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ShareChat enhances A/B testing with post-stratification for monetization metrics

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework to improve the statistical power of online experiments, particularly for monetization metrics that are heavy-tailed. This method combines post-stratification with CUPED, using pre-experiment data to enhance sensitivity without needing more traffic. When deployed at ShareChat, the technique significantly reduced variance and improved decision stability, achieving similar confidence levels with approximately 45% less traffic. AI

IMPACT Improves the reliability of A/B testing for AI-driven ranking and recommendation systems, enabling more efficient decision-making.

RANK_REASON The cluster contains an academic paper detailing a new statistical method for online experiments.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

ShareChat enhances A/B testing with post-stratification for monetization metrics

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The cluster contains an academic paper detailing a new statistical method for online experiments.
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2 independent sources
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90 days old
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Neeti Pokharna, Olivier Jeunen, Yatharth Saraf, Aleksei Ustimenko ·

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

    arXiv:2606.04110v1 Announce Type: cross 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 m…

  2. arXiv stat.ML TIER_1 English(EN) · Aleksei Ustimenko ·

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

    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…

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