Conversion rate optimization through evolutionary computation

ABSTRACT

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.

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Index Terms

• Applied computing

  • Electronic commerce

    • E-commerce infrastructure

• Computing methodologies

  • Machine learning

    • Learning paradigms

      • Reinforcement learning

        • Sequential decision making

    • Learning settings

      • Online learning settings

    • Machine learning approaches

      • Bio-inspired approaches

        • Genetic algorithms