What is a suggested method for fixing learning limited ad sets?

Combining ad sets and campaigns is a strategic approach to addressing learning limited ad sets because it allows the algorithm to pool more data and garner sufficient insights for optimization. When ad sets have low levels of activity, such as small budgets or limited audiences, they may not gather enough data for the delivery system to learn how to effectively optimize for conversions or desired outcomes.

By merging ad sets, you can generate a larger combined audience and budget, leading to higher impressions and engagements. This can facilitate a more effective learning phase, where the algorithm can identify and exploit successful patterns in audience interaction and conversion behavior. The aggregation of data helps to improve campaign performance and reach better outcomes more rapidly compared to maintaining several limited ad sets independently.

On the other hand, simply decreasing the ad budget can further restrict the ad's performance, while minimizing the number of ads running may not necessarily target the right audience effectively. Adjusting ad placement settings might improve performance indirectly but does not directly address the learning phase limitation relevant to ad sets. Thus, merging ad sets is a comprehensive strategy that encourages effective learning and campaign optimization.