Consumer Insights in E-commerce: Analyzing Sales Data Using Clustering Algorithm

This study offers a comprehensive analysis of ecommerce sales data, leveraging K-means clustering and the Sil-houette method to determine optimal cluster configurations. As online shopping continues to surge, comprehending sales data is crucial. By exploring various dimensions of e-commerce sales, including customer segmentation, geographical trends, and cus-tomer preferences, this paper provides actionable insights for refining marketing strategies, fostering product development, and enhancing customer engagement. Through RFM-based seg-mentation and detailed stock analysis, distinct consumer cohorts are identified, enabling targeted marketing efforts. Geograph-ical analysis unveils sales patterns and peak sales times across various countries, facilitating strategic decision-making and en-hancing insights within the dynamic realm of e-commerce.