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Multi-Armed Bandit

In traditional A/B testing methodologies, traffic is evenly split between two or more variations. A multi-armed bandit approach allows you to dynamically allocate traffic to variations that are performing well while allocating less and less traffic to underperforming variations. This testing approach is known to produce faster results since there’s no need to wait for a single winning variation.

The Multi Arm Bandit, also known as “reweight”, is one of Dynamic Yield’s machine-learning-based optimization algorithms used for automatic traffic allocation between variations, which shifts traffic in real-time towards the winning variation.

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