Interstitial removal and IAP impact: a study
By Loomit Team
What Happens When You Remove 80% of Your Ads? A Mobile Gaming Case Study
Published by Loomit | Ad Monetization Insights
A Hypothesis Worth Testing
One of the largest mobile gaming studios in Latin America was watching a troubling trend. Their highest-value user cohorts — the players most likely to make in-app purchases — were showing signs of decline. Playtime was dropping. Conversion rates were softening. Something was eroding the experience for their best players.
The founding team had a strong hypothesis: interstitial ads were hurting the experience of their top-tier users. Not as a vague concern, but as a specific, testable belief. And rather than act on gut instinct, they wanted the data.
The question was whether they had the tools to get it.
The Experiment
The studio decided to run a controlled removal of interstitial ads — which represented approximately 80% of their total ad stack — for a precisely defined user cohort. The parameters were deliberately narrow:
Users from a selected group of European countries
Installed within the last 30 days
Representing 10% of total active users
All other ad formats (Rewarded Video, banners) remained active
On the surface, this sounds straightforward. In practice, running a clean A/B test with these exact parameters — while keeping the rest of the app's ad stack intact, measuring the results with statistical confidence, and doing it without a new app release — used to be anything but simple.
In the past, this kind of segmented experiment would have required custom engineering work, multiple ad network integrations, and weeks of coordination. With Loomit, the team launched it in minutes.
The Results
The results were striking across every metric that mattered:
Average daily playtime nearly doubled: from 58 minutes to 108 minutes per user
Paying users / active users ratio increased by +25%
Ads ARPDAU decreased by -66% (expected, given interstitials were removed)
IAP ARPDAU increased by +50%
Total ARPDAU ended up at only -10%, as IAP revenue offset most of the ad loss
The headline number — playtime doubling — was dramatic. But the more important signal was the IAP lift. Removing ads didn't just make users play longer. It made them more likely to spend. The experience improvement translated directly into revenue, compensating for the majority of what was lost by turning off interstitials.
Why This Matters Beyond the Numbers
This experiment illustrates a principle that many mobile publishers understand intuitively but struggle to test rigorously: the relationship between ad load and user lifetime value is not linear.
Aggressive ad monetization can generate strong short-term ARPDAU while quietly eroding the engagement of your most valuable user segments. The users most likely to churn under heavy ad exposure are often the same ones most likely to convert to paying users — given the right experience.
The Latin American studio's results suggest that for certain user cohorts in certain markets, the true economic value of removing interstitials is actually positive: higher playtime, higher conversion, and a total revenue impact that is far smaller than the raw ARPDAU reduction would suggest.
The goal is no longer 'interstitials on vs. interstitials off.' It's finding the right balance between monetization and player experience — and that balance is different for every user segment.
What's Next: Optimization, Not Elimination
With a clear baseline established, the studio's next phase of experimentation is more nuanced than a simple on/off test. The team is now exploring:
Reducing interstitial pacing rather than eliminating interstitials entirely
Prioritizing less intrusive ad experiences (shorter formats, less disruptive placement timing)
Testing whether the playtime uplift can be sustained while recovering towards ARPDAU breakeven
The goal is to find the optimal configuration — not the most conservative one. And that kind of iterative experimentation is only possible with the right infrastructure: the ability to define granular user cohorts, configure precise ad strategies per segment, run clean A/B tests, and read results directly from the platform.
The Bigger Picture
This case study is an example of what Loomit calls 'Admon 2.0': moving beyond global ad stack optimization toward user-level decisioning. The same setup — interstitials for everyone, all the time — is almost never optimal. Different users have different sensitivities, different LTV profiles, and different responses to ad formats.
When experimentation is easy, studios stop guessing. They iterate, learn, and optimize for user experience and revenue at the same time. The studios that build this capability now will be the ones with the data-driven strategies that competitors can't easily replicate.
Want to see Loomit in action? Book a demo at loomit.ai
Related reading: Increase IAPs and subscriptions