ON THE USE OF BOOSTING ALGORITHMS FOR PREDICTING CONSUMER CHURN RATE ON AN E-COMMERCE PLATFORM
0
Online Published:
25/06/2026
Section:
Business Administration, Marketing, Commerce, and Tourism
Main Article Content
Abstract
This paper explores the application of various machine learning models to analyze the churn rate of an e-commerce platform. Logistic regression, decision tree, k-nearest neighbor, random forest, Adaboost, and XGboost were employed to predict customer churn and identify the most influential factors affecting it. The dataset includes 20 attributes, including tenure, complaint, preferred login device, and satisfaction score. The research aims to determine the most effective machine learning model for churn rate prediction. After comprehensive analysis and comparison of the models, the XG boost algorithm emerged as the best-performing one, exhibiting superior accuracy and predictive capabilities. Additionally, this study seeks to identify the key factors impacting customer churn. The results indicated that tenure and complaint were the most significant variables contributing to churn rate. Understanding these influential factors can aid e-commerce platforms in devising targeted strategies to mitigate churn and enhance customer retention. Based on the findings, this study proposes several managerial suggestions for e-commerce platforms to improve customer retention. First, implementing robust customer service processes is crucial to address complaints promptly and effectively. Offering of personalized assistance and timely resolutions can foster positive customer experiences, consequently reducing churn. Second, strategies to enhance customer retention such as loyalty programs, targeted discounts, and tailored marketing campaigns should be devised to incentivize customer loyalty and encourage repeated purchases.
Keywords
boosting algorithms; churn analysis; e-commerce; machine learning; prediction
Article Details
Field of Economic (JEL Codes)
References
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Aggarwal, V., Gupta, V., Singh, P., Sharma, K., & Sharma, N. (2019). Detection of Spatial Outlier by Using Improved Z-Score Test. 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), 788–790. https://doi.org/10.1109/ICOEI.2019.8862582
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Anzanello, M. J., & Fogliatto, F. S. (2011). Learning curve models and applications: Literature review and research directions. International Journal of Industrial Ergonomics, 41(5), 573–583. https://doi.org/10.1016/j.ergon.2011.05.001
Ascarza, E., Neslin, S. A., Netzer, O., Anderson, Z., Fader, P. S., Gupta, S., Hardie, B. G. S., Lemmens, A., Libai, B., Neal, D., Provost, F., & Schrift, R. (2018). In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions. Customer Needs and Solutions, 5(1–2), 65–81. https://doi.org/10.1007/s40547-017-0080-0
Athanasiou, V., & Maragoudakis, M. (2017). A Novel, Gradient Boosting Framework for Sentiment Analysis in Languages where NLP Resources Are Not Plentiful: A Case Study for Modern Greek. Algorithms, 10(1), 34. https://doi.org/10.3390/a10010034
Bentéjac, C., Csörgő, A., & Martínez-Muñoz, G. (2021a). A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54(3), 1937–1967. https://doi.org/10.1007/s10462-020-09896-5
Chen, N., & Yang, Y. (2021). The impact of customer experience on consumer purchase intention in cross-border E-commerce——Taking network structural embeddedness as mediator variable. Journal of Retailing and Consumer Services, 59, 102344. https://doi.org/10.1016/j.jretconser.2020.102344
Dhote, S., Vichoray, C., Pais, R., Baskar, S., & Mohamed Shakeel, P. (2020). Hybrid geometric sampling and AdaBoost based deep learning approach for data imbalance in E-commerce. Electronic Commerce Research, 20(2), 259–274. https://doi.org/10.1007/s10660-019-09383-2
Drummond, C., & Holte, R. C. (2003). C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling. Workshop on Learning from Imbalanced Datasets II, 11.
Fatonah, S., Yulandari, A., & Wibowo, F. W. (2018). A Review of E-Payment System in E-Commerce. Journal of Physics: Conference Series, 1140, 012033. https://doi.org/10.1088/1742-6596/1140/1/012033
Ferdinandy, B., Gerencsér, L., Corrieri, L., Perez, P., Újváry, D., Csizmadia, G., & Miklósi, Á. (2020). Challenges of machine learning model validation using correlated behaviour data: Evaluation of cross-validation strategies and accuracy measures. PLOS ONE, 15(7), e0236092. https://doi.org/10.1371/journal.pone.0236092
Ferreira, A. J., & Figueiredo, M. A. T. (2012). Boosting Algorithms: A Review of Methods, Theory, and Applications. In Ensemble Machine Learning (pp. 35–85). Springer New York. https://doi.org/10.1007/978-1-4419-9326-7_2
Hajli, M. N. (2014). A study of the impact of social media on consumers. International Journal of Market Research, 56(3), 387–404. https://doi.org/10.2501/IJMR-2014-025
Hargreaves, C. A. (2019). A Machine Learning Algorithm for Churn Reduction; Revenue Maximization: An Application in the Telecommunication Industry. International Journal of Future Computer and Communication, 8(4), 109–113. https://doi.org/10.18178/ijfcc.2019.8.4.550
Haryanti, T., & Subriadi, A. P. (2020). The Evolution Adoption Technology Theory for E-Commerce. International Journal of Electronic Commerce Studies, 11(2). https://doi.org/10.7903/ijecs.1910
Hossin, M., & Sulaiman, M. N. (2015). A Review on Evaluation Metrics for Data Classification Evaluations. International Journal of Data Mining & Knowledge Management Process, 5(2), 01–11. https://doi.org/10.5121/ijdkp.2015.5201
Khalid, S., Khalil, T., & Nasreen, S. (2014). A survey of feature selection and feature extraction techniques in machine learning. 2014 Science and Information Conference, 372–378. https://doi.org/10.1109/SAI.2014.6918213
Koul, S., & Philip, T. M. (2021). Customer Segmentation Techniques on E-Commerce. 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 135–138. https://doi.org/10.1109/ICACITE51222.2021.9404659
Kuhn, M., & Johnson, K. (2019). Feature Engineering and Selection. Chapman and Hall/CRC. https://doi.org/10.1201/9781315108230
Lawson, C., & Montgomery, D. C. (2006). Logistic Regression Analysis of Customer Satisfaction Data. Quality and Reliability Engineering International, 22(8), 971–984. https://doi.org/10.1002/qre.775
Lee, C. S., Cheang, P. Y. S., & Moslehpour, M. (2022). Predictive analytics in business analytics: decision tree. Advances in Decision Sciences, 26(1), 1–29.
Li, K., Zhang, W., Lu, Q., & Fang, X. (2014). An Improved SMOTE Imbalanced Data Classification Method Based on Support Degree. 2014 International Conference on Identification, Information and Knowledge in the Internet of Things, 34–38. https://doi.org/10.1109/IIKI.2014.14
Liang, T.-P., & Turban, E. (2011). Introduction to the Special Issue Social Commerce: A Research Framework for Social Commerce. International Journal of Electronic Commerce, 16(2), 5–14. https://doi.org/10.2753/JEC1086-4415160201
Liu, X., Ahmad, S. F., Anser, M. K., Ke, J., Irshad, M., Ul-Haq, J., & Abbas, S. (2022). Cyber security threats: A never-ending challenge for e-commerce. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.927398
Matuszelański, K., & Kopczewska, K. (2022). Customer Churn in Retail E-Commerce Business: Spatial and Machine Learning Approach. Journal of Theoretical and Applied Electronic Commerce Research, 17(1), 165–198. https://doi.org/10.3390/jtaer17010009
Miller, A. P., & Hosanagar, K. (2022). An Empirical Meta-analysis of E-commerce A/B Testing Strategies.
Mishra, S. (2017). Handling Imbalanced Data: SMOTE vs. Random Undersampling. International Research Journal of Engineering and Technology. www.irjet.net
Mitrović, S., Baesens, B., Lemahieu, W., & De Weerdt, J. (2021). tcc2vec: RFM-informed representation learning on call graphs for churn prediction. Information Sciences, 557, 270–285. https://doi.org/10.1016/j.ins.2019.02.044
Moriuchi, E., Landers, V. M., Colton, D., & Hair, N. (2021). Engagement with chatbots versus augmented reality interactive technology in e-commerce. Journal of Strategic Marketing, 29(5), 375–389. https://doi.org/10.1080/0965254X.2020.1740766
Omar, L., & Ivrissimtzis, I. (2019). Using theoretical ROC curves for analysing machine learning binary classifiers. Pattern Recognition Letters, 128, 447–451. https://doi.org/10.1016/j.patrec.2019.10.004
Priansa, D. J., & Suryawardani, B. (2020). Effects of E-Marketing and Social Media Marketing on E-commerce Shopping Decisions. Jurnal Manajemen Indonesia, 20(1). https://doi.org/10.25124/jmi.v20i1.2800
Raeisi, S., & Sajedi, H. (2020). E-Commerce Customer Churn Prediction By Gradient Boosted Trees. 2020 10th International Conference on Computer and Knowledge Engineering (ICCKE), 055–059. https://doi.org/10.1109/ICCKE50421.2020.9303661
Rizaldi, A., & Madany, Z. (2021). Impact of E-Commerce in Industry. International Journal of Research and Applied Technology, 1(2), 59–64. https://doi.org/10.34010/injuratech.v1i2.5914
Salazar, J. J., Garland, L., Ochoa, J., & Pyrcz, M. J. (2022). Fair train-test split in machine learning: Mitigating spatial autocorrelation for improved prediction accuracy. Journal of Petroleum Science and Engineering, 209, 109885. https://doi.org/10.1016/j.petrol.2021.109885
Schapire, R. E. (2013). Explaining AdaBoost. In Empirical Inference (pp. 37–52). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-41136-6_5
Shukla, V., Yagyasen, D., Gupta, R., & Singh, S. (2023). A REVIEW ON CUSTOMER DATA ANALYTICS IN E-COMMERCE. Www.Irjmets.Com @International Research Journal of Modernization in Engineering, 800. www.irjmets.com
Song, P., & Liu, Y. (2020). An XGBoost Algorithm for Predicting Purchasing Behaviour on E-Commerce Platforms. Tehnicki Vjesnik - Technical Gazette, 27(5). https://doi.org/10.17559/TV-20200808113807
Speiser, J. L., Miller, M. E., Tooze, J., & Ip, E. (2019). A comparison of random forest variable selection methods for classification prediction modeling. Expert Systems with Applications, 134, 93–101. https://doi.org/10.1016/j.eswa.2019.05.028
Vujovic, Ž. Ð. (2021). Classification Model Evaluation Metrics. International Journal of Advanced Computer Science and Applications, 12(6). https://doi.org/10.14569/IJACSA.2021.0120670
Wang, S., Dai, Y., Shen, J., & Xuan, J. (2021). Research on expansion and classification of imbalanced data based on SMOTE algorithm. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-03430-5
Wu, Z., Jing, L., Wu, B., & Jin, L. (2022). A PCA-AdaBoost model for E-commerce customer churn prediction. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04526-5
Yu, X., Guo, S., Guo, J., & Huang, X. (2011). An extended support vector machine forecasting framework for customer churn in e-commerce. Expert Systems with Applications, 38(3), 1425–1430. https://doi.org/10.1016/j.eswa.2010.07.049
Yu, Y., Wang, X., Zhong, R. Y., & Huang, G. Q. (2016). E-commerce Logistics in Supply Chain Management: Practice Perspective. Procedia CIRP, 52, 179–185. https://doi.org/10.1016/j.procir.2016.08.002
Zhang, P., Jia, Y., & Shang, Y. (2022). Research and application of XGBoost in imbalanced data. International Journal of Distributed Sensor Networks, 18(6), 155013292211069. https://doi.org/10.1177/15501329221106935
Aggarwal, V., Gupta, V., Singh, P., Sharma, K., & Sharma, N. (2019). Detection of Spatial Outlier by Using Improved Z-Score Test. 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), 788–790. https://doi.org/10.1109/ICOEI.2019.8862582
Ahn, J., Hwang, J., Kim, D., Choi, H., & Kang, S. (2020). A Survey on Churn Analysis in Various Business Domains. IEEE Access, 8, 220816–220839. https://doi.org/10.1109/ACCESS.2020.3042657
Anzanello, M. J., & Fogliatto, F. S. (2011). Learning curve models and applications: Literature review and research directions. International Journal of Industrial Ergonomics, 41(5), 573–583. https://doi.org/10.1016/j.ergon.2011.05.001
Ascarza, E., Neslin, S. A., Netzer, O., Anderson, Z., Fader, P. S., Gupta, S., Hardie, B. G. S., Lemmens, A., Libai, B., Neal, D., Provost, F., & Schrift, R. (2018). In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions. Customer Needs and Solutions, 5(1–2), 65–81. https://doi.org/10.1007/s40547-017-0080-0
Athanasiou, V., & Maragoudakis, M. (2017). A Novel, Gradient Boosting Framework for Sentiment Analysis in Languages where NLP Resources Are Not Plentiful: A Case Study for Modern Greek. Algorithms, 10(1), 34. https://doi.org/10.3390/a10010034
Bentéjac, C., Csörgő, A., & Martínez-Muñoz, G. (2021a). A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54(3), 1937–1967. https://doi.org/10.1007/s10462-020-09896-5
Chen, N., & Yang, Y. (2021). The impact of customer experience on consumer purchase intention in cross-border E-commerce——Taking network structural embeddedness as mediator variable. Journal of Retailing and Consumer Services, 59, 102344. https://doi.org/10.1016/j.jretconser.2020.102344
Dhote, S., Vichoray, C., Pais, R., Baskar, S., & Mohamed Shakeel, P. (2020). Hybrid geometric sampling and AdaBoost based deep learning approach for data imbalance in E-commerce. Electronic Commerce Research, 20(2), 259–274. https://doi.org/10.1007/s10660-019-09383-2
Drummond, C., & Holte, R. C. (2003). C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling. Workshop on Learning from Imbalanced Datasets II, 11.
Fatonah, S., Yulandari, A., & Wibowo, F. W. (2018). A Review of E-Payment System in E-Commerce. Journal of Physics: Conference Series, 1140, 012033. https://doi.org/10.1088/1742-6596/1140/1/012033
Ferdinandy, B., Gerencsér, L., Corrieri, L., Perez, P., Újváry, D., Csizmadia, G., & Miklósi, Á. (2020). Challenges of machine learning model validation using correlated behaviour data: Evaluation of cross-validation strategies and accuracy measures. PLOS ONE, 15(7), e0236092. https://doi.org/10.1371/journal.pone.0236092
Ferreira, A. J., & Figueiredo, M. A. T. (2012). Boosting Algorithms: A Review of Methods, Theory, and Applications. In Ensemble Machine Learning (pp. 35–85). Springer New York. https://doi.org/10.1007/978-1-4419-9326-7_2
Hajli, M. N. (2014). A study of the impact of social media on consumers. International Journal of Market Research, 56(3), 387–404. https://doi.org/10.2501/IJMR-2014-025
Hargreaves, C. A. (2019). A Machine Learning Algorithm for Churn Reduction; Revenue Maximization: An Application in the Telecommunication Industry. International Journal of Future Computer and Communication, 8(4), 109–113. https://doi.org/10.18178/ijfcc.2019.8.4.550
Haryanti, T., & Subriadi, A. P. (2020). The Evolution Adoption Technology Theory for E-Commerce. International Journal of Electronic Commerce Studies, 11(2). https://doi.org/10.7903/ijecs.1910
Hossin, M., & Sulaiman, M. N. (2015). A Review on Evaluation Metrics for Data Classification Evaluations. International Journal of Data Mining & Knowledge Management Process, 5(2), 01–11. https://doi.org/10.5121/ijdkp.2015.5201
Khalid, S., Khalil, T., & Nasreen, S. (2014). A survey of feature selection and feature extraction techniques in machine learning. 2014 Science and Information Conference, 372–378. https://doi.org/10.1109/SAI.2014.6918213
Koul, S., & Philip, T. M. (2021). Customer Segmentation Techniques on E-Commerce. 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 135–138. https://doi.org/10.1109/ICACITE51222.2021.9404659
Kuhn, M., & Johnson, K. (2019). Feature Engineering and Selection. Chapman and Hall/CRC. https://doi.org/10.1201/9781315108230
Lawson, C., & Montgomery, D. C. (2006). Logistic Regression Analysis of Customer Satisfaction Data. Quality and Reliability Engineering International, 22(8), 971–984. https://doi.org/10.1002/qre.775
Lee, C. S., Cheang, P. Y. S., & Moslehpour, M. (2022). Predictive analytics in business analytics: decision tree. Advances in Decision Sciences, 26(1), 1–29.
Li, K., Zhang, W., Lu, Q., & Fang, X. (2014). An Improved SMOTE Imbalanced Data Classification Method Based on Support Degree. 2014 International Conference on Identification, Information and Knowledge in the Internet of Things, 34–38. https://doi.org/10.1109/IIKI.2014.14
Liang, T.-P., & Turban, E. (2011). Introduction to the Special Issue Social Commerce: A Research Framework for Social Commerce. International Journal of Electronic Commerce, 16(2), 5–14. https://doi.org/10.2753/JEC1086-4415160201
Liu, X., Ahmad, S. F., Anser, M. K., Ke, J., Irshad, M., Ul-Haq, J., & Abbas, S. (2022). Cyber security threats: A never-ending challenge for e-commerce. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.927398
Matuszelański, K., & Kopczewska, K. (2022). Customer Churn in Retail E-Commerce Business: Spatial and Machine Learning Approach. Journal of Theoretical and Applied Electronic Commerce Research, 17(1), 165–198. https://doi.org/10.3390/jtaer17010009
Miller, A. P., & Hosanagar, K. (2022). An Empirical Meta-analysis of E-commerce A/B Testing Strategies.
Mishra, S. (2017). Handling Imbalanced Data: SMOTE vs. Random Undersampling. International Research Journal of Engineering and Technology. www.irjet.net
Mitrović, S., Baesens, B., Lemahieu, W., & De Weerdt, J. (2021). tcc2vec: RFM-informed representation learning on call graphs for churn prediction. Information Sciences, 557, 270–285. https://doi.org/10.1016/j.ins.2019.02.044
Moriuchi, E., Landers, V. M., Colton, D., & Hair, N. (2021). Engagement with chatbots versus augmented reality interactive technology in e-commerce. Journal of Strategic Marketing, 29(5), 375–389. https://doi.org/10.1080/0965254X.2020.1740766
Omar, L., & Ivrissimtzis, I. (2019). Using theoretical ROC curves for analysing machine learning binary classifiers. Pattern Recognition Letters, 128, 447–451. https://doi.org/10.1016/j.patrec.2019.10.004
Priansa, D. J., & Suryawardani, B. (2020). Effects of E-Marketing and Social Media Marketing on E-commerce Shopping Decisions. Jurnal Manajemen Indonesia, 20(1). https://doi.org/10.25124/jmi.v20i1.2800
Raeisi, S., & Sajedi, H. (2020). E-Commerce Customer Churn Prediction By Gradient Boosted Trees. 2020 10th International Conference on Computer and Knowledge Engineering (ICCKE), 055–059. https://doi.org/10.1109/ICCKE50421.2020.9303661
Rizaldi, A., & Madany, Z. (2021). Impact of E-Commerce in Industry. International Journal of Research and Applied Technology, 1(2), 59–64. https://doi.org/10.34010/injuratech.v1i2.5914
Salazar, J. J., Garland, L., Ochoa, J., & Pyrcz, M. J. (2022). Fair train-test split in machine learning: Mitigating spatial autocorrelation for improved prediction accuracy. Journal of Petroleum Science and Engineering, 209, 109885. https://doi.org/10.1016/j.petrol.2021.109885
Schapire, R. E. (2013). Explaining AdaBoost. In Empirical Inference (pp. 37–52). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-41136-6_5
Shukla, V., Yagyasen, D., Gupta, R., & Singh, S. (2023). A REVIEW ON CUSTOMER DATA ANALYTICS IN E-COMMERCE. Www.Irjmets.Com @International Research Journal of Modernization in Engineering, 800. www.irjmets.com
Song, P., & Liu, Y. (2020). An XGBoost Algorithm for Predicting Purchasing Behaviour on E-Commerce Platforms. Tehnicki Vjesnik - Technical Gazette, 27(5). https://doi.org/10.17559/TV-20200808113807
Speiser, J. L., Miller, M. E., Tooze, J., & Ip, E. (2019). A comparison of random forest variable selection methods for classification prediction modeling. Expert Systems with Applications, 134, 93–101. https://doi.org/10.1016/j.eswa.2019.05.028
Vujovic, Ž. Ð. (2021). Classification Model Evaluation Metrics. International Journal of Advanced Computer Science and Applications, 12(6). https://doi.org/10.14569/IJACSA.2021.0120670
Wang, S., Dai, Y., Shen, J., & Xuan, J. (2021). Research on expansion and classification of imbalanced data based on SMOTE algorithm. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-03430-5
Wu, Z., Jing, L., Wu, B., & Jin, L. (2022). A PCA-AdaBoost model for E-commerce customer churn prediction. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04526-5
Yu, X., Guo, S., Guo, J., & Huang, X. (2011). An extended support vector machine forecasting framework for customer churn in e-commerce. Expert Systems with Applications, 38(3), 1425–1430. https://doi.org/10.1016/j.eswa.2010.07.049
Yu, Y., Wang, X., Zhong, R. Y., & Huang, G. Q. (2016). E-commerce Logistics in Supply Chain Management: Practice Perspective. Procedia CIRP, 52, 179–185. https://doi.org/10.1016/j.procir.2016.08.002
Zhang, P., Jia, Y., & Shang, Y. (2022). Research and application of XGBoost in imbalanced data. International Journal of Distributed Sensor Networks, 18(6), 155013292211069. https://doi.org/10.1177/15501329221106935
How to Cite
Dinh, H. D., & Pham, A. (2026). ON THE USE OF BOOSTING ALGORITHMS FOR PREDICTING CONSUMER CHURN RATE ON AN E-COMMERCE PLATFORM. Journal of Finance - Marketing Research, 4(5). https://doi.org/10.52932/jfmr.v4i5.1254