In this episode of Facebook Ads with Fexingo, Lucas and Luna explore how Meta's ad system infers your audience's interests from their behavior — not just their stated preferences. They break down the difference between explicit and implicit signals, using a concrete example of a fitness brand that saw a 30 percent drop in cost per acquisition after shifting from interest-based targeting to behavior-based lookalikes. The conversation covers how Meta's machine learning weighs actions like video views, link clicks, and time spent, and why that matters for your campaign structure. If you've ever wondered why your detailed targeting interests don't always perform, or how to let Meta's algorithm find the right people, this episode gives you a practical framework for testing and optimizing. Tune in to understand how to speak Meta's language and get more from your ad budget.
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