Research On Customer Management Based On User Grouping And Churn Prediction Technology

The user is an important factor in the development of the enterprise,and the maintenance and management of the user reflects the ability of the enterprise to operate.The problem of user churn will affect the company’s revenue,and the reasons behind it also imply problems in product and enterprise development.It is necessary to find lost users in time,analyze the reasons and formulate corresponding recovery strategies.There are many studies on the prediction of lost users,among which the data mining modeling technology based on machine learning has a good performance in classification problems and is widely used in various fields.This paper selects the telecommunications user data published on the Internet,with a total of 6534 samples and 16 fields,involving basic user information,service information,and billing information.The telecommunications industry has a large number of users and a lot of information,and at the same time there is a lot of user loss.The long-term research on user management is of great value.This paper analyzes the current research status of the churn problem,and introduces the related concepts,data mining technology,and related algorithm principles of user churn.In practice,first,feature exploration and descriptive analysis are performed on the data.There are 5228 samples in the training set and 1306 samples in the test set.Correlation analysis is performed on features,and a scoring model is established based on churn correlation to predict churn users.The model finds more than 60% of users with an accuracy rate of 0.65 or more.Further,three clustering analysis methods of K-means,K-modes,and K-prototypes are used to classify the samples,and the Kprototypes method is selected to classify the samples into 0-1 two types of users.Type0 users may be short-term users or new users who love new things but the user relationship is not stable;Type 1 users are more stable overall.Predictive modeling is performed on the classified data and all data,and four methods of logistic regression,random forest,XGBoost,and Light GBM are selected and comprehensive evaluation indicators are selected.Finally,XGBoost’s classification data prediction model is selected,and the final recall rate of users of category 0 is 0.89456,and the recall rate of users of category 1 is 0.84211.Based on the analysis of the predicted results,the predicted lost users are divided into four user groups ABCD using key indicators,of which A-type users account for 52% of the most lost users.Analyze the reasons for user churn and determine user value.The main reasons for the loss of users are price factors,demand mismatch,life changes,and natural loss.Propose specific recovery strategies and try to put forward suggestions for the overall development of the company.