Long-Run Abstinence After Narcotics Abuse: What Are the Odds?
Management Science
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Machine Learning
Predicting Customer Behavior in Telecommunications
IEEE Intelligent Systems
Expert Systems with Applications: An International Journal
A novel evolutionary data mining algorithm with applications to churn prediction
IEEE Transactions on Evolutionary Computation
Recognizing plankton images from the shadow image particle profiling evaluation recorder
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Handling class imbalance in customer churn prediction
Expert Systems with Applications: An International Journal
Combined rough set theory and flow network graph to predict customer churn in credit card accounts
Expert Systems with Applications: An International Journal
An extended support vector machine forecasting framework for customer churn in e-commerce
Expert Systems with Applications: An International Journal
An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction
Expert Systems with Applications: An International Journal
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 | 12.06 |
In subscription services, customers who leave the company can be divided into two groups: customers who do not renew their fixed-term contract at the end of that contract, and others who just stop paying during their contract to which they are legally bound. Those two separate processes are often modeled together in a so-called churn-prediction model, but are actually two different processes. The first type of churn can be considered commercial churn, i.e., customers making a studied choice not to renew their subscriptions. The second phenomenon is defined as financial churn, people who stop paying because they can no longer afford the service. The so-called marketing dilemma arises, as conflicting interests exist between the sales and marketing department on the one hand, and the legal and credit department on the other hand. This paper shows that the two different processes mentioned can be separated by using information from the internal database of the company and that previous bad-payment behavior is more important as a driver for financial than for commercial churn. Finally, it is shown on real-life data that one can more accurately predict financial churn than commercial churn (increasing within period as well as out-of-period prediction performance). Conversely, when trying to persuade customers to stay with the company, the impact of 'loyalty' actions is far greater with potential commercial churners as compared to financial churners. Evidence comes from a real-life field experiment.