AWS blog shares how contextual bandits are used for personalised conversion optimisation
What happened
On 2 October 2026, the AWS Machine Learning Blog published a first-hand report on Amazon Payments using a multi-objective contextual multi-armed bandit (LinUCB) on Amazon SageMaker AI to personalise content selection in its product acquisition funnel. The report says that in a seven-week online A/B test, one audience segment achieved a high single-digit relative improvement in final funnel conversion, while another showed no improvement, attributing the difference to content rather than the model.
Written by AI from the reports · updated 19 min ago
Timeline
Follow the reports to see the event from each side.
- AWS Machine Learning BlogUplifting conversion across the acquisition funnel with personalization using contextual bandits on AWS
In this post, we share how Amazon Payments applied AI-based personalization to a product acquisition funnel, using a multi-objective contextual multi-armed bandit (MAB) on Amazon SageMaker AI. In a seven-week online A/B test we currently see a high single-digit percentage relative lift in final-funnel conversion for one customer population, while another saw no improvement over the existing experience.
Heat of this event
Not enough continuous observations yet to draw a trend.