Sentient Ascend: AI-Based Massively Multivariate Conversion Rate Optimization

Authors

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

Keywords:

Conversion optimization, e-commerce, evolutionary computation, design

Abstract

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, 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.

Downloads

Published

2018-04-27

How to Cite

Miikkulainen, R., Iscoe, N., Shagrin, A., Rapp, R., Nazari, S., McGrath, P., Schoolland, C., Achkar, E., Brundage, M., Miller, J., Epstein, J., & Lamba, G. (2018). Sentient Ascend: AI-Based Massively Multivariate Conversion Rate Optimization. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11387