Uplifting conversion across the acquisition funnel with personalization using contextual bandits on AWS

What happened

Uplifting conversion across the acquisition (one company buying another) funnel with personalization using contextual bandits on AWS, AWS Machine Learning Blog announced. Amazon Payments used a multi-objective contextual bandit on Amazon SageMaker (Python library) AI to personalize an acquisition funnel, achieving a high single-digit conversion lift for one audience, and learning why content, not the model, was the constraint. Generative AI has made it possible to produce large amounts of personalized content quickly and at low cost. Amazon Web Services is a cloud computing company based in Seattle.

The new challenge is now one of selection. The problem turned out to be the content, not the model.

Growing role of multi-armed bandits in the generative AI era A multi-armed bandit (MAB) is a reinforcement learning method built for settings with many options and limited traffic. It treats each content variation as an “arm,” tries each against live traffic, and steadily shifts impressions toward the arms that perform, while holding a fraction back to keep testing the rest.