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@article{182368,
        author = {Siva Kannan Ganesan},
        title = {DATA-DRIVEN DECISION MAKING IN RETAIL MARKETPLACE: LEVERAGING ML/AI ANALYTICS FOR BUSINESS GROWTH},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {3743-3751},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182368},
        abstract = {In today’s competitive retail environment, the integration of machine learning (ML) and artificial intelligence (AI) into data-driven decision-making is revolutionizing how retailers operate, from inventory management to personalized marketing. This review examines the role of ML and AI in optimizing decision-making processes, exploring key components such as data collection, integration, and actionable insights generation. While the adoption of these technologies presents numerous benefits, including increased operational efficiency and enhanced customer satisfaction, there are also significant challenges. These challenges include data quality and availability, infrastructure limitations, skill gaps, ethical concerns, and the scalability of AI applications. The review proposes a comprehensive framework for integrating AI and ML into retail operations and discusses the key assumptions, limitations, and future research directions to address existing gaps. By highlighting the potential applications and challenges, this review aims to offer valuable insights for both researchers and practitioners looking to leverage AI and ML in retail decision-making.},
        keywords = {Machine Learning, Artificial Intelligence, Data-Driven Decision Making, Retail, Inventory Management, Personalized Marketing, Predictive Analytics, Consumer Behavior, Ethical AI, Data Integration},
        month = {July},
        }