A player loses a level, watches an ad to get another chance, tries again and closes the game. Your app can record every step.
Did the ad help the player keep going? Did it load too slowly? Did the player come back tomorrow? Did they buy anything later?
The answers may sit in several different reports. As your app grows, checking all those reports takes more time than a small team has. AI agents can help connect the data, find problems and test changes that could improve revenue and growth.
What data does a game collect
An event is a record of something that happened in the app. It could be a completed level, an ad shown or a purchase.
In an AWS case study about Supercell’s earlier infrastructure, the company could process up to 45 billion in-game events a day. That figure describes the setup covered in the case study, rather than its current traffic.
Consider this example: one million daily players, three sessions each and 65 tracked events per session. That produces 195 million events a day, or 5.85 billion over 30 days.
Ad activity adds another set of records. At ten ad opportunities per session, the same example has 30 million app ad requests a day. If each request reaches eight eligible bidders, it creates 240 million bid requests. An app request and a request sent to a bidder are different counts.
These are example assumptions, rather than industry averages. The real volume depends on player activity, what the app tracks and how ads are requested.
Let’s group data in 4 layers:
| Data layer | Examples | Question it can help answer |
|---|---|---|
| Player | Device, country, install source, return visits | Which players arrive, stay and become valuable? |
| App activity | Sessions, levels, currency spent, crashes | What do players do and where do they get stuck? |
| Purchases | Offer, price, first purchase, repeat purchases | What do players pay for and when? |
| Advertising | Requests, bids, ads returned, ads shown, revenue | Where does the ad process earn or lose money? |
A failed level tells you one thing. Connecting it with an ad, a purchase and a later return gives you a fuller picture of that player’s experience.
Connect the player’s experience with the money it generates. Use group-level reports when individual records cannot be linked.
Why the data becomes hard to follow
A total or average can hide a problem. Your app’s overall revenue might look fine while one ad placement stops working on Android.
To find the issue, the team may need to check:
- Which countries and app versions are affected.
- Whether the problem involves rewarded ads, banners or another format.
- Whether new players behave differently from returning players.
- Whether the change began after a release, a campaign or an ad setting update.
Each extra detail adds more groups to compare. An illustrative setup with 100 countries, two platforms, six placements, eight ad networks, five device tiers, four app versions and ten player groups. Multiply those dimensions and you get 1.92 million possible combinations.
Example dimensions – many combinations will have little or no data, so they cannot all support separate decisions.
An analyst can study a chosen problem closely. Keeping watch over every changing combination, all day and night, becomes impractical for a small team.
An agent also needs enough evidence. If a group has very few users or purchases, it should wait or combine similar groups. Our guide to how much traffic AI monetization needs explains this in more detail.
Find where ad revenue leaks
In a bidding setup, an ad request passes through several stages. The app asks for an ad, eligible buyers bid, a winning ad is returned, the app shows it and its impression value is recorded.
A simplified view of a bidding setup. Check the stage where performance changes before deciding what to adjust.
The stage matters:
- Few buyers bid: check demand, buyer eligibility and the signals available for that traffic.
- Few requests return an ad: check errors, available demand and whether pricing rules reject too many bids.
- Ads return but rarely appear: check loading, placement timing and whether players leave before the ad is shown.
AdMob’s reporting glossary separates matched requests from impressions. Its show rate measures the share of returned ads that are displayed. That helps an agent distinguish a problem finding an ad from a problem showing one.
Real developers are already asking for help
After adding analytics to a puzzle game, one developer wrote in r/gamedev: “All this is going over my head.” They had reports about activity, user groups and long-term value, but struggled to make sense of them.
A commenter suggested starting with basic questions: do players finish the tutorial, where do they leave, and do they return the next day? These questions give a new studio a useful place to start.
In a separate r/admob discussion, a publisher reported falling revenue while daily user numbers stayed stable. They traced the drop to ad performance in Brazil, which accounted for around 40–45% of their audience. The post describes their own experience; the initial update did not establish why the drop happened.
An agent could help find the affected countries and placements, check recent changes and gather the evidence needed to choose a response.
How an AI agent helps
An AI agent is software that can work through a task using connected tools. For this job, it needs access to the right reports, a clear goal and rules about what it can change.
A useful agent can:
- Check for missing, duplicated or delayed data.
- Find which user groups or ad placements changed.
- Compare possible causes and explain the evidence.
- Run an approved test through a supported integration.
- Track the result and undo the change if agreed limits are crossed.
The database still counts the events. The analytics system still provides reports. The agent uses those tools to follow the investigation and decide which question to check next.
Four kinds of work that can support those decisions:
| Agent job | Simple explanation | Example |
|---|---|---|
| Predict | Use past patterns to estimate what might happen next. | Flag a player group likely to stop returning. |
| Test the cause | Compare with an unchanged group to check whether the action helped. | Check whether a pricing change added revenue. |
| Check confidence | Show a range and how much evidence supports the estimate. | Wait when a result depends on a handful of bids. |
| Keep learning | Try a few allowed options and send more test traffic to better performers. | Compare minimum ad prices while protecting revenue and player experience. |
A forecast can guide a test. It still needs checking against what actually happened. If ad revenue rises during a seasonal increase in advertiser spending, a before-and-after report alone cannot show how much the agent contributed.
For example, AdMob can report revenue for each ad shown, including its currency and whether the value is estimated or precise. Linking those records with player activity can help show which groups produce ad revenue.
Our mobile-game monetization stack guide explains how ad platforms, analytics, marketing measurement and purchase systems fit together.
An example of finding a revenue problem
Imagine a game where revenue from rewarded ads falls after an update. This example is fictional.
- Check the reports. Are the numbers complete, or is some data arriving late?
- Find the affected users. Split the results by country, app version and ad placement.
- Check the cause. Suppose the new Android version has more ad loading failures, even though players still request rewards.
- Send the evidence to the right team. The developers can inspect the loading problem. If an ad setting caused the issue, an approved test or rollback may be appropriate.
- Measure the result. Check revenue, ad completion and player experience after the fix.
The financial cost helps the team decide how urgently to act. If the game normally earns $200 an hour, a 30% shortfall over ten hours means an estimated $600 loss. These are assumed numbers. They show how to size a problem, without promising that an agent would recover the full amount.
An example workflow. People set the goal, the allowed changes and the limits for stopping a test.
What real automation results tell us
Firebase’s Halfbrick case study reports a 16% increase in average revenue per daily active user after using Remote Config personalization. Engagement, retention and ratings were unaffected. The system used player behavior to adjust when and how often ads appeared.
Halfbrick used machine-learning personalization. An AI agent could help with the work around a system like this, such as checking the data, managing tests and alerting the team when results worsen.
CrazyLabs’ case study shows the workload at a larger scale. The team tested up to 30 ad and purchase configurations per title, across as many as 15 games. Firebase helped automate testing, and the team analyzed results in BigQuery.
Halfbrick and CrazyLabs show what personalization and automated testing can do. The agent workflows in this article are proposed examples of how to build on that work.
How this helps the app business grow
More ad revenue today can come with fewer players tomorrow. The team needs to measure the wider effect of a change.
Our Follow the Money story about MONOPOLY GO! looks at how marketing, social play, events and spending work together. The same thinking helps a smaller studio decide whether a change improves the business.
Purchases have their own data trail: install, early sessions, level progress, first purchase and later purchases. An agent could compare those journeys to suggest where to test a starter offer, or whether reducing ads for paying players improves total revenue. An early purchase prediction remains an estimate until enough time has passed to check it.
Consider this fictional test. eCPM means the revenue earned per 1,000 ads shown.
| Test group | Ads shown | eCPM | Ad revenue |
|---|---|---|---|
| Existing setup | 80,000 | $8 | $640 |
| New setup | 60,000 | $9 | $540 |
The new setup earns more per 1,000 ads, yet total ad revenue is $100 lower. Purchases and player returns could change the result further.
When judging a change, track:
- Total revenue: money from ads and purchases over the test period.
- Player returns: whether people keep using the app after the change.
- App experience: crashes, slow ads and interrupted play.
- Marketing payback: whether the players you acquire earn back their acquisition cost over time.
This helps the team decide which changes to keep and where it can afford to spend on growth. Our guide to increasing ad revenue while protecting retention covers the tradeoffs.
Start with one problem
Choose one question your team regularly struggles to answer. For example, why do fewer players finish rewarded ads after an app update?
Before the agent acts, agree on:
- The reports it can read and the changes it can make.
- The outcome that would count as an improvement.
- The limits that would stop the test or trigger a rollback.
- Who reviews uncertain results or problems outside its scope.
Keep a control group of users on the existing setup so you can compare outcomes. Our guide to measuring the extra revenue AI AdOps creates explains how to test whether a change caused the improvement.
For a practical guide to access and limits, read what an AI AdOps agent should be allowed to change.
Frequently asked questions
Can a small app use AI agents?
Yes. A small app can use an agent to check reports, spot problems and suggest next steps. Testing changes for very small user groups may require more time or larger groups to get a reliable result.
Do AI agents replace analytics tools?
Agents use analytics tools and databases to do their work. They help connect reports, follow up on findings and take actions through supported integrations.
Can an agent fix every revenue problem?
Some problems need a developer, a product change or a discussion with an ad partner. An agent can gather evidence and send the issue to the right person. Its ability to make changes depends on its tools and permissions.
What should I measure besides ad revenue?
Track purchase revenue, player returns, ad completion and app performance. If you buy users through advertising, also compare the cost of acquiring a group with the revenue it earns over time.
How do I know the agent is helping?
Compare an approved change with a control group, check that your data is complete and allow enough time for the result you care about. Count the platform’s cost when deciding whether the gain is worth it.
How UndrAds helps game studios and app developers
UndrAds offers AI-powered ad operations for game studios and app developers. It works with existing ad setups, including AdMob and AppLovin MAX, to help teams manage monetization without checking every report by hand.
Its agents help monitor ad performance, spot revenue drops, test ad settings and adjust pricing across supported setups. A human account manager supports the team along the way.
An iOS campaign example from 18–27 September 2026, the company reports that its agents identified unusually expensive user groups outside working hours and shared acquisition recommendations. It is a company-reported example of using data while a campaign is running.
If your team spends hours watching dashboards and chasing ad revenue problems, talk to UndrAds about your current setup. Start with a clear revenue problem and a test that shows what the platform adds to your business.
