As an important means to prevent the loss of customers in automobile 4S stores,customer chum early warning not only provides an effective economic benefit guarantee for contemporary car companies,but also brings a new research basis for car companies to make future decisions.In order to establish the grading standard of customer chum early warning in automobile 4S stores,this paper starts from 29 indicators such as customer basic information,vehicle age,vehicle sales price,loan amount,maintenance times,and maintenance time,and establishes a standard model for customer chum early warning in automobile 4S stores based on the particle swarm optimization BP neural network algorithm.The model first predicts the probability of customer chum,and then divides it into 5 levels from 1 to 5 according to the probability size of the value between 0 and 1,where 1 indicates that the probability of chum is very small,and 5 indicates that the probability of chum is very large.In the end,71.39％,3.75％,3.50％,5.86％and 15.50％of the test set customer chum warnings were obtained,respectively.At the same time,78.65％of the customers in the training set are not churned as the prior probability,and the prediction probability is less than or equal to the prior probability of customer chum,and the overall accuracy of the model is 91.71％.
维普
万方数据