Research on a complaint prediction model utilizing joint neural networks
By conducting in-depth exploration on the key factors affecting repeat complaints of telecom operators,this study aimed to improve service quality and construct a risk prediction model.Based on the operator's customer service data,the study employed Logistic regression,BP neural network,and their combined modeling methods.The Logistic regression model identified five major influencing factors,predicting the probability of repeat complaints with an accuracy of 80.0%.The BP neural network selected 81 influencing factors,achieving a prediction accuracy of 90.6%.On this basis,a combined model was constructed with an accuracy rate of up to 92.8%.After practical appli-cation in a provincial telecom operator,the repeat complaint rate decreased by 3.2%,demonstrating a significant im-pact.Strong support is provided for improving the service quality of telecom operators and reducing repeat com-plaints,which is of great significance for the development of the telecom industry in China.
AI customer servicejoint modelingrepeated complaintLogistic regressiondeep learning model
万方数据
维普
