The Multi-Armed Bandit (MAB) in A/B testing

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Continuously optimize while exploring multiple variants

📌 Definition

Multi-Armed Bandit (MAB) is an adaptive experimentation approach used to test multiple variants of a page or element while maximizing results in real time.
Unlike traditional A/B testing, where traffic is distributed in a fixed manner (e.g., 50/50) between variants until the end of the test, MAB dynamically reallocates traffic to the best-performing variants as soon as a positive signal is detected.

The name comes from the concept of the "one-armed bandit": a player faced with several slot machines must determine which one gives the best payouts, while continuing to play to refine their decisions.

⚖️ Exploration vs. Exploitation

The Multi-Armed Bandit test is based on a balance between:

• Exploration: testing different options to learn their performance,
• Exploitation: directing more traffic to the most promising variants.

The algorithm automatically adjusts traffic distribution to minimize lost opportunities and maximize cumulative gains during the test period.

🧪 Differences from a traditional A/B test

Traditional A/B testingMulti-Armed Bandit testingFixed traffic distribution (e.g., 50/50)Adaptive distribution based on performanceEnd-of-test analysis with statistical significanceContinuous learning with dynamic adjustmentGoal: learn what worksGoal: learn and maximize performance nowRecommended for exploratory testing or strategic decisionsIdeal for live campaigns, short-term optimizations

🎯 Use cases adapted to CRO

• Marketing campaigns or homepage banners where you want to maximize real-time conversions (e.g., sales, limited offers).
• Testingemails, landing pages, or activation messages,
• Continuous optimization of product recommendations,
• Situations where traffic is limited in time, making traditional A/B testing less effective.

✅ Benefits for the CRO

• ⏱️ Save time: no need to wait for the test to finish before taking action.
• 💰 Maximizes conversions during testing (useful on high-traffic or context-sensitive pages),
• 🔄 Continuous learning: the algorithm automatically adjusts to changing trends (e.g., seasonality, behavioral changes).

⚠️ Limitations and precautions

• Less suitable for tests where rigorous post-test analysis is required (e.g., validation of a complete UX redesign).
• It is difficult to interpret the results as accurately as an A/B test (no fixed p-value or conventional significance analysis).
• Traffic to losing variants may be very low, making qualitative analysis difficult.
• Requires robust technical infrastructure or a suitable tool.

🛠️ Tools that offer Multi-Armed Bandit

• Optimizely, AB Tasty, VWO, Dynamic Yield, and Kameleoon offer bandit-type options (often under names such as "auto-optimization," "AI-powered allocation," etc.).

🔍 When should you choose a Multi-Armed Bandit test?

Use a MAB if you want:

• Quickly maximize a KPI, such as click-through rate or conversion rate.
• Act on short campaigns (less than 2 weeks),
• Adapt your traffic based on actual performance without delay.

Avoid it if you need:

• Rigorous results with high strategic or scientific value,
• Complete control over statistical analysis and result allocation.

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