Lookalike audience modeling is a machine-learning method that finds new prospects who closely resemble your best existing customers. Platforms analyze a source audience’s attributes and behaviors, then score and rank broader populations to identify similar individuals at varying similarity thresholds (for example, 1% for precision, 10% for reach). Marketers use it to scale audience targeting, improve relevance, and raise conversion efficiency while controlling waste. Effective lookalike modeling depends on high-quality seed data, clear outcomes (acquisition, lead gen, or awareness), compliance with privacy rules, and continuous testing to fine-tune size, signals, and performance.