An Enhanced Customer Segmentation Algorithm Based on the RFM Model Using K-Means Clustering

: Customer segmentation plays a crucial role in designing effective marketing strategies and understanding consumer behavior. While traditional RFM (Recency, Frequency, Monetary) analysis combined with K-Means clustering has been widely used to group customers based on past purchasing patterns, it often lacks the ability to capture the temporal evolution of customer behavior. In this study, we present an applied and progressive extension of the traditional RFM framework by integrating additional behavioral and temporal indicators, namely the trend in purchase frequency (Slope F), the trend in monetary value (Slope M), the average transaction value, and the average basket size. These features are designed to capture customer behavioral dynamics that are typically absent from static RFM models. K-Means clustering is applied to the enriched feature space, and the resulting segments are evaluated using standard internal validation metrics. The experimental results show that the inclusion of behavioral and trend-based variables leads to more coherent and better-separated customer segments, thereby supporting more effective and targeted marketing strategies.