Customer valuation has emerged as a strategic imperative in healthcare supply firms operating within increasingly competitive and data-driven environments. The transition toward Healthcare 4.0 and digital transformation has significantly enhanced the capacity of organizations to utilize analytical frameworks for decision-making, particularly in evaluating long-term customer profitability. This study investigates the application of a Recency-Frequency-Monetary (RFM)-based analytical framework to predict client longevity worth in pharmaceutical and medical supply distribution contexts.

The research adopts a structured analytical approach combining RFM segmentation with predictive modeling concepts to evaluate customer lifetime value (CLV). Drawing upon knowledge management perspectives, digital transformation capabilities, and stakeholder relationship dynamics, the study develops an integrated framework tailored to healthcare supply chains. The methodology emphasizes behavioral data interpretation, customer segmentation, and performance optimization aligned with strategic objectives.

The findings indicate that RFM-based segmentation significantly enhances the accuracy of client valuation when combined with dynamic capability perspectives and data analytics enablers. Firms that integrate digital transformation strategies with customer analytics demonstrate improved forecasting of long-term value and more effective resource allocation. Additionally, the study reveals that stakeholder relationship management and ecosystem-based approaches influence customer retention and profitability patterns.

The research contributes to both theory and practice by bridging customer analytics with healthcare digital transformation literature. It highlights the importance of integrating data-driven methodologies with organizational capabilities and knowledge systems. Limitations include reliance on conceptual modeling and absence of empirical dataset validation, suggesting future research opportunities involving real-world implementation and advanced machine learning integration.

Overall, the study provides a comprehensive framework for healthcare supply firms to enhance strategic decision-making through predictive customer analytics, thereby supporting sustainable competitive advantage in evolving healthcare ecosystems.

Customer Lifetime Value, RFM Model, Healthcare Supply Chain, Predictive Analytics, Digital Transformation, Customer Segmentation, Healthcare 4.0, Data Analytics, Stakeholder Management

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