@article{Miikulainen_Brundage_Epstein_Foster_Hodjat_Iscoe_Jiang_Legrand_Nazari_Qiu_Scharff_Schoolland_Severn_Shagrin_2020, title={Ascend by Evolv: AI-Based Massively Multivariate Conversion Rate Optimization}, volume={41}, url={https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/5256}, DOI={10.1609/aimag.v41i1.5256}, abstractNote={<p><span lang="EN-IN">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 article describes Ascend by Evolv,1 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 online with real users, making it possible to discover and use 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 fourfold improvements over human design. Ascend can therefore be seen as massively multivariate CRO made possible by artificial intelligence.</span></p>}, number={1}, journal={AI Magazine}, author={Miikulainen, Risto and Brundage, Myles and Epstein, Jonathan and Foster, Tyler and Hodjat, Babak and Iscoe, Neil and Jiang, Jingbo and Legrand, Diego and Nazari, Sam and Qiu, Xin and Scharff, Michael and Schoolland, Cory and Severn, Robert and Shagrin, Aaron}, year={2020}, month={Apr.}, pages={44-60} }