Lucas and Luna unpack how Meta's lookalike audiences actually work — not as magic, but as a statistical model trained on your best customers. They walk through the mechanics: how Meta builds a vector representation of your seed audience, how it scores the broader population, and why the 1 percent lookalike is not always the best choice. They get specific about source audience size (a thousand events is a rough floor), about the trade-off between similarity and reach, and about how to read your results when a lookalike underperforms. There's a concrete example with a DTC skincare brand that shifted from a 1 percent to a 3 percent lookalike and saw cost per acquisition drop. They also touch on how to layer lookalikes with interest and behavior targeting, and how to refresh your seed as your customer base evolves. The tone is practical, with a clear takeaway: lookalikes are a tool, not a shortcut — the quality of your seed determines the quality of your model. A listener should walk away understanding how to think about lookalikes as a marketer, not just a button to click.
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