Building comprehensible customer churn prediction models: A multiple kernel support vector machines approach

The in-depth understanding of customers´ behavior is a crucial means in CRM for identifying its driving force and developing effective and personalized service activities. Relatively little research notices that it is key important in the competitive market to build comprehensible customer churn prediction models which can provide enterprises explicit customer behavior patterns. In this paper, a multiple kernel support vector machines (MK-SVMs) based customer churn prediction model is proposed to encapsulate three knowledge discovery tasks, which are feature selection, class prediction and decision rule extraction, into a whole framework A two-stage iteration of two convex optimization problems is designed for simultaneously feature selection and class prediction. Based on the selected features, support vectors are used to extract decision rules. An open CRM dataset is used to evaluate the performance of this approach. Experimental results show that MK-SVMs achieve promising performance on the heavily skewed dataset by means of a re-balanced strategy, and the extracted rules achieves high coverage and low false-alarm with small number of preconditions.