Percent Change Estimation in Large Scale Online Experiments
Methodology
2019-08-23 v2
Abstract
Online experiments are a fundamental component of the development of web-facing products. Given their large user-bases, even small product improvements can have a large impact on user engagement or profits on an absolute scale. As a result, accurately estimating the relative impact of these changes is extremely important. I propose an approach based on an objective Bayesian model to improve the sensitivity of percent change estimation in A/B experiments. Leveraging pre-period information, this approach produces more robust and accurate point estimates and up to 50% tighter credible intervals than traditional methods. The R package abpackage provides an implementation of the approach.
Cite
@article{arxiv.1711.00562,
title = {Percent Change Estimation in Large Scale Online Experiments},
author = {Jacopo Soriano},
journal= {arXiv preprint arXiv:1711.00562},
year = {2019}
}