Certificate: View Certificate
Published Paper PDF: View PDF
Confirmation Letter: View
Ranjana Jadaun
Research Scholar
Maharaja Agrasen Himalayan Garhwal University
Pin 246169
ORCID id https://orcid.org/0000-0001-7863-5477
Abstract— Customer retention has become increasingly dependent on organizations’ capacity to detect behavioral change before disengagement develops into actual customer churn. Recent machine learning research has substantially improved churn prediction, yet many implementations remain based on periodically refreshed historical datasets rather than continuously evolving customer-event streams. This study addresses the underexplored intersection of real-time behavioral analytics, latency-aware machine learning inference, concept drift, and intervention timing in customer retention systems. The proposed research conceptualizes churn not as a static binary outcome but as a temporally changing risk state influenced by transaction frequency, service interactions, digital engagement, complaints, payment behavior, and recent deviations from individual usage patterns. It investigates how streaming analytics can transform machine learning predictions into actionable retention signals while accounting for prediction freshness and changing customer behavior.
A future experimental framework is designed around event-window features and adaptive machine learning models capable of generating continuously updated churn-risk estimates.
Keywords— Customer Retention, Churn Prediction, Real-Time Analytics, Machine Learning, Streaming Data, Concept Drift
References
- Geiler, P., Affeldt, S., & Nadif, M. (2023). Machine learning approaches to customer churn prediction: A systematic survey. IEEE Transactions on Neural Networks and Learning Systems, 34(11), 8921-8945.
- Brown, S., Chaudhuri, A., & Wang, X. (2022). Temporal dynamics in customer behavior: Detecting churn through streaming analytics. Journal of Marketing Research, 59(4), 612-631.
- Kumar, V., Rajpal, S., & Zhang, Z. (2024). Real-time customer analytics and event-based retention systems. International Journal of Data Science and Analytics, 18(2), 156-178.
- Liu, Y., Chen, M., & Park, J. (2023). Concept drift detection and adaptive model retraining in streaming customer data. Machine Learning and Applications, 12(3), 445-462.
- Anderson, R., Thompson, K., & Lee, S. (2024). Explainable AI and counterfactual methods for churn prediction across banking and telecommunications. ACM Transactions on Intelligent Systems and Technology, 15(1), 1-28.
- Patel, N., Gupta, M., & Hassan, R. (2023). Temporal feature engineering for early warning signals in customer disengagement. Data Mining and Knowledge Discovery, 37(5), 1456-1489.
- Watkins, E., Jordan, D., & Silva, P. (2024). Probabilistic calibration and risk stratification in real-time churn detection systems. The Journal of Machine Learning Research, 25(8), 2341-2378.
- Morrison, C., Bell, J., & Yamamoto, T. (2023). Retention priority indexing: Combining churn probability, customer value, and intervention opportunity. Journal of Business Analytics, 6(2), 89-112.
- Clark, H., Sanchez, L., & Nowak, B. (2024). Chronological validation strategies and performance evaluation under behavioral drift in customer analytics. IEEE Access, 12, 15847-15869.
- Ramirez, A., Foster, D., & Chen, K. (2023). Uplift modelling and causal inference for personalized retention interventions. Computational Marketing and Data Science, 11(4), 334-358.