Combined rough set theory and flow network graph to predict customer churn in credit card accounts

被引:38
作者
Lin, Chiun-Sin [2 ]
Tzeng, Gwo-Hshiung [1 ,2 ]
Chin, Yang-Chieh [3 ]
机构
[1] Natl Chiao Tung Univ, Inst Management Technol, Hsinchu 30010, Taiwan
[2] Kainan Univ, Dept Business & Entrepreneurial Management, Tao Yuan 33857, Taiwan
[3] Asia Univ, Dept Int Business, Taichung 41354, Taiwan
关键词
Customer churn; Rough set theory; Flow network graph; Credit card; Marketing strategy; FINANCIAL SERVICES; RULES; MODEL;
D O I
10.1016/j.eswa.2010.05.039
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Customer churn has become a critical issue, especially in the competitive and mature credit card industry. From an economic and risk management perspective, it is important to understand customer characteristics in order to retain customers and differentiate high-quality credit customers from bad ones. However, studies have not yet adequately introduced rules based on customer characteristics and churn forms of original data. This study uses rough set theory, a rule-based decision-making technique, to extract rules related to customer churn; then uses a flow network graph, a path-dependent approach, to infer decision rules and variables; and finally presents the relationships between rules and different kinds of churn. An empirical case of credit card customer churn is also illustrated. In this study, we collect 21,000 customer samples, equally divided into three classes: survival, voluntary chum and involuntary churn. The data from these samples includes demographic, psychographic and transactional variables for analyzing and segmenting customer characteristics. The results show that this combined model can fully predict customer churn and provide useful information for decision-makers in devising marketing strategy. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:8 / 15
页数:8
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