Customer attrition analysis for financial services using proportional hazard models

Poel, Dirk Van den; Larivière, Bart

doi:10.1016/s0377-2217(03)00069-9

Cited by 287 publications

(150 citation statements)

References 53 publications

Supporting

Mentioning

141

Contrasting

Unclassified

Order By: Relevance

“…Kim and Yoon (2004) found that whereas some types of satisfaction positively affects survival time, other do not have any influence. Van den Poel and Larivière (2004) present some studies that did not find any effect of satisfaction on survival time.…”

Section: Model Estimationmentioning

confidence: 77%

“…Buckinx and Van den Poel (2005), Hadden et al (2005), Reinartz and Kumar (2003), Song et al (2004), and Van den Poel and Larivière (2004) present literature reviews of customer churn studies. The Appendix 1 presents a review of the literature about customer churn prediction in the TI in contractual settings and continuous time, which is the scope of this study.…”

Section: Because (I) the Retention Of Unprofitable Customermentioning

confidence: 99%

See 1 more Smart Citation

Detecting Customer Defections: An Application of Continuous Duration Models

Portela¹,

Menezes²

2011

JGSM

View full text Add to dashboard Cite

show abstract

Section: Model Estimationmentioning

confidence: 77%

Section: Because (I) the Retention Of Unprofitable Customermentioning

confidence: 99%

Detecting Customer Defections: An Application of Continuous Duration Models

Portela¹,

Menezes²

2011

JGSM

View full text Add to dashboard Cite

show abstract

“…Maintaining a long relationship with credit card customers is also of primary importance for these companies once, in general, acquiring a new customer is more expensive than retaining an existing one (Van den Poel & Larivière, 2004). Anti-attrition scoring is used to promote customer retention.…”

Section: Typology Of Credit Risk Forecasting Modelsmentioning

confidence: 99%

Using multi-state markov models to identify credit card risk

Régis

Artes

2015

Prod.

View full text Add to dashboard Cite

The main interest of this work is to analyze the application of multi-state Markov models to evaluate credit card risk by investigating the characteristics of different state transitions in client-institution relationships over time, thereby generating score models for various purposes. We also used logistic regression models to compare the results with those obtained using multi-state Markov models. The models were applied to an actual database of a Brazilian financial institution. In this application, multi-state Markov models performed better than logistic regression models in predicting default risk, and logistic regression models performed better in predicting cancellation risk.

show abstract

“…Researchers believe that as a result of high costs of absorbing and attracting new customers and significant benefits of maintaining current customers, studying customer's turnover is completely essential in competitive industries [14]. One of these industries is the industry of private banking in Iran in which the competition is getting tighter every day by introduction of new rivals and new services.…”

Section: Issn(e): 2223-1331/issn(p): 2226-5724mentioning

confidence: 99%