Enhancing Customer Churn Analysis by Using Real-Time Machine Learning Model

Customer churn, the loss of customers to competitors, poses a significant challenge for businesses, particularly in competitive industries such as banking and telecommunications. As a result, several customer churn analysis models have been proposed to identify at-risk customers and enable top managers to implement strategic decisions to mitigate churn and improve customer retention. Although the existing models provide top managers with promising insights for churn prediction, they rely on a batch-based training approach using fixed datasets collected at periodic intervals. While this training approach enables existing models to perform well in relatively stable environments, they, unfortunately, struggle to adapt to dynamic settings, where customer preferences shift rapidly, especially in industries with volatile market conditions, such as banking and telecom. Where, in dynamic environments, data distribution can change significantly over short periods, disabling existing models to maintain efficiency and leading to poor predictive performance, increased misclassification rates, and suboptimal decision-making by top executives, ultimately exacerbating customer churn. To address these limitations, this research proposes RCE, a real-time, continual learning-based, ensemble learning model. RCE integrates an event-driven development approach for real-time churn analysis with a replay-based continual learning mechanism to adapt to evolving customer behaviors without catastrophic forgetting, and RCE implements a stacked ensemble learning for customer churn classification. Unlike existing models, RCE continuously processes streaming data, ensuring adaptability and generalization in fast-changing environments, and providing instantaneous insights that enable decision-makers to respond swiftly to emerging risks, market fluctuations, and customer behavior changes. RCE is evaluated using the Churn Modelling benchmark dataset for European banks, achieving performance with a 95.65% accuracy; however, in dynamic environments, RCE accomplishes an average accuracy (ACC) of 86.75% and an average forgetting rate (FR) of 13.25% across tasksT_i. The results demonstrate that RCE outperforms existing models in predictive accuracy, adaptability, and robustness across multiple tasks, especially in dynamic environments. Finally, this research discusses the proposed model’s limitations and outlines directions for future improvements in real-time customer churn analysis.

@article{Ghallab2025,
  title     = {Enhancing Customer Churn Analysis by Using Real-Time Machine Learning Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Haitham Ghallab and Mona Nasr and Hanan Fahmy},
  doi       = {10.14569/IJACSA.2025.0160538},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160538}
}