Bag of Activities for Customer Churn Prediction in e-Book Subscription Domain
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5934-7_14
Reference21 articles.
1. Al Najjar, D., Al-Rousan, N., Al-Najjar, H.: Machine learning to develop credit card customer churn prediction. J. Theor. Appl. Electron. Commer. Res. 17, 1529–1542 (2022). https://doi.org/10.3390/jtaer17040077
2. Deligiannis, A., Argyriou, C.: Designing a real-time data-driven customer churn risk indicator for subscription commerce. Int. J. Inf. Eng. Electron. Bus. 12, 1–14 (2020). https://doi.org/10.5815/ijieeb.2020.04.01
3. Figalist, I., Elsner, C., Bosch, J., Olsson, H.: Customer churn prediction in B2B contexts (2020)
4. Geiler, L., Affeldt, S., Nadif, M.: A survey on machine learning methods for churn prediction. Post-Print HAL-03824873, HAL (2022). https://doi.org/10.1007/s41060-022-00312-5. https://ideas.repec.org/p/hal/journl/hal-03824873.html
5. Hosein, P., Sewdhan, G., Jailal, A.: Soft-churn: optimal switching between prepaid data subscriptions on e-sim support smartphones. In: 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), pp. 1–6 (2021). https://doi.org/10.1109/DSAA53316.2021.9564163
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