A lookalike audience is a targeting tool offered by paid social platforms - primarily Meta and TikTok - that uses machine learning to find new users who share behavioral and demographic characteristics with a defined source audience. You provide the platform with a seed list (your best customers, your email subscribers, your highest-LTV purchasers), and the algorithm identifies patterns in that group's platform behavior to find millions of similar users who have never interacted with your brand but statistically resemble people who have converted.
For Shopify brands, lookalike audiences are one of the foundational prospecting tools in paid social acquisition. The quality of the lookalike is directly determined by the quality and specificity of the source audience. A lookalike built from all website visitors (a broad, mixed-intent group) will be significantly less precise than one built from customers who have purchased three or more times (a high-intent, high-value group). The most effective seed audiences for e-commerce are typically: top-percentile customers by LTV, customers acquired in the last 90-180 days (recent signals tend to be more accurate), and email subscribers who have opened and clicked (indicating engagement beyond a passive opt-in).
The post-iOS14 privacy changes significantly impacted lookalike audience performance on Meta by reducing the signal available from pixel-based tracking. Brands that shifted toward first-party data sources for their seed lists - uploading customer email lists directly through Customer Match rather than relying solely on pixel-matched audiences - saw more resilient lookalike performance through this transition. This is one of the most concrete reasons why email list growth and zero-party data collection are treated as growth priorities rather than just retention tactics.
On Meta, lookalike audiences are typically built at 1-3% similarity for the tightest match (most similar to the seed, smallest audience) scaling to 5-10% for broader reach at the cost of precision. TikTok's equivalent - Value-Based Lookalikes - can incorporate purchase value signals to specifically weight high-spending users in the output audience. Both platforms are increasingly automating audience selection through broad targeting and Advantage+ approaches, but understanding how lookalikes work remains essential for brands that want to retain control over their acquisition targeting strategy.
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