What is a Lookalike Audience?
A lookalike audience is a targeting tool that uses machine learning to identify new potential customers who share behavioral, demographic, and interest-based characteristics with your best existing users. Ad platforms analyze your "seed" audience and find similar users at scale.
This approach combines the precision of targeting your best customers with the scale needed for growth, typically outperforming broad targeting by 2-3x.
How Lookalikes Work
- Create Seed Audience: Upload a list of your best customers (purchasers, high-LTV users)
- Platform Analysis: ML algorithms identify common patterns and signals
- Expansion: Platform finds users matching those patterns who aren't yet customers
- Targeting: Ads are served to this expanded audience
Lookalike Size vs Quality
| Expansion % | Audience Size | Quality | Best For |
|---|---|---|---|
| 1% | Smallest | Highest | High-value conversions |
| 1-2% | Small | Very High | Purchase events |
| 2-5% | Medium | High | Balanced campaigns |
| 5-10% | Large | Medium | Awareness, reach |
| 10%+ | Very Large | Lower | Top-of-funnel only |
Best Seed Audiences
- Purchasers: Users who made in-app purchases (best for revenue)
- High-LTV Users: Top 20% by lifetime value
- Subscribers: Active subscription customers
- Engaged Users: D7 retained users with high session counts
- Event Completers: Users who completed key milestones
Pro Tip: Value-Based Lookalikes
When possible, include purchase value data with your seed audience. Platforms like Meta can optimize for users likely to have similar spending patterns, not just similar demographics.
Platform Comparison
| Platform | Name | Min Seed Size |
|---|---|---|
| Meta (Facebook/Instagram) | Lookalike Audiences | 100 users |
| Google Ads | Similar Audiences | 1,000 users |
| TikTok | Lookalike Audiences | 1,000 users |
| Snapchat | Lookalikes | 1,000 users |
| Twitter/X | Lookalike Expansion | 500 users |
Lookalike Best Practices
- Quality Over Quantity: Better to have 1,000 purchasers than 10,000 installers
- Refresh Regularly: Update seed audiences monthly as user base evolves
- Test Expansion Sizes: Start tight (1-2%), expand if performance holds
- Exclude Existing Users: Don't waste budget targeting current customers
- Layer with Other Targeting: Combine with geo, age, or interest filters