AI-Driven Customer Behavior Analysis in E-Commerce Platforms

  • Authors

    • Dr. Rajesh Kumar Sharma Professor, University of Delhi, India. Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2018PIIJ6M3

    Published 09-04-2018

  • Artificial Intelligence, E-Commerce Analytics, Customer Behavior Analysis, Machine Learning, Predictive Modeling, Recommendation Systems, Data Mining, Customer Segmentation

    Issue

    Vol. 1 No. 2 (2018)

    Section

    Articles

    How to Cite

    [1]
    R. K. Sharma, “AI-Driven Customer Behavior Analysis in E-Commerce Platforms”, ijmiet, vol. 1, no. 2, pp. 01–15, Sep. 2018, doi: 10.67228/30715628/IJMIET-2018PIIJ6M3.

  • Abstract

    The rapid growth of e-commerce has generated massive customer interaction data, creating opportunities for intelligent systems to analyze and predict consumer behavior. Traditional analytical methods often struggle to capture the complex and nonlinear nature of customer decision-making. In this context, Artificial Intelligence (AI) has emerged as a transformative tool, enabling advanced behavioral analytics through machine learning, data mining, and predictive modeling. This paper explores AI-based customer behavior analysis in e-commerce, focusing on improving personalization, recommendation systems, and customer retention. It utilizes techniques such as supervised learning, unsupervised clustering, and deep learning to analyze purchasing patterns, browsing behavior, and customer segmentation. The proposed framework integrates multi-source data, including clickstream data, transaction history, demographic details, and product interactions. By applying feature engineering and behavioral modeling, the system identifies hidden patterns in customer decisions. Predictive models like logistic regression, decision trees, and neural networks are used to forecast purchasing and churn behavior. A key contribution is the hybrid architecture combining clustering and classification methods, enabling better customer profiling and adaptive response systems.The study emphasizes the importance of data preprocessing—such as normalization, noise removal, and handling missing values—for improving model performance. Results show that AI-based models outperform traditional statistical approaches in accuracy, scalability, and adaptability. Additionally, AI enhances customer experience through personalized recommendations and targeted marketing. Overall, the paper highlights AI as a powerful enabler in modern e-commerce analytics, offering scalable and efficient solutions for understanding complex customer behavior. Future work may focus on real-time analytics and integrating reinforcement learning to further improve customer engagement.

  • References

    [1] R. Agrawal and R. Srikant, “Fast algorithms for mining association rules,” Proc. 20th Int. Conf. Very Large Data Bases (VLDB), pp. 487–499, 1994.

    [2] D. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, Cambridge, MA, USA: MIT Press, 2001.

    [3] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, New York, NY, USA: Springer, 2009.

    [4] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed., San Francisco, CA, USA: Morgan Kaufmann, 2011.

    [5] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.

    [6] C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.

    [7] J. B. MacQueen, “Some methods for classification and analysis of multivariate observations,” Proc. 5th Berkeley Symp. Mathematical Statistics and Probability, pp. 281–297, 1967.

    [8] G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Trans. Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749, 2005.

    [9] Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer, vol. 42, no. 8, pp. 30–37, 2009.

    [10] X. Su and T. M. Khoshgoftaar, “A survey of collaborative filtering techniques,” Advances in Artificial Intelligence, vol. 2009, pp. 1–19, 2009.

    [11] [P. Resnick and H. R. Varian, “Recommender systems,” Communications of the ACM, vol. 40, no. 3, pp. 56–58, 1997.

    [12] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.

    [13] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems (NeurIPS), pp. 1097–1105, 2012.

    [14] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.

    [15] J. Chen, H. Zhang, X. He, L. Nie, W. Liu, and T. S. Chua, “Attentive collaborative filtering: Multimedia recommendation with item- and component-level attention,” Proc. 40th Int. ACM SIGIR Conf., pp. 335–344, 2017.

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