Look-a-Like Audiences

Look-a-Like Audiences: Finding New Customers Similar to Your Best Ones

Look-a-like audiences (also known as lookalike audiences or similar audiences) are targeting segments created by advertising platforms using machine learning to find new users who share characteristics with a source audience you define. By analyzing patterns in your existing customer data, platforms like Meta, Google and LinkedIn can identify prospects with similar demographics, interests and behaviors who are likely to be interested in your offering.

How Look-a-Like Audiences Work

You provide the platform with a source audience, such as a list of your best customers, website converters or engaged email subscribers. The platform's machine learning algorithms analyze hundreds of data points about these users, including demographics, online behavior, device usage and social connections. It then searches its user base for people who match these patterns but are not yet in your source audience.

The quality of your look-a-like audience depends entirely on the quality and size of your source audience. A source audience of your top 1,000 customers by lifetime value will produce a much more effective look-a-like than a source audience of all website visitors. Be strategic about which customers you use as your seed.

Creating Effective Source Audiences

The best source audiences share specific, valuable characteristics:

• High-value customers: Customers with the highest lifetime value, repeat purchase rate or average order value. This tells the algorithm to find people likely to become valuable customers.
• Recent converters: Customers who purchased in the last 30-90 days. Recency ensures the source audience reflects current market conditions.
• Engaged users: Users who regularly interact with your content, emails or app. High engagement signals genuine interest.
• Specific product purchasers: If you want to grow a specific product line, use buyers of that product as your source.

Aim for a source audience of at least 1,000 users for Meta and 1,000 for Google. Larger source audiences generally produce better results because the algorithm has more data to identify meaningful patterns.

Audience Size and Similarity

Most platforms let you control the size of your look-a-like audience, typically expressed as a percentage of the platform's total user base in your target country. A 1 percent look-a-like is the most similar to your source audience but the smallest. A 10 percent look-a-like is larger but less similar.

Start with smaller, more similar audiences (1-3 percent) and expand as you find what works. Test different sizes against each other to find the optimal balance between reach and relevance. In many cases, you can layer multiple look-a-like sizes to create a tiered bidding strategy where you bid higher for the most similar audiences.

Platform-Specific Considerations

Meta's lookalike audiences are among the most powerful due to the depth of user data Meta has access to. Use them for both prospecting and to supplement retargeting efforts. Google's similar audiences (now evolving into optimized targeting and audience expansion) work across Search, Display and YouTube. LinkedIn's lookalike audiences are particularly effective for B2B because the source data includes professional attributes.

Each platform creates look-a-likes differently, so a source audience may produce different quality look-a-likes across platforms. Test on each platform independently and compare results as part of your channel experiments.

Optimizing Look-a-Like Performance

Regularly refresh your source audiences to reflect current customer characteristics. Customers acquired two years ago may differ significantly from those acquired recently. Test multiple source audiences simultaneously, such as high-value customers versus recent converters, to see which produces the best look-a-like performance.

Combine look-a-like targeting with interest or behavioral targeting to create more focused segments. Exclude existing customers and recent website visitors to ensure your look-a-like campaigns focus purely on new prospect acquisition.

Frequently Asked Questions

What is the minimum source audience size?

Meta requires at least 100 people but recommends 1,000-5,000 for best results. Google recommends at least 1,000 users. LinkedIn requires 300 matched members. Larger source audiences almost always produce better look-a-likes because the algorithm has more data to work with.

Should we use customer email lists or pixel-based audiences?

Both work, but email lists of verified customers typically produce higher-quality look-a-likes because they represent confirmed buyers rather than just website visitors. If using pixel-based audiences, filter for converters or high-engagement users rather than all visitors. Ensure proper measurement to track which source type performs best.

How often should we refresh look-a-like audiences?

Refresh your source audiences monthly for best results. Customer characteristics shift over time, and platforms need fresh data to maintain look-a-like quality. If you notice declining performance from look-a-like campaigns, refreshing the source audience is often the first fix to try.