How to setup Multi-Armed Bandit test

Multi-Armed Bandit (MAB)

A multi-armed bandit is an algorithm used in an A/B test setup to dynamically allocate traffic to different variations of an experiment based on their performance, thus maximizing the total reward or desired outcome. 

A/B Testing vs Multi-Armed Bandit

A/B Testing works best when the business doesn’t have a strong hypothesis about what will work, and there’s enough budget and time to run the test. 

On the other hand, the Multi-Armed Bandit approach is best suited when you need to quickly optimize experiences and can’t afford unnecessary exploration, or when the same test needs to be executed many times.

How to implement MAB?

Mida.so is an AI-powered A/B testing platform that businesses can use to automate A/B testing, freeing up resources to focus on other crucial areas.

Now, let’s go through the simple steps to set up a multi-arm bandit experiment on Mida.so. 

Step 1: Create a new experiment

In the dashboard, click on Create a new test button, select A/B test to start a new experiment.

Step 2: Setup the experiment

Provide an experiment name, hypothesis, metrics, and other necessary details until you reach Configuration tab.

Step 3: Traffic Allocation

Here’s where the magic happens.

Instead of assigning traffic evenly, select the Automatically give more traffic to better-performing variants option. This enables machine learning to learn from the data gathered during the test to dynamically adjust visitor allocation in favor of better-performing variants.

And voila! You’ve set up your Multi-Arm Bandit experiment. Balancing exploration and exploitation is crucial, and with the powerful combination of A/B testing and AI, Mida.so aims to offer a solution that is both effective and efficient. Enjoy the optimization journey!