Lookalike Audience explained
Lookalike audiences are the closest thing to a cheat code in paid advertising. Instead of guessing who might buy, you tell the platform "find me more people like the ones who already bought" and let the algorithm do the heavy lifting.
Here is how it works: you upload a source audience, typically your customer list, website visitors, or people who took a specific action. The platform analyzes hundreds of data points about those people (demographics, interests, online behavior, purchase patterns) and builds a statistical model. Then it finds new users who match that model but have never interacted with your brand.
On Meta, you create lookalikes from Custom Audiences with percentage ranges from 1% to 10%. A 1% lookalike is the top 1% of the country most similar to your source, smaller but highest quality. A 10% lookalike casts a wider net with more variation.
The quality of your lookalike depends entirely on the quality of your source. A lookalike based on your top 100 repeat customers will outperform one based on all website visitors. Garbage in, garbage out.
There is a catch though: lookalike audiences have gotten less powerful as privacy changes (iOS 14.5, cookie deprecation) limit the data platforms can use. In 2026, the best media buyers use lookalikes as a starting point but rely increasingly on broad targeting combined with strong creative to let the algorithm find buyers through engagement signals rather than data matching.
Examples
- 1Creating a 1% lookalike audience on Meta based on your top 500 customers by lifetime value
- 2Using a TikTok lookalike built from people who watched 75% of your best-performing video ad
- 3Building a Google Similar Audience from your email subscriber list to target on YouTube
- 4Testing a 1% vs 3% vs 5% lookalike to find the sweet spot between quality and scale