Predicting Advertising Success Beyond Traditional Measures: New Insights from Neurophysiological Methods and Market Response Modeling

The past decade has seen a tremendous increase in the use of neurophysiological methods to better understand marketing phenomena among academics and practitioners. However, the value of these methods in predicting advertising success remains under-researched. Using a unique experimental protocol to assess subjects' responses to 30-second TV ads, we capture many measures of advertising effectiveness across six commonly used methods (traditional self-reports, implicit, eye tracking, biometrics, EEG, and fMRI). These measures are shown to reliably tap into higher-level constructs commonly used in advertising research: attention, affect, memory, and desirability. Using time-series data on sales and Gross Ratings Points for the same TV ads, we then attempt to relate individual-level response neurophysiological measures when participants viewed the ads in the lab to their aggregate, market-level elasticities. We show that fMRI measures explain the most variance in advertising elasticities beyond the baseline traditional measures. Notably, activity in the ventral striatum is the strongest predictor of real-world, market-level response to advertising. We discuss how these findings have significant implications for theory, research and practice.