A Comparative Study of Machine Learning Algorithms for Customer Churn Predictive in e-Commerce
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Abstract
Customer churn rate is one of the main challenges that e-Commerce businesses must address, as it may affect revenue and long-term growth. In this paper, predictive analysis was used to identify potential customers who would be lost. It uses two different types of data to evaluate and compare the performance of five Machine Learning (ML) models, namely logistic regression, decision tree, support vector machine, gradient boost and random forest. Accuracy, precision, recall rate and F1 score are used as indicators of model performance. Among them, random forest achieved the highest performance across both datasets, demonstrating superior predictive power and consistency. By validating results on multiple datasets, this paper not only ensures reliability but also highlights the stability of churn prediction models across varying data conditions. The findings indicate that effective churn prediction can support proactive customer retention strategies, enabling e-Commerce businesses to minimize losses and promote sustainable growth. The use of two datasets represents a notable contribution, as it provides a stronger basis for assessing model generalizability and avoiding bias toward a single data source. The findings indicate that accurate churn prediction can support proactive retention strategies, enabling e-Commerce businesses to reduce churned customers, enhance loyalty, and maintain a competitive advantage. Overall, this paper underscores the value of data-driven approaches, demonstrates the benefits of employing multiple datasets for model validation, and provides actionable insights for businesses aiming to implement effective and sustainable customer retention strategies.
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References
[1] Y. Feng, Y. Yin, D. Wang, J. Ignatius, T. C. E. Cheng, M. Marra, and Y. Guo, “Enhancing e-Commerce customer churn management with a profit- and AUC-focused prescriptive analytics approach,” Journal of Business Research, vol. 184, Art. no. 114872, 2024, doi: 10.1016/j.jbusres.2024.114872.
[2] D. Raut, A. Nimje, D. Bhagwatkar, N. Rakesh, R. Sharma, and M. Gulhane, “Predicting customer churn rate using machine learning,” in 2025 10th International Conference on Signal Processing and Communication (ICSC), Feb. 2025, pp. 529–534. doi: 10.1109/ICSC64553.2025.10968946.
[3] N. Danaan, A. Deme, G. Bibu, M. A. Lawal, and I. Z. Yakubu, “An integrated machine learning model for e-Commerce churn prediction,” International Journal of Innovative Science and Research Technology, vol. 10, no. 6, pp. 2762–2778, Jul. 2025. doi: 10.38124/ijisrt/25jun1816.
[4] S. J. Shobana, Ch. Gangadhar, R. K. Arora, P. N. Renjith, J. Bamini, and Y. D. Chincholkar, “E-Commerce customer churn prevention using machine learning-based business intelligence strategy,” Measurement: Sensors, vol. 27, Art. No. 100728, Jun. 2023. doi: 10.1016/j.measen.2023.100728.
[5] I. Jahan and T. F. Sanam, “An improved machine learning-based customer churn prediction for insight and recommendation in e-Commerce,” in 2022 25th International Conference on Computer and Information Technology (ICCIT), Cox’s Bazar, Bangladesh: IEEE, Dec. 2022, pp. 1–6. doi: 10.1109/ICCIT57492.2022.10054771.
[6] R. Alexander, A. Maria Nancy, E. Aswini, and P. S. Sarao, “Comparative performance analysis using machine learning for churn prediction in e-Commerce,” in 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS), Pudukkottai, India: IEEE, Dec. 2023, pp. 537–542. doi: 10.1109/ICACRS58579.2023.10404319.
[7] S. Upadhyay and M. Rajalakshmi, “Customer churn prediction using machine learning,” in 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India: IEEE, Jul. 2023, pp. 1–7. doi: 10.1109/ICCCNT56998.2023.10307748.
[8] J. Sharma and S. Neema, “CusCP: AI-driven system for predictive modeling of customer churn in e-Commerce using machine learning,” in 2023 Seventh International Conference on Image Information Processing (ICIIP), Solan, India: IEEE, Nov. 2023, pp. 924–929. doi: 10.1109/ICIIP61524.2023.10537663.
[9] IBM, “What is machine learning (ML)?,” IBM Think, Sep. 22, 2021. [Online]. Available: https://www.ibm.com/think/topics/machine-learning
[10] M. A. Al Rahib, N. Saha, R. Mia, and A. Sattar, “Customer data prediction and analysis in e-Commerce using machine learning,” Bulletin of Electrical Engineering and Informatics, vol. 13, no. 4, pp. 2624–2633, Aug. 2024, doi: 10.11591/eei.v13i4.6420.
[11] IBM, “What Is a Machine Learning Algorithm?,” IBM Think, Nov. 3, 2023. [Online]. Available: https://www.ibm.com/think/topics/machine-learning-algorithms
[12] M. Afzal, S. Rahman, D. Singh, and A. Imran, “Cross-sector application of machine learning in telecommunications: enhancing customer retention through comparative analysis of ensemble methods,” IEEE Access, vol. 12, pp. 115256–115267, 2024, doi: 10.1109/ACCESS.2024.3445281.
[13] X. Xiahou and Y. Harada, “B2C e-Commerce customer churn prediction based on K-Means and SVM,” Journal of Theoretical and Applied Electronic Commerce Research, vol. 17, no. 2, pp. 458–475, Apr. 2022, doi: 10.3390/jtaer17020024.
[14] A. R. Lubis, S. Prayudani, J. Polmed, O. Nugroho, Y. Y. Lase, and M. Lubis, “Comparison of model in predicting customer churn based on users’ habits on e-Commerce,” in 2022 5th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), Yogyakarta, Indonesia: IEEE, Dec. 2022, pp. 300–305. doi: 10.1109/ISRITI56927.2022.10052834.
[15] P. Aggarwal and V. Vijayakumar, “Customer churn prediction in the telecom sector,” in 2024 3rd International Conference on Artificial Intelligence for Internet of Things (AIIoT), Vellore, India: IEEE, May 2024, pp. 1–6. doi: 10.1109/AIIoT58432.2024.10574660.
[16] S. Kim and H. Lee, “Customer churn prediction in influencer commerce: an application of decision trees,” Procedia Computer Science, vol. 199, pp. 1332–1339, Jan. 2022, doi: 10.1016/j.procs.2022.01.169.
[17] S. Brinthakumari, K. Patil, K. Pawar, and S. Jogale, “Customer churn prediction using machine learning,” International Journal of Scientific Research in Engineering and Management (IJSREM), vol. 8, no. 4, pp. 1–5, Apr. 2024. doi: 10.55041/IJSREM29907.
[18] G. Chaubey, P. R. Gavhane, D. Bisen, and S. K. Arjaria, “Customer purchasing behavior prediction using machine learning classification techniques,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 12, pp. 16133–16157, Dec. 2023, doi: 10.1007/s12652-022-03837-6.
[19] J. Li, “Customer churn prediction using machine learning: a case study of e-Commerce data,” International Journal of Computer Applications, vol. 186, no. 48, pp. 22–25, Nov. 2024, doi: 10.5120/ijca2024924140.
[20] T. M. Ghomeeda, “Utilizing machine learning algorithms for the comprehensive prediction and analytical study of customer churn in electronic commerce,” in ICSE 2024, Libya: BWU, 2024. [Online]. Available: http://bwu.edu.ly/icse2024.
[21] I. Boukrouh and A. Azmani, “Explainable machine learning models applied to predicting customer churn for e-Commerce,” IAES International Journal of Artificial Intelligence, vol. 14, no. 1, pp. 286–297, Feb. 2025, doi: 10.11591/ijai.v14.i1.pp286-297.
[22] “Digital Marketing | E-Commerce | Customer Behavior,” Kaggle, [Online]. Available: https://www.kaggle.com/datasets/ermismbatuhan/digital-marketing-ecommerce-customer-behavior. Accessed: Jul. 5, 2025.
[23] “Ecommerce Customer Churn Analysis and Prediction,” Kaggle, [Online]. Available: https://www.kaggle.com/datasets/ankitverma2010/ecommerce-customer-churn-analysis-and-prediction. Accessed: Jul. 5, 2025.
[24] J. Hang, “Customer churn prediction model for E-Commerce using an enhanced adaptive boosting algorithm,” Discover Applied Sciences, vol. 8, Art. no. 747, 2026, doi: 10.1007/s42452-026-08745-4.
[25] R. B. Santoso and B. W. Sari, “Analisis simulasi prediksi customer churn e-Commerce menggunakan algoritma Random Forest berbasis data sintetis,” JMBI UNSRAT (Jurnal Ilmiah Manajemen Bisnis dan Inovasi Universitas Sam Ratulangi), vol. 13, no. 1, pp. 317–331, 2026, doi: 10.35794/jmbi.v13i1.67545.