In modern business, understanding customer behavior is of paramount importance. It allows for the delivery of personalized experiences, which can profoundly influence engagement and revenue. Behavioral segmentation is a potent approach known to enhance customer engagement by up to 73 percent and boost revenue by 15 percent. This study employs DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to address the research problem of segmenting Amazon customers based on a wide range of features. This methodology enables a comprehensive analysis of customer behavior. The key findings of this study reveal customer segments at high risk of churn, a critical concern in the e-commerce industry. This insight is invaluable for businesses looking to tailor retention strategies, ultimately enhancing customer satisfaction and fostering sustainable growth within the fiercely competitive e-commerce landscape. The research showcases the practical application of DBSCAN for customer segmentation and churn risk analysis in the context of Amazon. Academics, managers, and decision-makers can leverage these findings to refine their strategies and better serve their customers, contributing to increased customer satisfaction and long-term business success.