The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques
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Cites
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Switching barriers and repurchase intentions in services
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Cited by
Verbeke, W., Dejaeger, K., Martens, D., Hur, J., & Baesens, B. (2012) · European Journal of Operational Research · doi:10.1016/j.ejor.2011.09.031 · Cited by 462
Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., Hawalah, A., & Hussain, A. (2016) · IEEE Access · doi:10.1109/access.2016.2619719 · Cited by 310
Saradhi, V. V., & Palshikar, G. K. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.07.134 · Cited by 188
Churn prediction for high-value players in casual social games
Runge, J., Gao, P., Garcin, F., & Faltings, B. (2014) · 2014 IEEE Conference on Computational Intelligence and Games · doi:10.1109/cig.2014.6932875 · Cited by 152
Coussement, K., Benoit, D. F., & Van den Poel, D. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.07.029 · Cited by 117
Self-citationA Survey on Churn Analysis in Various Business Domains
Ahn, J., Hwang, J., Kim, D., Choi, H., & Kang, S. (2020) · IEEE Access · doi:10.1109/access.2020.3042657 · Cited by 108
“On the other hand, some churn studies have often used a top 10% decline lift performance metric [13], [67].”
BackgroundAn effective hybrid learning system for telecommunication churn prediction
Huang, Y., & Kechadi, T. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.04.020 · Cited by 90
A reference model for customer-centric data mining with support vector machines
Lessmann, S., & Voß, S. (2009) · European Journal of Operational Research · doi:10.1016/j.ejor.2008.12.017 · Cited by 86
Random Forests for Uplift Modeling: An Insurance Customer Retention Case
Guelman, L., Guillén, M., & Pérez-Marín, A. M. (2012) · Lecture Notes in Business Information Processing · doi:10.1007/978-3-642-30433-0_13 · Cited by 61
Citizens as consumers: Profiling e-government services’ users in Egypt via data mining techniques
Mostafa, M. M., & El-Masry, A. A. (2013) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2013.03.007 · Cited by 60
Churn Prediction in MMORPGs Using Player Motivation Theories and an Ensemble Approach
Borbora, Z., Srivastava, J., Hsu, K.-W., & Williams, D. (2011) · 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing · doi:10.1109/passat/socialcom.2011.122 · Cited by 58
de la Llave, M. Á., López, F. A., & Angulo, A. (2019) · Scandinavian Actuarial Journal · doi:10.1080/03461238.2018.1531781 · Cited by 21
A SLR on Customer Dropout Prediction
Sobreiro, P., Martinho, D. D. S., Alonso, J. G., & Berrocal, J. (2022) · IEEE Access · doi:10.1109/access.2022.3146397 · Cited by 8
Customer Churn Prediction in Superannuation: A Sequential Pattern Mining Approach
Culbert, B., Fu, B., Brownlow, J., Chu, C., Meng, Q., & Xu, G. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-92013-9_10 · Cited by 2
Metadata
- Authors
- Coussement, K., & Van den Poel, D.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2008-01
- Volume
- 34
- Issue
- 1
- Pages
- 313-327
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W1966021193
- Cited by
- 432
- References
- 8
- FWCI
- 41.06
- Type
- journal article
- Language
- en
- Open access
- closed
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
