Analysis Of Telecom Customer Churn Based On Data Mining

With the development of the telecommunications market,market competition is becoming fiercer.Operators have higher costs to develop new customers,and customer churn analysis has gradually become an important part of telecommunications company business analysis.How to build an accurate and effective customer churn early warning model and identify customers who have a tendency to churn is of great significance to telecom operators.The data used in this article comes from the customer data of a telecommunications operator,including the basic information of the customer and the operator service information ordered.The process of building a telecommunications customer churn early warning model includes data cleaning,unbalanced processing,and classifier training and evaluation.This paper first uses the Adaboost algorithm to output the feature importance of the variables,selects 12 feature variables,and then uses the SMOTE algorithm to balance the data set,and then uses Logistic regression,Adaboost algorithm,and XGBoost algorithm to establish a customer churn prediction model,and uses the model in The accuracy,sensitivity,specificity,F1 value,accuracy,AUC value and KS value on the test set are comprehensively compared.The results show that the overall prediction effect of XGBoost model is higher than that of Adaboost model and Logistic regression model,but the sensitivity of Adaboost model is higher than that of XGBoost model.From the analysis of the XGBoost model and the Adaboost model,it can be seen that the monthly consumption amount and the duration of using the product have a strong ability to divide whether the telecommunications customers are lost.The AUC value of the XGBoost model is 0.905 and the KS value is 0.609,indicating that the classification effect of the model is better.Operators can apply this model to predict the risk of customer churn,and take corresponding retention policies for the reasons for customer churn to provide a basis for decision-making on customer churn prevention management.