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June 05, 2023
12 min read

How Apphud helped AEZAKMI Group double app revenue and increase the conversion rate to subscription by 61% with A/B experiments

In this case study, we will describe how our client, AEZAKMI Group, increased the conversion rate and maximized their subscription apps’ revenue by launching A/B experiments in Apphud.

How Apphud helped AEZAKMI Group double app revenue and increase the conversion rate to subscription by 61% with A/B experiments

The modern mobile application market is a highly competitive environment. It especially affects the utility apps area, where a large number of companies compete for app user attention by offering a fairly similar set of features. In such an environment, it is critical not only to get traffic to your app but also to handle it properly. If your app gets installs but doesn't have enough subscription conversions, and a good number of rebills, it will be extremely difficult to survive in such competition.

About the Company

AEZAKMI Group has strong expertise in the creation and promotion of Subscription Utility Apps in the App Store. They are open to partnership options from app publishing, app promotion, and consulting to selling high-quality applications of their development with organic traffic. Partnership proposals can be sent to partnership@aezakmi.group.

A/B Testing Importance

Many factors influence a user's decision to subscribe to an app: price, the presence or absence of a free trial, the design of the paywall, the product information on the paywall, and the location and appearance of the closing button on the paywall. It is the combination of these factors that influences the conversion rate.

As a result, to improve the app performance, you need to test all these combinations for each niche separately. Based on the AEZAKMI Group experience, some niches perform much better with a trial subscription, and some work much better with long subscription periods (3 months, a year), but there are also those where a regular monthly subscription would be the best option. This is just the type of subscription, but there are still a huge number of other variables. 

So, you need validation. The best option for that is simultaneous A/B testing. After all, even if you run the a/b test consistently, external factors can significantly change the final result. The application may drop in terms of traffic quality, or it may simply start a seasonal decline in a niche in the middle of the test, and the results will be blurred. For example, the new paywall will show better conversion rates, but this can be completely counteracted by lower traffic solvency in different periods. Therefore, qualitative results can only be obtained through A/B testing, where all options are tested simultaneously under the same conditions.

Experiments Case Study results with ApphudExperiments Case Study results with Apphud

Apphud Solution

For icons and screenshots, App Store Connect provides the ability to run its product page optimization tests, but at the same time, it is not possible to test the internal app content. This is where Apphud's Experiments tool comes in. 

Through a simple integration and a convenient guide, you can configure options for everything in the app, from the appearance of paywalls to app onboarding and subscription types - all within the A/B testing framework. AEZAKMI Group used Experiments in many cases where it helped them to understand niches and improve app performance.

Subscription Types A/B Testing: Case Study Description


The AEZAKMI Group entered the PDF scanner niche with their new app and didn't know what type of subscription would work best. They needed to test what would bring the most profit: a regular monthly subscription, a weekly subscription, or a weekly subscription with a trial. Of course, many more similar tests could be run, such as a month with a trial, a year, 3 months, etc., but here you have to understand that trying a large number of options at the same time is resource-consuming, so they decided to start with one described test and then run other variables to compare with the winner.

In theory, it's possible to run this type of test without using third-party tools. However, it's inconvenient for many reasons: manual traffic allocation, difficulties in results analysis, and test completion. In addition, this traffic separation is not encouraged by Apple, as it makes it more complicated for them to test and track activity that violates their policies.


All these problems have been solved with Apphud Experiments. The simple integration of the SDK allows you to quickly create options and specify the percentage of traffic for each of them, as well as select the desired subscription and paywall. Another advantage of Experiments is that it displays detailed statistics and allows you to easily apply the winning option.

Paywalls options for the testPaywalls options for the test


Test results of different subscription types for the PDF Scanner app:

Experiment result, Apphud toolExperiment result, Apphud tool

It is easy to see that the trial subscription conversion rate (2.29%) is almost twice as high as the monthly subscription conversion rate (1.21%). It is also higher than the weekly subscription conversion rate (1.63%), but this is not the most important thing. 

The point is that Experiments allow you to track ARPU - a very important metric. ARPU is the indicator that accumulates both the conversion rate and the rebills. After all, one subscription may convert very well, but users will immediately cancel it, and the other may convert 2 times worse, but at the same time, they may have a 4 times higher subscription renewal rate, which means it will be more profitable in the long run.

Most importantly, ARPU is calculated for each user group. In other words, user rebills are tracked separately for each variation. You can see that the ARPU of a monthly subscription is 2 times lower than the ARPU of a trial, and here you might think that this is due to the conversion rate, which is twice higher. However, the price of a weekly subscription is 2 times lower than that of a monthly subscription, and the user is monetized better because he is subscribed to a greater number of subscription periods. This can also be seen in ARPPU: trial users bring in more revenue and stay subscribed longer.

This test allowed the AEZAKMI Group to understand that the weekly subscription with a trial is 2 times better than the original monthly subscription, and also allowed them to double the app revenue.

Onboarding A/B Testing: Case Study Description

The purpose of this test was to select the best-performing onboarding for the LED Light Controller app.

Onboarding options for the testOnboarding options for the test

For this test, the AEZAKMI Group had 6 onboarding options. As Apphud Experiments allows the users to run up to 5 testing variations simultaneously, they split the test into 2 phases.


First phase:

Experiment result for the 1st test, Apphud toolExperiment result for the 1st test, Apphud tool

Second phase:

Experiment result for the 2nd test, Apphud toolExperiment result for the 2nd test, Apphud tool
This case shows the importance of testing options at the same time. Option 1 showed a conversion rate of 1.12% in the first test, and 1.29% in the second test, even though nothing has changed in this variant or other parts of the app.

As a result, the AEZAKMI Group increased conversion to subscription from 0.8% to 1.29% (+61%) and increased ARPU from $0.12 to $0.14 (+16%). For an app with a large number of app users, it has a very significant impact. Especially, when you consider that you brought this insight from just one test.


A/B tests are a critical tool for subscription apps, as they provide invaluable insights into user experience and behavior. As these cases show, A/B tests help subscription apps increase conversion rates and finally the app revenue. In this way, A/B tests are essential in ensuring the success of subscription apps. 

Apphud Experiments helped AEZAKMI Group test its hypotheses and achieve its revenue goals. Apphud provides the clients with a statistically valid result that is used to determine whether the outcome of the experiment is random. For our experiments, we define a statistical significance threshold of 5% (or P-value = 0.05). Apphud delivers data you can trust.

Want to multiply your app revenue too? Book a demo with our team and we’ll answer all your questions.

Head of Marketing at Apphud
7+ years in product marketing. Nataly is responsible for marketing strategy development and execution. Committed adherent of the agile methodology.

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