A Two Phase Clustering Method for Intelligent Customer Segmentation

被引：0

作者：

Namvar, Morteza ^{[1
]}

Gholamian, Mohammad R. ^{[1
]}

KhakAbi, Sahand ^{[1
]}

机构：

[1] Iran Univ Sci & Technol, Dept Ind Engn, Tehran, Iran

来源：

UKSIM-AMSS FIRST INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS, MODELLING AND SIMULATION | 2010年

关键词：

customer relationship management; segmentation; data mining; clustering; Iran; MODEL; RFM;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：215 / +

页数：2

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