Sales Forecast of Marketing Brand Based on BP Neural Network Model

Abstract

With the advancement of globalization, the market competition among enterprises has become increasingly intense. To win a good market, an enterprise must understand and grasp the laws of the market economy and accordingly predict the future of the market. Efficient market estimates are based on a careful study of various types of market data. Therefore, enterprises must engage in preliminary research and data collection, based on a complete data system, and ensure the accuracy of vision predictions by developing a scientific market vision. Only by ensuring correct estimates can companies develop a right business plan and ultimately capture the market. More traditional sales forecasting methods generally only involve some details of sales, not accounting for relatively complex interactions among those factors (price, consumer income, etc.) that affect demand, and as a result, the models built are relatively simple. Artificial neural networks have excellent capabilities for infinite mapping and passive learning. This affects the requirements among the various factors, as well as the more complex relationships between them. In terms of weights, it is safe for neural networks. Therefore, BP neural network technology is used by most people to predict the number of sales, and a more coherent sales forecast method has been established for this purpose. Predicting sales targets is a very complex process, as the experimental results show. The prediction accuracy of this model is much higher than that of other common prediction methods. Its prediction accuracy is more than 30% higher than that of conventional methods, and it also has better comprehensive performance. This has a certain application value for sales forecasting work.