recent converters "” that the model uses as a template to find similar new users.
Why is lookalike quality tied to first-party data?
Because a precise, owned first-party seed produces sharper models than broad third-party segments, and first-party data is the most durable input after cookie deprecation.