Segmentasi Nasabah pada Data Campuran dengan K-Means Clustering

Abstract

Customer segmentation is crucial for banks to develop marketing strategies tailored to specific customer groups. While the RFM model is commonly used, enhancing service usage expansion requires data on customer transaction preferences, which are typically categorical in nature. Therefore, this study segments bank customers based on their transaction history, utilizing not only numerical data but also categorical data representing transaction preferences using K-Means Clustering. The clustering model effectively groups customers into four clusters with distinct characteristics.