Prediction And Analysis Of Telecom Customer Churn Warning Model Based On Machine Learning

In the rapidly changing age,telecom consumers have new demands in terms of products,content,and services.All current revenue-oriented operations in the telecom industry,a single package marketing strategy,and the lack of personalized services have been unable to fully meet customer needs.As the market is close to saturation,inter-industry competition mainly lies in the retention of users.Under the background of the dilemma between revenue growth and customer satisfaction,the telecommunications industry needs to respond quickly and flexibly to market changes,reduce the probability of loss of the local network,and seize the market of lost users from different networks.Therefore,based on actual internship experience in a certain operator and previous studies,I proposed a grouping to construct a loss early warning model.Based on the predecessor's predictive churn model,more emphasis is placed on analyzing user churn tendency and reasons based on the characteristics of the user group.Firstly,100,000 telecommunication users,a total of 99 columns of user characteristic data are cleaned,and 66 characteristic variables are retained after the Pearson correlation test.The remaining variables are divided into the three dimensions of usage,cost and basic information.The embedding method based on machine learning is innovatively adopted for each dimension for feature screening,and a total of 18 indicators in three dimensions are selected.After screening,the users of each dimension are clustered and grouped,and the KMeans clustering algorithm is used to divide each dimension into 2 categories,and accurately display the consumption level,voice usage level,activity level and other information.This article uses PCA dimensionality reduction to complete user grouping,and finally divides users into 8 categories.At the same time,through information regulation,typical portrait descriptions of all user groups are carried out,so that managers and operators can better understand user behavior habits and preference information.Secondly,start to predict the probability of loss by grouping.Use a good effect of previous use of machine learning algorithms to predict churn,including random forests,Adaboost,GBDT,Extra Tree,XGBoost and voting methods,and innovatively use of a voting method using soft voting classifiers as a fusion mechanism.By comparing the prediction results of the models of all users,the voting method in the form of soft voting was selected,with the F1 score of 71.8%.Construct models for user group classification with different characteristics and predict user churn tendency labels.The model F1 scores before and after classification are 71.8%and 75.4%respectively,which proves that the clustering is effective.In order to improve the effect of the model,combining previous experience,the SMOTE algorithm is used to balance the positive and negative samples.After sample balancing,the F1 score is increased by up to 6.04%,which further better predicts the tendency of users to churn.Finally,the characteristics of different types of users and the ranking of important factors affecting the tendency of users to churn are used to analyze the reasons for user churn,and provide user retention suggestions and corresponding decisions.On the basis of completing customer classification,grasping the personalized consumption needs of users,and looking forward to achieving more detailed and accurate marketing recommendations.