The Application of Customers Segmentation Using RFM Analysis Method and K-Means Clustering to Improve Marketing Strategy - Robo, Salahudin; Melani, Putri Indah; Fernatyanan, Patrisia; Widiantoro, Muh Riandi; Bariyyah, Sitti Khairul (IJISTECH (International Journal of Information System and Technology), 2024)
This research aims to overcome problems in improving marketing strategies in the Retail Business industry by using effective customer segmentation. The method used is RFM (Recency, Frequency, Monetary) analysis to measure the time proximity, frequency and monetary value of customer transactions, as well as K-Means Clustering to group customers based on their purchasing behavior. The results showed that the combination of these two methods successfully grouped customers into ten different segments, such as “Champions” and “Hibernating,” which provided deep insight into customer needs and behavior. The application of this segmentation provides practical benefits in increasing the efficiency of marketing strategies, customer retention and resource optimization. Overall, this research proves that applied customer segmentation techniques can significantly increase customer satisfaction and loyalty, making a valuable contribution to the field of retail marketing.