Multi-Armed Bandit Testing: Smarter Traffic Allocation Than Standard A/B Tests

A multi-armed bandit is an algorithm that dynamically reallocates website traffic toward better-performing variations in real time, rather than maintaining a fixed split until a test concludes. Unlike traditional A/B testing, which prioritizes statistical precision before declaring a winner, bandit algorithms balance exploration of untested options with exploitation of the current best performer. Three core algorithms drive most bandit implementations: epsilon-greedy, Thompson sampling, and Upper Confidence Bound, each offering a different strategy for managing the explore-exploit tradeoff. The key metric guiding these algorithms is 'regret' — the gap between conversions actually earned and the maximum possible had the best variation been served throughout. Bandit testing is best suited for scenarios where maximizing conversions during the experiment matters more than obtaining a clean, unbiased statistical result.