Abstract
As a brand-new marketing method, network marketing has gradually become one of the main ways and means for enterprises to improve profitability and competitiveness with its unique advantages. Using these marketing data to build a model can dig out useful information that the business is concerned about, and the company can then formulate marketing strategies based on this information. Sales forecasting is to speculate on the future based on historical sales. It is a tool for companies to determine production volume and ensure the balance of product supply and sales. It can help companies make correct business decisions to maximize profits. The neural network can approximate the nonlinear function with arbitrary precision, and the time series prediction model based on the neural network can well reflect the nonlinear development trend of information. Based on the analysis of the shortcomings of the traditional BP network, this paper uses a genetic algorithm with good global search capabilities to improve the neural network. The thought and theory of optimizing the initial weight and threshold of the neural network of the GA algorithm are discussed in detail. While expounding the forecasting method, it uses specific examples to analyze the performance and characteristics of the GA-BP network in the enterprise network marketing forecasting. The results show that the GA-BP neural network is higher than the traditional BP neural network in terms of prediction accuracy and adaptability.
Highlights
According to Kolmogorov’s theorem, EBP neural network can approximate any rational function with any accuracy. at is, a 3-layer EBP network can complete any m-dimensional to n-dimensional mapping [16]. e main purpose of this article is to combine the backpropagation (BP) network and the genetic algorithm (GA) to form a GA-BP network to train and predict time series. is is because of the following: (1) Each node and weight of a pure BP network will affect the output
When the algorithm reaches a certain convergence requirement, the neural network is used for secondary training to avoid local optimization and achieve the purpose of improving network training accuracy and speed. is article uses the GA-BP forecasting method to predict the status of corporate network marketing because the status of corporate network marketing is constantly changing over time
With the country’s increasing investment in science and technology and the education industry, Internet marketing is gradually becoming the main tool of corporate marketing during the period of high growth in the population of Internet users in my country. rough learning and using network knowledge to flexibly use marketing theory and develop network marketing practice, there is a broad and bright prospect for enterprises to obtain greater benefits. is paper proposes a corporate marketing forecasting model based on a genetic algorithm to optimize the BP neural network, which can effectively overcome the shortcomings of the existing research
Summary
Evaluation indicators are the standards for measuring performance, reflecting the key success factors of corporate network marketing, and revealing the content of corporate network marketing performance evaluation. It is a specific way for the subject of network marketing performance evaluation to fully understand the evaluation object. E financial benefit mainly describes the degree of influence on some financial indicators of the enterprise before and after the use of network marketing. E competitive benefit mainly describes the company’s use of network marketing to improve its competitiveness in the same industry, including the increase in product market expansion rate and product market share. After-sales service fee describes the cost of providing after-sales service to customers
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