Multi-variate testing

Using our dynamic multi-variate testing system, you’ll get a better converting App Store page faster and more efficiently, enabling you to scale both organic and paid growth.

app store conversion increasing over time

Forget the one-offs

No more manually setting up one-off A/B tests one by one. Feed the machine all your creative assets at once, and our algorithm will continuously optimize for the highest converting combination.

Smarter, Faster ASO

Traditional A/B Testing
One Time Testing Setup
Automated Multi-Variate Testing
App Lifecycle and Granular Creative Reporting
Test Cost Planning Tool

What we do

With the Upptic App Store optimization platform you upload multiple variations of any number of elements at once. Once you activate the test, the system will automatically generate relevant experiments and iterate through each variation as you send traffic.

Our system prioritizes the most high-impact elements first and uses an advanced statistical model to quickly identify winners to automatically kill down losing assets.

This saves both time and money. Your team doesn’t need to monitor the performance, and we will automatically move on to the next most impactful test never wasting a testing dollar.

What they do

We won’t name any names, but currently all of the ASO tools in the market only allow you to do simple and inefficient one-off A/B tests.

To do an Icon test, you set up two variants, send traffic, monitor it, and at some point make a human decision based on the confidence interval of the test results.

Then you repeat for your screenshot, feature image, copy, etc. It's highly inefficient both in human-hours and marketing budget.

We automate this entire process saving you time and money.

A more efficient way to optimize your app store

ASO Testing Expenditure Over Time
"Using Upptic ASO platform we were able to increase Roller Coaster Tycoon's store page conversion by 21.7%. We were pleased to see the test results increase our real world conversions once we adopted Upptic's machine learning analysis."
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