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Let’s first be clear about what A/B testing is and how is it beneficial to us?
A/B testing is a test conducted on mobile apps by dividing an audience into two or probably more groups to observe how a variable affects user behavior. The best possible user experience is identified by this test and it brings out the best possible results. An example for better understanding can be assuming that Sony had tested different calls to action on their banner ads, like “Make it Personal” against “Customize your VAIO” which later leads to the conclusion that the second one increased CTRs by 6% and increased shopping rate by 21.3%
A/B testing for mobile apps is a wide practice performed in every industry because of its numerous benefits and high confidence level marketers and has in their analysis. A/B testing is an important and necessary step in the development of any mobile app. The benefits can be listed as:
The easiest and most effective way of creating content that can convert more visitors into buyers.
Helps to achieve higher values for the products or services.
Simple to analyze real, factual results.
Gaining a better understanding of user behavior.
Useful in low data rate testing.
The overall advantage of A/B testing is that it eliminates guesswork and helps the app marketers to rely on data drove conclusions. The sooner one starts with A/B testing the better chance of getting the app in the best possible state.
There are at present two types of A/B testing, relevant to app developers. Both types work on the same principle of using a comparable audience group to find a positive variable. But apparently, they have a different function. Namely, these are In-app A/B testing and A/B testing for marketing campaigns. The first is concerned with the changes in the app’s UX and UI impact metrics such as session time, engagements, retention rate, stickiness, and LTV and the latter is simply a way of optimizing conversion rates, drive installs and successfully retarget users for app marketers.
So after the basic necessary information about A/B testing, we can now focus on the process followed to complete it. A/B testing is a cyclic process that can be used to continually optimize an app and its campaigns. So, here follows the process for A/B testing:
Collect Data and Identify the Goals: One’s data will always help to determine where to start the optimization. It helps to begin with high traffic areas of the site or app as that will allow gathering data fast. Search for pages with low conversion rates or high drop-off rates and that will be the area, to begin with. The conversion rates are the basis on which one can determine whether or not the variation is more successful than the original version. Goals can be anything from clicking a button or link to product purchase and e-mail sign-ups.
Develop a Hypothesis: After researching and analyzing the data available, a hypothesis needs to be generated. Without it, one would not be able to define which variable to test. The hypothesis must be developed based on why you think it will be better than the current version. Once the list of ideas is ready, prioritize them in terms of expected impact and difficulty of implementation. In case one finds it difficult to decide what they would like to test, they can begin by just merely outlining a problem that can be solved. Thus, it will provide a good starting point and further one can define what could be monitored to solve the issue.
Create Variations: With the completion of the generation of the hypothesis, the next step would be creation variations in the apps. Using any A/B testing software like Optimizely, VWO, Google Experiments, etc. makes the desired changes to the element of your application. The change can be anything as required be it changing the color of a button or logo, be it swapping the order of elements, hiding navigation elements, or something entirely custom. Make sure the experiment passes QA (Quality Assurance) to be sure that it works as expected.
Segment your Audience: With the hypothesis and variations in place, the experiment is ready to be tested on audience samples. At this point, visitors of the app will participate in the experiment and will be randomly assigned to either the control or the variation of the experience. Their experience and interaction will be measured and then it will be compared to the former to determine how each performs.
Analysis: Once the experiment is over, the results must be analyzed. The A/B testing software used, will soon bring up the results by presenting the comparison data from the experiment and show the difference between the two versions of the app and whether or not there is a statistically significant difference. Remember to look at every metric that has been influenced because this will help you to learn much more from a single test. For example, even if one intends at increasing conversions, there may have been an unexpected impact on engagement or session time.
Implement the changes: If the result is positive then it can surely be exposed to a larger audience to the successful changes. And check whether the learnings from the experiment can be applied to other sections of the app and continue iterating on the experiment to improve results. If the experiment generates a negative result use its learning experience and generate a new hypothesis.
A/B testing enables us to continually develop a hypothesis over time. One should always be testing to learn new ways to boost conversions because there will always be ways to improve. It is an essential tool that helps marketers improve their campaigns. No matter whether the obtained results are positive or negative continue the building of the hypothesis on fresh and new data and implement new tests to stay ahead of the competition. To get the best A/B testing for your mobile, you can always go for Lytechx Digital Pvt. Ltd, the best web developer company in Jaipur.