Customer segmentation based on survival character |
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Authors: | Yun Chen Guozheng Zhang Dengfeng Hu Chuan Fu |
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Affiliation: | (1) School of Public Economics & Administration, Shanghai University of Finance & Economics, Shanghai, 200433, P.R. China |
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Abstract: | Customer Segmentation is an increasingly pressing issue in today’s over-competitive commercial area. More and more literatures
have researched the application of data mining technology in customer segmentation, and achieved sound effectives. But most
of them segment customer only by single data mining technology from a special view, rather than from systematical framework.
Furthermore, one of the key purposes of customer segmentation is customer retention. Although previous segment methods may
identify which group needs more care, it is unable to identify customer churn trend for taking different actions. This paper
focus on proposing a customer segmentation framework based on data mining and constructs a new customer segmentation method
based on survival character. The new customer segmentation method consists of two steps. Firstly, with K-means clustering
arithmetic, customers are clustered into different segments in which customers have the similar survival characters (churn
trend). Secondly, each cluster’s survival/hazard function is predicted by survival analyzing, the validity of clustering is
tested and customer churn trend is identified. The method mentioned above has been applied to a dataset from China Telecom,
which acquired some useful management measures and suggestions. Some propositions for further research is also suggested. |
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Keywords: | Customer segmentation Survival character Data mining Survival analysis |
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