A Study On Z Bank's Retail Customer Churn Based On Data Mining

With the rapid development of the economy,the acceleration of interest rate marketization,and the formation of the buyer's market,the competition in the banking industry has become increasingly fierce,especially for customer resources.Studies have shown that the cost of obtaining a new customer is much higher than the cost of retaining old customers.In this context,banks are paying more and more attention to customer churn prediction and analysis retention.Therefore,how can we predict customers in advance? Whether it has a high probability of loss,and thus adopting effective marketing measures and formulating a reasonable marketing strategy to retain these customers has become an urgent problem for banks in terms of customer operations and business development.Through theoretical analysis and empirical research on data mining,this paper provides new research for commercial banks in the loss and retention of retail customers.First of all,this paper puts forward the problem of customer churn on the basis of clarifying the status quo of retail customers of commercial banks and Z Bank.This has become a serious problem hindering the development of banks.However,banks' analysis and retention methods for customer churn are more traditional and backward,and it is difficult to find possible loss.Customers,and also lack of targeted retention methods,this paper proposes a data mining-based big data analysis method to predict potential lost customers in advance,and based on this,customer-retained solutions.Secondly,this paper focuses on the analysis of Z Bank's customer churn problem.Based on the data mining theory and XGBoost algorithm,the cross-industry data mining standard process(CRISP-DM)is used to construct the retail customer churn prediction model for Z Bank.In the empirical research process of the model,through the establishment of the loss tendency-customer value two-dimensional matrix,it is proposed to subdivide the customer into four categories,adopt different perspectives of recovery strategies,and focus on customers with high loss tendency and high customer value.Retain empirical research to demonstrate the practical validity of the model.Finally,the common analysis of the lost customers of the model results,explore the common characteristics of the lost customers in product configuration,trading behavior,etc.,and carry out the retention strategy analysis for the four typesof specific loss scenarios of Z Bank,and propose a targeted retention method.At the same time,it is pointed out that banks should make full use of financial technology tools and innovative means to improve the quality of financial products and services,improve customer stickiness,and comprehensively improve the development of banking business.This research can help banks to identify potential lost customers in advance,timely and targeted customer retention,and avoid losses caused by customer loss,which has certain practical significance.At the same time,based on data mining methods and empirical research work for industry reference Has a certain academic value.