Multi-Armed Bandit Testing: How It Works and When to Use
A multi-armed bandit is an algorithm that decides how to split traffic across several variations while a test is still running, shifting more visitors toward whichever option is performing best. Instead of holding an even split until a fixed sample size is reached, a bandit continuously reallocates traffic to earn conversions during the test itself. This guide explains how multi-armed bandits…
Multi-armed bandit testing is a method that dynamically allocates traffic to different variations of a webpage during an ongoing experiment. Rather than holding a fixed split until a predetermined sample size is reached, the algorithm continuously reallocates visitors to the option that appears to be performing best at any given moment. This allows the majority of visitors to be directed to the winning variation while still collecting data to validate the decision.
There are three main algorithms used in multi-armed bandit tests: epsilon-greedy, Thompson sampling, and Upper Confidence Bound.
Epsilon-greedy is the simplest approach, where a small percentage (epsilon) of visitors are randomly routed to a control variation each time, while the majority are sent to the variation currently performing best. This provides a balance between exploration and exploitation, but relies on manually tuning the epsilon value.
Thompson sampling takes a Bayesian approach, maintaining a probability distribution of each variation's true conversion rate as more data is collected. On each decision, it samples from these distributions and directs traffic to the arm with the highest sampled value. This naturally increases exploration for arms with limited data.
Upper Confidence Bound selects the arm with the highest upper confidence limit on its potential conversion rate. This encourages exploration of less-tested variations that may have high actual conversion rates.
Compared to a fixed A/B test, multi-armed bandit tests continuously optimize for conversion rate rather than just statistical significance. This allows them to potentially achieve better results, but at the cost of having a cleaner statistical analysis. Bandit tests are best suited for situations where the experiment can run long enough to collect significant data and where the goal is to maximize conversions rather than just confirm a hypothesis.
Optimizely and other experimentation platforms have built in support for multi-armed bandit algorithms to automatically implement these dynamic allocation strategies. By leveraging the explore-exploit tradeoff, bandit tests can efficiently identify the winning variation while still collecting enough data to make a confident decision about the final winner.
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