What are Lookalike Audiences?
Lookalike Audiences use Meta's machine learning algorithms to find new people who are similar to your existing customers or website visitors. By analyzing the characteristics and behaviors of your source audience Meta identifies potential customers who share similar traits and are likely to be interested in your products or services.
How Lookalike Audiences Work:
Meta analyzes your source audience data to identify common characteristics including:
- Demographic information like age location and interests
- Behavioral patterns and online activities
- Purchase history and spending patterns
- Device usage and app preferences
Source Audience Options:
- Customer lists - Use your best customers as the foundation
- Website visitors - Target people similar to your site traffic
- App users - Find users similar to your mobile app audience
- Engagement audiences - Target people similar to those who engage with your content
Audience Size Selection:
Choose your lookalike percentage (1-10%) based on your goals:
- 1% audiences are most similar but smaller
- 10% audiences are larger but less precise
- Test different percentages to find optimal balance
Best Practices for Lookalike Success:
- Use high-quality source audiences with at least 100-1000 people
- Refresh source data regularly as your customer base evolves
- Test multiple source audiences and percentages
- Combine with other targeting criteria for precision