The most common Meta Ads mistake isn't bad creative or wrong bidding strategy - it's targeting the wrong audience. You can have a great ad that converts well for the right person and performs terribly when shown to the wrong one. Getting your audience right is the highest-leverage thing you can do before touching anything else in your account.
Start with your warmest audiences first. Custom audiences built from your website visitors, customer email list, or video viewers are the highest-converting segments available to you - because these people already know who you are. If you're not running retargeting campaigns to these audiences, you're leaving the easiest conversions on the table.
Lookalike audiences are the next tier. Meta builds these by finding people who share characteristics with your existing customers or website visitors. A 1% lookalike of your customer list is typically the strongest starting point - it's the smallest and most similar audience. As you scale, you can expand to 2-3%, but expect conversion rates to drop as the audience broadens.
Interest-based targeting is where most businesses start - and where most budgets get wasted. The problem is that interest categories on Meta are broad and imprecise. Someone who liked a cooking page five years ago is still categorized as interested in cooking. Layer multiple interests together, combine them with behavioral signals, and always add demographic filters that match your actual customer profile.
Audience size matters more than most advertisers realize. An audience that's too small (under 50,000) gives Meta's algorithm too little room to optimize and leads to ad fatigue quickly. An audience that's too large (over 10 million) dilutes your targeting to the point where it's barely better than run-of-network. For most businesses, 500,000 to 3 million is a workable range for a cold audience campaign.
Test one audience variable at a time. If you change the audience, the creative, and the offer simultaneously, you won't know what drove the difference in results. Build a testing discipline where each experiment answers one question - then apply what you learn systematically across your campaigns.