Research On E-commerce Customer Churn Prediction

In recent years,Internet technology has developed rapidly,and the competition in the domestic e-commerce market has become increasingly fierce.Companies are paying more attention to customer churn management.Increasing customer retention rate is an important part of the company's cost control.The core job of reducing customer churn is to identify potential lost customers.How to accurately predict customer churn is a research issue that cannot be underestimated in the field of customer relationship management.This paper takes e-commerce enterprise customers as the research object and conducts research on customer churn prediction.Firstly,considering the positive impact of customer segmentation on customer churn prediction,this paper analyzes the impact of customer buying behavior and social behavior on customer value and adds the customer social impact indicator based on the RFM model to construct the RFMI value recognition model.Starting from the two dimensions of customer's personal value and social network value,the company's customers are effectively segmented based on customer value.Then,according to the problem of high feature dimension and large data volume in e-commerce customer churn research,the random forest algorithm is used for feature screening,and the customer churn prediction model is built based on the integrated learning algorithm XGBoost.In order to solve the problem that the two types of error losses may be different,the XGBoost algorithm is improved.We add a penalty factor to its loss function,making one type of error loss higher than the second type of error loss and improving the prediction accuracy of the overall model.This paper uses the customer data of an e-commerce company in China to verify and analyze the model.The study found that:(1)The improved XGBoost algorithm can effectively reduce the probability of occurrence of a type of error and has higher accuracy,with the accuracy rate increased by 2.8%;(2)After the subdivision,the forecasting effect is better,in which the probability of occurrence of a type of error in the core value customer is reduced by 10.8%,and the accuracy rate is increased by 7.8%;(3)Compared with other classification algorithms,the improved XGBoost algorithm has better performance in terms of AUC value,accuracy and precision.