Implementation of Data Mining for Analyzing Consumer Purchasing Patterns at TeTa Ino Cafe

Abstract

TeTa Ino is a micro, small, and medium enterprise by university students focusing on the production and marketing of innovative products based on butterfly pea tea. The significant decrease in sales volume from approximately 50–80 units per promotional event to only 20–40 units indicates a potential issue in the current marketing strategy. Therefore, this study aims to identify consumer purchasing patterns that can serve as the foundation for developing more targeted marketing strategies to enhance the competitiveness of TeTa Ino. This research employs the Cross Industry Standard Process for Data Mining (CRISP-DM) approach by applying the K-Means Clustering algorithm to four months of transaction data, including variables such as number of transactions, total transaction value, and discounts offered. The analysis resulted in four distinct consumer clusters: passive consumers, loyal consumers, non-loyal consumers, and consumers with moderate purchasing frequency. Each cluster is recommended to be approached with tailored marketing strategies, such as loyalty programs, product benefit education, and bundling promotions. The clustering evaluation achieved a Silhouette Score of 0.9008 and a Calinski Harabasz Score of 7630.34, indicating good segmentation quality and clear separation among clusters. This study concludes that applying the K-Means Clustering algorithm is effective in mapping consumer purchasing behavior as a basis for data-driven marketing strategy formulation. Future research is recommended to incorporate time-related variables and explore other clustering methods to further strengthen the analysis.