Customer Segmentation Using Generated Coffee Export Transaction Data

This study uses synthetically generated coffee export transaction data to explore customer segmentation, identifying distinct segments based on purchasing behavior to enhance coffee cooperatives' marketing strategies and efficiency. Real transaction data served as the foundation for generating synthetic data, which was analyzed. The K-means clustering method segmented customers based on Recency, Frequency, and Monetary (RFM) metrics, resulting in distinct groups. The analysis revealed high-value and low-value segments. These insights are crucial for coffee companies to develop effective marketing strategies and personalize services for different segments. Companies can optimize marketing by focusing on high-value customers through personalized services and loyalty programs and targeting low-value customers with tailored promotions and incentives. This study shows that analyzing transaction data helps understand market behavior and enhances business performance in the coffee industry.