RFMI-based Customer Segmentation with K-means

The development of e-marketing over recent decades has led offline and online retail enterprises to adopt various data analysis technologies to enhance their understanding of consumer behavior and increase revenue. One common approach involves segmenting consumers into distinct groups based on designed metrics, targeting high-value segments for specialized services. To evaluate customer worthiness, the popular RFM model uses three dimensions: recency (the time since their last purchase), frequency (how often they make purchases), and monetary value (total spending). Higher scores under this model are indicative of greater potential profitability for businesses. While this approach provides valuable insights, it may not fully capture all profitable customer behaviors accurately. To address these limitations, this paper introduces a new model, namely the RFMI (i.e., recency, frequency, monetary, and interval) model, for comprehensively evaluating customer value. The new model employs an analytic hierarchy process to derive the RFMI values of customers. Subsequently, we employ K-means clustering customers to group customers into six segments. Moreover, the experimental dataset was sourced from a real UK e-commerce platform. The experimental results indicate that the new model effectively distinguishes between various consumption patterns among customers. This enhanced understanding can enable retailers to improve their marketing strategies more precisely, optimize customer service, and increase profitability.