Author(s)

Xinyan Li

Corresponding Author

Xinyan Li

Abstract

Through the comprehensive analysis of huge data sets, enterprises can have a deeper understanding of market trends, consumer behavior and product performance, so as to formulate more targeted market strategies. At the same time, enterprises can also make risk assessment and decision-making optimization by simulating different market situations to adapt to the complex and changeable market environment. These data not only reflect consumers' purchasing preferences and behavior patterns, but also provide strong support for enterprises' market forecasting and decision-making. On the one hand, with the help of data mining and machine learning technology, enterprises can identify consumers' buying patterns and preferences, thus realizing personalized marketing.On the other hand, by analyzing historical sales data and external environmental data, enterprises can make market demand forecasts. Modern consumers are increasingly inclined towards personalized shopping experiences. Through big data technology, companies can analyze consumers' behavior habits and push products or services that meet their interests. Therefore, e-commerce market prediction and decision support based on big data analysis are particularly important. E-commerce market prediction and decision support based on big data analysis is not only an effective means for modern enterprises to enhance competitiveness, but also an inevitable choice to adapt to market changes.

Keywords

Big data analysis; Electronic Commerce; Information technology; Forecast; Policy decision