Enhanced Customer Segmentation Using LRFSM Behavior Model and Multi-Algorithm Clustering Approaches

Customer segmentation plays a critical role in optimizing personalized marketing and driving customer engagement in online retail. Building on the previously established LRFS (Length, Recency, Frequency, Spend) model, this study introduces an enhanced LRFSM framework by incorporating Monetary value as a fifth behavioral dimension. This expansion enables a deeper analysis of customer purchasing behavior and economic contribution. By applying advanced segmentation techniques and evaluating cluster quality through both internal validation scores and external classification metrics, this work ensures both statistical rigor and business relevance. Detailed profiling of each customer segment further provides meaningful insights into targeted strategy development. The results demonstrate that the inclusion of Monetary value not only improves segmentation precision but also supports the creation of scalable, adaptive models tailored to dynamic retail environments, bridging the gap between data science and actionable business outcomes.