What Is A/B Testing for Mobile Apps?
No matter how much research you do, not every marketing campaign will bring you positive results. But with mobile A/B testing, you’ve got a data-driven way to calculate the best app optimization strategies for your mobile app.
- 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 with mobile A/B testing
- Make sure you control variables strictly to produce accurate results
What Should You A/B Test?
When it comes to mobile A/B testing, you can test just about anything you can think of in your app.
New features? No problem. From changing in-app purchase button colors to boost both visibility and revenue to increasing CPIs to drive higher volumes. Once you’ve sussed out a great A/B testing workflow, you can also observe how new app features perform with your users.
Why Is Split-Testing Creatives Important?
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:
- Image choices
- Call to action (CTA) button and boxes
- Ad dimensions
- Colors and background
Mobile A/B Testing: What Else Can You Test?
You could A/B test ad distribution in mobile games to see how changes impact user behavior. Think about assessing the frequency of your ads being shown or the placement of your 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 cause them to churn.
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?
If you don’t conduct the tests properly, you can get poor results, or worse – results that lead to you making poor decisions.
The A/B testing process works by following a four-step process.
Step 1: Hypothesize
The first step in A/B testing is to identify what you want to test and a hypothesis. State this in the form of a question – and tailor your question to fix a problem you want to solve.
The more straightforward your hypothesis, the easier it will be to assess the results.
In mobile A/B testing, you want to focus on a single variable. Be wary of any assumptions you might have; be open to surprising outcomes. You can’t really predict how users will behave – and you don’t have to. You will have the data to show for it.
Step 2: Test
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 variant for test samples for reliable results, make sure you prioritize test significance and statistical confidence during testing:
- Test with the right sample: Ensure your target demographic is represented in your sample.
- Test with the right sample size: You can more easily 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. 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.
- Run one test at a time, where possible: 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.
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, 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 for – or even any answer at all. Your theory may have been flawed, or it simply may not apply to users in the sample. Besides that, 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 its variant? If you want to be sure of your test validity, you can separately test download times to be sure you really know what is impacting your user’s decisions.
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 it might seem as though most consumers generally preferred the freemium version to a paid subscription, the results of this test could be 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 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. 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.
With concrete data on user behavior, you can implement little changes to make big progress on reaching your KPIs.
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.
You’re dealing with a competitive industry of app marketers. When you carry out successful A/B testing to optimize your app, you’re more likely to successfully unlock engaged users. And finding out what makes your users tick means you can devise effective strategies to advertise your app or monetize your app. A no-brainer for you and your team. Eliminate bias, coincidences, and guesswork – and the risk of wasting time, money, or resources on features in your app that don’t convert.
Mobile 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.
It’s simply another name for A/B testing. Similar groups are shown two different variables to see if one produces the desired result.
A/B testing follows a four-step process: creating a theory or hypothesis, testing the theory, analyzing the test results, and repeating the process.
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.
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.