Multi-Armed Bandit

Contributor

Pritul Patel,

Data Scientist

What is a Multi-Armed Bandit?

A Multi-Armed Bandit (MAB) test is an adaptive experimentation method where traffic allocation changes as results come in. Instead of splitting traffic evenly like in A/B testing, MAB tests gradually shift more traffic to better-performing variants while reducing exposure to underperformers.

The name comes from the classic slot machine problem: you have multiple “arms” (variants), each with unknown payouts. How do you pull the arms in a way that earns you the most overall? MAB solves this by balancing two actions:

Exploration: Test all variants to learn their potential
Exploitation: Send more traffic to the top performer(s) as results emerge

MAB testing helps you learn and optimize at the same time, especially useful when time, traffic, or opportunity is limited.

How Multi-Armed Bandit Tests Work

Multi-armed bandits rely on algorithms that continuously analyze performance and dynamically reallocate traffic. Common methods include:

Epsilon-Greedy: Mostly sends traffic to the best variant, occasionally explores others
Upper Confidence Bound (UCB): Chooses the variant with the best balance of performance and uncertainty
Thompson Sampling: A Bayesian approach that samples based on probability distributions for each variant

Advanced forms like Contextual Bandits personalize the traffic allocation based on user-specific data like location, device, or behavior. Instead of finding one “best” variant, they try to show the right one for each user.

“A Multi-Armed Bandit test (MAB) is ideal for high-opportunity-cost scenarios (e.g., Black Friday or Cyber Monday) where traditional testing is not practical, long-term learnings are not the primary goal, and quick optimization matters more than understanding why.

During these 3-4 day events, visitors are heavily “sale-biased,” fundamentally different from a typical user, and unlikely to return, making permanent variant launches unnecessary. Traditional A/B testing wastes valuable conversion opportunities during these critical periods.

Launch 3-4 variants (maximum 6-7) for quick exploration and optimization. MABs rapidly identify and automatically shift traffic to top-performing variants, maximizing goal completion within a short timeframe. Be aware that more variants extend exploration time, reducing effectiveness for time-sensitive events.

All major ad platforms use specialized MABs to place different Ad copy in front of users. It auto-shuts non-performing variants and allocates budgets to winners. They also power product variant recommendations (e.g., size L) based on user context at large e-commerce stores.”

Pritul Patel, Data Scientist

When to Use a Multi-Armed Bandit Test

Multi-Armed Bandits are best used when:

Time is limited: e.g., Black Friday, short-run campaigns
Users won’t return: One-time visitors mean long-term learnings aren’t useful
Quick optimization > long-term insight: The goal is performance now
Content is dynamic: Headlines, product displays, or promotions that change often
Multiple low-risk components: When A/B testing each one separately, it would be inefficient

Examples include:

Google Ads uses MAB-like logic to optimize which ad gets served
News sites use bandits to test and rotate article headlines
E-commerce sites dynamically recommend products and layouts