Enhancing Sales Determination for Coffee Shop Packages through Associated Data Mining: Leveraging the FP-Growth Algorithm

Abstract

The coffee shop business offers a diverse range of coffee and food options. However, customers often experience delays during transactions due to the extensive selection of menu items and combinations. This inconvenience not only discomforts new customers but also hampers their likelihood of returning, potentially impacting the overall business turnover. To address this issue, this study aims to establish association rules by combining the least and most popular menu items for the upcoming month. These rules will serve as a guideline for creating shopping packages that streamline the decision-making process. The FP-Growth algorithm is employed to analyze sales transaction data from January to March 2023, comprising 2,336 transactions in .csv format. Among the generated association rules, two rules stand out with the highest support and confidence values. The first rule exhibits a support value of 0.3% and a confidence of 70.0%, while the second rule showcases a support value of 0.4% and a confidence of 69.2%. By considering these two rules alongside the existing menu options, coffee shop owners can effectively curate shopping packages that cater to customer preferences. It is anticipated that these packages will elevate the quality of service, attract a greater number of customers, and subsequently enhance the overall business turnover.