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What Is Mobile A/B Testing?

A/B testing for mobile apps – or A/B testing ads – allows developers to compare how two variables in their app or ads perform or impact user behavior. These variables could include anything from colors, copy, or buttons.

With mobile A/B testing processes set up, developers can approach app and ad optimizations in a data-driven way by using and understanding their findings.
Key Takeaways
  • A/B testing ads can improve a given experience or a goal, such as higher installs or user engagement
  • There is usually a four-step process: creating a theory or hypothesis, testing the theory, analyzing the test results, and repeating
  • Developers should strictly control variables to produce accurate results

Why Should App Developers Use Mobile A/B Testing?

App developers – particularly user acquisition teams – can use mobile A/B testing to test anything in their apps. They might, for example, want to better understand the user’s ad experience.

Teams may want to compare experiences in Android and iOS environments or change in-app purchase button colors to boost visibility and revenue. They might want to tweak copy in ad creatives so that they convert better. Developers might also adjust how they deliver their ads to different audiences to maximize the conversion and install rate.

mockup of two phone screens that are undergoing mobile ab testing

With the variety of ad formats out there, it is also useful to compare the performance of various ad formats with various audiences. How do users behave when they have the option to engage with an offerwall, rewarded videos, or other rewarded ads? With video ads, similarly, developers can test a variety of things to optimize the view-to-install rate.

What Can Developers A/B Test in Their Apps?

Here are some specific creative elements that developers can test:

  • Copy
  • Image choices
  • Call to action (CTA) button and boxes
  • Ad dimensions
  • Content
  • Colors and background
  • Ad distribution: frequency, format, or placement 
  • Buying ads: raising bids, CPI rates

Developers should always focus on a single variable and avoid any assumptions they may have before testing.

How Does A/B Testing for Mobile Apps Work?

If developers don’t conduct their A/B tests properly, this leads to poor results or results that lead to poor optimization decisions.

The mobile A/B testing process typically works by following a four-step process. 

Step 1: Hypothesize

The first step in A/B testing for mobile apps is to identify a straightforward hypothesis and how it can be tested. This hypothesis can be in the form of a question and tailored to fix a problem that developers want to solve.

a hypothesis example in AB testing: if we do X will it result in Y?

Step 2: Test

Developers are then ready to test their A and B variants by exposing their users to them. Testing can be automated to randomly show these A and B versions to those in the sample set. 

There must be a control variant for test samples in order to achieve reliable results. Developers should consider the following:

  • Test with the right sample: Ensure the target demographic is represented in the sample.
  • Test with the right sample size: Developers can more easily draw reasonable conclusions with a larger sample size. If the sample size is too small, they risk making the wrong optimizations for their app. Microchanges generally need larger sample sizes to accurately judge their effectiveness. 
  • Test for the right duration and seasonally: Developers shouldn’t run their test for too long – this risks introducing more variables. Similarly, they shouldn’t cut tests short, even if they’re not receiving the results they want or need. They should also never interrupt tests to incorporate new additional versions and changes.

Step 3: Analyze

At this stage, developers can confirm whether one variant performed better than the other.

By looking at every key metric, from retention, session duration, install rate, and in-app purchase, developers can better understand how optimizations will impact the in-app experience.

bar graph showing how mobile ab testing can help boost KPIs in adtech

Sometimes, tests don’t provide the answer developers were looking for – or even any answer at all. The hypothesis might have been flawed, or it simply might not apply to users in the sample. 

Any developers involved in the mobile A/B testing process should understand that they really understand the results. Is it that ad creative that users really prefer or does it just take less time to load than its variant? It’s sometimes just as valuable to know when something does not make a difference to users.

Finally, developers need to identify whether the optimizations positively benefit higher-quality users or post-install metrics. Even if install rates increase due to ad creative optimizations, these installs might not convert to engaged or paying users.

Step 4: Repeat

If developers have a conclusive positive result from their mobile A/B testing data, they can adapt the hypothesis, implement necessary changes, and repeat the A/B test using a larger sample size to verify the results collected so far.

If results are inconclusive, developers can still adapt and test their hypothesis – and develop it over the course of their future findings.


To stay ahead of competitors in the mobile app economy, app optimizations should always be founded on fresh data.

A/B testing for mobile apps is not a one-off experiment. Continuous optimizations are an important part of an app’s monetization and UA strategy. Developers can eliminate bias, coincidences, and guesswork – along with the risk of wasting time, money, or resources – on app optimizations or ad creatives that don’t convert.  


What Is Mobile A/B Testing?

A/B testing is a testing process that enables developers to compare two versions of something and assess which provides the best results. It helps them better understand user behavior and problems with conversion rates.

How Does A/B Testing for Mobile Apps Work?

A/B testing follows a four-step process: creating a theory or hypothesis, testing the theory, analyzing the test results, and repeating.

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