Everything you need to know about A/B testing for mobile apps
With a more competitive app market than ever before, learning how to optimize your app—and your marketing campaigns—is crucial. Even a small change in your app’s user experience can have a significant impact on conversion rates, so it’s important to test what works. E-commerce company WallMonkeys proved this with a 550% increase in conversion rate simply by using A/B testing.
A/B testing is an essential tool for user acquisition marketers to get clarity on how your app and its marketing campaigns can be optimized. In this guide, we’ll show you everything you need to know about mobile A/B testing, including best practices for optimal results.
What is mobile A/B testing?
A/B testing for mobile apps is the testing of multiple versions of a defined variable, such as creative or copy. This form of testing works by segmenting an audience into two or more groups, exposing each segment to a different version of the variable, and analyzing how the variable affects user behavior.
For example, let’s say you want to drive installs for your mobile game. As part of your UA strategy you strategically target 18-24 year old males based in the U.S. with video ads. Instead of throwing money at ads that haven’t been proven to work, it’s smarter to expose your ads to a small group or that audience to start – and even smarter to A/B test your video ad. The A/B test shows that video B, which had larger text than video A, had a 20% higher conversion rate. With these results, it makes sense to expose a larger audience to the video with larger text.
What are the benefits of A/B testing?
A/B testing for mobile apps has historically been perceived as difficult due to technical challenges and the need to test on both Android and iOS environments. However, it is critical to app marketing. The results of A/B tests help marketers identify the recipe for the best possible user experience, which in turn improves app engagement and delivers the best possible campaign results.
A/B testing allows app developers and marketers to:
- Optimize in-app engagements
- Learn what works for different audience groups
- Observe the impact of a new feature
- Gain a better understanding of user behavior
- Improve key performance indicators (KPIs)
Ultimately, the learnings gained from A/B tests eliminate guesswork, allowing app marketers to rely on data-driven conclusions. This is something you can’t afford to avoid, and the earlier you can begin A/B testing, the sooner you can ensure your app (and your ads) are in the best possible iteration.
Let’s look at a real-world example. Polish e-commerce provider, Grene, ran a test on its mini-cart page to explore which page elements needed to be more or less prominent. The results led to:
- An increase in in-cart page visits
- An overall increase in e-commerce conversion rate from 1.83% to 1.96%
- 2x increase in total purchased quantity
Different types of A/B testing for mobile apps
There are two types of A/B testing that are relevant to app marketers and developers. These both work with the same principle (comparing one variable among a split audience) but have different functions.
In-app A/B testing
This is how developers test how changes to an app’s UX and UI impact metrics such as session time, engagements, retention rate, stickiness, and lifetime value (LTV). There will also be specific metrics that developers will measure the impact of depending on the specific function of the app.
A/B testing for marketing campaigns
For app marketers, A/B testing is a way of maximizing the effectiveness of marketing campaigns. Testing can uncover which ad creative works best for UA campaigns, which advertising channel is most effective, or which messaging makes churned users most likely return. A/B testing campaigns can help to optimize conversion rates, drive installs, and successfully retarget users.
How to do A/B testing right
A/B testing is a cyclical process that you can use to continually optimize both your app and campaigns. Before you get started, ask yourself:
- What do you want to test?
- Who is your target audience?
- What do you anticipate will be the outcome of the test?
- How will you proceed if your hypothesis is proven/disproven?
With this in mind, here’s the ultimate A/B testing checklist:
Develop a hypothesis and define your variable
Developing a hypothesis before implementing any tests can help give companies the most actionable insights that can help them achieve their goals. If you are struggling to define what you’d like to test, start by outlining a problem you’d like to solve. This will give you a good starting point whereby you can define what should be monitored to solve that issue. Review any data you currently have available to help define one variable to test. If you test multiple variables simultaneously, it will be much harder to identify what has influenced your campaign’s performance. Perform one test at a time, each with a different variable.
For example, your hypothesis could be that having fewer products on show upon opening your e-commerce app will increase session time. This hypothesis, which should be informed by prior research, can then be used to define your variable (the number of products on your home page).
Segment your audience
With your hypothesis and variable in place, you’re ready to test variations on your audience. Use an A/B testing platform* to segment your audience groups into test groups which will be exposed to different versions of the variable. Remember that you need a statistically significant sample size. If your audience is too small, you risk misidentifying optimizations for your app that will not have the desired influence on larger audience groups.
Analyze the results
You can now determine which variant delivers the best results. Remember to look at every important metric that may have been influenced, because this allows you to learn much more from a single test. For example, even though you’re looking to increase conversions, there may have been an unexpected impact on engagement or session time.
If you have found a positive result, you can confidently expose a larger audience to the successful variant. If your test was inconclusive, this is still useful data that should be used when updating your hypothesis.
Adapt your hypothesis, and repeat
A/B testing enables you to continually develop your hypothesis over time. You should always be testing to learn new ways to boost conversions because there will always be ways to improve. Continue to build your hypothesis on fresh data, and implement new tests to stay ahead of the competition.
*What A/B testing platforms can I use?
Because A/B testing for mobile apps is so important to the development of any app, there are many tools available to app marketers. Adjust’s Audience Builder is a segmentation tool that is proven to drive growth by A/B testing and retargeting. Immediately define detailed audience segments with ease, saving you and your team considerable time and energy. With audience groups defined for A/B testing, send your advertising partners a dynamic URL containing all of the information needed to reach those users.
5 best practices for A/B testing
Define what you want to test
This may seem like a simple step, but knowing why you are implementing these tests ensures you aren’t wasting time and money on a test that won’t deliver actionable insights. Do not start testing before you have a clear hypothesis and know how you will proceed based on different outcomes.
Be open to surprises in your analysis
User behavior will always be complex, and that means your A/B tests will sometimes reveal surprising results. It’s important to be open-minded and follow up on these learnings. Otherwise, you risk leaving money on the table by failing to learn from your own data.
Don’t cut your tests short – even if you aren’t seeing results
A/B tests are valuable even when your hypothesis turns out to be false, or when the result appears to be inconclusive very early into the testing period. It’s essential to stick with your tests long enough that you have a high confidence level in the result.
Don’t interrupt your tests with additional changes
It’s crucial not to make any mid-test changes. This diminishes the confidence you can have in your findings because you will no longer know which variables have produced the outcome. Remember, you are trying to find a cause and effect based on conclusive results.
Regardless of vertical, your results will be subject to the time period in which you’ve tested. You can, therefore, test the same variables in different seasons and find different results. For example, it could be that a particular creative that didn’t perform well in summer would see impressive results in winter.
Learn from your own tests, not just case studies
In his article on A/B testing case studies, Yaniv Navot, Vice President of Marketing at omnichannel personalization platform Designer Yield, claims that “Generalizing any A/B testing result based on just one single case would be considered a false assumption. By doing so, you would ignore your specific vertical space, target audience, and brand attributes.” He adds, “Some ideas may work for your site and audience, but most will not replicate that easily.” With so many A/B tests available for marketers to read and learn from, remember that their findings won’t necessarily work for your audience. Instead, the development and testing of your own hypothesis should indicate what gets results.
A/B testing is clearly an essential tool that helps marketers continually improve their campaigns. If you’d like to explore more ways to improve your campaigns, check out our ultimate guide to scaling your app to one million users.
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