How to avoid wasting ad spend with lookalike audiences

This title was summarized by AI from the post below.

I love seeing businesses leverage paid social media advertising to reach their target audience, but I still see many marketers making this critical mistake: Ignoring the importance of lookalike audience refinement. While platforms like Facebook and LinkedIn are good at creating lookalike audiences based on your existing customer data, multiply this issue across a whole ad campaign and you are guaranteed to run into issues with audience overlap and ad fatigue. You are not getting the true value because you are wasting ad spend on users who are either too similar to your existing customers or not relevant enough, diluting your campaign's overall performance. Here’s how to fix it: - Use audience layering to refine your lookalike audiences by combining them with other relevant data segments, such as engagement or intent signals, to achieve higher precision. - Implement a regular audience refresh cycle to ensure your lookalike audiences stay up-to-date and aligned with your latest customer data for better performance. - Switch from relying solely on one-platform lookalike audiences to using cross-platform lookalike modeling to reach a more diverse and relevant audience. Has anyone else seen this costing businesses real money? What would you add to the solution list? #PaidSocialAdvertising #DigitalMarketing #MarketingTips #SocialMediaStrategy #AdOptimization #MarketingSuccess

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