<p>This study empirically validates a fuzzy logic-based system for evaluating customer satisfaction, using a global technology leader as a case study. Recognizing diverse customer expectations in a competitive market, the research employed a mixed-method approach, combining surveys with in-depth interviews to gather comprehensive data on product quality, service quality, and brand image. The findings demonstrate that the fuzzy logic model effectively handles ambiguity, offering greater accuracy and flexibility than traditional methods. It yielded higher average output values for key dimensions; for instance, product quality scored 4.3—a 0.2 point (5%) increase over the classical method. Regression analysis identified product quality (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\beta \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>β</mi> </math></EquationSource> </InlineEquation>=0.42) as the most significant driver of satisfaction, followed by service quality (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\beta \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>β</mi> </math></EquationSource> </InlineEquation>=0.35) and brand image (<InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\beta \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>β</mi> </math></EquationSource> </InlineEquation>=0.28). Significant differences were also found between customer segments, with loyal customers reporting higher satisfaction levels than new ones. The study provides a novel, holistic framework that simultaneously considers multiple criteria. It confirms that targeted improvements, such as enhancing battery life, positively impact long-term satisfaction trends, offering actionable insights for strategic decision-making.</p>