Enhancing E-Commerce Decision-Making Through Data Science–Based Customer Behavior Prediction
Keywords:
Behavioural modelling, Clickstream analysis, Customer behaviour prediction, Customer segmentation, Data science, E-commerce, Machine learning, Predictive analytics, Purchase intent, Recommendation systemsAbstract
Understanding and predicting customer behaviour is necessary for enlightening decision-making, improving user experience, and growing sales in e-commerce platforms. This research presents a data science–based approach to customer behaviour prediction using machine learning techniques, demonstrated through a real-world case study. The study analyses customer interaction data including browsing history, clickstream patterns, purchase logs, demographics, and product preferences to identify key behavioural indicators that influence purchasing decisions. Pre-processing steps such as data cleaning, feature engineering, and exploratory analysis were performed to enhance model performance. Predictive models including Logistic Regression, Random Forest, Gradient Boosting, and Neural Networks were evaluated, with Gradient Boosting achieving the highest accuracy, precision, and F1-score. The results show that data-driven behaviour prediction can significantly improve personalized recommendations, targeted marketing, and conversion-rate optimization. The study highlights the effectiveness of integrating machine learning with e-commerce analytics and provides a framework that can be adopted by online businesses to enhance customer engagement and strategic planning.
References
E. Deniz and S. Çökekoğlu Bülbül, “Predicting Customer Purchase Behaviour Using Machine Learning Models,” Information Technology in Economics and Business, vol. 1, no. 1, Jul. 2024, doi: https://doi.org/10.69882/adba.iteb.2024071
D. Liu, H. Huang, H. Zhang, X. Luo, and Z. Fan, “Enhancing customer behavior prediction in e-commerce: A comparative analysis of machine learning and deep learning models,” Applied and Computational Engineering, vol. 55, no. 1, pp. 190–204, Jul. 2024, doi: https://doi.org/10.54254/2755-2721/55/20241475
T. S. Panchal, “Predicting Customer Behavior in E-Commerce Using an Association Rule Mining Approach,” Ictact Journal on Data Science and Machine Learning, vol. 6, no. 3, pp. 801–807, Jun. 2025, doi: https://doi.org/10.21917/ijdsml.2025.0162
S. Chavan and S. Singh, “Enhancing Smart Decision Making in E-Commerce: Deep Learning Mechanism Using Behavioural Learning Analysis,” African Journal of Biomedical Research, vol. 27, no. 45, 2024, doi: https://doi.org/10.53555/ajbr.v27i4s.7373
P. Pande, A. K. Kulkarni, B. P., B. S, and V. Ramalingam, “Big Data Analytics in E-commerce Driving Business Decisions Through Customer Behaviour Insights,” ITM Web of Conferences, vol. 76, pp. 05001–05001, Jan. 2025, doi: https://doi.org/10.1051/itmconf/20257605001
Y. D. Al-Otaibi, “Enhancing E-Commerce Strategies: A Deep Learning Framework for Customer Behaviour Prediction,” Engineering, Technology & Applied Science Research, vol. 14, no. 4, pp. 15656–15664, Aug. 2024, doi: https://doi.org/10.48084/etasr.7945
E. F. Zineb, R. Najat, and A. Jaafar, “An Intelligent Approach for Data Analysis and Decision Making in Big Data: A Case Study on E-commerce Industry,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 7, 2021, doi: https://doi.org/10.14569/ijacsa.2021.0120783
R. K. Mishra, D. Mishra, and R. Agarwal, “Applications of data science in e-commerce,” ResearchGate, vol. 10, no. 2, pp. 66–75, Nov. 2024, doi: https://doi.org/10.5281/zenodo.14257784
R. Shynkarenko, “Using Big Data and AI to Predict Consumer Behaviour in E-Commerce,” Scientific Notes of Lviv University of Business and Law, no. 44, pp. 115–127, 2025, Available: https://nzlubp.org.ua/index.php/journal/article/view/1549
S. L. Gupta, V. Khandelwal, V. Katria, A. Sharma, and A. Pandey, “Analysing the Efficacy of K-Means Clustering and Logistic Regression for Diabetes Prediction,” South Eastern European Journal of Public Health, vol. 25, pp. 1255–1262, Nov. 2024, doi: https://doi.org/10.70135/seejph.vi.2454
N. Begum, “Big Data Analytics and Its Impact on Customer Behaviour Prediction in Retail Businesses,” Pacific Journal of Business Innovation and Strategy, vol. 1, no. 1, pp. 49–59, 2024, doi: https://doi.org/10.70818/pjbis.2024.v01i01.017
M. T. Ribeiro, S. Singh, and C. Guestrin, “‘Why Should I Trust you?’: Explaining the Predictions of Any Classifier,” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’16, no. 44, pp. 1135–1144, Aug. 2016, doi: https://doi.org/10.1145/2939672.2939778
S. Hussain, “Data Science and Machine Learning Approach to Improve E-Commerce Sales Performance on Social Web,” International Journal of Advanced Research in Engineering and Technology (IJARET), vol. 12, no. 4, pp. 401–424, Apr. 2021, Accessed: Dec. 19, 2025. Available: https://iaeme.com/home/article_id/ijaret_12_04_040
T. Mahmud, R. Chakma, R. Akther, “Leveraging Data-Driven Decision Making for E-Commerce Growth: A Machine Learning Framework,” Lecture Notes in Networks and Systems, pp. 210–219, Jan. 2024, doi: https://doi.org/10.1007/978-3-031-73318-5_21
