Customer behavior analysis and segmentation: A Big Data approach in Vietnam’s banking sector

Abstract: The rapid development of digital technologies and Big Data in the banking sector has facilitated the application of analytical models to identify customer behavior. This study applies the Recency-Frequency-Monetary (RFM) model to analyze the behavior of individual banking customers. The research dataset comprises 4,642 transaction records from 845 customers during the period from October 1, 2024, to October 30, 2025. The data were extracted from the data warehouse, the Core Banking system, and the Customer Relationship Management (CRM) system. The analytical results enable the identification of the characteristics and service usage behavior of different customer groups, thereby providing a basis for banks to develop business strategies and customer care policies tailored to the needs and expectations of each segment. These findings contribute to improving customer retention and strengthening banks’ competitive capabilities.