Machine Learning
Logistic Regression Using the SAS System: Theory and Application
Logistic Regression Using the SAS System: Theory and Application
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Toward a successful CRM: variable selection, sampling, and ensemble
Decision Support Systems
Expert Systems with Applications: An International Journal
Handling class imbalance in customer churn prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Using text classification and multiple concepts to answer e-mails
Expert Systems with Applications: 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
Customer churn prediction using improved one-class support vector machine
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Mining ideas from textual information
Expert Systems with Applications: An International Journal
Mining data with random forests: A survey and results of new tests
Pattern Recognition
Tuning metaheuristics: A data mining based approach for particle swarm optimization
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
Expert Systems with Applications: An International Journal
Time-varying effects in the analysis of customer loyalty: A case study in insurance
Expert Systems with Applications: An International Journal
Random forests based monitoring of human larynx using questionnaire data
Expert Systems with Applications: An International Journal
Assessing print quality by machine in offset colour printing
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
International Journal of Information Retrieval Research
Hi-index | 12.08 |
Predicting customer churn with the purpose of retaining customers is a hot topic in academy as well as in today's business environment. Targeting the right customers for a specific retention campaign carries a high priority. This study focuses on two aspects in which churn prediction models could be improved by (i) relying on customer information type diversity and (ii) choosing the best performing classification technique. (i) With the upcoming interest in new media (e.g. blogs, emails,...), client/company interactions are facilitated. Consequently, new types of information are available which generate new opportunities to increase the prediction power of a churn model. This study contributes to the literature by finding evidence that adding emotions expressed in client/company emails increases the predictive performance of an extended RFM churn model. As a substantive contribution, an in-depth study of the impact of the emotionality indicators on churn behavior is done. (ii) This study compares three classification techniques - i.e. Logistic Regression, Support Vector Machines and Random Forests - to distinguish churners from non-churners. This paper shows that Random Forests is a viable opportunity to improve predictive performance compared to Support Vector Machines and Logistic Regression which both exhibit an equal performance.