# Lookalike Audiences

**Category:** platform  
**Short Description:** Advanced targeting segments that use machine learning to find users similar to valuable customers.  
**Last Updated:** 2026-05-30T00:00:00Z

## Definition

Lookalike audiences are sophisticated targeting segments created through platform algorithms that analyze patterns in existing customer data to find new users who share similar characteristics, behaviors, and interests. These audiences leverage machine learning to identify complex patterns across demographics, interests, behaviors, and engagement signals, enabling advertisers to scale their reach while maintaining targeting precision.

## Examples

- Facebook Lookalike Audiences based on top 1% of customer lifetime value
- Platform-specific similar audiences using website conversion data
- Multi-tiered lookalike strategies testing different similarity thresholds
- B2B lookalikes based on ideal customer profiles

## Best Practices

- Use high-value customer segments as seed audiences
- Test different audience expansion levels
- Regularly update source audiences
- Combine with other targeting parameters

## FAQs

### What are lookalike audiences?

Lookalike audiences (called Similar audiences on some platforms) are targeting audiences a platform builds by finding new users who resemble a source group you provide — typically your best customers or converters. The platform analyzes the traits and behaviors of your seed audience and finds others who look similar, letting you prospect for new people most likely to behave like your existing high-value customers.

### How are lookalike audiences built?

You supply a seed (source) audience — a custom audience like your customer list, purchasers, or high-value users — and the platform's algorithm identifies common characteristics, then finds other users who match that profile across its user base. You usually choose a size (how broadly to expand), trading similarity for reach. The quality of the seed strongly determines the quality of the lookalike.

### Why do lookalike audiences work?

Because your existing customers are the best predictor of who else will convert — people who resemble them in behavior and attributes are more likely to want what you offer than a random or broad audience. Lookalikes let you prospect efficiently by extending the patterns of proven customers to new people, often outperforming generic interest or demographic targeting for finding net-new buyers.

### How do I choose the best seed audience?

Use a high-quality, relevant source: your best customers, recent purchasers, or high-value/high-LTV users rather than all visitors or a low-intent list. A seed of genuinely valuable converters teaches the algorithm to find more like them; a noisy or low-intent seed produces a weak lookalike. Larger seeds give the algorithm more signal, but quality (who's in it) matters more than size.

### How big should a lookalike audience be?

It's a similarity-versus-reach trade-off. A smaller percentage (e.g. 1%) is most similar to the seed and usually highest-quality but limited in reach; larger percentages (e.g. 5–10%) expand reach at the cost of similarity. Start tighter for quality, then test expanding for scale. With modern broad-targeting trends, some advertisers rely less on narrow lookalikes and more on the algorithm plus signals, but lookalikes remain a useful prospecting tool.

## Related Terms

### Component Terms

- **[Custom Audiences](/resources/glossary/general/custom-audiences)**: The source audiences used to generate lookalike segments based on existing customer data
- **[Audience Segmentation](/resources/glossary/general/audience-segmentation)**: The process of grouping customers by characteristics that informs lookalike creation

### Child Terms

- **[Audience Targeting](/resources/glossary/general/audience-targeting)**: The broader practice of defining and reaching specific user segments

### Similar Terms

- **[Behavioral Targeting](/resources/glossary/general/behavioral-targeting)**: A related targeting approach that uses behavior patterns to identify valuable audiences