RFM K-Means Clusteringfor Meat Products

This dataset contains the results of a Recency, Frequency, and Monetary (RFM) analysis combined with K-Means clustering applied to customer transaction data for meat product purchases. The data has been preprocessed and filtered based on specific product categories and time periods, with key variables including customer ID, purchase recency, purchase frequency, and monetary value. The purpose of this dataset is to support customer segmentation analysis, enabling the identification of distinct behavioral clusters for targeted marketing strategies. This dataset can be used for research in data analytics, customer relationship management (CRM), and machine learning applications in marketing, particularly in developing automated workflows for segmentation and personalized recommendation systems.