Definition
Lookalike Audiences
Lookalike audiences are targeting groups created by platforms that find new users who share characteristics with your existing customers or high-value audiences.
What it means
Lookalike audiences use machine learning to identify people who 'look like' a seed audience you provide—typically your customers, email list, or high-value converters. The platform analyzes patterns in demographics, interests, behaviors, and engagement signals to find similar users who haven't encountered your brand yet. Lookalikes solve the scale problem: your customer list might be 10,000 people, but a 1% lookalike can reach millions with similar profiles. The quality of your lookalike depends on the quality of your seed: a lookalike based on your best customers will outperform one based on all purchasers. Lookalike size matters too—smaller percentages (1%) are more precise but smaller; larger percentages (5-10%) offer more scale but less similarity. Combining lookalikes with strong UGC creative creates a powerful prospecting engine.
Why it matters
- Lookalikes help you find new customers without guessing at interest targeting.
- They encode your customer insights into targeting, scaling what's already working.
- Performance often exceeds broad or interest-based targeting when seed audiences are high-quality.
- They enable efficient prospecting at scale once you have enough customer data.
How to improve it
- Build lookalikes from your highest-value customers, not just all purchasers.
- Test multiple seed audiences: purchasers vs. repeat purchasers vs. high-AOV customers.
- Start with 1% lookalikes for precision, then expand to 1-3% or 1-5% as you scale.
- Refresh seed audiences quarterly as your customer base evolves.
- Pair lookalikes with strong creative—targeting finds the right people, creative converts them.
Common mistakes
- Using weak seed audiences (all site visitors instead of purchasers) that dilute lookalike quality.
- Jumping to large percentage lookalikes before testing tighter, more precise ones.
- Never refreshing seed audiences, causing lookalikes to become stale and less effective.
- Relying solely on lookalikes without testing broad targeting, which sometimes outperforms with strong creative.
Related terms
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