Introducing Autotune: Statsig’s Multiarmed Bandit

The multi-armed bandit (MAB) problem involves optimizing resource allocation by balancing exploration of new options and exploitation of known ones, commonly applied in digital testing scenarios like online advertisements and product promotions. MABs are advantageous in situations with limited resources or multiple variations and work well with a single, well-defined metric. Statsig's Autotune, an application of MAB principles, utilizes Bayesian Thompson Sampling to automate decision-making in digital experiments, demonstrated by a 55-day test that improved click-through rates on their website without manual intervention. This method allowed for more efficient traffic allocation compared to traditional A/B testing. The test results indicated a significant improvement in selecting the optimal button text for their website, showcasing Autotune's capability in maximizing exposure to the most effective variant.

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