K-means Clustering Algorithm Is Applied To The Customer Segmentation Of The Banking CRM System

With the gradual development of the Internet financial and the rise of small loan companies,banking business has received the larger impact,traditional business plan of the bank is no longer suitable for today's fierce competition situation,for the current banking,assets concept has been less important,the most important thing is the customer concept,who caught the needs of customers,who can master the market and the wealth in the future.So how to differentiate the customer's type,the precise location of customer demand,is critical to the development of the banking industry.To understand the customer's needs,we need to dig through the various data in the interaction between the bank and the customer to find useful information that is implicit in the data.In the customer relationship management(CRM)system in the bank,there is a huge amount of customer data that is the basis for mining customer requirements.However,it is a problem for the banking industry to find useful information in the vast amount of data.The emergence of data mining technology is the right way to solve this problem for the banking industry.The k-means clustering algorithm is an important algorithm in data mining technology,which is an important technique for obtaining hidden information.It can process the cluttered customer data in the CRM data system and then divide them into different classes of different features,the bank can target customers according to the characteristics of different kinds of clients,then the Bank can really meet customers' needs.This paper introduces the basic theory of customer segmentation and CRM,and then analyzes the structure and function of banking CRM according to the characteristics of current banking operations.Then we introduce the basic principle,classification and advantages and disadvantages of the common clustering method.At the same time put forward to use the k-means clustering algorithm to analyze the basic customer data of the CRM system.Because K-means cluster analysis is sensitive to the initial clustering center of the problem,so this paper puts forward an improved method,use artificial synthesis of the initial clustering center instead of a system of random initial clustering center,according to the error variance criterion proved that the improved algorithm is more effective.Then according to certain principles selecting indicators for the customer data of CRM system,using the improved clustering algorithm to classify the bank's customers,the more reasonable classification results were obtained,which solved the problem of the bank's low customer classification and low customer segmentation.Finally,according to the classification and other characteristics,the strategy and suggestion of banking service are put forward.