An Intergrated Data Mining and Survival Analysis Model for Customer Segmentation

Abstract

More and more literatures have researched the application of data mining technology in customer segmentation, and achieved sound effects. One of the key purposes of customer segmentation is customer retention. But the application of single data mining technology mentioned in previous literatures is unable to identify customer churn trend for adopting different actions on customer retention. This paper focus on constructs a integrated data mining and survival analysis model to segment customers into heterogeneous group by their survival probability (churn trend) and help enterprises adopting appropriate actions to retain profitable customers according to each segment’s churn trend. This model contains two components. Firstly, using data mining clustering arithmetic cluster customers into heterogeneous clusters according to their survival characters. Secondly, using survival analysis predicting each cluster’s survival/hazard function to identify their churn trend and test the validity of clustering for getting the correct customer segmentation. This model proposed by this paper was applied in a dataset from one biggest china telecommunications company. This paper also suggests some propositions for further research.KeywordsData MiningCustomer Relationship ManagementCustomer SegmentCustomer RetentionHeterogeneous ClusterThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.