Meta Business Help Centre

Understanding auction overlap

When you create ad sets with overlapping audiences, it's possible that multiple ads from your Page could enter the same ad auction. We call this auction overlap. Understanding auction overlap is important for diagnosing performance.

How auction overlap affects performance

When two or more ads from your Page enter the same ad auction, we choose the ad with the highest total value to compete in the auction. As a result, the other ads from your Page are not considered in this auction. This ensures that your ads will not bid against one another.

Since auction overlap prevents ads from entering auctions, auction overlap can prevent an ad set from spending its full budget or achieving enough results to exit the learning phase. When your ad is in the learning phase, the delivery system is exploring the best way to deliver your ad set. During this phase, performance is less stable and cost per action (CPA) is usually worse.

Too much auction overlap across your Page typically results in less predictable performance – especially when scaling your budget.

How to diagnose and reduce auction overlap

There are multiple ways to see if ad set performance is affected by auction overlap:

  • Use the Auction overlap module in the inspect tool: The inspect tool lets you track an active ad set's auction overlap over time. For example, an ad set with 20% auction overlap has been excluded from 20% of eligible auctions due to auction overlap.
  • Use delivery recommendations: In some cases, you may see a delivery recommendation in Account overview if we believe that reducing auction overlap could improve your performance. We'll show you what to do.
  • Create an automated rule to automatically detect and manage auction overlap.

You can reduce auction overlap by:

  • Combining similar ad sets. Combining ad sets (and their budgets) will help you get the results you need faster, which means you'll see stable results sooner.
  • Turning off overlapping ad sets. We typically recommend turning off the ad sets that are learning limited or have the fewest results. After turning off an ad set, move its budget to the active ad set to maintain a similar or greater number of optimisation events.
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