International Journal of Man-Machine Studies
Flow Graphs and Intelligent Data Analysis
Fundamenta Informaticae - Contagious Creativity - In Honor of the 80th Birthday of Professor Solomon Marcus
The class imbalance problem: A systematic study
Intelligent Data Analysis
Toward a hybrid data mining model for customer retention
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Predicting credit card customer churn in banks using data mining
International Journal of Data Analysis Techniques and Strategies
Business Aviation Decision-Making Using Rough Sets
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
The state of CRM adoption by the financial services in the UK: an empirical investigation
Information and Management
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences: an International Journal
An application of support vector machines for customer churn analysis: credit card case
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Exploring the preference of customers between financial companies and agents based on TCA
Knowledge-Based Systems
A hybrid KMV model, random forests and rough set theory approach for credit rating
Knowledge-Based Systems
Do impression management tactics and/or supervisor-subordinate guanxi matter?
Knowledge-Based Systems
International Journal of Information Retrieval Research
Customer attrition in retailing: An application of Multivariate Adaptive Regression Splines
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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 churn 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.