<p>In an environment where each customer has unique preferences and needs, companies must continuously adapt to remain competitive. This necessity has driven organizations to modify their business strategies, focusing on understanding their clients and developing personalized approaches for specific customer segments. The main challenge lies in identifying distinct customer types and determining the most suitable strategy for each group. Customers can be categorized based on their social media activity through analyses of reliability, frequency, and helpfulness. However, interpreting the results of such analyses can be difficult when using traditional techniques. Therefore, it is essential to employ tools that support decision-makers in understanding these outcomes. In this paper, we propose a multi-hierarchical linguistic–semantic interpretation methodology. This methodology fuzzifies the information obtained from clusters to assign a linguistic label to each variable by comparing fuzzy variables with predefined linguistic labels. An extension of the classic RFM (Recency, Frequency, Monetary) model–referred to as the RFH (Recency, Frequency, Helpfulness) model—is applied. The proposed methodology enhances the interpretability of clustering models compared to classical techniques and modern explainable paradigms. By addressing phenomena such as the "semantic gap" and "semantic erasure," our approach provides intrinsic, magnitude-aware semantic profiles. Furthermore, the structural robustness and generalizability of the framework are quantitatively validated across diverse domains, before applying it to a real-world business case of customer profiling in the tourism sector.</p>