If you’ve ever had any doubts and fears about the effectiveness of changes that are to be made in your mobile app, then AB tests are just what the doctor ordered. Besides pitching in conversion rate increase, they allow for further optimization of mobile apps to boost revenue. What are the steps of an A/B testing campaign and when are they no god for your project?
What is A/B testing and how does it work?
Also known as bucket tests and split or split-run testing, A and B testing is a marketing experiment, in which users are offered two variants to choose from. Its key objective lies in identifying what changes are more likely to increase interest in a product and resonate better with mobile users. When running an A/B test, the current version of an app is normally taken as a null hypothesis*. One or two variables are interchangeably offered to a mobile app user. These A and B variables are often almost identical, besides a slight variance which is likely to affect users’ behavior. These changes are normally simultaneously offered to users. During AB tests, key metrics are also measured in parallel. When it comes to mobile app development, UX (images, colors, and layouts, for instance), app content, in-app messaging and push notifications are A/B tested most frequently. Basic steps of each and every A/B testing campaign:
o Optimizely is an experimentation platform for mobile apps, and not only; o Apptimize carries out A/B testing and instant updates of native iOS and Android apps, and o Leanplum offers multi-channel campaigns from messaging to in-app experience to test and drive customer engagement.
R-Style Lab recommends avoiding A/B testing, as follows:
No matter what is the size of your project, A/B testing is a great way to gather data on the target audience and integrate necessary changes to better resonate with users and boost higher engagement. Split testing should not surely underpin all your online marketing initials, and must be carried out elaborately and wisely in order to ensure the most valuable results!. *Null hypothesis means a commonly accepted fact that is intended to be nullified by implementing alternative hypotheses [1]. **Statistical significance level means the likelihood that the difference between a given variation and the null hypothesis is not because of a random chance [2].
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