Predicting customer churn using machine learning: A case study in the software industry
Author:
Funder
Fundação para a Ciência e a Tecnologia
Publisher
Springer Science and Business Media LLC
Subject
Marketing,Statistics, Probability and Uncertainty,Strategy and Management,Economics, Econometrics and Finance (miscellaneous)
Link
https://link.springer.com/content/pdf/10.1057/s41270-023-00269-9.pdf
Reference38 articles.
1. Ahn, J., et al. 2020. A Survey on Churn Analysis in Various Business Domains. IEEE Access 8: 220816–220839. https://doi.org/10.1109/ACCESS.2020.3042657.
2. Aldhafferi, N., et al. 2022. Learning Trends in Customer Churn with Rule-Based and Kernel Methods. International Journal of Electrical and Computer Engineering 12 (5): 5364–5374. https://doi.org/10.11591/IJECE.V12I5.PP5364-5374.
3. Almeida, S., S. Mesquita, and I. Carvalho. 2022. The COVID-19 Impacts on the Hospitality Industry Highlights from Experts in Portugal. Tourism and Hospitality Management 28 (1): 61–81. https://doi.org/10.20867/THM.28.1.3.
4. AlShourbaji, I., et al. 2023. An Efficient Churn Prediction Model Using Gradient Boosting Machine and Metaheuristic Optimization. Scientific Reports. https://doi.org/10.1038/S41598-023-41093-6.
5. Bogaert, M., and L. Delaere. 2023. Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art. Mathematics. https://doi.org/10.3390/MATH11051137.
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