Mobile App Terminology simply explained

What Is A/B Testing?

A/B testing goes by a couple of different names. It’s also called split testing or bucket testing, for example. However, it all boils down to the same thing: You’re comparing the statistical performance of two different variables and observing their impact on user behavior.

No matter how much research you do, not every mobile app campaign will yield positive results. This is why A/B testing is important. A/B testing is a data-driven way to calculate the best optimization strategies for your app.
Key Takeaways
  • Use A/B testing data to improve a given experience or a single goal, such as higher installs or user engagement
  • Optimize your in-app ads, your ad creatives, and your user experience
  • Make sure you control variables strictly to produce accurate results

Split Test Examples: What Should You A/B Test?

In reality, you can test just about anything you can think of in your app: from changing in-app purchase button colors to boost performance to increasing CPIs to drive higher volumes. New features? No problem. Once you’ve sussed out a great A/B testing workflow, you can also observe how new app features perform with your users.

Let’s explore a few split test examples you might consider to improve the performance of your app.

A/B Testing Ad Creatives

You might choose to A/B test ads to see which ad creative your users respond to better. By A/B testing ad creatives, you can see which creative drives more installs from a certain demographic. 

But you also need to identify whether these are your higher-quality users. Remember, you can get a lot of installs, but if your ads are misleading, these might not convert to engaged or paying users.

Here are some specific creative elements that you can test:

  • Headlines
  • Image choices
  • Call to action (CTA) button and boxes
  • Ad dimensions
  • Content
  • Colors and background
infographic showing the different variables tested in A/B testing ad creatives

Testing Ad Distribution

You could A/B test ad distribution in mobile games to see how changes impact user behavior. Think about assessing the frequency of ads being shown or the placement of ads – for example, where users have the option to engage with an offerwall, rewarded videos, or other rewarded ad formats.

It is absolutely critical for apps to distribute their ads in the most optimal way. Running too many ads might cause users to run a mile from your app and hurt retention rates. But not enough rewarded ads and/or requiring too many in-app purchases (IAP) might make users feel alienated and drive them away from your app.

Buying Ads 

You may want to assess the impact of raising bids on ads or cost per install (CPI) rates on conversion rates, return on investment (ROI), and app retention rates. 

How Does A/B Testing Work?

The A/B testing process works by following a four-step process:

  1. Hypothesize
  2. Test
  3. Analyze
  4. Repeat

Step 1: Hypothesize

The first step in A/B testing is to identify what you want to test and a hypothesis. You can do this by stating it in the form of a question – and tailoring the question to fix a problem you want to solve.

If we do X, will it result in Y?

For example, if we increase CPI bids, will we acquire a higher volume of quality users?

The more straightforward your hypothesis, the easier it will be to assess the results. 

In A/B testing, you want to keep everything focused on a single variable. Be weary of any preconceptions you might have; be open to surprising outcomes. You can’t really predict how users will behave – and you don’t have to. Instead, it’s best practice to ask yourself at this stage: What will you do next if your hypothesis is proven correct or incorrect? 

Step 2: Test

You’re now ready to test your A and B variables by exposing your audience samples to them. Testing can be automated to randomly show your A and B versions to those in your sample set. 

Besides ensuring you have a control variable, where possible, to produce results you can trust, observe the following guidelines during your testing phase. These will give you test significance and statistical confidence.

  • Test with the right sample: Ensure your target demographic is represented in your sample.
  • Test with the right sample size: You are better equipped to draw reasonable conclusions with a larger sample size. If your sample size is too small, you risk making the wrong optimizations for your app. A heads-up – microchanges generally need larger sample sizes to accurately judge their effectiveness. 
  • Test for the right duration: Don’t run your test for too long – this risks introducing more variables. Similarly, don’t cut your tests short, even if you’re not receiving the results you want or need. You need to stick with your A/B test long enough to know you can be confident in the results. You should also never interrupt your test to incorporate new additional versions and changes.

Step 3: Analyze

It’s time for the unveiling! You can now confirm whether one version performed better than the other. By looking at every key metric – even the metrics you assumed would remain unaffected – you can deep-dive into how a new feature you want to roll out or optimize will impact your user experience. 

You’ll often find that your tests don’t provide the answer you were looking or hoping for – or even any answer at all. Your theory may have been flawed, or it simply may not apply to users in the sample. 

For example, a game developer might test whether a freemium version with in-app purchase options performs better than a monthly paid subscription model. Although the majority of customers may indicate that they generally preferred the freemium version to a paid subscription, the results of this test could be deemed inconclusive. 

That doesn’t mean, however, that the tests were a failure. One of the key reasons you test different variables is to find out what works and what doesn’t work. In this case, the developer decided it didn’t matter, so they spent time tweaking other variables instead. Sometimes, it’s just as valuable to know when something does not make a difference to users.

Step 4: Repeat

If you have a conclusive positive result from your A/B testing data, you can adapt your hypothesis, implement your changes, and repeat your A/B test using a larger sample size to verify the results you’ve collected so far.

Don’t be disheartened if your results were inconclusive. You can still adapt and test your hypothesis – and subsequently develop it over the course of your future findings. Stay ahead of your competitors by ensuring your app optimizations are always founded on fresh data.

Why Is A/B Testing Important?

You’re dealing with a competitive industry of app marketers. When you engage in successful A/B testing to optimize your app, you’re more likely to successfully unlock engaged users. A no-brainer for you and your team. With concrete data on user behavior, you can implement little changes to make big progress on reaching your KPIs. With A/B testing, you also eliminate bias, coincidences, and guesswork – and the risk of wasting time, money, or resources on features in your app that don’t convert.

Disclaimer: A/B testing is not a one-off experiment. Just as you expect to keep users returning to your app, you should expect to keep optimizing user experience as part of your monetization and UA strategy. Doing this will help you refine your user flow, your creatives, in-app engagements, and any other app marketing component.

5 Best Practices: Dos and Don’ts of A/B Testing

Running effective and efficient A/B tests can provide the results you need to improve performance. However, if you don’t conduct the tests properly, you can get poor results – or worse: results that lead to you making poor decisions.

  • Always have a control group if you’re testing existing features. This is a version where nothing changes. For example, your first group might see a new red button, while the control group sees the one you currently have. This allows you to collect more insightful results about the feature you’d like to change.
  • Run one test at a time, where possible. You don’t want to multitask here; trust us. Running more than one test at the same time will not save you time. As soon as you start changing multiple elements, your A/B testing data tends to become invalid. 
  • Test your test. Make sure your randomization, allocation of users, and test groups are not faulty before launching your A/B tests. Run a small sample size to check that everything is working optimally.
  • Check that you really understand your results. Is it that ad creative that users really prefer or does it just take less time to load than the other version? If you want to be sure of your test validity, you can separately test other components to be sure you really know what is impacting your user’s decisions. 
  • Simultaneously test your variables. You can test the same variables in different seasons and come to different conclusions. A/B testing your ad creatives one week compared with another week can produce wildly different test results based on inconsistent traffic patterns. Test both creatives in the same period, but randomize your sample.


A/B testing is quite literally another way of saying something has been “tried and tested.” A/B testing data is still valuable – even when your hypothesis is disproven, or when it seems inconclusive early on in the testing period. The bottom line is that you can see what works and what doesn’t work for your mobile app. What’s more, all the data you compile about your users will give you the kind of confidence and conviction that you need to make powerful and informed decisions about your app strategy.

Whether you’re looking to advertise your app and grow a loyal user base or monetize your app, the first step in your strategy should be to understand what makes your user tick. Understanding that will help you suss out how to acquire them, how to monetize them, and how to ultimately retain them.   

FAQs About A/B Testing

What Is A/B Testing?

A/B testing is a testing process to compare two versions of something and assess which provides the results you want. It helps you understand user behavior and identify potential problems with the user experience in your app.

What Is A/B Split Testing?

It’s simply another name for A/B testing. Similar groups are shown two different variables to see if one produces the desired result.

How Does A/B Testing Work?

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

Why Does A/B Testing Matter to Mobile Developers?

Valid A/B testing data gives mobile developers statistical confidence to change features of their app in a way that is very likely to deliver positive KPIs. They can learn and observe how users are using their app and make necessary optimizations to boost user retention and engagement. This is why A/B testing is important for mobile developers.

What Are A/B Testing Best Practices?

You should always have a control group if you’re testing existing features in your app – you might find that this feature is fine just the way it is. Other best practices include running one test at a time, testing whether your test works, making sure you really understand your results so that you don’t interpret them wrong, and testing your variables simultaneously to avoid inconsistencies.

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