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Online experiments are a fundamental component of the development ofweb-facing products. Given their large user-bases, even small productimprovements can have a large impact on user engagement or profits on anabsolute scale. As a result, accurately estimating the relative impact of thesechanges is extremely important. I propose an approach based on an objectiveBayesian model to improve the sensitivity of percent change estimation in A/Bexperiments. Leveraging pre-period information, this approach produces morerobust and accurate point estimates and up to 50% tighter credible intervalsthan traditional methods. The R package abpackage provides an implementation ofthe approach.

Percent Change Estimation in Large Scale Online Experiments