  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.
