BEYOND THE CRYSTAL BALL: RETHINKING THE EXACTITUDE OF CONSUMER BEHAVIOR MODELS

Abstract

This study examines the limitations of traditional consumer behavior models and explores the shift toward more adaptive, emotion-based, and data-driven frameworks. Traditional models have long relied on assumptions of rationality and predictability, often failing to account for the complexities and dynamic nature of consumer decision-making. The rise of behavioral economics, psychological insights, and advancements in data analytics has highlighted the need for models that incorporate emotional, social, and contextual factors, alongside rational elements, to better capture consumer behavior. This research discusses the limitations of conventional models, particularly their inability to account for irrational decision-making, emotional influences, and the evolving preferences of consumers. Additionally, it emphasizes the importance of incorporating real-time data and advanced technologies, such as artificial intelligence and machine learning, to create adaptive models that can respond to changing consumer behaviors and market conditions. The study also explores the growing significance of personalization in consumer behavior modeling, as well as the role of emotions, social influences, and cultural contexts in shaping decisions. Furthermore, it advocates for the development of more nuanced, multidimensional models that reflect the complexity of modern consumer behavior, moving beyond rigid frameworks. The findings suggest that both marketers and researchers must embrace uncertainty, complexity, and flexibility in their approaches, using data-driven insights to create more personalized and relevant consumer experiences. In conclusion, this study highlights the need for continuous adaptation of consumer behavior models, as well as the importance of incorporating a broader range of psychological and social factors to enhance the effectiveness of marketing strategies and consumer engagement in the digital age.