Lookalike Audience

Lookalike audiences use machine learning to analyze your best customers, identify the attributes and behaviors that distinguish them, and find new users who match that profile. You provide a seed audience, such as your top purchasers or most engaged users, and the platform's algorithms find statistically similar people who have not yet interacted with your brand.

For growth teams, lookalike audiences are one of the most reliable scaling mechanisms for paid acquisition. They automate the process of identifying high-potential prospects beyond your known audience. The quality of the seed audience is the primary determinant of performance, so growth engineers should test different seed definitions: highest-LTV customers, recent converters, most engaged trial users. AI models powering lookalike systems vary by platform, with some using thousands of signals to find similarities. As third-party data access declines, lookalike quality depends increasingly on the richness of your first-party seed data. Teams that maintain clean, well-segmented customer data will build better lookalikes than those relying on thin behavioral signals.