Research On The Forecast Aanlysis Of Long-term Revenue For Telecom Operators Under The Context Of Lowering Internet Price

The essay mainly analyzes that in order to response to the "Lower Fees" requirement,the telecom operators has launched sums of fee reduction measures,which directly led to the reduction of the current or sustained income.In this essay,the analysis based on the changes of user stickiness as well as the neural network models,Bayes network models and Support Vector Machine(SVM)-based model all applied to predict the operator revenue over the long term period.Under the background of the global telecom industry,with the development of the China’s 4G-orientd telecom industry,the essay has researched on the comparison of domestic and international 4G tariff so that to learn the 4G-oriented situation and development.Then,the commonly prediction methods has been introduced,especially the neural network models,Bayes network models and SVM-based model.Taking the a provincial telecom operator forwarding company as an example,the essay analysis the impact of "Lower Fees" strategy through the actual operating data and the corresponding user stickiness changes after the "Lower Fees" strategyintroduced.The essay takes one year of actual operation data as a sample to predict the operator revenue over the long term period.Applied to the neural network models,Bayes network models and SVM-based model,the essay has do the prediction and comparison of the results.By comparing the results in the three methods,it’s found that the BP neural network model based on regression analysis is the most proper way for the research on the operator revenue prediction over the long term period.Therefore,this essay puts forward suggestions on the future development strategy of telecom operators corresponding to the Lower Feesrequirement.