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Optimizely Knowledge Base

Multi-armed bandits: When to experiment and when to optimize

  • Understand what Optimizely’s multi-armed bandit optimization is and how it works
  • Decide when to choose a multi-armed bandit optimization instead of an A/B experiment

In Optimizely, you can run tests to do one of two things: either experiment or optimize.

  • When you experiment, you’re trying to test a hypothesis or validate a claim. The goal is to determine whether a variation is fundamentally different (via statistical significance) with the aim of generalizing learnings from that knowledge into future deployments or experiments.

  • When you optimize, on the other hand, you’re using a set-it-and-forget-it algorithm designed to squeeze as much lift from a set of variations as possible, without concern for visibility into whether a variation is fundamentally better or worse. What that means is, you’re ignoring statistical significance in favor of maximizing your goal.

Multi-armed bandit (MAB) optimizations aim to maximize performance of your primary metric across all your variations. They do this by dynamically re-allocating traffic to whichever variation is currently performing best. This will help you extract as much value as possible from the leading variation during the experiment lifecycle, so you avoid the opportunity cost of showing sub-optimal experiences.

In other words, the better a variation does, the more traffic a multi-armed bandit will send its way. A/B tests don't do this. Instead, they keep traffic allocation constant for the experiment's entire lifetime, no matter how each variation performs:


Click here for a thorough explanation of what's happening in this graph.

When to use a multi-armed bandit

Here are a couple cases that may be a better fit for a multi-armed bandit optimization than a traditional A/B experiment:

  • Promotions and offers: users who sell consumer goods on their site often focus on driving higher conversion rates. One effective way to do this is to offer special promotions that run for a limited time. Using a multi-armed bandit optimization (instead of running a standard A/B experiment) will send more traffic to the over-performing variations and less traffic to the underperforming variations.

  • Headline testing: headlines are short-lived content which lose relevance after a fixed amount of time. If a headline experiment takes just as long to reach statistical significance as the lifespan of a headline, then any learnings gained from the experiment will be irrelevant going forward. Therefore, a multi-armed bandit optimization is a natural choice to allow you to maximize your impact without worrying about balancing experiment runtime and the natural lifespan of a headline.

Set up a multi-armed bandit optimization

To set up multi-armed bandit optimization on your experiment, select Multi-Armed Bandit from the Create New... dropdown when you first create your optimization.

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You can use multi-armed bandit optimizations in Full Stack; however, you can't use them for feature rollouts in Feature Management.  

Interpreting MAB results

If you're an Optimizely user, you probably have a good understanding of how to interpret the results of a traditional A/B test. Those interpretations won't work for MABs, for two important reasons:

  • Multi-armed bandits don't generate statistical significance, and 

  • Multi-armed bandits don't use a control or a baseline experience

Because of this, the MAB results page focuses on improvement over equal allocation as its primary summary of your experiment's performance.

MABs do not show statistical significance

With a traditional A/B test, the goal is exploration: collecting data to discover if a variation performs better or worse than the control. This is expressed through the concept of statistical significance.

Statistical significance tells you whether a change had the effect you expected. You can use those lessons to make your variations better each time. Fixed traffic allocation strategies are usually the best ways to reduce the time it takes to reach a statistically significant result.

On the other hand, Optimizely’s multi-armed bandit algorithms are designed for exploitation: MABs will aggressively push traffic to whichever variations are performing best at any given moment, because the MAB doesn’t consider the reason for that superior performance to be all that important.

Since multi-armed bandits essentially ignore statistical significance, Optimizely will do the same. This is why statistical significance does not appear on the results page for MABs: It avoids confusion about the purpose and meaning of multi-armed bandit optimizations.

MABs do not use a baseline

In a traditional A/B test, statistical significance is calculated relative to the performance of one baseline experience. But MABs don’t do this. They’re intended to explicitly evaluate the tradeoffs between all variations at once, which means there is no control or baseline experience to compare to.

What’s more, MABs are "set-and-forget" optimizations. In an A/B test, you follow up an experiment with a decision: do you deploy a winning variation, or stick with the control? But since MABs continuously make these decisions throughout the experiment’s lifetime, there’s never any need for a baseline reference point for that decision, because you'll never need to make it yourself.

Improvement over equal allocation

Improvement over equal allocation represents the gain in total conversions in the current MAB test over a hypothetical state, in which an A/B test with fixed, equal traffic allocation had been run instead. We can estimate this improvement using the following formula:


In other words, we assume that the observed conversion rate using most recent data is an accurate estimate of the true conversion rate for that arm of the experiment. Then, in the case that our pool of N total visitors is evenly distributed across the k arms of the experiment so that there are N/k total visitors in each experience, we can apply the observed conversion rate to each pool of visitors to arrive at a good estimate of how many total conversions would have been generated by each arm in an equal-allocation A/B experiment.

MAB optimization vs. A/B testing: a demonstration

In this head-to-head comparison, simulated data is sent to both an A/B test with fixed traffic distribution and a multi-armed bandit experiment. Traffic distribution over time and the cumulative count of conversions for each mode are both observed. The true conversion rates driving the simulated data are:

  • Original: 50%

  • Variation 1: 50%

  • Variation 2: 45%

  • Variation 3: 55%


The multi-armed bandit algorithm senses that Variation 3 is higher-performing from the start. Even though it doesn’t yet have high statistical significance for this signal, it still begins to push traffic to Variation 3 in order to exploit the perceived advantage and gain more conversions.

For the ordinary A/B experiment, the traffic distribution remains fixed in order to more quickly arrive at a statistically significant result. Because fixed traffic allocations are optimal for reaching statistical significance, MAB-driven experiments generally take longer to find winners and losers than A/B tests.

By the end of the simulation, the multi-armed bandit has optimized the experiment to achieve roughly 700 more conversions than if traffic had been held constant.