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Customer segmentation based on survival character

机译：基于生存特征的客户细分

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摘要

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.
机译：在当今竞争异常激烈的商业领域，客户细分是一个日益紧迫的问题。越来越多的文献研究了数据挖掘技术在客户细分中的应用，并取得了良好的效果。但是，从特殊的角度来看，它们中的大多数仅通过单一数据挖掘技术来细分客户，而不是系统框架。此外，客户细分的主要目的之一是客户保留率。尽管以前的细分方法可能会确定哪个组需要更多注意，但无法识别采取不同措施的客户流失趋势。本文着重提出了一种基于数据挖掘的客户细分框架，并构造了一种基于生存特征的客户细分方法。新的客户细分方法包括两个步骤。首先，利用K-means聚类算法，将客户聚类到不同的细分中，其中客户具有相似的生存特征（流失趋势）。其次，通过生存分析来预测每个集群的生存/危害功能，测试集群的有效性并确定客户流失趋势。上述方法已应用于中国电信的数据集，该数据集获得了一些有用的管理措施和建议。还提出了一些进一步研究的建议。

著录项

  • 来源
    《Journal of Intelligent Manufacturing》 |2007年第4期|513-517|共5页
  • 作者

    Yun Chen; Guozheng Zhang; Dengfeng Hu; Chuan Fu;

  • 作者单位

    School of Public Economics ampamp Administration Shanghai University of Finance ampamp Economics Shanghai 200433 P.R. China;

    School of Public Economics ampamp Administration Shanghai University of Finance ampamp Economics Shanghai 200433 P.R. China;

    School of Public Economics ampamp Administration Shanghai University of Finance ampamp Economics Shanghai 200433 P.R. China;

    School of Public Economics ampamp Administration Shanghai University of Finance ampamp Economics Shanghai 200433 P.R. China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Customer segmentation; Survival character; Data mining; Survival analysis;

    机译：客户细分;生存特征;数据挖掘;生存分析;

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