Lookalike Audience

A Lookalike Audience is a targeting option in Meta Ads (and other platforms) that uses machine learning to find users who statistically resemble a “seed” audience — typically your existing customers, high-value purchasers, or email subscribers — but who haven’t yet interacted with your brand.

How Lookalike Audiences Work

You provide Meta with a seed audience: a list of customer emails, a Custom Audience of purchasers, or a value-based customer list with associated spend data. Meta analyzes the shared characteristics of people in that seed — demographics, interests, behaviors, engagement patterns — and builds a model to find similar profiles among all Meta users in a given country.

You then specify a “percentage” for the lookalike — typically 1% to 10% of the target country’s population:

• 1% LAL — most similar to the seed, smallest audience, typically best conversion rates
• 5–10% LAL — broader, more reach, less precise match

Value-Based Lookalikes

The most effective lookalike audiences are seeded with high-LTV customers rather than all purchasers. If you seed with your top 20% of customers by total spend, Meta optimizes toward finding people who resemble your best customers — not just anyone who ever bought.

This distinction matters: a lookalike of all purchasers includes many one-time buyers with low LTV. A lookalike of high-value customers skews toward people likely to become repeat buyers.

Lookalikes in 2026: Are They Still Effective?

The effectiveness of Lookalike Audiences has diminished since iOS 14 reduced the signal quality available to Meta. Many advertisers find that broad targeting (no audience restrictions) combined with Meta’s Advantage+ placement and campaign optimization now performs comparably to — or better than — hand-crafted Lookalike Audiences for cold prospecting.

The current best practice is to test broad vs. Lookalike audiences within Advantage+ Shopping Campaigns and let Meta’s optimization decide where to deliver. Many D2C brands have found broad outperforming 1% LALs, particularly for catalog and DPA formats.

Lookalikes Still Win For

• Email list-based prospecting (seeding with your CRM, which Meta can’t infer from pixel data)
• Niche products where the customer profile is very distinct
• International expansion into new markets where Meta has less purchase signal