Improving User Experience in E-commerce Through Intelligent Demand Forecasting and Inventory Visualization

E-commerce has grown significantly, creating an urgent need for businesses to enhance user experience. This paper proposes innovative strategies to achieve this through intelligent demand forecasting and inventory visualization. We introduce a system that leverages historical sales data alongside current market trends to deliver more accurate demand predictions. By implementing machine learning algorithms, retailers can proactively anticipate customer needs, thereby optimizing their stock levels and minimizing the chances of stockouts. Complementing this, an interactive inventory visualization tool is presented, enabling users to track stock availability in real-time. This tool not only supports retailers in making data-driven decisions but also fosters customer satisfaction by ensuring products are accessible. Experimental results from e-commerce platforms illustrate that our approach significantly boosts customer engagement and enhances sales performance, emphasizing the critical role of advanced analytics in transforming the e-commerce environment for improved user experiences.