Research on intelligent cigarette purchase model based on multi-source data

Abstract

Cigarette retailers usually rely on past sales data, intuition and experience to select products. Such selection strategy suffers from some problems, such as low efficiency, single selection index, serious homogenization, and focusing only on immediate interests. As a result, it is unable to respond to market changes sensitively, resulting in a loss of store profits. Under the background of big data, this paper mainly studies how to use machine learning algorithms to make intelligent and efficient store selection of cigarette commodities, by combining internal data of the enterprise and big data of external people, goods and stores. Six stores involving a total of 30 tobacco retailers were taken as the experimental objects, the location of which cover Kunshan, Zhangjiaxiang, Taicang, Changshu and Wujiang districts. The commodity sales in the second and the third quarters were compared. By using the selection system, the customer unit price of tobacco retailers increased by an average of 9% year-on-year, the cigarette profit increased by an average of 5% year-on-year, and the cigarette inventory turnover rate increased by 12% year-on-year. It shows that the model has satisfactory performance. Furthermore, the cigarette selection model can be dynamically updated and optimized. It provides the most suitable and real-time cigarette selection suggestion scheme for retailers and helps the retailers to improve cigarette sales.