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Amazon· Bedrock / NovaUplifting conversion across the acquisition funnel with personalization using contextual bandits on AWS
Amazon Payments uses contextual bandits to personalize a multi-stage acquisition funnel.
Amazon Payments applied a multi-objective contextual multi-armed bandit (LinUCB) on Amazon SageMaker AI to personalize a product acquisition funnel.
A seven-week A/B test showed a high single-digit percentage relative lift in final-funnel conversion for one customer population, while another saw no improvement, attributed to the content rather than the model.
The post explains the bandit approach, the extension to optimize an entire funnel, and the AWS architecture.
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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. The problem turned out to be the content, not the model.
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