By analyzing the distribution of estimated ad revenue per capita, you can determine the basis for segmenting user value per interaction. Additionally, by examining the distribution of daily ad viewing frequencies, you can identify the basis for segmenting user ad viewing frequencies, thereby reasonably segmenting users.

To determine the "optimal frequency" of ad playback, you can use retention analysis models to reveal the correlation between changes in ad playback frequency and a decrease in user retention. Based on data performance, the balance point between the two can be found.

You need to combine analysis models such as event analysis, retention analysis, and attribute analysis to explore the changes and correlations between ad estimated revenue, user retention, and playback frequency, in order to find the optimal ad playback frequency settings.

You should also monitor the performance of core metrics using ad positions and ad types as key dimensions. This helps identify ad positions and ad types with greater operational value, thereby more effectively maximizing ad revenue.