机器学习驱动的信用卡客户细分与营销探究OA

This paper takes the credit card consumption dataset as the analysis object and relies on Machine Learning technology as the core tool to systematically carry out customer segmentation and high-value target customer mining.Firstly,it gains in-depth insight into the intrinsic characteristics of the data through data preprocessing and optimizes the data structure by combining PCA dimensionality reduction and feature standardization methods.Secondly,it compares the clustering effects of the K-means algorithm and the hierarchical clustering algorithm and finally determines that the optimal number of clusters is 4.On this basis,it divides customers into 4 groups with distinct characteristics and further screens out 11 high-value target customers from them.The research results can provide quantitative data support for the formulation of precision marketing plans,the implementation of dynamic risk management and control,and the design of differentiated customer maintenance strategies in the credit card business,and effectively help the business achieve the dual goals of revenue growth and risk controllability.