A recommender system to avoid customer churn: A case study

Authors:
Yi-Fan Wang;Ding-An Chiang;Mei-Hua Hsu;Cheng-Jung Lin;I-Long Lin
Affiliations:
Department of Information Management, Chang Gung Institute of Technology, Taiwan;Department of Information Engineering, Tamkang University, Taiwan;Center for General Education, Chang Gung Institute of Technology, 261, Wen-Hwa 1st Road, Kwei-Shan, Taoyuan, Taiwan;Department of Information Engineering, Tamkang University, Taiwan;Department of Information Management, National Central Police University, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

A major concern for modern enterprises is to promote customer value, loyalty and contribution through services such as can help establish a long-term, honest relationship with customers. For purposes of better customer relationship management, data mining technology is commonly used to analyze large quantities of data about customer bargains, purchase preferences, customer churn, etc. This paper aims to propose a recommender system for wireless network companies to understand and avoid customer churn. To ensure the accuracy of the analysis, we use the decision tree algorithm to analyze data of over 60,000 transactions and of more than 4000 members, over a period of three months. The data of the first nine weeks is used as the training data, and that of the last month as the testing data. The results of the experiment are found to be very useful for making strategy recommendations to avoid customer churn.