Variance Reduction for Heavy-Tailed Monetization Metrics in Ranking Experiments via Post-Stratification

Abstract:Online evaluation of ranking and retrieval systems often relies on downstream monetization metrics such as app revenue or creator earnings. These metrics are typically heavy-tailed, with a small fraction of users dominating both mean and variance, leading to low statistical power and unreliable conclusions in A/B experiments -- especially under limited traffic. We present a practical framework for variance reduction in online experiments by combining post-stratification with CUPED. Our approach leverages pre-experiment covariates to improve the sensitivity of monetization experiments without requiring additional traffic. Deployed at ShareChat across ranking-driven monetization experiments, the method substantially reduces variance and improves decision stability, achieving equivalent statistical confidence with ~45\% less traffic than standard metrics. We further discuss practical design choices, guardrails, and limitations, providing guidance on when post-stratification is appropriate for real-world information retrieval and Recommendation systems.

Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2606.04110 [cs.LG]

(or arXiv:2606.04110v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.04110 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1145/3805712.3808428 Focus to learn more DOI(s) linking to related resources

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