The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Applying data mining to telecom churn management
Open accessAbstract
Taiwan deregulated its wireless telecommunication services in 1997. Fierce competition followed, and churn management becomes a major focus of mobile operators to retain subscribers via satisfying their needs under resource constraints. One of the challenges is churner prediction. Through empirical evaluation, this study compares various data mining techniques that can assign a ‘propensity-to-churn’ score periodically to each subscriber of a mobile operator. The results indicate that both decision tree and neural network techniques can deliver accurate churn prediction models by using customer demographics, billing information, contract/service status, call detail records, and service change log.
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Cites
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis
Zhang, G., Y. Hu, M., Eddy Patuwo, B., & C. Indro, D. (1999) · European Journal of Operational Research · doi:10.1016/s0377-2217(98)00051-4 · Cited by 677
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
Saradhi, V. V., & Palshikar, G. K. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.07.134 · Cited by 188
A 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
“Therefore, if customer churn is prevented and appropriate incentives are provided, it is of great help in maintaining sales [16], [32]–[34].”
BackgroundWhy you should stop predicting customer churn and start using uplift models
Devriendt, F., Berrevoets, J., & Verbeke, W. (2021) · Information Sciences · doi:10.1016/j.ins.2019.12.075 · Cited by 90
An 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
Metadata
- Authors
- Hung, S.-Y., Yen, D. C., & Wang, H.-Y.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2006-10
- Volume
- 31
- Issue
- 3
- Pages
- 515-524
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2161634631
- Cited by
- 514
- References
- 2
- FWCI
- 24.16
- Type
- journal article
- Language
- en
- Open access
- bronze
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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.
