Evaluating customer aid functions of online stores with agent-based models of customer behavior and evolution strategy

With competitive pressure growing in online markets, many Internet stores provide various customer aid functions such as personalized pages to help customers shop more effectively and efficiently. Evaluating such customer aid functions is usually costly because it requires full or partly-working systems and many human testers. In order to address this problem, this research presents a novel approach to evaluating customer aid functions with agent-based models of customer behavior and evolution strategies. Agent-based modeling is used to imitate users’ rational behavior at Internet stores with regard to browsing and collecting product information. It is assumed that users evolve their browsing skill and strategy over time, to maximize the efficiency and effectiveness of their shopping, and hence, evolution strategy, an optimization method, is combined with the agent-based model to find the rational behavior of each user. The rational behavior is then used to simulate the virtual shopping of users and to evaluate the performances of target customer aid functions. Several experiments were performed to illustrate the use of the approach, where the personalized recommendation page of a virtual online DVD rental store is evaluated in comparison with more general functions such as listing most popular products or sorting categories. The results show that a personalized page might not always be the best customer aid function for all users compared to the simpler ones.