MODERN METHODOLOGICAL FRAMEWORK FOR CONSUMER BEHAVIOR RESEARCH IN THE SERVICE SECTOR: FROM RETROSPECTIVE ANALYSIS TO PREDICTIVE ANALYTICS

Abstract

This article investigates the evolution of methodological approaches to studying consumer behavior in the service sector amidst digital transformation. It systematically analyzes the transition from traditional retrospective methods – such as surveys, focus groups, and experiments – to contemporary real-time tools including digital footprint analytics, neuromarketing techniques, and sentiment analysis, as well as predictive models based on artificial intelligence and machine learning. The key advantages and limitations of each approach are critically evaluated, revealing that retrospective methods capture stated preferences but suffer from recall bias, real-time analytics provide objective behavioral data yet lack explanatory depth, and predictive models offer forecasting capabilities but depend heavily on data quality. The study argues that no single method can fully comprehend consumer behavior in modern service environments, substantiating the necessity of integrating these approaches into comprehensive research ecosystems. Such integration enables a multidimensional, continuously updated understanding of consumer motivations, preferences, and loyalty drivers essential for competitive advantage. A conceptual hybrid model is proposed, operationalizing the integration of qualitative, quantitative, and predictive methods within a five-stage iterative cycle: hypothesis generation through qualitative inquiry; quantitative validation via surveys and behavioral analytics; predictive model development using machine learning; experimental testing through A/B validation; and continuous refinement through feedback loops. This cyclical architecture ensures deepening consumer understanding and evidence-based managerial decisions. The practical implications for customer experience management are significant, enabling organizations to transition from reactive analysis to proactive anticipation and real-time personalization, thereby enhancing satisfaction, retention, and loyalty. The findings underscore that the value of modern consumer research lies in intentionally designed integrated ecosystems where diverse methodological insights continuously inform and amplify one another, equipping researchers and practitioners to navigate the evolving landscape and achieve superior customer experience outcomes in an increasingly complex digital environment.