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@article{192692,
        author = {Subashree and Dr.J.Savitha},
        title = {Artificial Intelligence and Data-Driven Analytics in E-Commerce: A Comprehensive Review of Methods, Applications and Research Challenges},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {2241-2259},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192692},
        abstract = {The fast growth of e-commerce platforms has generated massive volumes of heterogeneous data, necessitating intelligent and scalable analytical solutions to support decision-making, personalization, and business sustainability. This review paper presents a comprehensive synthesis of recent research on artificial intelligence (AI), machine learning (ML), deep learning (DL), and big data analytics applied to e-commerce environments. The reviewed literature spans key application domains, including customer demand forecasting, profit prediction, churn analysis, recommendation systems, dynamic pricing, product classification, website usability evaluation, and intelligent decision support systems. Studies employing advanced models such as transformer architectures, convolutional and recurrent neural networks, ensemble learning methods, reinforcement learning, and large language models are critically examined alongside traditional data mining and statistical techniques. The review highlights the strengths of AI-driven approaches in capturing complex consumer behavior, improving predictive accuracy, and enabling real-time, data-driven strategies. At the same time, it identifies persistent challenges related to interpretability, data imbalance, scalability, privacy, and real-world deployment. By organizing existing contributions into coherent thematic categories, this review provides a structured overview of methodological trends, datasets, and performance outcomes. The paper concludes by outlining future research directions toward adaptive, explainable, privacy-aware, and business-aligned AI systems for next-generation e-commerce platforms.},
        keywords = {E-commerce analytics, Artificial intelligence, Machine learning, Deep learning, Customer churn prediction, Demand forecasting, Recommendation systems, Big data analytics, Intelligent decision support and Data mining.},
        month = {February},
        }