Customer classification based on spatial transition probability and Deep Forest

Accurate customer classification can help company save costs and create profits more effectively. In previous studies, few research uses spatio-temporal data for customer classification. In this paper, we put forward a hybrid classification method named MDF based on transition probability matrix andDeep Forest in order to improve the performance in customer classification. The innovation of the proposed new method is that it converts spatio-temporal data to construct the transition probability matrix and then it adopts Deep Forest to classify customers into different types. Experiments on real-world customer classification task from retail company have been done and we have compared MDF with some benchmark methods. Experimental results shows that the proposed method MDF have better performance than other techniques. The new customer classification method provides useful a tool for customer relationship management.