Reimagining Consumer Analytics: Predictive and Real-Time Insights Through Dynamic Structural Equation Modeling

Abstract

Background: Prior research had predominantly emphasized traditional Structural Equation Modeling (SEM), with limited exploration of dynamic SEM. Yet, dynamic SEM was essential, as it enhanced the precision of real-time consumer behavior prediction. 
Objectives: This study critically examined recent advances in dynamic SEM, focusing on their effectiveness in improving predictive accuracy, model efficiency, and adaptive decision-making in response to temporal variations in consumer behavior.
Design/methodology/approach: A review of peer-reviewed empirical studies published between 2010 and 2025 was conducted. Using predefined inclusion and exclusion criteria, relevant works were retrieved from Scopus and Web of Science. Comparative synthesis highlighted differences between traditional and dynamic SEM applications.
Findings/Results: The results demonstrate that dynamic SEM substantially outperforms traditional SEM by incorporating temporal dynamics, capturing interindividual variations, and effectively managing intensive longitudinal data. Its strength lies in analyzing large-scale, high-frequency datasets from digital platforms such as Google Analytics, enabling accurate prediction and monitoring of consumer behavior over time. The study further contributes original constructs - including behaviorally relevant triggers, sentiment indicators, personalization measures, and engagement metrics - thus extending the scope of consumer analytics.
Conclusion: Dynamic SEM was shown to exert a transformative impact on consumer behavior research and marketing management by supporting real-time behavioral adjustments and agile decision-making. However, challenges remained regarding its computational capacity with large and complex datasets, underscoring the need for advanced data governance and sophisticated analytical tools.
Originality/value: The study evaluated the methodological innovations in a unique and systematic way and gave an advice on how to improve the SEM applications and theory when handling large and complex datasets when dealing with the temporal changes in consumer behavior. Researchers, policy-makers and practitioners were given the actionable recommendations on how to improve and utilize the dynamic SEM as a future-proof marketing analytics approach.

Keywords: big data analytics, dynamic structural equation modelling, latent growth modeling, marketing decision-making, temporal dynamics