Application of K-Means Clustering Algorithm for E-Commerce Data Analysis - Laila Ali Putri; Mazayah Tsaqofah; Dea Syahfira Hasibuan; Hasti Fadillah; Maria Ulfa; Mhd.Furqan, (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2025)
The development of information technology has driven significant changes in consumer behavior, especially in online shopping transactions through e-commerce platforms. Increasingly fierce business competition requires companies to not only focus on the product, but also understand the characteristics and needs of customers in order to maintain their loyalty. This research aims to identify customer behavior patterns so that segmentation can be carried out that is useful for a more personalized, effective, and efficient marketing strategy. The results of the analysis show that there is a segmentation of customers into several groups based on different transaction intensity and value. This segmentation can be used as a basis for strategic decision-making, especially in marketing planning and customer relationship management. By understanding customer behavior patterns through the clustering process, companies can develop a more personalized and effective service strategy to increase loyalty and business profitability.