Research And Implementation Of The Early Warning And Recommendation System For The Loss Of Telecom Package Users

With the continuous improvement of user demand and the continuous segmentation of user groups,the matching package types emerge in endlessly,with various forms and complete functions.However,the large number of packages does not mean that telecom packages with high market share,high frequency of use,complex information and various contents not only affect the management and decision-making of telecom operators,but also not convenient for users to choose.The loss of users in the network has become one of the most troublesome problems for telecom operators.In order to expand market share and accelerate enterprise development,telecom operators have constantly tried to launch package products that adapt to market changes,but the effect has been counterproductive,the instability of users has increased,and the loss rate has increased.Therefore,the purpose of this study is to help telecom operators conduct early warning analysis of lost users,take timely measures,and retain the customers who are about to lose and stabilize the existing stock of users to the greatest extent by providing users with reasonable and personalized package recommendation services and analyzing user needs.The research significance of this paper is to provide enterprises with comprehensive data analysis,reduce user loss as much as possible,improve efficiency,optimize the existing product structure,and improve work efficiency for enterprise employees.The research method of this paper is to mine and analyze the user data,consumption behavior data and package use data accumulated by telecom operators,use data mining algorithms,machine learning algorithms and other popular algorithms,and use data mining technologies such as preprocessing,rule classification,association,etc.to find out the characteristic factors that affect the use of the user’s package and lead to poor stability,Use these characteristic factors to build a pre-warning model for the loss of package users based on data mining technology and fusion mining strategy,identify the users who are about to lose in advance,and make retention measures.At the same time,put forward the combination of the advantages of XGBoost model and LightGBM model,propose a composite recommendation algorithm model,analyze the use preferences and needs of package users,and provide the most suitable package for online users and users who are about to lose,Designed a set of early warning and recommendation system for telecom package user churn to help enterprises conduct user market analysis and decision-making,user retention and accurate recommendation,effectively solved the problems of data redundancy,low mining efficiency and insufficient recommendation accuracy,optimized the problems of data sparsity,data and rule mining difficulties and over-fitting in traditional algorithms,and improved the efficiency and accuracy of churn prediction and recommendation,Provide customized services for users with various needs,accurately recommend and retain users.The research in this paper realizes the key information in the existing package model and user data through data mining technology,studies the pricing form of the package content,strongly correlates the existing telecom package model with the user’s relevant data,proposes the eigenvalue extraction algorithm for continuous attribute data,and uses the composite mining model of rule mining and classification mining to analyze the user’s consumption behavior and predict the loss,Optimize the content and structure of the package.Use the high-quality data mining and machine learning algorithm fusion to establish a user churn warning and recommendation model to achieve maximum retention and accurate recommendation for telecom package users.Finally,through a large number of experiments,the accuracy of loss prediction and recommendation efficiency have been significantly improved.