Multi-armed bandits in UX experiments: Faster testing with smarter traffic splits

Traditional A/B testing involves splitting traffic equally between design versions, which can produce statistically accurate results but may negatively impact conversion rates due to exposure to less effective designs. In contrast, the multi-armed bandit (MAB) strategy dynamically adjusts traffic allocation based on performance, enabling faster completion of A/B, A/B/n, and multivariate tests while optimizing conversion rates by directing more traffic to the better-performing design. While MAB offers advantages such as shorter test durations and reduced waste of traffic, it has limitations like potential statistical bias and lack of guaranteed statistical significance. The effectiveness of MAB depends on the algorithm's quality, and it is best suited for scenarios prioritizing reduced traffic waste over statistical rigor.

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