Design and Implementation of Network Marketing System Based on Neural Network Algorithm

In the era of DT, traditional product-centered marketing methods can not accurately locate customer preferences, and it is increasingly difficult to improve the competitiveness of enterprises in the market. The new marketing plan emphasizes the need to make full use of DT resources to exactly analyze VOC, classify client before product marketing, predict whether client will order products, and conduct targeted marketing for different client. ANN has excellent nonlinear characteristics, which is especially suitable for the processing of highly nonlinear systems. Intelligent prediction based on neural network is an effective method to solve nonlinear prediction problems. Therefore, this paper studies the BP NN algorithm in ANN, and designs and implements a network marketing system for targeted marketing to client. Finally, MATLAB NN toolbox is used to conduct empirical research, measure the customer’s willingness to buy products index, identify different types of client, and complete the training and testing of the model. The empirical results show that the model is effective in training and testing, the data fit is high, the error rate between the actual output and the expected output is small, and the prediction accuracy of the model is high. BP NN technology improves the efficiency and accuracy of customer data processing, and has high feasibility and practicability in the field of customer accurate identification.