Abstract
With the rapid development of network and informatization of the consumer market in my country, the application and maturity of technologies such as the Internet, terminal equipment, logistics, and payment and the continuous improvement of people’s consumption concepts, online shopping has gradually become the mainstream purchase method for Chinese consumers, and e-commerce has gradually become one of the important driving forces to promote the sustained and vigorous development of China's economy. Under the traditional marketing model, companies do not fully understand the needs of users. The sales staff's thinking is only how to sell products to users. They do not know the specific consumer needs, so they can only focus on the product. Based on these foundations, this research uses convolutional neural networks and applies this model to precision marketing to obtain accurate portraits of consumers, thereby increasing the company's turnover. After comparing different models and conducting some experiments, it is concluded that (1) through the collection and analysis of W enterprise data, the training and testing conditions of the CNN model, LSTM model, LSTM attention model, and CNN + LSTM attention model are compared. It is concluded that the CNN + LSTM attention model and the LSTM attention model perform better, and the accuracy of testing and training is higher. (2) Through the fitting of the model, it is found that Sn(%) = 70.71, Sp(%) = 86.25, Acc(%) = 81.07, and MCC = 0.752 of the CNN + LSTM attention model are the best fitting models. The men and women stratification and gender stratification of users are predicted, and it is found that men in the W company are the main purchasing power, and in the age stratification, it is found that the population of 41–50 accounts for the highest proportion. (3) The average accuracy rate of the LSTM attention model is as high as 66.6%, the average recall rate is 82.3%, and the F1 score is 73.1%. This model has met expectations for precision marketing forecasts. (4) Using the CNN + LSTM attention model to predict the marketing input for the next year, it is found that the use of precision marketing will increase the profit of W company. The average annual data show that the monthly revenue of precision marketing has increased by 73.5%.
Highlights
IntroductionIn the past ten years, with the rapid development of network and informatization of China’s consumer market, the application and maturity of technologies such as the Internet, terminal equipment, logistics, and payment and the continuous improvement of people’s consumption concepts, online shopping has gradually become the mainstream purchase method for Chinese consumers
(4) Using the Convolutional neural networks (CNN) + LSTM attention model to predict the marketing input for the year, it is found that the use of precision marketing will increase the profit of W company. e average annual data show that the monthly revenue of precision marketing has increased by 73.5%
A multilabel classifier system is based on deep learning features of convolutional neural network to infer the classification of goods. e system is based on the two-dimensional representation of the sample: first, obtain a one-dimensional feature vector, extract the characteristics of the marketing amount and marketing model, find out the interaction and the structure and feature similarity information with other products of different categories, and reshape the original one-dimensional feature vector to obtain a twodimensional matrix table of commodities
Summary
In the past ten years, with the rapid development of network and informatization of China’s consumer market, the application and maturity of technologies such as the Internet, terminal equipment, logistics, and payment and the continuous improvement of people’s consumption concepts, online shopping has gradually become the mainstream purchase method for Chinese consumers. Convolutional neural networks can be used to automatically learn structured data, extract effective features, and use the extracted effective features to make predictions and make correct decisions, which can help manage company employees, predict consumer preferences, etc. E ATC classification system considers the distribution of population characteristics, brings into the model effect characteristics, and predicts unknown problems in its category Such predictions can be used to infer the active ingredients of system performance and to infer other possible active ingredients. E CNN model can be used to decode the hidden focus of attention related to EEG events in the object selection process It compares the performance of CNN and the commonly used linear discriminant analysis (LDA) classifier, applies it to different dimensional data sets, and analyzes the transfer learning ability. With the continuous exploration of science and technology by human beings, CNN is undergoing continuous innovation and transformation, with more extensive applications and continuous enhancement of computing capabilities [14, 15]
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