Research On Customer Segmentation And Marketing Mata Analysis Of N Bank

With the development of the times,data networks have a tremendous impact on people's lives.Among them are many various fintech products that emerged at the historic moment.Their diversified,personalized,and even high liquidity and high-yield characteristics continue to attract customer funds,which affects the bank's deposit business and capital income,and impacts banks' Traditional business.At present,it is very important to study customer needs,distinguish customer types and adopt targeted marketing strategies.There are a large number of customer data recorded in the customer management system of the bank,and the use of these data has become a problem faced by the bank in the new era.Firstly,based on the related data mining theory and customer relationship management theory,this paper puts forward some suggestions to solve this problem.Secondly,through the use of data mining methods to classify and filter bank customer information data and modeling analysis,the K-means clustering analysis is mainly used to process bank customer information,reduce the complexity of variables,optimize the selection of variable data,select variables with higher correlation for evaluation,and avoid the evaluation model caused by too many variables in modeling analysis.Thirdly,the logistic regression model is used to establish the prediction regression equation to judge the purchase intention of customers' products,so as to improve the utilization of existing data.Then,the likelihood ratio test and R2 test are carried out to get the test results of the model,which can be used in practice.On the basis of the test results,the paper puts forward customer management suggestions and targeted marketing suggestions based on data analysis to realize effective tracking management of customers,precise marketing of bank products and maintenance of customer relations.