Data-Driven Approach to Enhancing Supermarket Vegetable Sales Strategies

Abstract

China's vegetable commodity production has developed rapidly and made great progress. Due to the short shelf life of most vegetable products, they cannot be sold the next day. Therefore, by studying the distribution law of sales volume and the relationship between categories and pricing, the supermarket can accurately predict the sales situation, reasonably arrange replenishment plans, and formulate pricing strategies. Comprehensive analysis of automatic replenishment and pricing strategies of vegetable commodities is of great significance to the profit of fresh supermarkets. Aiming at the automatic pricing and replenishment strategies of vegetable products, this paper visualizes each item and each category to obtain the distribution and relationship of time, sales volume, and sales frequency of different items and categories. Spearman grade correlation coefficient is used for correlation analysis, and a single linear regression model is established. The relationship between cost profit rate and cost, selling price, and total sales volume of each category is obtained. Then, the ARIMA prediction model is used to predict the automatic pricing and replenishment strategies for each category and each vegetable item during the week of July 1-7, 2023, and July 1, 2023.