Potential Users' Behavior Analysis Based On Data Mining

With the continuous development of the communication industry in China,the three operators have been competing for the user resources of the existing market by all means,and the competition is very fierce.With the rapid development of mobile Internet,mass user communication information,traffic consumption information,business consumption information and other behavioral data become available.This provides a broader platform for the development of data mining technology,which operator can get user needs faster,more accurately and more effectively,which operator can win.If we get business opportunities,we will be in a dominant position in the market.The data mining based on consumer behavior helps operators to better carry out the business of accurate marketing,customer loss early warning,potential customer recognition and blacklist user identification,so as to improve customer satisfaction and enhance the competitiveness of operators,so as to formulate specific marketing strategies to achieve accurate marketing for different enterprises.Users need to provide individualized solutions,and ultimately achieve the purpose of saving user marketing costs and improving corporate profits.But the customer's behavior data mining is faced with the problems such as the huge amount of data and the variety of data types.The traditional data mining method is difficult to achieve satisfactory results.According to the behavior characteristics of the telecom operators,this paper uses two algorithms of random forest and Xgboost to model and analyze a large number of user data,determine the optimization scheme of the algorithm model,and optimize the parameters.Finally,the model performance of the two algorithms is evaluated and contrasted,and the classification efficiency of the Xgboost algorithm is obtained.The result is better,and its results are used to predict.