Welcome to our Lookalike Audience guide! Lookalike Audiences help you reach new people who share similar characteristics with your existing customers. By using a high-quality source audience, you can expand your reach while maintaining relevance and performance.
How Lookalike Audiences Work
A Lookalike Audience is created by using a Custom Audience as the source. The system analyzes the attributes of people in your source audience and finds new users who are most similar. This is especially useful for scaling campaigns without losing targeting accuracy.
Getting Started
Follow these steps to create a Lookalike Audience:
- Go to Tools in your dashboard.
- Select Audience Management.
- Click Create Audience.
- Choose Lookalike Audience.
- In the Lookalike Audience tab:
- The Source field will appear once you’ve created a Custom Audience.
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Recommendation: Include as much data as possible for best results.
📌Note: If your source audience consists of converted users and you believe they are unlikely to repurchase in the short term, you can exclude the source when creating the Lookalike Audience.
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Set Audience Size:
- A narrow size limits reach.
- A broad size may lead to lower performance.
- We recommend keeping it balanced.
Creating a Custom Audience (Check Out Here for Additional Guide!)
To create a Lookalike Audience, you’ll need to upload a Custom Audience:
- File-based: Upload a data file containing IDFA/GAID or email details. You can download a sample CSV file from the upload page for formatting reference.
- App rule based: Build an audience using in-app actions.
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Engagement rule based: Build an audience using user interactions, such as impression, click and conversion.
Best Practices
- Use a source audience with enough size and high-quality data.
- Test different audience sizes to see which works best for your goals.
- When possible, run an A/B test by comparing one ad set using a Lookalike Audience against another without it to measure performance differences.
- Refresh your source audience periodically to keep targeting accurate.
📌 Note: The more complete and accurate your source data, the better your lookalike performance will be.