Home / Current Issue / Paper 1724207
Dynamic Churn Prediction and Value-Aware Customer Retention in E-Commerce
Subject area: Science,Engineering and Technology · Area of research: Predictive Analytics
DOI: 10.64388/IREV10I4-1724207
Abstract
Customer retention is essential for business profit in the cut-throat world of e-commerce. Nevertheless, classic customer churn prediction techniques tend to implement static batch learning models that require periodic retraining and are concentrated only on prediction accuracy and do not pay enough attention to the financial side of things. In this paper, an intelligent approach to identify potential at-risk customers based on a dynamic and value-centric model is introduced. The suggested method is based on using RFM criteria and customer behavioural patterns to give an insight into customer activity and purchasing behaviour. Incremental learning through stochastic gradient descent (SGD) decreases the need for full retraining by continually adjusting the model parameters as new observations arrive, thus solving the limitations associated with static learning. SHapley Additive exPlanations (SHAP), which illustrates the contribution of each feature in every possible outcome, increases the interpretability of the model. A combination of both probability of attrition and customer value is used by Value-Aware Prioritization in assessing the Expected Financial Loss, thus ensuring that the available retention efforts are focused on customers with more financial impact. Risk levels and customer value are then converted into suitable retention actions via a rule-based decision engine. By including adaptive learning, transparent decision support, and financially informed prioritisation, the suggested framework provides e-commerce companies with a workable solution to turn churn analysis into a continuous customer retention strategy.
Keywords
customer churn prediction; e-commerce; online learning; SHAP; RFM analysis; value-aware prioritization
References
[1] Alshamsi SA. Customer churn prediction in e-commerce sector [master’s capstone project]. Dubai: Rochester Institute of Technology, RIT Dubai; 2022.
[2] Chang M. E-commerce live streaming data-based customer churn prediction [master’s thesis]. Rotterdam: Erasmus School of Economics, Erasmus University Rotterdam; 2023. Available from: http://hdl.handle.net/2105/66734
[3] Shobana J, Gangadhar C, Arora RK, Renjith PN, Bamini J, Chincholkar YD. E-commerce customer churn prevention using machine learning-based business intelligence strategy. Measurement: Sensors. 2023;27:100728. https://doi.org/10.1016/j.measen.2023.100728
[4] Xiahou X, Harada Y. B2C e-commerce customer churn prediction based on K-means and SVM. Journal of Theoretical and Applied Electronic Commerce Research. 2022;17(2):458–75. https://doi.org/10.3390/jtaer17020024
[5] Li J. Machine learning-based customer churn prediction: an analysis of e-commerce data. International Journal of Computer Applications. 2024;186(48):22–5. https://doi.org/10.5120/ijca2024924140
[6] Panchal NM. Theoretical perspectives on customer churn prediction in e-commerce using machine learning and big data analytics. SSRN [Preprint]. 2025. Available from: https://ssrn.com/abstract=7013501
How to cite this paper
@article{1724207,
author = {Anand Prakash, Ritu Kumari, Anjali Tyagi, Rehan Dhanuka},
title = {Dynamic Churn Prediction and Value-Aware Customer Retention in E-Commerce},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {3297-3307},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724207.pdf},
abstract = {Customer retention is essential for business profit in the cut-throat world of e-commerce. Nevertheless, classic customer churn prediction techniques tend to implement static batch learning models that require periodic retraining and are concentrated only on prediction accuracy and do not pay enough attention to the financial side of things. In this paper, an intelligent approach to identify potential at-risk customers based on a dynamic and value-centric model is introduced. The suggested method is based on using RFM criteria and customer behavioural patterns to give an insight into customer activity and purchasing behaviour. Incremental learning through stochastic gradient descent (SGD) decreases the need for full retraining by continually adjusting the model parameters as new observations arrive, thus solving the limitations associated with static learning. SHapley Additive exPlanations (SHAP), which illustrates the contribution of each feature in every possible outcome, increases the interpretability of the model. A combination of both probability of attrition and customer value is used by Value-Aware Prioritization in assessing the Expected Financial Loss, thus ensuring that the available retention efforts are focused on customers with more financial impact. Risk levels and customer value are then converted into suitable retention actions via a rule-based decision engine. By including adaptive learning, transparent decision support, and financially informed prioritisation, the suggested framework provides e-commerce companies with a workable solution to turn churn analysis into a continuous customer retention strategy.},
keywords = {customer churn prediction; e-commerce; online learning; SHAP; RFM analysis; value-aware prioritization},
month = {October},
doi = {https://doi.org/10.64388/IREV10I4-1724207}
}