Why One A/B Test a Month Is Not ASO

The average ASO team runs one store listing experiment per month.

At that rate, testing just one icon might take three months. Testing screenshots in five locales could stretch to a year. If you want to test your full store listing—icons, screenshots, feature graphics, and short descriptions—across your portfolio, you could be looking at years, not weeks.

Meanwhile, many competitors are able to test and learn every week.

This slow pace makes it harder to learn quickly. Google Play actually lets you run several experiments at once, across different parts of your store listing. If you only run one at a time, you’re missing out on faster progress and growth.

The real challenge isn’t just about what to test first, but how quickly you can test. If you don’t address testing speed, your ASO strategy will struggle to keep up with the market.

The one-test-a-month reality

Most teams stick to this pace for good reasons. Resources are limited, designers are busy, and setting up experiments in Google Play Console takes manual work. Reviewing results and making decisions also takes time and confidence.

As a result, the backlog keeps growing. Dozens of ideas can sit in a spreadsheet while the team runs just one top-priority test each month, reviews the results, and repeats. At 12 tests a year, it could take over three years to get through the list.

But the real cost is missing out on improvements you could have made sooner. Every month an idea goes untested, your store listing might keep underperforming. For example, if your icon is converting 2% below its potential, that could mean thousands of installs lost each month. (Store listing experiments, 2026)

Multiply that across a portfolio of ten apps, and slow testing can quietly become a major hidden cost for your growth.

What testing velocity actually means for CVR

There is a clear link between how many experiments you run and how much your conversion rate improves. More tests don’t guarantee better results, but they do help you learn faster, and they generate data. That data informs the next hypothesis. The next hypothesis generates a better experiment. The cycle compounds.

If you run one test a month, you get 12 learning cycles a year. If you run 50 experiments at once, you can complete hundreds. The gap in what you learn grows much faster than just the number of tests.

Google Play lets you run multiple tests at the same time, across different parts of your store listing. You can test an icon, a set of screenshots, and a short description all at once, with no overlap. Most teams only use one slot, but the platform can handle much more.

Publishers running 50 or more experiments at once are just using the platform as it was meant to be used: at the backlog problem: why prioritization alone isn’t enough to fix.

When teams see a growing backlog, the first instinct is often to double down on prioritization. That might mean using new frameworks or stricter scoring models, but prioritization alone doesn’t solve a throughput problem. If you can only run one experiment a month, even the best ranking system still limits you to 12 tests a year.tests per year.

The real solution isn’t just better prioritization. It’s increasing your testing throughput.

Think of it like a factory that can only build one car a month. Picking which car to build first doesn’t solve the delivery problem. Increasing production capacity does.

ASO testing works the same way. The real challenge isn’t which experiment to run next, but how many you can run at once.

Book a demo to see how PressPlay by Phiture can help you automate experimentation.

References

(2026). Store listing experiments. Google Play Console. https://play.google.com/console/about/store-listing-experiments