A/B Testing is running two or more versions of a store-listing element against live traffic to measure which converts better — replacing opinion with data.
A/B testing splits real store visitors between a control and one or more variants of an asset — an icon, a screenshot set, a preview video — and measures which drives the higher install rate. It's the rigorous way to do conversion optimization, because store audiences routinely behave in ways no internal opinion predicts. The two platforms expose it differently: Google Play has native Store Listing Experiments, while on iOS the equivalent is Product Page Optimization, which tests up to three variants against your default.
Sound tests change one major thing at a time, run until the result is statistically meaningful rather than stopping at the first promising day, and account for traffic that varies by weekday and season. The discipline matters because conversion gains compound with everything upstream: a winning screenshot lifts installs from every impression you already earn, so a few points of conversion can outweigh a lot of new ranking work.
Example
A team tests a benefit-led first screenshot against a feature-list one; the benefit version wins by 9% on install rate, so they ship it and apply the lesson across the rest of the gallery.