Estimating customer future value of different customer segments based on adapted RFM model in retail banking context

被引:46
|
作者
Khajvand, Mahboubeh [1 ]
Tarokh, Mohammad Jafar [1 ]
机构
[1] KN Toosi Univ Technol, Fac Ind Engn, IT Grp, Tehran, Iran
关键词
Customer lifetime value; Customer segmentation; Data mining; RFM analysis; Time series; ARIMA model;
D O I
10.1016/j.procs.2011.01.011
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
One of the important challenges in customer-based organizations is customer cognition, understanding difference between them, and ranking them. Customer need-based segmentation was common in past years, but recently customer value as a quantifiable parameter could be used for customer segmentation. In this regard, customer segmentation based on customer lifetime value (CLV) and estimating the value of each segment would be useful for making decision in marketing and customer relationship management (CRM) program which can be adapted with the characteristics of each segment. Customer future value as a part of customer lifetime value can be estimated based on customer segmentation. In this regard, this study provide a framework for estimating customer future value based on adapted weighted RFM analysis which is a CLV calculating model, for each segment of customer in retail banking scope. (C) 2010 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Guest Editor.
引用
收藏
页数:6
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