Prediction Of Telecom Customer Loss Based On Deep Learning

With the rapid development of social economy,the telecommunications industry is becoming more and more mature,and the industry competition is becoming more and more fierce.Major operators gradually turn their focus to customer-centric,highlighting the position of customers in the operators,and how to reduce the loss of customers and timely retrieve customers will become the key.With the rapid development of artificial intelligence,the application of artificial intelligence technology to the customer analysis of the telecom industry can effectively reduce the operating cost of the telecom industry and improve the customer retention rate.At present,most of the researches use traditional machine learning methods,which need a lot of data preprocessing work,especially in feature engineering.With the explosive growth of the number of customers,traditional machine learning methods have been unable to meet the analysis needs of telecom customers under big data.Deep learning can automatically learn better data features by simulating the thinking mode of human brain,learning the internal laws and representation levels of sample data,and has achieved remarkable results in text,image,voice and other aspects.The focus of this paper is to obtain customer information,account information and value-added services from a telecom company.After data preprocessing including missing value processing,digitization,normalization and equalization,deep learning algorithm is used to build a deep neural network model for customer churn prediction based on the behavior data of telecom customers,and artificial features are used The selected logistic regression and support vector machine(SVM)models are compared.Aiming at the problem that the number of layers of deep neural network is too deep,which is easy to cause the gradient to disappear,batch is used_Noramlization replaces dropout,and adds a direct connection channel in the deep neural network,further preventing the gradient disappearance problem caused by too deep layers.The simulation results show that the deep learning method has better prediction effect than the traditional machine learning method.