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Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry
Abstract
Competition in the wireless telecommunications industry is fierce. To maintain profitability, wireless carriers must control churn, which is the loss of subscribers who switch from one carrier to another.We explore techniques from statistical machine learning to predict churn and, based on these predictions, to determine what incentives should be offered to subscribers to improve retention and maximize profitability to the carrier. The techniques include logit regression, decision trees, neural networks, and boosting. Our experiments are based on a database of nearly 47,000 U.S. domestic subscribers and includes information about their usage, billing, credit, application, and complaint history. Our experiments show that under a wide variety of assumptions concerning the cost of intervention and the retention rate resulting from intervention, using predictive techniques to identify potential churners and offering incentives can yield significant savings to a carrier. We also show the importance of a data representation crafted by domain experts. Finally, we report on a real-world test of the techniques that validate our simulation experiments.
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Citations
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Predicting player churn in the wild
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“…have proposed that, in terms of The associate editor coordinating the review of this manuscript and approving it for publication was Le Hoang Son . net return on investment, marketing campaigns for retaining existing customers are more efficient than putting efforts to attract new customers [16].”
BackgroundResultInfluentialAn effective hybrid learning system for telecommunication churn prediction
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Churn Prediction in Online Games Using Players’ Login Records: A Frequency Analysis Approach
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Churn Prediction in MMORPGs Using Player Motivation Theories and an Ensemble Approach
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Metadata
- Authors
- Mozer, M. C., Wolniewicz, R., Grimes, D. B., Johnson, E., & Kaushansky, H.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2000-05
- Volume
- 11
- Issue
- 3
- Pages
- 690-696
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2142752589
- Cited by
- 347
- References
- 1
- FWCI
- 16.29
- Type
- journal article
- Language
- en
- Open access
- closed
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