The distribution-based recency, frequency and monetary (RFM) score segmentation method: A novel RFM model for enhanced marketing strategies

Customer segmentation is important for marketing and campaign management, and the RFM (recency, frequency and monetary) model is widely used for this purpose. However, existing algorithms typically segment customers based on overall similarity in R, F and M scores, which may not provide the most meaningful insights for retailers. A more effective approach involves grouping customers according to the distribution of their R, F and M scores rather than clustering those with similar values. To address this limitation, this paper introduces the DbRFMSS (distribution-based RFM score segmentation) method. The proposed method categorises customers based on the distribution of R, F and M scores, offering more actionable segmentation. While many tools, including Excel and other applications, can perform RFM analysis, the DbRFMSS method leverages machine learning algorithms to handle large datasets more precisely. Comparative analysis with the traditional RFM approach demonstrates that the proposed method performs better in terms of computation time, iteration efficiency and silhouette measures. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.