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A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry

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Cited by:

  1. Joydeb Kumar Sana & Mohammad Zoynul Abedin & M Sohel Rahman & M Saifur Rahman, 2022. "A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-21, December.
  2. Amin, Adnan & Al-Obeidat, Feras & Shah, Babar & Adnan, Awais & Loo, Jonathan & Anwar, Sajid, 2019. "Customer churn prediction in telecommunication industry using data certainty," Journal of Business Research, Elsevier, vol. 94(C), pages 290-301.
  3. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
  4. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
  5. Lewlisa Saha & Hrudaya Kumar Tripathy & Tarek Gaber & Hatem El-Gohary & El-Sayed M. El-kenawy, 2023. "Deep Churn Prediction Method for Telecommunication Industry," Sustainability, MDPI, vol. 15(5), pages 1-21, March.
  6. Gattermann-Itschert, Theresa & Thonemann, Ulrich W., 2021. "How training on multiple time slices improves performance in churn prediction," European Journal of Operational Research, Elsevier, vol. 295(2), pages 664-674.
  7. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
  8. Seema & Gaurav Gupta, 2024. "Development of fading channel patch based convolutional neural network models for customer churn prediction," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(1), pages 391-411, January.
  9. Feng, Yi & Yin, Yunqiang & Wang, Dujuan & Ignatius, Joshua & Cheng, T.C.E. & Marra, Marianna & Guo, Yihan, 2024. "Enhancing e-commerce customer churn management with a profit- and AUC-focused prescriptive analytics approach," Journal of Business Research, Elsevier, vol. 184(C).
  10. Liu, Zhenkun & Zhang, Ying & Abedin, Mohammad Zoynul & Wang, Jianzhou & Yang, Hufang & Gao, Yuyang & Chen, Yinghao, 2024. "Profit-driven fusion framework based on bagging and boosting classifiers for potential purchaser prediction," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
  11. Janssen, Patrick & Sadowski, Bert M., 2021. "Bias in Algorithms: On the trade-off between accuracy and fairness," 23rd ITS Biennial Conference, Online Conference / Gothenburg 2021. Digital societies and industrial transformations: Policies, markets, and technologies in a post-Covid world 238032, International Telecommunications Society (ITS).
  12. De Bock, Koen W. & Coussement, Kristof & Caigny, Arno De & Słowiński, Roman & Baesens, Bart & Boute, Robert N. & Choi, Tsan-Ming & Delen, Dursun & Kraus, Mathias & Lessmann, Stefan & Maldonado, Sebast, 2024. "Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda," European Journal of Operational Research, Elsevier, vol. 317(2), pages 249-272.
  13. Chen, Shuixia & Wang, Jian-qiang & Zhang, Hong-yu, 2019. "A hybrid PSO-SVM model based on clustering algorithm for short-term atmospheric pollutant concentration forecasting," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 41-54.
  14. Latifah Almuqren & Fatma S. Alrayes & Alexandra I. Cristea, 2021. "An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach," Future Internet, MDPI, vol. 13(7), pages 1-19, July.
  15. Arno Caigny & Kristof Coussement & Matthijs Meire & Steven Hoornaert, 2025. "Investigating the impact of undersampling and bagging: an empirical investigation for customer attrition modeling," Annals of Operations Research, Springer, vol. 346(3), pages 2401-2421, March.
  16. Chandrasekhar Valluri & Sudhakar Raju & Vivek H. Patil, 2022. "Customer determinants of used auto loan churn: comparing predictive performance using machine learning techniques," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(3), pages 279-296, September.
  17. Lessmann, Stefan & Coussement, Kristof & De Bock, Koen W. & Haupt, Johannes, 2018. "Targeting customers for profit: An ensemble learning framework to support marketing decision making," IRTG 1792 Discussion Papers 2018-012, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  18. Ebru Pekel Ozmen & Tuncay Ozcan, 2022. "A novel deep learning model based on convolutional neural networks for employee churn prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 539-550, April.
  19. Liu, Zhenkun & Jiang, Ping & De Bock, Koen W. & Wang, Jianzhou & Zhang, Lifang & Niu, Xinsong, 2024. "Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
  20. Delu Wang & Xun Xue & Yadong Wang, 2021. "Overcapacity Risk of China’s Coal Power Industry: A Comprehensive Assessment and Driving Factors," Sustainability, MDPI, vol. 13(3), pages 1-21, January.
  21. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
  22. Hui Zhu, 2023. "Oil Demand Forecasting in Importing and Exporting Countries: AI-Based Analysis of Endogenous and Exogenous Factors," Sustainability, MDPI, vol. 15(18), pages 1-19, September.
  23. Meire, Matthijs, 2021. "Customer comeback: Empirical insights into the drivers and value of returning customers," Journal of Business Research, Elsevier, vol. 127(C), pages 193-205.
  24. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.
  25. Lewlisa Saha & Hrudaya Kumar Tripathy & Soumya Ranjan Nayak & Akash Kumar Bhoi & Paolo Barsocchi, 2021. "Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review," Sustainability, MDPI, vol. 13(9), pages 1-35, May.
  26. Jean Robert Kala Kamdjoug & Hyacinthe Djanan Sando & Jules Raymond Kala & Arielle Ornela Ndassi Teutio & Sunil Tiwari & Samuel Fosso Wamba, 2024. "Data analytics-based auditing: a case study of fraud detection in the banking context," Annals of Operations Research, Springer, vol. 340(2), pages 1161-1188, September.
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