Analisis Churn Menggunakan Metode K-Means Clustering Berdasarkan Model LRFM Untuk Meningkatkan Retensi Pada Mahes Printing - Bagus Joko Winarso; Diana Laily Fithri; Soni Adiyono (Universitas Bandar Lampung (UBL), 2025)
In the competitive digital era, customer retention has become a critical factor for business sustainability, particularly in the digital printing industry which faces intense competition. Mahes Printing, despite recording a high transaction volume, continues to experience low repurchase rates due to fragmented and manual management of customer data and transaction history. This study aims to implement churn analysis within a Sales Management Information System using a Customer Relationship Management (CRM) approach supported by the LRFM (Length, Recency, Frequency, Monetary) model and the K-Means clustering algorithm. The results indicate that customers can be effectively grouped into three main clusters representing low, medium, and high churn risk levels. This segmentation facilitates the identification of customers with high churn potential, characterized by low Recency and Frequency values, thereby providing strategic insights to support data-driven decision-making and the development of more targeted and effective customer retention strategies.