<![CDATA[In this competitive digital era, customer behavior is key to maintaining loyalty and increasing profitability. This study aims to implement customer segmentation using the Length, Recency, Frequency, Monetary, Volume (LRFMV) approach and the K-Means algorithm to identify customer behavior characteristics and determine high-value segments. The combination of these five dimensions has rarely been used in previous studies, thus providing a new contribution to data-based customer behavior analysis. This study adopts an exploratory descriptive quantitative approach. The data used consists of 2,098 transactions from 452 customers, sourced from a public GitHub dataset. The data analysis process includes preprocessing, determining LRFMV values, and segmentation using K-Means Clustering. The Silhouette Coefficient is used to evaluate cluster quality and determine the optimal number of clusters. The results show that the best configuration is obtained at k=5 with a Silhouette value of 0.842. The findings show five customer segments with different characteristics and Customer Lifetime Value (CLV) values. Clusters 0 and 2 are categorized as Loyal Customers (L↑R↓F↑M↑V↑) with the highest CLV. Clusters 3 and 1 are Inactive New Customers (L↓R↑F↓M↓V↓) with low contribution. Cluster 4 consists of Inactive Customers (L↓R↓F↓M↓V↓), indicating overall inactivity. These segmentation results are used to develop more targeted strategies, such as loyalty programs or reactivation campaigns, to optimize marketing strategies based on customer value.]]>
