Individual Customer Segmentation Of Commercial Banks Based On Machine Learning

In the era of big data,with the development of internet finance and the intensification of market competition,major commercial banks have gradually transformed from the traditional "horse racing and enclosure" extensive market grabbing model to the "intensive cultivation and meticulous cultivation" refined marketing model.Under the dual pressure of survival and profit,how to take the opportunity of Digital transformation,accurately identify customers,invest limited resources to attract and maintain more high-end customers,and meet the basic financial needs of low-end customers has become the number one problem for major commercial banks.As is well known,customers value service,provide high-quality services,and improve user experience as the main means to retain high-end customers and increase profits.Based on this,commercial banks’ customer segmentation and differentiated service marketing plans are particularly important.This article is based on the method of big data customer segmentation and related customer segmentation theories,using a combination of K-means clustering algorithm and business expert experience,K-means clustering algorithm and DBSCAN clustering algorithm,as well as CART decision tree classification algorithm.Using grid search to find the optimal parameters for each algorithm,the study focuses on a specific group of customers who use the service of salary distribution.Starting from the customers’ basic information and financial asset attributes,the study effectively segmented and accurately located the high-value customers among the salary distribution customers,and explored their asset features and financial preferences.Differentiated service solutions were provided according to the different customer segments,providing powerful data support for precision marketing,in order to enhance customer stickiness and loyalty,and increase their potential value.The research results show that the clustering algorithm needs to analyze and merge multiple clustering results according to the actual business situation to find the best clustering result.The experimental results have high value in practical applications.The CART tree classification algorithm is based on the labels generated by the clustering algorithm and uses pre-pruning and grid search to find the optimal parameters to construct the classification decision tree.The classification accuracy on sample data reached over 97%.