  Online controlled experiments, such as A/B-tests, are commonly used by modern
tech companies to enable continuous system improvements. Despite their
paramount importance, A/B-tests are expensive: by their very definition, a
percentage of traffic is assigned an inferior system variant. To ensure
statistical significance on top-level metrics, online experiments typically run
for several weeks. Even then, a considerable amount of experiments will lead to
inconclusive results (i.e. false negatives, or type-II error). The main culprit
for this inefficiency is the variance of the online metrics. Variance reduction
techniques have been proposed in the literature, but their direct applicability
to commonly used ratio metrics (e.g. click-through rate or user retention) is
limited.
  In this work, we successfully apply variance reduction techniques to ratio
metrics on a large-scale short-video platform: ShareChat. Our empirical results
show that we can either improve A/B-test confidence in 77% of cases, or can
retain the same level of confidence with 30% fewer data points. Importantly, we
show that the common approach of including as many covariates as possible in
regression is counter-productive, highlighting that control variates based on
Gradient-Boosted Decision Tree predictors are most effective. We discuss the
practicalities of implementing these methods at scale and showcase the cost
reduction they beget.
