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Original Article
Churn Guard AI: Production Customer Intelligence System for Real-Time Churn Prediction Using Explainable Machine Learning
S.Ramya1
Kavipriya.M2
Abinaya.D3
Jayanthi.R4
1 Assistant Professor, Department of Information Technology, Er. Perumal Manimekalai College of Engineering Hosur,nTamil Nadu, India. 2 3 4 Department of Information Technology, Er. Perumal Manimekalai College of Engineering, Hosur, Tamil Nadu, India.
Published Online: March-April 2026
Pages: 401-405
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260602056
References
S. Coussement, K. W. De Bock, and N. M. G. Van den Poel, ”Improving customer churn prediction utilizing actionable interpretable
machine learning,” IEEE Transactions on Engineering Management, vol. 68, no. 4, pp. 1100-1115, 2021.
2. A. Ahmed, H. Sabir, and T. M. K. Ali, ”Baseline methodologies in demographic customer churn modeling,” IEEE Access, vol. 8, pp.
24510-24519, 2020.
3. M. Q. Z. Ali and A. Khan, ”Deployment challenges and API architec-tures for robust machine learning services,” ACM Transactions
on Web Systems, vol. 12, no. 3, pp. 45-61, 2022.
4. S. M. Lundberg and S. I. Lee, ”A unified approach to interpreting model predictions,” Advances in Neural Information Processing
Systems, vol. 30, 2017.
5. T. R. K. Kumar and M. J. Lee, ”Benchmarking linear and non-linear predictive algorithms for telecommunications churn,” Springer
Journal of Data Science, vol. 14, pp. 104-123, 2019.
6. H. Chen, Y. Wang, and Z. Liu, ”Random Forest optimizations applied to high-dimensional customer activity logs,” Elsevier Expert
Systems with Applications, vol. 143, p. 113060, 20207. J. Zhang, C. Q. Wu, and D. Chen, ”XGBoost and variance reduction in predictive commercial domains,” IEEE Network, vol. 35,
no. 1, pp. 162-168, 2021.
8. G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T. Liu, ”LightGBM: A highly efficient gradient boosting decision
tree,” Advances in Neural Information Processing Systems, vol. 30, pp. 3146-3154, 2017.
9. L. Kumar, S. R. Naidu, and P. Sharma, ”Analyzing sequential user logs utilizing recurrent neural networks for churn detection,”
IEEE Transactions on Artificial Intelligence, vol. 3, no. 2, pp. 210-221, 2022.
10. D. R. E. A. Brown and S. J. Smith, ”Bridging the gap: From predictive machine learning constructs to actionable operational
microservices,” IEEE Software, vol. 40, no. 5, pp. 22-30, 2023.
11. A. M. Garcia,”Expla
machine learning,” IEEE Transactions on Engineering Management, vol. 68, no. 4, pp. 1100-1115, 2021.
2. A. Ahmed, H. Sabir, and T. M. K. Ali, ”Baseline methodologies in demographic customer churn modeling,” IEEE Access, vol. 8, pp.
24510-24519, 2020.
3. M. Q. Z. Ali and A. Khan, ”Deployment challenges and API architec-tures for robust machine learning services,” ACM Transactions
on Web Systems, vol. 12, no. 3, pp. 45-61, 2022.
4. S. M. Lundberg and S. I. Lee, ”A unified approach to interpreting model predictions,” Advances in Neural Information Processing
Systems, vol. 30, 2017.
5. T. R. K. Kumar and M. J. Lee, ”Benchmarking linear and non-linear predictive algorithms for telecommunications churn,” Springer
Journal of Data Science, vol. 14, pp. 104-123, 2019.
6. H. Chen, Y. Wang, and Z. Liu, ”Random Forest optimizations applied to high-dimensional customer activity logs,” Elsevier Expert
Systems with Applications, vol. 143, p. 113060, 20207. J. Zhang, C. Q. Wu, and D. Chen, ”XGBoost and variance reduction in predictive commercial domains,” IEEE Network, vol. 35,
no. 1, pp. 162-168, 2021.
8. G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T. Liu, ”LightGBM: A highly efficient gradient boosting decision
tree,” Advances in Neural Information Processing Systems, vol. 30, pp. 3146-3154, 2017.
9. L. Kumar, S. R. Naidu, and P. Sharma, ”Analyzing sequential user logs utilizing recurrent neural networks for churn detection,”
IEEE Transactions on Artificial Intelligence, vol. 3, no. 2, pp. 210-221, 2022.
10. D. R. E. A. Brown and S. J. Smith, ”Bridging the gap: From predictive machine learning constructs to actionable operational
microservices,” IEEE Software, vol. 40, no. 5, pp. 22-30, 2023.
11. A. M. Garcia,”Expla
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