Customer Churn Prediction Algorithm Based On Logistics Business

In the current situation of mutual expansion among enterprises devouring the market,customers become the biggest chip of enterprise competition.Customers are lost for a variety of reasons,such as dissatisfaction,high cost,poor quality,lack of expected functionality,and privacy issues.The rapid development of e-commerce leads to increasingly fierce competition in the logistics industry.While consumers have more choices,logistics enterprises themselves are faced with a significantly increased probability of customer loss.Therefore,logistics customer loss prediction is a necessary and important task.In the current research,CCP is mainly implemented from two different perspectives.On the one hand,the researchers focus on the optimization of the CCP model and work on proposing more complex models to improve the prediction performance.On the other hand,researchers want to understand the main factors driving customer loss and identify the important drivers of customer loss.Finding the drivers of customer loss choice is the key issue.In view of the above problems,this paper conducts research on group recommendation and proposes the research and design of customer loss prediction algorithm based on logistics business.The main work is as follows:This paper will make use of the complex text information left by the customers on the online logistics platform,combine the lost customer behavior characteristics in the historical customer database,and combine the customer openness to enable new applications in the field of customer loss prediction.In this paper,in this study proposes a customer classification model based on improved LDA and supervision,to effectively predict the customer characteristics of openness,this study extends the model based on fuzzy measure and fuzzy logic to extract the customer mainly expressed by the preference and customer emotional intensity,and the customer open modeling,the model can be effectively adapted to deal with large and sparse data.Based on the measurement of customer openness and considering the unique characteristics of customer loss in logistics industry,this paper combines rough set and BP neural network to realize the prediction of customer loss in logistics industry.In order to make up for the shortcomings of BP neural network,the rough set is used as a pre-processing tool in this paper.First,the rough set is used to extract rules for customers with normal behaviors and customers with abnormal behaviors to distinguish the classes of customers in the logistics industry,and the data is discretized based on the information entropy of logistics customer loss attribute.Finally,discrete customer churn attributes and open measurement are input into BP neural network for training,and Adam algorithm is introduced to build an adaptive BP neural network training model according to the strong liquidity of logistics customers.Based on the above theories and methods,this paper will build a prototype system for predicting customer churn and provide application demonstrations in the latest logistics scenarios.Prototype system development process to complete the design goals,logistics customer churn prediction model is the overall architecture,database design,and to achieve the user classification,potential customers tag and loss prevention,function module,verify the feasibility of the proposed method and theory,and demonstrate its application to predict the effect of the real customer churn in the scene.