Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques
By Kristof Coussement, Dirk Van den Poel. Expert systems with applications, 2008
471 citations recorded by the source corpus.
Subfields: Marketing, Organizational Behavior and Human Resource Management
View Paper: https://www.semanticscholar.org/paper/92230fcb6e68a86db7757267813db75f99f4ccc0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Marketing
- David Martens
- Wouter Verbeke
- Jonathan Burez
- Bart Baesens
- Joonyoung Hur
- Karel Dejaeger
- Thorsten Strufe
- Conor Hayes
In Organizational Behavior and Human Resource Management
- Jonathan Burez
- David Martens
- Wouter Verbeke
- Joonyoung Hur
- Karel Dejaeger
- Bart Baesens
- Kwetishe Joro Danjuma
- Thorsten Strufe
Citations in the corpus
- Churn prediction in new users of Yahoo! answers (2012, background): “around the wireless telecommunication industry [6, 12, 15, 7, 4, 5, 14, 16, 18].”
- New insights into churn prediction in the telecommunication sector: A profit driven data mining approach (2012, methodology): “…including only Table 1 (continued) Classification technique Previous studies in churn prediction SVM with radial basis function kernel (rbfSVM) Coussement and Van den Poel (2008) and Kumar and Ravi (2008) LSSVM with linear kernel (Suykens and Vandewalle, 1999) (linLSSVM) LSSVM with radial…”
- Churn in Social Networks (2010, methodology): “Churn has been analysed in a wide range of industries: most widely in the telcom sector [44, 50, 41, 66, 17, 32, 13, 65, 30], but also in the field of retail business [12], banking [69], Internet service providers [31], service industries [53], P2P networks [28] and online games [34].”
- Handling class imbalance in customer churn prediction (2009, methodology): “…be interesting to compare the modelling techniques used in this study to some state of the art techniques, like support vector machines (Viaene et al., 2001; Coussement & Van den Poel, 2008), Bayesian methods (Baesens, Viaene, Van den Poel, Vanthienen , & Dedene, 2002), neural networks and so on.”
- Performance Evaluation of Machine Learning Algorithms in Post-operative Life Expectancy in the Lung Cancer Patients (2015, background): “The decision tree is popular and widely used because of its shorter learning curve and interpretability, and the tree ability to handle covariates attributes measured at different level [36].”