Application of Big Data in the Promotion of Fast Selling Products

• Main Author: Cui Wenxin
• Format: Article
• Language: English
• Published: EDP Sciences 2021-01-01
• Series: E3S Web of Conferences
• Online Access: https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/11/e3sconf_netid2021_03078.pdf

In the traditional marketing mode of fast-selling products, the sales mode of physical stores is adopted. However, in the background of the current big data era, it is a trend to optimize the promotion form by applying big data technology. Therefore, this paper puts forward the application research of big data in the promotion of fast-selling products. This paper makes an indepth study on big data technology and commodity marketing. It is believed that there is a lot of information hidden behind the information data, and the application of big data technology pays more attention to consumer behavior than before. In this paper, according to the characteristics of fast-selling products promotion activities, combined with big data technology, the effect evaluation model are established, which can better solve the shortcomings of traditional promotion activities which are difficult to evaluate. And according to the actual needs of the promotion of fast-selling products, targeted optimization is carried out. In order to further verify the data analysis ability of big data technology in the promotion of fast selling products, this paper establishes the corresponding investigation experiment. The experimental data show that big data technology can better analyze the actual effect of various promotion tools and promotion strategies, provide technical support for enterprises before and after the promotion of fast-selling products, and facilitate enterprises to adjust strategies and summarize experience. The analysis shows that big data technology has brought a variety of convenience to the promotion activities, which not only broadens the sales channels, but also provides a new basis for the decision-making of enterprises.