智能客户细分的两阶段聚类方法

M. Namvar, M. Gholamian, Sahand KhakAbi
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引用次数: 67

摘要

在当今竞争激烈的商业领域,客户细分是一个日益重要的问题。许多文献回顾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。但在大多数情况下,它是从一个特殊的角度来使用客户数据,而不是从考虑CRM所有阶段的系统方法来执行的。本文借助数据挖掘工具,构建了一种基于RFM、人口统计学和LTV数据的客户细分新方法。新的客户细分方法包括两个阶段。首先,使用K-means聚类,根据客户的RFM将其聚类到不同的细分市场。其次,利用人口统计数据,将每个聚类再次划分为新的聚类。最后,使用LTV为客户创建一个配置文件。将该方法应用于伊朗一家银行的数据集,得出了一些有用的管理措施和建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Two Phase Clustering Method for Intelligent Customer Segmentation
Customer Segmentation is an increasingly significant issue in today’s competitive commercial area. Many literatures have reviewed the application of data mining technology in customer segmentation, and achieved sound effectives. But in the most cases, it is performed using customer data from a special point of view, rather than from systematical method considering all stages of CRM. This paper, with the aid of data mining tools, constructs a new customer segmentation method based on RFM, demographic and LTV data. The new customer segmentation method consists of two phases. Firstly, with K-means clustering, customers are clustered into different segments regarding their RFM. Secondly, using demographic data, each cluster again is partitioned into new clusters. Finally, using LTV, a profile for customer is created. The method has been applied to a dataset from Iranian bank, which resulted in some useful management measures and suggestions.
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