Lookalike Audiences
Lookalike audiences use machine learning to find new people who share characteristics with your best customers. They are the foundation of AI-powered prospecting at scale.
How Lookalike Models Work
AI analyzes your seed audience (customers, converters, or high-value users) and identifies behavioral patterns, interests, and attributes that distinguish them from the general population. The model then scores the broader population on similarity.
Seed Audience Strategy
| Seed Type | Quality | Best For |
|---|---|---|
| Top Customers (LTV) | Highest | Finding high-value new customers |
| Recent Purchasers | High | General customer acquisition |
| Email Subscribers | Medium | Top-of-funnel prospecting |
| Website Visitors | Medium-Low | Broadest reach, lower precision |
| Add-to-Cart Users | High | Finding high-intent shoppers |
Platform Lookalike Features
Meta Lookalikes
1-10% expansion sizes, value-based lookalikes, and Advantage+ Lookalike that automatically optimizes expansion percentage.
Google Similar Audiences
Integrated into Performance Max and Display campaigns. Google uses first-party data and signals to find similar users.
LinkedIn Matched Audiences
Company and contact-based lookalikes for B2B targeting with professional attribute matching.
TikTok Lookalikes
Custom and automatic lookalike audiences based on pixel events, customer lists, and engagement data.
Optimization Strategies
- Test Expansion Sizes: Start with 1% (most similar) and test 3%, 5%, and 10% to find the sweet spot of reach vs. precision
- Layer with Interests: Combine lookalikes with interest targeting for narrower, higher-performing audiences
- Refresh Regularly: Update seed audiences monthly as your customer base evolves
- Exclude Existing Customers: Always exclude current customers from prospecting lookalikes to avoid waste
- Test Multiple Seeds: Create separate lookalikes from different seed sources and compare performance
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