Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling:: The case of financial services

被引:87
作者
Larivière, B [1 ]
Van den Poel, D [1 ]
机构
[1] State Univ Ghent, Dept Marketing, B-9000 Ghent, Belgium
关键词
data mining; customer relationship management; cross-sell analysis; multinomial probit; survival analysis; savings and investment features; retention behavior;
D O I
10.1016/j.eswa.2004.02.002
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The enhancement of existing relationships is of pivotal importance to companies, since attracting new customers is known to be more expensive. Therefore, as part of their customer relationship management (CRM) strategy, many researchers have been analyzing 'why' customers decide to switch. However, despite its practical relevance, few studies have investigated how companies can react to defection prone customers by offering the right set of products. Additionally, within the current customer attention 'hype', one tends to overlook the nature of different products when investigating customer defection. In this research, we study the defection of the savings and investment (SI) customers of a large Belgian financial service provider. We created different SI chum behavior categories by introducing two dimensions: (i) duration of the products (fixed term versus infinity) and (ii) capital/revenue risks involved. Considering these product features, we first gain explorative insight in the timing of the chum event by means of Kaplan-Meier estimates. Secondly, we elaborate on the most alarming group of customers that emerged from the former explorative analysis. A hazard model is built to detect the most convenient product categories to cross-sell in order to reduce their churn likelihood. Complementary, a multinomial probit model is estimated to explore the customers' preferences with respect to the product features involved and to test whether these correspond with the findings of the survival analysis. The results of our study indicate that customer retention cannot be understood by solely relying on customer characteristics. In sum, it might be true that 'not all customers are created equal', but neither are all products. (C) 2004 Elsevier Ltd. All rights reserved.
引用
收藏
页码:277 / 285
页数:9
相关论文
共 21 条
[1]  
ALLISON DA, 1999, LOGISTIC REGRESSION, P167
[2]   Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers [J].
Baesens, B ;
Verstraeten, G ;
Van den Poel, D ;
Egmont-Petersen, M ;
Van Kenhove, P ;
Vanthienen, J .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2004, 156 (02) :508-523
[3]   Statistics notes - Survival probabilities (the Kaplan-Meier method) [J].
Bland, JM ;
Altman, DG .
BRITISH MEDICAL JOURNAL, 1998, 317 (7172) :1572-1572
[4]  
Breslaw J. A., 2002, ECONOMET J, V5, P417
[5]   Goal-oriented sequential pattern for network banking chum analysis [J].
Chiang, DA ;
Wang, YF ;
Lee, SL ;
Lin, CJ .
EXPERT SYSTEMS WITH APPLICATIONS, 2003, 25 (03) :293-302
[6]   ESTIMATING A MULTINOMIAL PROBIT MODEL OF BRAND CHOICE USING THE METHOD OF SIMULATED MOMENTS [J].
CHINTAGUNTA, PK .
MARKETING SCIENCE, 1992, 11 (04) :386-407
[8]  
Dawes J, 1999, INT J BANK MARK, V17, P36, DOI [10.1108/02652329910254037, DOI 10.1108/02652329910254037]
[10]   ALTERNATIVE COMPUTATIONAL APPROACHES TO INFERENCE IN THE MULTINOMIAL PROBIT MODEL [J].
GEWEKE, J ;
KEANE, M ;
RUNKLE, D .
REVIEW OF ECONOMICS AND STATISTICS, 1994, 76 (04) :609-632