Which ad optimization strategy can be effective for improving a learning limited ad set?

Expanding audience size can be an effective ad optimization strategy for improving a learning limited ad set because it increases the volume of potential impressions and interactions. When an audience is larger, there are more opportunities for the algorithm to gather data on which users are more likely to engage with the ad and complete valuable actions, such as clicks or conversions.

In situations where an ad set has reached a learning limit, it indicates that the algorithm has insufficient data to optimize performance effectively. By expanding the audience, you provide the algorithm with a broader range of data points, allowing it to better understand user behavior and optimize placements and bids accordingly. This broader data can facilitate quicker learning and enhance the overall effectiveness of the ad campaign.

The other strategies may not provide the same level of benefit. For instance, decreasing bids could limit the reach even further, while limiting ad types narrows options and could potentially reduce engagement. Using only automatic placements can be a good practice, but if the audience is too constrained, it might not yield enough data for effective optimization.