DEAL · COMPANIES · #1371
Amazon Payments deploys LinUCB contextual bandit on Amazon SageMaker AI to personalize acquisition funnel
Amazon Payments implemented a multi-objective contextual multi-armed bandit (LinUCB) on Amazon SageMaker AI to personalize content across a three-step acquisition funnel. In a seven-week online A/B test they report a high single-digit percentage relative lift in final-funnel conversion for one customer population while another saw no improvement; the team published the AWS architecture, method details, and a code repository for experimentation on synthetic data.
KEY POINTS
- Amazon Payments implemented a multi-objective contextual multi-armed bandit (LinUCB) on Amazon SageMaker AI to personalize content across a three-step acquisition funnel.
- In a seven-week online A/B test they report a high single-digit percentage relative lift in final-funnel conversion for one customer population while another saw no improvement; the team published the AWS architecture, method details, and a code repository for experimentation on synthetic data.
- This demonstrates a production application of contextual bandits (LinUCB) for scalable, auditable personalization alongside generative-AI-driven content generation, showing measurable lift in some populations and providing reusable code and architecture for practitioners.
WHY IT MATTERS
This demonstrates a production application of contextual bandits (LinUCB) for scalable, auditable personalization alongside generative-AI-driven content generation, showing measurable lift in some populations and providing reusable code and architecture for practitioners.