Design And Implementation Of Postal Savings Bank Customer Relationship Management System Under The Background Of Big Data

With the expansion of the scale of the postal banking business,the number of customers has also increased dramatically.The traditional customer relationship management system is unable to carry out efficient customer data processing due to the backwardness of information technology.Secondly,most postal and storage banks have not paid enough attention to customer relationship management,and have failed to integrate with corporate culture,which has hindered the development of enterprises.To this end,this paper designs and implements a postal bank customer management system based on big data.It aims to realize the mining of customer value and the construction of new customer relationships through the use of big data,and promote the sustainable and healthy development of the bank.This paper follows the general process of software engineering system development to build a credit card management system.First,the system construction objectives are clarified through system requirements analysis,and the UML use case method is used to analyze the system functional requirements,and the non-functional requirements of the system are analyzed from the aspects of data security,reliability and system performance.Secondly,the results are analyzed according to system requirements.Established the system's business framework,which is customer basic information management,customer asset management,customer churn management,customer service management,customer point management and customer churn management.In the process of system design,this paper mainly designs the functional modules in the form of time series diagrams.At the same time,the system database design is completed from two aspects: logical structure and physical structure.Considering the comprehensive development of the online and offline business of the Postal Savings Bank,the bank has accumulated a large amount of data,and traditional analysis methods have been difficult to meet the needs of the real world.This paper innovatively introduces big data technology and improves the K-means algorithm.A distributed clustering algorithm based on MapReduce model is proposed.The algorithm uses the K-means algorithm to measure the distance from each object to the center point as an independent operation.The idea of three-sided relation theorem is used to improve the object classification process.Acceleration ratio experiments demonstrate the superiority of the performance of the algorithm.In the system implementation,this paper mainly discusses the form of interface screenshot and operation flow chart in detail.Finally,the black box test method is used to test thesystem from both system function and performance.The test results show that the system achieves the expected goal and the final quality of the system meets the expected level.The system uses the improved K-means algorithm to achieve customer clustering and data value mining,and completes the segmentation of the bank customers,which is expected to promote the bank's precise marketing and product intelligent recommendation.