Building an always-on ad revenue monitor for AppLovin MAX studios (feedback welcome)

This isn't about the business/law side. Looks like a product announcement and request for feedback, so moving to Your Announcements.

The “is this us or the market?” question is exactly where I would start.

For a useful alert, I would want a short evidence trail rather than only a market/self-made label:

• Did requests, impressions, or fill rate move with revenue?

• Is it limited to one OS, country, format, placement, or ad unit?

• Did floors, source mix, SDK, consent, or traffic change near the same time?

• Do comparable cohorts show the same movement?

The benchmarking part could be valuable, but only when the cohort is tight: country + format + OS + app category + volume band. A broad benchmark may create more noise than clarity.

For a small studio, the most useful alert may be: “Revenue fell 18%, mainly because X changed; here are the next two checks.” That feels more actionable than a generic anomaly notification.

I’m curious how you plan to handle seasonality and small-sample noise in the first version.

@ecpm_insights Thank you, this is the kind of feedback I was looking for.

• On the evidence trail, I had implemented it previously. Every anomaly has an impact correlated with the impressions/revenue of the same day (not "market" or "self"), so you have something like "Revenue down by 22%, mostly due to eCPM decrease by 18% and stable impressions, therefore this is probably price related, not volume driven anomaly" and also two suggestions of what to check based on how it reads.

• On tight cohorts vs. broad benchmarking: I agree that a broad benchmark is likely to introduce too much noise into the system, therefore it's not worth it, so I removed it from the analysis - if your cohort volume falls below the real-studios sample size threshold, you'll no longer see percentiles/market lines, synthetic number with a disclaimer will not be generated either. It should be mentioned that the peer benchmark will not be available right on launch due to this. Tight cohorts (by country, format, OS, category, volume band) will be the way to go once there is enough volume to populate it. Same goes for the changes in floor/SDK/source mix, this is not something which is exposed by the reporting API, so for now it's a suggested manual check rather than one which is automated.

• On seasonality and small-sample noise, I implemented two changes based on what you mentioned: The baseline now prefers using the same week day (Saturday, for example) as a historical comparison once there are more than 3 occurrences of it, falling back to the flat trailing average during the early project life when such data is not available yet. The flag threshold will increase proportionally with the decrease of volume in the day compared to the typical volume of the project up to 2.5x times, so the same percentage change would trigger flagging on an average day and no flagging on a day with the traffic being significantly lower, as it is likely just sampling effect. Filtering to one OS, country, format, placement is possible. This requires getting a larger pull from AppLovin's reporting API, it's on the todo list but not done yet.

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  Thank you again for the feedback!

That sounds like a thoughtful direction. I especially like avoiding synthetic benchmarks until the cohort is real.

Showing the confidence or baseline quality in the alert could be a useful later addition, but the explanation plus next checks already sounds valuable for small studios. Good luck with it.