Research On Enterprise Customer Relationship Management Based On Data Mining Technology

The era of Internet economy is a witness to rapid development the logistics industry and big data technologies.But with the fiercer market competition of the domestic and foreign logistics enterprises and increase of individualized logistics demands,China's logistics enterprises are still faced with huge transformation pressure.As they are in the service industry,customers are the sources of third-party logistics enterprises and customer resources are the core power of enterprise development.Therefore,logistics enterprises should transform from the “productcentered” operation model into “customer-centered” one.At present,China's logistics industry is still in the preliminary stage for exploration development,logistics information technologies are underused,and massive business data resources accumulated in the logistics enterprises cannot be transformed into valuable business information in a timely manner,which increases difficulty in guiding and perfecting customer relationship management.Given this,the paper,by combining the research status of logistics enterprises at home and abroad,and based on the data mining technology and logistics customer relationship management theories,builds logistics customer relationship model in the data mining algorithm and makes empirical study.The paper mainly expounds on three aspects.Firstly,it outlines the related theories in the paper,including the theories of data mining technology,customer relationship management and extended logistics enterprise customer relationship management,and the theory of logistics enterprise customer relationship management emphasizes the importance of customer relationship management in third-party logistics enterprises.Then,it introduces and researches the four algorithms of cluster analysis,association rules,decision-making tree classification and neural network about data mining,and strengthens the understanding of data mining algorithms through concepts,flows,case study,etc.,Lastly,it builds different algorithm models according to different modules in the logistics customer relationship management,such as association rules mining in precision cross-selling,cluster analysis under customer value segmentation,BP neural network model for customer churn prediction.It makes flow analysis from the four aspects of data preparation,model building,model evaluation and implementation deployment in a uniform manner for different models,and analyzes and researches related algorithms by combining cases.It is of certain economic significance to build the logistics enterprise customer relationship management model based on data technologies,and of reference value to perfect the effective customer relationship management of logistics enterprises(especially of third-party logistics enterprises),and the model helps logistics enterprises lower management costs and improve their market competitiveness.