What you need to know about A/B testing for apps | Adjust

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.

Implement optimizations

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.