Lookalike Audience
What is LAL?
A Lookalike Audience is a targeting option in Meta Ads that finds new people who share similar characteristics with your existing customers or audience. Meta analyzes your source audience (like a customer list or website visitors) and uses machine learning to find users with matching demographics, interests, and behaviors.
Example
You upload a list of 1,000 customers who have purchased in the last 90 days. Meta analyzes their common traits and creates a 1% Lookalike Audience of ~2.3 million people in the US who share similar attributes. You then target ads to this Lookalike Audience.
Industry Benchmarks
| Platform / Segment | Value | Note |
|---|---|---|
| 1% Lookalike | ~2.3M (US) | Most similar, highest quality |
| 1–3% Lookalike | ~6.9M (US) | Good balance of quality and scale |
| 5% Lookalike | ~11.5M (US) | Broader, lower similarity |
| 10% Lookalike | ~23M (US) | Maximum reach, least similar |
Most similar, highest quality
Good balance of quality and scale
Broader, lower similarity
Maximum reach, least similar
Benchmarks are approximate averages and vary by industry, targeting, season, and geography.
Optimization Tips
- 1
Use your highest-value customers as the source audience rather than all customers for better quality matches.
- 2
Start with 1% Lookalikes for best performance, then expand to 1–3% to scale while maintaining quality.
- 3
Create separate Lookalike audiences from different sources (purchasers, leads, engagers) and test them against each other.
- 4
Refresh your source audience quarterly to ensure the Lookalike reflects your current best customers.
Frequently Asked Questions About LAL
Track Your LAL in Real Time
Adship gives you a unified dashboard to monitor LAL, optimize campaigns, and improve performance across Meta and TikTok.