Ascend by Evolv: AI-Based Massively Multivariate Conversion Rate Optimization (2020)
Risto Miikkulainen, Myles Brundage, Jonathan Epstein, Tyler Foster, Babak Hodjat, Neil Iscoe, Jingbo Jiang, Diego Legrand, Sam Nazari, Xin Qiu, Michael Scharff, Cory Schoolland, Robert Severn, Aaron Shagrin
Conversion rate optimization (CRO) means designing an e-commerce 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, evaluating all combinations of two or three variables through multivariate testing, or evaluating multiple variables independently. Traditional CRO is thus limited to a small fraction of the design space only, and often misses important interactions between the design variables. This paper describes Ascend by Evolv, 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.
AI Magazine, 41:44-60, 2020.

Risto Miikkulainen Faculty risto [at] cs utexas edu