Lookalike Audience
A targeting option that finds new users who share characteristics with your existing customers or high-value audiences.
Definition
Lookalike audiences (called "similar audiences" on some platforms) use machine learning to find new users who share characteristics and behaviors with a source audience you define—typically your customers, converters, or email list. The platform analyzes patterns in your seed audience and finds others who match those patterns.
You can typically adjust lookalike size: smaller audiences (1-2%) are more similar to your source but have limited reach, while larger audiences (5-10%) have broader reach but less similarity. The optimal balance depends on your campaign goals and market size.
Why It Matters
Lookalike audiences are among the most effective prospecting tools in digital advertising because they apply your first-party data to find genuinely similar potential customers. Rather than relying solely on demographic or interest targeting, lookalikes find behavioral patterns that predict likelihood to convert.
They're particularly valuable for scaling successful campaigns beyond your existing audience while maintaining targeting quality.
Examples in Practice
An e-commerce brand creates a lookalike of their highest-LTV customers, finding new customers who spend 40% more than average.
A B2B company builds a lookalike from closed-won opportunities, improving lead quality from paid social significantly.
A subscription service uses a lookalike of long-retained subscribers to reduce churn rates among new customer acquisition.