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@article{178025,
        author = {Suhasan Chintadripet dillibatcha},
        title = {DATA-DRIVEN PRODUCT STRATEGY FOR E-COMMERCE: A CASE STUDY IN HOME APPLIANCE SALES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {3291-3301},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178025},
        abstract = {The growing importance of data-driven strategies in e-commerce has led to significant changes in how businesses, especially in the home appliance sector, manage product offerings, pricing, inventory, and consumer engagement. This review explores the integration of multiple data sources, such as consumer behavior, social media insights, transaction history, and competitive pricing, to create a dynamic and personalized approach to product strategy. By leveraging machine learning, artificial intelligence, and predictive analytics, the proposed model offers improved accuracy in demand forecasting, personalized recommendations, and dynamic pricing. Through a comparative analysis with existing baseline models, we demonstrate that the proposed model significantly enhances predictive accuracy and operational efficiency. The implications for e-commerce practitioners, policymakers, and future research are discussed, emphasizing the importance of investing in data infrastructure, fostering innovation in e-commerce technologies, and ensuring ethical data usage. This review highlights how a robust, data-driven product strategy can unlock the full potential of e-commerce businesses, driving growth, customer satisfaction, and profitability.},
        keywords = {Data-driven strategy, e-commerce, home appliance sales, predictive analytics, machine learning, dynamic pricing, consumer behavior, personalization, inventory management, real-time data, AI, social media insights, product recommendations, competitive analysis, inventory optimization.},
        month = {May},
        }