| China's e-commerce has risen since the end of the last century.With the rapid development of the Internet era,competition among enterprises is gradually intensifying.Previous research results have shown that the cost of retaining old customers is much lower than developing new customers.At present,the competition for customer resources has become the focus of enterprise competition.At the same time,machine learning technology has gradually been applied to customer relationship management in e-commerce enterprises.Enterprises use machine learning technology to find customers with a tendency to churn in time,which can reduce costs Retain it to win in a fiercely competitive environment.This study clarifies the research route by collecting and collating relevant literature on customer churn.First of all,the purpose of the study was derived by explaining the necessity of customer churn for the company.Secondly,it analyzes the relationship between customer churn and customer relationship management,and introduces the basic principles of subsequent algorithms.Third,an empirical analysis is performed.Based on the recent order data of users of an e-commerce platform,relevant data for purchasing products,evaluation and other browsing behavior data,a customer churn prediction model is constructed using Logistic regression and deep learning algorithms.The confusion matrix of the model is obtained,and the accuracy,precision,recall,F1 value and ROC curve of the model are calculated to evaluate the model.Through multi-directional evaluation,the model accuracy reached 75%.The prediction accuracy of the two models constructed in this study is relatively high.After comparative evaluation,it is found that the model constructed by the deep learning algorithm is slightly better than the logistic regression in the application of customer loss in e-commerce.Finally,the main conclusions of this research are summarized,and the directions for further research are put forward. |
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