You strive to integrate findings from all variations to make critical business decisionsĭuring an experiment, MAB sends most traffic to a variation that performs better than all others and sequential A/B testing, as we mentioned earlier, distributes traffic equally and shows you the winner, and this knowledge can be further integrated into your app store product page and utilized for other business purposes. Multi-armed bandit testing informs you what to do with traffic right now (it allocates traffic among all variations in the course of an experiment), while sequential A/B testing informs you what to do after an experiment: which variation (the winner) should be leveraged.ģ. Sequential A/B tests on SplitMetrics enable you to analyze how users behave on your app store product page, what elements draw their attention, whether they tend to scroll through your screenshots and watch app previews before pressing the download button or leaving the page. You’d like to analyze the performance of all variations after an experiment is finished At the same time, sequential A/B testing experiments are the perfect way to get statistical significance you’re seeking.įor example, if you are at the prelaunch stage and still working on a new app or game, you might want to gather as much information as possible on the performance of your creatives, especially screenshots, to better understand which features are more important for your target audience, and further incorporate your learnings into your app.Ģ. Multi-armed bandit testing, which we will consider later on, is actually not the best choice if you are aiming to get a statistically significant winner. You are seeking to identify variations with statistical significance
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