Automated decision support system for predicting online shopping behavior of e-commerce users

The relevance of this study is caused by the rapid development of electronic commerce and the growing need for effective tools to predict user behavior in online retail environments. The main problem lies in the fact that existing solutions in this domain are often limited to specific datasets, lack sufficient scalability, and rarely support real-time automation of the forecasting process. The purpose of this study is to develop a decision support system that enables the estimation of the probability of future purchase completion based on the analysis of user behavioral data and provides decision-makers with actionable recommendations for subsequent marketing activities. The methodological framework of the study is based on the use of a web analytics system as a source of information on user activities, data preprocessing and structuring procedures, and the application of gradient boosting as a machine learning algorithm for predicting the probability of purchase. To identify internal and external factors that could have a positive or negative impact on achieving the goal, a SWOT analysis was conducted. Experimental validation of the system was conducted using data from four online stores representing different business domains. The results demonstrate that the overall F-score exceeds 80 % across all experiments. The materials presented in this article have practical relevance for e-commerce professionals, data analysts, and marketing specialists, as well as for decision-makers, since the proposed system enables automated prediction of purchasing behavior, the formation of interpretable user segments, and the application of the obtained results to marketing personalization and optimization of managerial decision-making.

1. Esmeli R., Bader-El-Den M., Abdullahi H. Towards early purchase intention prediction in online session based retailing systems. Electronic Markets. 2020;31:697–715. https://doi.org/10.1007/s12525-020-00448-x

2. Wang W., Xiong W., Wang J., et al. A User Purchase Behavior Prediction Method Based on XGBoost. Electronics. 2023;12(9). https://doi.org/10.3390/electronics12092047

3. Ketipov R., Angelova V., Doukovska L., Schnalle R. Predicting User Behavior in e-Commerce Using Machine Learning. Cybernetics and Information Technologies. 2023;23(3):89–101. https://doi.org/10.2478/cait-2023-0026

4. Chaudhuri N., Gupta G., Vamsi V., Bose I. On the platform but will they buy? Predicting customers' purchase behavior using deep learning. Decision Support Systems. 2021;149. https://doi.org/10.1016/j.dss.2021.113622

5. Xu J., Wang J., Tian Y., et al. SE-stacking: Improving user purchase behavior prediction by information fusion and ensemble learning. PLoS ONE. 2020;15(11). https://doi.org/10.1371/journal.pone.0242629

6. Abhichandani D., Vadrevu N.R.T., Doshi P., Shrivastava Sh. Predicting Online Purchases Using Six Machine Learning Models Based on Customer Demographics. In: 2025 6th International Conference on Data Intelligence and Cognitive Informatics (ICDICI), 09–11 July 2025, Tirunelveli, India. IEEE; 2025. P. 1787–1792. https://doi.org/10.1109/icdici66477.2025.11135228

7. Gkikas D.C., Theodoridis P.K. Predicting Online Shopping Behavior: Using Machine Learning and Google Analytics to Classify User Engagement. Applied Sciences. 2024;14(23). https://doi.org/10.3390/app142311403

8. Shi X. The application of machine learning in online purchasing intention prediction. In: ICBDC '21: Proceedings of the 6th International Conference on Big Data and Computing, 22–24 May 2021, Shenzhen, China. New York: ACM; 2021. P. 21–29. https://doi.org/10.1145/3469968.3469972

9. Hamami F., Muzakki A. Machine learning pipeline for online shopper intention classification. AIP Conference Proceedings. 2021;2329(1). https://doi.org/10.1063/5.0043452

10. Liu Ch.-J., Huang T.-Sh., Ho P.-T., Huang J.-Ch., Hsieh Ch.-T. Correction: Machine learning-based e-commerce platform repurchase customer prediction model. PLoS ONE. 2024;19(12). https://doi.org/10.1371/journal.pone.0315518

11. Hesvindrati N., Aminuddin A., Mahadhni J., Pambudi A., Sudaryatno B. Behavior-Based Purchase Intent Prediction in E-Commerce: A Machine Learning Approach. International Journal of Current Science Research and Review. 2025;8(8):3970–3980. https://doi.org/10.47191/ijcsrr/V8-i8-03

12. Prasad A.K., M D.K., Macedo V.D.J., Mohan B.R., N A.P. Machine Learning Approach for Prediction of the Online User Intention for a Product Purchase. International Journal on Recent and Innovation Trends in Computing and Communication. 2023;11(1s):43–51. https://doi.org/10.17762/ijritcc.v11i1s.5992

13. Zhang W., Wang M. An improved deep forest model for prediction of e-commerce consumers' repurchase behavior. PLoS ONE. 2021;16(9). https://doi.org/10.1371/journal.pone.0255906

14. Zhou S., Hudin N.S. Advancing e-commerce user purchase prediction: Integration of time-series attention with event-based timestamp encoding and Graph Neural Network-Enhanced user profiling. PLoS ONE. 2024;19(4). https://doi.org/10.1371/journal.pone.0299087

15. Satu M.Sh., Islam S.F. Modeling online customer purchase intention behavior applying different feature engineering and classification techniques. Discover Artificial Intelligence. 2023;3(1). https://doi.org/10.1007/s44163-023-00086-0

16. Tanvir A.-A., Khandokar I.A., Islam A.K.M.M., Islam S., Shatabda S. A gradient boosting classifier for purchase intention prediction of online shoppers. Heliyon. 2023;9(4). https://doi.org/10.1016/j.heliyon.2023.e15163

17. Liu Y., Tian Y., Xu Y., et al. TPGN: A time-preference gate network for e-commerce purchase intention recognition. Knowledge-Based Systems. 2021;220. https://doi.org/10.1016/j.knosys.2021.106920

18. Liu Zh., Zhang Y., Abedin M.Z., et al. Profit-driven fusion framework based on bagging and boosting classifiers for potential purchaser prediction. Journal of Retailing and Consumer Services. 2024;79. https://doi.org/10.1016/j.jretconser.2024.103854

19. Mamiev O.A., Finogenov N.A., Sologub G.B. Using Machine Learning Methods to Solve Problems of Forecasting the Amount and Probability of Purchase Based on E-Commerce Data. Modelling and Data Analysis. 2020;10(4):31–40. (In Russ.). https://doi.org/10.17759/mda.2020100403

20. Tokuç A.A., Dağ T. Customer Purchase Intent Prediction using Feature Aggregation on E-Commerce Clickstream Data. In: 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP), 21–22 September 2024, Malatya, Turkiye. IEEE; 2024. P. 1–5. https://doi.org/10.1109/idap64064.2024.10711144

21. Wang H., Wang L., Zhu F. E-Commerce User Behavior Analysis and Prediction Based on Artificial Neural Network and Data Mining. In: 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 20–22 September 2024, Chongqing, China. IEEE; 2024. P. 583–586. https://doi.org/10.1109/itnec60942.2024.10733243

22. Kumari L., Bhattacharjee K., Sharma N., Kumar Sh., Kumari A. Machine Learning Models in Customer Behaviour Prediction: A Comparative Analysis. In: 2024 7th International Conference on Contemporary Computing and Informatics (IC3I), 18–20 September 2024, Greater Noida, India. IEEE; 2024. P. 957–959. https://doi.org/10.1109/ic3i61595.2024.10828637

23. Al-Otaibi Y.D. Enhancing e-Commerce Strategies: A Deep Learning Framework for Customer Behavior Prediction. Engineering, Technology & Applied Science Research. 2024;14(4):15656–15664.

24. Deniz E., Çökekoğlu Bülbül S. Predicting Customer Purchase Behavior Using Machine Learning Models. Information Technology in Economics and Business. 2024;1(1):1–6. https://doi.org/10.69882/adba.iteb.2024071

25. Lv Q. E-Commerce Big Data Analysis and User Behavior Prediction Algorithm Based on Deep Learning. In: 2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS), 29–31 July 2024, Bristol, United Kingdom. IEEE; 2024. P. 219–224. https://doi.org/10.1109/aiars63200.2024.00046

26. Liu D., Huang H., Zhang H., Luo X., Fan Zh. Enhancing customer behavior prediction in e-commerce: A comparative analysis of machine learning and deep learning models. Applied and Computational Engineering. 2024;55:181–195. https://doi.org/10.54254/2755-2721/55/20241475

27. Fu Z., Han J. Research on Marketing Strategies of Pinduoduo based on SWOT Analysis. SHS Web of Conferences. 2023;154. https://doi.org/10.1051/shsconf/202315402009

28. Budiman S., Ahidin U. Optimizing digital marketing strategies for Indonesian retail companies through SWOT analysis and strategic development. Journal of Industrial and Logistics Management. 2025;9(1):86–98. https://doi.org/10.30988/jmil.v9i1.1612

29. Chauleva B., Capeska Bogatinoska D., Karadimce A. Optimizing Customer Journey through Advanced Analytics Techniques over Google Analytics 4 Data in Google BigQuery. WSEAS Transactions on Computers. 2024;23:336–346. https://doi.org/10.37394/23205.2024.23.33

30. Svyatov R.S. Forecasting e-commerce user purchase behavior based on event Data. Modeling, Optimization and Information Technology. 2025;13(4). (In Russ.). https://doi.org/10.26102/2310-6018/2025.51.4.064

31. Svyatov R.S. Ontology-based approach to predicting consumer purchasing behavior in e-commerce. Modeling, Optimization and Information Technology. 2026;14(2). (In Russ.). https://doi.org/10.26102/2310-6018/2026.53.2.018

32. Bhutani P., Baranwal Sh.K., Jain S. Semantic Framework for Facilitating Product Discovery. In: ACI'21: Workshop on Advances in Computational Intelligence at ISIC 2021, 25–27 February 2021, Delhi, India. 2021. P. 30–36.

33. Kim H. Developing a Product Knowledge Graph of Consumer Electronics to Manage Sustainable Product Information. Sustainability. 2021;13(4). https://doi.org/10.3390/su13041722

34. Schulze R., Schreiber T., Yatsishin I., Dahimene R., Milovidov A. ClickHouse – lightning fast analytics for everyone. Proceedings of the VLDB Endowment. 2024;17(12):3731–3744. https://doi.org/10.14778/3685800.3685802

35. Schneider M., Martínez D. A comparative benchmark analysis of transactional and analytical performance in PostgreSQL and MySQL. International Journal of Modern Computer Science and IT Innovations. 2025;2(10):51–63.