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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/59844

    Title: A recommender system to avoid customer churn: A case study
    Authors: Wang, Yi-Fan;Chiang, Ding-An;Hsu, Mei-Hua;Lin, Cheng-Jung;Lin, I-Long
    Contributors: 淡江大學資訊工程學系
    Keywords: CRM;Data mining;Decision tree;Recommender system
    Date: 2009-05
    Issue Date: 2013-06-07 10:46:43 (UTC+8)
    Publisher: Kidlington: Pergamon
    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.
    Relation: Expert Systems With Applications 36(4), pp.8071-8075
    DOI: 10.1016/j.eswa.2008.10.089
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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