Lookalike Audiences: What They Are & How to Use Them in Ads

If you could copy-paste your best customers… you’d do it, right? You can’t clone people, but you can clone behavior. Data modeling is the closest thing marketing has to cloning. And right now, marketers need it more than ever.

Between signal loss, privacy updates, and rising ad costs, the margin for wasted impressions is gone. That’s why lookalike audiences have become a marketer’s secret weapon. They take what’s already working, learn from it, and scale it across every platform. Instead of finding random new users, you’re multiplying the ones who already buy.

Learn how audience modeling fits into broader cannabis audience targeting strategies that connect data-driven tactics like lookalike modeling with full-funnel campaign frameworks.

What Is a Lookalike Audience?

Think of a lookalike audience as your best customers’ digital twins—new people who act, browse, and buy in similar ways. These audiences are built from a seed list of your existing customers, site visitors, or loyalty members. From there, an ad platform’s algorithm analyzes patterns like demographics, engagement, and purchase behavior to identify new users who closely match those traits.

This predictive modeling approach helps you grow your reach efficiently. Instead of spending on broad targeting, you reach new prospects statistically most likely to convert.

If you’re building seed lists from first-party data, see our guide to the cannabis eCommerce funnel for ways to strengthen privacy-safe audience sources.

Why Advertisers Use Lookalike Audiences

Lookalike audiences take the guesswork out of audience expansion. Instead of targeting everyone who might be interested, you target people who are algorithmically proven to behave like your highest-value customers.

Here’s why performance marketers rely on them:

• Scalability: Expand your reach beyond retargeting while maintaining relevance.
  
• Higher conversion rates: Ads reach people predisposed to your offer or message.
  
• Efficient ad spend: Focus your budget on statistically higher-value prospects.
  
• Privacy-friendly targeting: Model from first-party data while staying compliant with modern privacy frameworks.
  

According to Meta’s internal case studies, advertisers using lookalike audiences have reported up to a 40% lower cost per acquisition (CPA) compared to broad targeting.

How to Create a Lookalike Audience

Learn how to set up and optimize your own lookalike audience with step-by-step guidance below. 

Step 1: Build a high-quality seed audience.

Start with your most valuable data source: loyal customers, high-value transactions, or engaged site visitors. The smaller and cleaner the list, the better the model.

Step 2: Upload your seed list.

In your ad manager, import a customer file (CSV) or sync from your CRM or pixel data.

Step 3: Choose audience size.

Most platforms offer percentage-based similarity ranges such as 1% for the closest match and up to 10% for broader reach. Smaller audiences are more precise, while larger ones provide greater scale.

Step 4: Test, track, and refine.

Run A/B tests comparing different lookalike sizes or sources. Refine your targeting every few months as behaviors and privacy regulations evolve. For managed campaign support or hands-on implementation, explore our cannabis programmatic advertising services.

How Lookalike Modeling Differs Across Platforms

Each ad platform has its own approach to audience modeling. Understanding the differences helps you plan smarter budgets and creative strategies.

Pro Tip: For cannabis advertisers, Meta’s lookalike modeling often delivers stronger results when paired with engagement-based seed data such as video views or landing page engagement rather than product-specific conversions that may be restricted by policy.

Best Practices for Using Lookalike Audiences

If your first lookalike campaign struggles, it’s usually not the algorithm; it’s the input. Follow these tactics for better results:

• Feed it quality, not quantity. A small list of verified buyers is more effective than a massive list of newsletter subscribers.
• Layer your targeting. Combine lookalikes with geographic or demographic filters to stay compliant with state laws and audience restrictions.
• Refresh regularly. Consumer behavior changes frequently. Update your seed every 90 days for more accurate modeling.
• Test multiple lookalikes. Run 1%, 3%, and 5% versions at the same time to balance reach and relevance.
• Pair with first-party and contextual data. As third-party cookies fade, your owned data becomes the backbone of scalable modeling.

Common Challenges and Privacy Limitations

The biggest challenge with lookalike audiences today isn’t setup; it’s signal strength.

With privacy laws tightening and platforms phasing out third-party cookies, the data available to model from is shrinking. Meta lost significant cross-app visibility after Apple’s iOS 14.5 update. Google retired Similar Audiences in 2024 in favor of Optimized Targeting, which relies on aggregated first-party data.

The solution is to invest in first-party data capture through loyalty programs, on-site interactions, and CRM integrations, then use those to power compliant audience modeling. When combined with contextual and geographic targeting, lookalikes can still outperform most broad targeting strategies.

Learn more in our article on cannabis identity resolution.

Turning Data Into Demand

Cloning customer behavior isn’t guesswork. It’s precision. Lookalike audiences turn your best buyers into models for repeatable success. When you stop chasing strangers and start cloning your best customers, every campaign becomes a smarter version of the last. Ready to scale smarter? 

Contact MediaJel to build data-driven campaigns that convert, or learn how our programmatic experts can help you master audience modeling for your next ad strategy.