Abstract
The use of psychological targeting—employing machine learning to predict consumer personality from digital footprints and subsequently tailoring persuasive messages—has emerged as a controversial yet prominent practice in digital marketing. Despite frequent claims about its potential to enhance message resonance and behavior change, a comprehensive, cross-disciplinary assessment of its effectiveness has been lacking. We address this gap with the first meta-analysis to systematically evaluate the two core components of psychological targeting: inferring personality from digital footprints and the impact of personality-tailored messages on consumer outcomes, as well as their combined, end-to-end effectiveness. Across 41 studies spanning marketing, psychology, and computer science, we find that only about 5% of the variance in personality can be predicted from digital footprints, and personality-tailored messages show negligible effects on behavior. We document pervasive methodological issues, highlighting that methodological rigor, not model class or data type, primarily determines reported accuracy. When design and evaluation flaws are controlled, the combined end-to-end effectiveness of psychological targeting approaches zero. We conclude by providing recommendations to strengthen future research in this field.