Conversion rate optimization through evolutionary computation
By Risto Miikkulainen, N. Iscoe, Aaron Shagrin, Ronald E. Cordell, Sam Nazari, Cory Schoolland, M. Brundage, Jonathan Epstein, Randy Dean, Gurmeet Lamba. 2017
19 citations recorded by the source corpus.
Conversion optimization means designing a web interface so that as many users as possible take a desired action on it, such as register or purchase. Such design is usually done by hand, testing one change at a time through A/B testing, or a limited number of combinations through multivariate testing, making it possible to evaluate only a small fraction of designs in a vast design space. This paper describes Sentient Ascend, an automatic conversion optimization system that uses evolutionary optimization to create effective web interface designs. Ascend makes it possible to discover and utilize interactions between the design elements that are difficult to identify otherwise. Moreover, evaluation of design candidates is done in parallel online, i.e. with a large number of real users interacting with the system. A case study on an existing media site shows that significant improvements (i.e. over 43%) are possible beyond human design. Ascend can therefore be seen as an approach to massively multivariate conversion optimization, based on a massively parallel interactive evolution.
Subfields: Computer Vision and Pattern Recognition, Signal Processing
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In Computer Vision and Pattern Recognition
- Risto Miikkulainen
- Aditya Rawal
- Daniel Fink
- Jason Zhi Liang
- Elliot Meyerson
- Babak Hodjat
- J. Urgen Schmidhuber
- Kenneth O. Stanley
In Signal Processing
Citations in the corpus
- Evolving Deep Neural Networks (2017, methodology): “ humans write imperfect versions of programs, and evolutionary algorithms are then used to repair them [15]; humans de•ne the space of possible web designs, and evolution is used to •nd e‡ective ones [35]. „is same approach can be applied to the design of DNN architectures. „is problem includes thre”