Segmentasi Pelanggan Berbasis RFM dengan Algoritma K-Means pada Data Transaksi Online Retail - Darma Oktavian, Vedly Vedliyan; Ramadhan , Ridho; Fadhilla, Daffa Rayhan (Jejaring Penelitian dan Pengabdian Masyarakat (JPPM), 2025)
This research focuses on customer segmentation using the RFM (Recency, Frequency, Monetary) model and the K-Means algorithm on online retail transaction data. Customer segmentation is the process of categorizing customers into different groups based on their transactional behavior patterns. The RFM model allows us to evaluate customers based on three critical dimensions: how recently a customer made their last purchase (Recency), how often a customer makes purchases (Frequency), and the total monetary value generated by the customer (Monetary). By combining RFM data and the K-Means algorithm, we can divide customers into homogeneous segments. This analysis provides deep insights into the characteristics and value of each customer segment, enabling companies to develop more targeted and effective marketing strategies. The segmentation results are expected to assist companies in enhancing customer retention, maximizing customer lifetime value,and improving the effectiveness of marketing campaigns.