Abstract : Market segmentation remains a critical strategic function for optimizing customer targeting and enhancing product positioning in competitive business environments. Traditional segmentation approaches, however, often fail to capture the multidimensional relationships between customer behavior and product performance. The present study proposes an integrated analytical framework that combines customer-centric behavioral variables with product-level performance indicators to optimize segmentation outcomes. Using a quantitative modeling approach, key parameters such as purchase frequency, customer lifetime value, engagement rate, product usage frequency, and feature adoption rate were analyzed through principal component analysis, k-means clustering, and canonical correlation analysis. The results revealed the emergence of distinct customer segments characterized by varying levels of engagement intensity and product adaptability, with higher alignment between behavioral patterns and product utilization associated with improved segmentation optimization efficiency. The integrated framework demonstrated enhanced predictive capability in identifying high-value segments and evaluating cross-domain associations influencing customer retention and value realization. These findings highlight the strategic advantage of combining customer and product analytics in developing scalable, data-driven segmentation models capable of supporting targeted interventions and efficient resource allocation.