An Implementation of Loyalty Program Theory Based on Recency Frequency Monetary Score in Information Systems to Increase Customer Loyalty
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Abstract
This study aims to help online retail stores find the right strategy for treating customers through customer segmentation based on Recency, Frequency, and Monetary (RFM) Score. With a quantitative approach, this study uses the K-Means Clustering algorithm to group customers based on their RFM values and applies it within the Loyalty Program Theory framework. The results show that the Best Customers segment has the highest percentage at 26.3%, which emphasizes the importance of retaining high-value customers through exclusive loyalty programs such as VIP access and premium offers. In contrast, the Lost Customers segment at 24.8% requires attention through retargeting and discount programs to attract them back. This study proves that data-based customer segmentation and the implementation of relevant strategies can strengthen long-term relationships with customers, increase loyalty, and ultimately help the development of online retail businesses.
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