The Research Of Railway Freight Customer Churn Theory And Approach

With the gradual advance of the national policy of supply-side structural reform and the current intense competition of transportation market,the serious situation of railway freight industry is facing the risk of customer churn.How to achieve efficient customer churn management is a long-term problem for railway freight marketing departments.This paper proposes to use data mining technology to excavate and analyze the big data of railway freight customers,which is used for freight customer churn management.And based on the actual demand of railway freight industry,this paper puts forward the research method of customer segmentation and churn prediction.Then,combining with the research results of the customer segmentation and churn prediction,this paper puts forward the research on freight customer churn retention value,which has theoretical and practical significance.The main research work of this paper includes:Firstly,based on the characteristics of railway freight industry and railway freight customer delivery,this paper puts forward a model of freight customer segmentation KFAV based on RFM model.This model has good practice effect,which introduces the freight customer's shipping turnover,freight customer's delivery tendency,and customer's freight contribution.In addition,the improved k-means clustering algorithm is proposed to solve the traditional k-means' problem of clustering center random selection.Lastly,through the simulation platform based on the Hadoop,KFAV model of customer segmentation effect is verified,and the improved efficiency of the improved k-means algorithm is proved,and the superiority of the Hadoop platform in dealing with big data is also verified.Secondly,on the basis of customer segmentation,the customer identification method is proposed for railway bulk freight customers and scattered white freight customers,and then customer churn prediction model is established respectively.For bulk freight customers,on the basis of KFAV model,a parallel SVM customer churn prediction method based on freight customer value classification is established.For scattered white freight customers,customer churn prediction model is established by introducing some parameters such like social economy,and in view of the parameter characteristics of the customer churn prediction model,a improved C4.5 based on discretization method for continuous attributes based on information entropy is introduced as the calculation method of this model.Then,the simulation environment based on Hadoop platform is established for two kinds of freight customers,and the accuracy and predictive ability of two kinds of freight customer churn models are verified.Thirdly,based on the segmentation of freight customers and the prediction of freight customer churn,this paper proposes the research method of customer churn retention value mode,this model fully considers the future potential of freight losing customers and the rescue effect and the rescue cost of freight losing customers,by setting the threshold value,we can judge whether the retention results and coping strategies of the lost freight customers are effective,and the retention priority of the losing customers can be set according to the retention value.The simulation also proves that it is better to develop corresponding coping strategies for the same segmentation of freight losing customers.