Multi-Armed Bandit Testing: How It Works and When to Use It

Multi-Armed Bandit (MAB) testing is an adaptive experimentation method that dynamically shifts traffic toward better-performing variations while a test is running. Unlike traditional A/B testing, which typically uses a fixed traffic split, MAB aims to maximize conversions during the learning period.

How does Multi-Armed Bandit testing work?

A bandit algorithm continuously evaluates the performance and uncertainty of each variation. As more data becomes available, it sends more visitors toward promising variations while reducing traffic to weaker ones.

Common approaches include:

Epsilon-greedy: Balances exploitation with a fixed level of exploration.
Upper Confidence Bound (UCB): Considers both performance and uncertainty.
Thompson Sampling: Uses probability distributions to balance exploration and exploitation.
Contextual bandits: Selects variations based on individual user characteristics.
When should you use MAB testing?

MAB testing can be useful for:

Short-term campaigns and promotions
Ad creative optimization
Testing multiple variations
Product recommendations
Personalized experiences
Always-on optimization
Situations where sending traffic to poor-performing variants has a high opportunity cost
When should you use traditional A/B testing?

Traditional A/B testing is generally better when you need reliable causal estimates, precise effect measurement, or defensible statistical conclusions. It is especially appropriate for major UX changes, pricing decisions, regulatory validation, and experiments involving important guardrail metrics.

MAB testing also does not solve low-traffic problems. Limited data can still produce uncertain performance estimates and prevent an algorithm from converging reliably.

Final takeaway

A/B testing and Multi-Armed Bandit testing serve different purposes. A/B testing focuses on learning and statistical validation, while MAB focuses on optimizing performance while learning. Choosing the right approach depends on your traffic, experiment duration, business objective, and need for statistical inference.

Read the complete guide:
https://www.brillmark.com/multi-armed-bandit-testing-how-it-works-and-when-to-use-it/