Enhancing customer segmentation in Chinese city commercial banks: A machine learning approach

Abstract

This paper examines the progression and implementation of customer segmentation theory in banking, focusing particularly on an improved RFM (Recency, Frequency, Monetary) model. It highlights the essential role of identifying high-value customers for the sustained growth and success of commercial banks. The study elucidates the pivotal role of Customer Relationship Management (CRM) and detailed management in the realm of retail banking as crucial for achieving success. By analyzing real-case scenarios from the Chinese banking sector, this paper illuminates the evolving nature of customer segmentation theory and its varied applications. The objective is to provide insightful and practical recommendations for commercial banks aiming to develop or enhance their customer segmentation frameworks. This research contributes both to the theoretical understanding of customer segmentation and offers a pragmatic guide for banks seeking to improve their customer management approaches in a market that is becoming increasingly competitive.