Sentient Ascend: AI-Based Massively Multivariate Conversion Rate Optimization

Conversion rate optimization (CRO) means designing an ecommerce web interface so that as many users as possible take a desired action such as registering for an account, requesting a contact, or making a purchase. Such design is usually done by hand, evaluating one change at a time through A/B testing, or evaluating all combinations of two or three variables through multivariate testing. Traditional CRO is thus limited to a small fraction of the design space only. This paper describes Sentient Ascend, an automatic CRO system that uses evolutionary search to discover effective web interfaces given a human-designed search space. Design candidates are evaluated in parallel on line with real users, making it possible to discover and utilize interactions between the design elements that are difficult to identify otherwise. A commercial product since September 2016, Ascend has been applied to numerous web interfaces across industries and search space sizes, with up to four-fold improvements over human design. Ascend can therefore be seen as massively multivariate CRO made possible by AI.

Authors

• Risto Miikkulainen The University of Texas at Austin Cognizant AI Labs
• Neil Iscoe
• Aaron Shagrin
• Ryan Rapp
• Sam Nazari
• Patrick McGrath
• Cory Schoolland
• Elyas Achkar
• Myles Brundage
• Jeremy Miller
• Jonathan Epstein
• Gurmeet Lamba

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