Customer lifetime value modeling and its use for customer retention planning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The class imbalance problem: A systematic study
Intelligent Data Analysis
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Learning from imbalanced data in surveillance of nosocomial infection
Artificial Intelligence in Medicine
A novel evolutionary data mining algorithm with applications to churn prediction
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Neural Networks
Data Mining for Business Applications: Introduction
Proceedings of the 2010 conference on Data Mining for Business Applications
Churn prediction in new users of Yahoo! answers
Proceedings of the 21st international conference companion on World Wide Web
Improving customer retention in financial services using kinship network information
Expert Systems with Applications: An International Journal
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This work focuses on one of the central topics in customer relationship management (CRM): transfer of valuable customers to a competitor. Customer retention rate has a strong impact on customer lifetime value, and understanding the true value of a possible customer churn will help the company in its customer relationship management. Customer value analysis along with customer churn predictions will help marketing programs target more specific groups of customers. We predict customer churn with logistic regression techniques and analyze the churning and nonchurning customers by using data from a consumer retail banking company. The result of the case study show that using conventional statistical methods to identify possible churners can be successful.