Optimizely for Amazon Personalize: Build winning experiences with the leaders in A/B testing and personalization

Use Optimizely Full Stack to test different Amazon Personalize models, drive engagement and increase revenue.

Launch and optimize personalized experiences faster

Machine learning can deliver more relevant recommendations and content, but tuning models is time-consuming.

A/B test the algorithms that Amazon built for their own business on your site or app, and create more personalized customer experiences, faster.

Increase customer engagement with personalization

A/B test different ML algorithms with unique customer segments and deliver recommendations that drive more conversions

  • Innovate faster

    Iterate on multiple versions of your ML algorithms and improve user experiences quickly.

  • Measure impact

    Get real-time results on how your personalized experiences are performing, reduce guesswork and quantify the impact on your application and business metrics.

Use Optimizely Full Stack to test different Amazon Personalize models

Optimizely's experimentation platform randomly assigns users to different experiences and tracks the impact on business metrics. A real-time dashboard provides results with statistical confidence.

Safely roll out and roll back different personalization models to different customer segments

The Optimizely platform has audience targeting and feature management solutions so you can set which customers see which experiences. Roll out personalization updates to a percentage of customers, and roll back without a deploy.

Improve engagement and conversion by powering real-time product and content recommendations

Amazon Personalize is based on over 20 years of research in machine learning at Amazon, it’s like having your very own Amazon.com recommendation system.

Optimizely for Amazon Personalize

Interested in learning how Optimizely for Amazon Personalize can help you deliver recommendations and relevant content to your customers? Sign up here for a brief demo.

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