Reasearch On Clustering Analysis In The Application Of Banking Customer Segmentation

With the rapid development of society and economy in recent years, market competition become more and more fierce, enterprise competition is become more and more fierce,the same is true of the banking sector. how can remain invincible in such a cruel environment turns the utmost primary to all banks. especially the advent of big data era, all enterprises including banks have accumulated a large amount of data. But how to find the best advantages in mass data and to promote the competitiveness with customer segmentation and precisely marketing is a big problem that banks are confronted.However, the data mining technology can solve this problem, the cluster analysis is an important algorithm in technology, it is particularly important for the future development of the bank, but some research in clustering analysis methods all have some disadvantages. By combining theory and practice, this paper analyze the application of K-means clustering in customer segmentation.This paper is approximately divided into four parts. Part one introduces the research background, present situation and research results at home and abroad. Then analyze three aspects of CRM and enterprise customer segmentation, including the concept, features and current situation. Part three is about the selection for customer segmentation index with the perspective of banking business, and the clustering analysis by using the model of K-means clustering algorithm. The last part summarizes the result of the model, comprehensively understand and analyze the characteristics of customer individually, and finally provides the feasible Suggestions for development of the banking industry.