SELFLYZER-Enabled Deep Trace: Unveiling the Jungian Shadow through AI to Predict Consumer Behavior

Background: Traditional psychometrics, with their reliance on conscious self-report, fail to capture the vast, unconscious drivers of human behavior. This study challenges these limitations by introducing ShadowTrace, a pioneering AI-powered framework developed within the SELFLYZER ecosystem. Grounded in Jungian theory, ShadowTrace is not merely a predictive tool but a computational model of the Jungian shadow, designed to detect and operationalize repressed emotional and cognitive patterns that fundamentally shape consumer decision-making. Methods: The model translates analytical psychology into a multi-layered computational architecture, integrating affective computing, narrative interpretation, and machine learning. A pilot study with 100 simulated profiles and a real-world test with 100 participants (aged 18–55) were conducted. A supplemental validation with an N=200 cohort and XGBoost classifiers was also performed to ensure robustness. Results: ShadowTrace achieved 84% accuracy in identifying shadow-driven behavioral patterns. The model predicted consumer choices with up to 88% accuracy, significantly outperforming conventional approaches. The analysis revealed five distinct, interpretable "shadow personality" clusters, each with unique behavioral and emotional signatures. Conclusion: By providing a scalable and empirical method for accessing the unconscious, ShadowTrace offers a paradigm shift in psychological profiling. The system adheres to the highest ethical AI standards, including informed consent and GDPR compliance. This work lays the foundation for a new generation of psychologically-aware technologies with transformative applications in marketing, digital therapy, and personal development.