Application of Association Rule Mining in Supermarket Sales Strategies

Abstract

The study used the association rule mining method to conduct data analysis on the purchase records of 999 products from 4624 customers of a large Taobao online store, in order to quantitatively describe the association relationship between products. By using support and confidence indicators, businesses can calculate the ratio of the number of customers who purchase two products at the same time to the total number of customers to obtain the correlation probability. This model can help businesses develop more targeted promotional strategies, improve sales efficiency, and increase customer satisfaction and loyalty. In order to verify the feasibility and effectiveness of the model, data simulation experiments were conducted and the theoretical results of the model were compared with actual data. The experimental results indicate that the theoretical results of the model are consistent with actual data, further verifying its feasibility. In addition, actual promotional strategies were adjusted for some products, and changes in sales data were observed. The results indicate that the promotion strategy developed using this model can effectively improve sales efficiency, and increase customer satisfaction and loyalty.