Modeling user interactions for conversion rate prediction in M-Commerce

Recent developments such as the introduction of new mobile banking and mobile payment services represent both an opportunity and a challenge for banks. While there is great potential to increase revenue by providing new services to customers, this goes together with the need to improve the understanding of customer data through deeper analysis, and to react quickly to changes in customer demands. It becomes increasingly important to maintain and update mobile apps with rapid release cycles. However, evaluating the results of changes in data analysis tools and their applications, such as recommender systems, sometimes requires live experiments on deployed systems. In this paper, a model based on stochastic process algebra is described for the interaction between a user and a recommending engine through a mobile app, and quantitative analysis is performed to show how changing features and parameters at the engine side may have an effect on user experience. This activity can be replicated on models representing an existing system, as a way to assess possible impacts before experimenting with live changes.