The increasing complexity of health product logistics has intensified the need for advanced analytical tools to evaluate customer value and retention profitability. In this context, Recency-Frequency-Monetary (RFM) analysis has emerged as a robust data-driven framework for assessing customer engagement and long-term profitability. This study explores the application of RFM techniques in health product logistics firms, where supply chain efficiency, demand variability, and service reliability significantly influence client retention outcomes. Unlike traditional customer profitability models, RFM analytics offers a behavioral perspective by integrating transaction recency, purchase frequency, and monetary contribution into a unified decision-making framework.

The research develops a structured analytical model that integrates RFM metrics with operational logistics variables such as supply chain responsiveness, inventory control mechanisms, and service delivery efficiency. Drawing upon established theories in lean manufacturing, demand flow optimization, and predictive maintenance systems, the study bridges the gap between customer analytics and logistics performance evaluation (Feld, 2000; Grosfeld-Nir et al., 2000). Furthermore, the incorporation of predictive analytics and system health monitoring frameworks enhances the capability of firms to forecast retention trends and optimize resource allocation (Ferrell, 2000; Liao & Lee, 2010).

The study employs a conceptual modeling approach supported by theoretical synthesis of supply chain optimization, prognostics, and decision-support systems. It demonstrates how RFM segmentation can be aligned with logistics strategies such as push-pull systems, just-in-time production, and demand-driven distribution to maximize profitability from retained clients. The findings indicate that integrating behavioral analytics with logistics performance metrics significantly improves decision accuracy in customer segmentation and enhances long-term profitability.

The research contributes to both academic literature and industry practice by proposing a hybrid framework that combines customer analytics with operational logistics intelligence. It also identifies critical limitations related to data integration, model scalability, and domain-specific variability in healthcare logistics. The study concludes by recommending future research directions focusing on AI-driven RFM extensions and real-time predictive analytics for healthcare supply chains.

RFM Analysis, Customer Retention, Health Product Logistics, , Supply Chain Optimization, Customer Profitability, Data-Driven Decision Making, Predictive Analytics, Lean Supply Chain, Demand Flow Systems

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