Abstract: Sales data is currently one of the most widely used indicators to evaluate the performance of products in retail stores, which determines product inventory management and marketing strategies. Current research still lacks comprehensive evaluation methods of product feedback, product revenue and inventory-sale conversion based on consumer behavior. Therefore, this study established a set of evaluation methods for shoe products in offline stores, and conducted empirical research to verify the effectiveness of the method. Firstly, we carried out standardized calculations on the five data, including attention number, sales number, try-on number, unit price and stock-to-sale ratio, and established an intuitive quantitative evaluation model using radar chart and area formula. Then, by analyzing the data collected during the spring, we obtained shoe categories with high sales, high attention, high unit price and high conversion rate. The results showed that the best overall product category for the store during this period was casual shoes. Through a more comprehensive analysis, this method provided a more reliable basis for formulating a marketing strategy plan for footwear products, and brought higher revenue to the store.