Lookalike Audience Targeting: How to Find More of Your Best Customers
You've run a campaign, found users who convert, and now you want more of them. Lookalike audience targeting is how you do that at scale. Instead of guessing at who might be interested in your offer, you use behavioral data from your best existing users as a blueprint to find new ones who share the same patterns. In this post, we'll break down how lookalike targeting works, why it outperforms standard broad targeting, and how to put it to use in your RTB campaigns.
What Is a Lookalike Audience?
A lookalike audience is a group of users identified by an ad platform or data provider because they share behavioral, demographic, or contextual characteristics with a defined "seed" group — your existing customers, converters, or high-value visitors.
The idea is straightforward: if a specific type of user consistently buys your product or clicks through to your offer, other users with similar browsing behavior, device usage patterns, content interests, and geography are statistically more likely to do the same. The platform finds those users for you automatically.
In programmatic advertising and RTB, lookalike modeling works by:
- Analyzing the attributes of your seed audience (site visitors, converters, email list, pixel-tracked users)
- Building a statistical profile of what those users have in common
- Matching that profile against a large pool of available impressions in the ad exchange
- Prioritizing bids on users who score high against the model
The result is a targeting layer that's smarter than demographic targeting alone, without requiring you to manually define every parameter.
Why Lookalike Targeting Outperforms Cold Audiences
Broad targeting — running ads to a wide audience without behavioral signals — is a valid approach when you're launching something new and have no data to work from. But it's expensive to optimize from scratch. You're paying for impressions across a wide spectrum of users, many of whom will never convert.
Lookalike targeting compresses that learning curve. Because the model is based on real converters, you start closer to the user segments that perform. This typically means:
- Lower cost per acquisition (CPA) — you reach users pre-qualified by behavioral similarity
- Faster ramp-up — you're not spending budget to discover the audience from zero
- Easier scaling — expand the model slightly to reach more users while maintaining quality
- Better campaign efficiency — budget concentrates on higher-probability impressions
It also pairs well with other targeting layers. You can combine lookalike audiences with geographic targeting, browser and device targeting, or dayparting to further sharpen who sees your ads and when. (For more on layering targeting options, see our posts on audience segmentation and behavioral targeting.)
Building an Effective Seed Audience
The quality of your lookalike model is only as good as your seed data. A few principles that make the seed more effective:
Use converters, not just visitors
A seed audience built from everyone who landed on your site will be too broad. Use users who took a meaningful action — purchases, form completions, sign-ups, or other defined conversion events. The more specific your seed, the tighter the lookalike model.
Make the seed large enough to model from
If your seed is only a handful of users, the statistical signal is weak and the model won't be reliable. As a general rule, a few hundred to a few thousand converters gives the platform enough signal to build a useful profile. If you're just starting out and don't have that yet, focus on gathering conversion data first before leaning on lookalike targeting.
Refresh your seed regularly
User behavior shifts over time. Audiences that converted six months ago may not look like the users converting today — especially if your offer, creative, or product has changed. Update your seed audience periodically to keep the model current.
Segment by value when possible
If some converters are significantly more valuable than others — higher order values, repeat purchasers, longer-term customers — consider building separate lookalike audiences for each tier. Targeting users who look like your highest-value converters will generally outperform a mixed seed.
Lookalike Targeting in RTB Campaigns
In a real-time bidding environment, lookalike audiences work by attaching a scoring or segment layer to the bid decision. When an impression becomes available, the platform checks whether the available user matches your lookalike profile, and adjusts the bid accordingly.
This means you can:
- Set a higher bid multiplier for impressions that score strongly against your lookalike model
- Exclude users already in your retargeting pool to avoid wasting budget on existing customers (retargeting and lookalike are complementary, not redundant — see our retargeting guide)
- Run A/B tests comparing lookalike audiences to standard behavioral or demographic targeting, to quantify the performance lift
When setting up lookalike targeting in an RTB campaign, pay attention to the similarity threshold. Platforms typically allow you to control how close the match needs to be. A tighter match reaches fewer users but maintains stronger similarity to your seed. A looser match expands reach but introduces more variance. Start tight, prove performance, then widen gradually as you scale.
How to Measure Performance
Track your lookalike audience campaigns against these key metrics:
- CPA (cost per acquisition): The clearest indicator of whether the lookalike model is working — compare against your historical baseline from broad or behavioral targeting.
- Conversion rate: Are users in the lookalike segment converting at a higher rate than general traffic?
- CTR vs. conversion rate: A high click-through rate with a low conversion rate suggests the audience is interested but the landing page or offer may need work. A strong conversion rate with lower CTR suggests the creative isn't compelling enough to generate the click.
- Audience overlap: Make sure your lookalike segments aren't heavily overlapping with your retargeting pools, which can cause budget inefficiency.
Pair this analysis with token tracking to attribute performance at the individual impression level. Our post on using token tracking to optimize your Squren campaigns walks through how to set that up.
Conclusion
Lookalike audience targeting is one of the most reliable ways to scale an ad campaign once you have real conversion data to work from. Instead of spending budget to discover who your audience is, you let your existing converters define it — and then find more users who match. The result is better efficiency, lower acquisition costs, and a faster path to scaled performance.
Ready to put smarter targeting to work for your campaigns? Sign up as an advertiser on Squren.com to access advanced targeting tools including behavioral and lookalike audience options — and reach your best customers at scale across our RTB network. Questions? Our 24/7 support team is here to help you get started.
