La Escuela de Negocios del ITAM invita a la conferencia

Russ Winer

Department of Marketing

New York University

Predicting Advertising Success:

New Insights from Neuroscience and Market Response Modeling

Joint Work with Vinod Venkatraman, Angelika Dimoka, Paul Pavlou, Khoi Vo, William Hampton, Bryan Bollinger, Hal Hershfield and Masakazu Ishihara.

Organizations spend millions on advertisements and are always seeking the key drivers of advertising success. While traditional advertising research has focused on rational and conscious processes through consumer self-report measures, recent advances in neuromarketing methods emphasize the importance of understanding the role of emotions and non-conscious processes. Here, we seek to systematically elucidate the role of different methodologies in predicting the effectiveness of commercials measured using market response models.

The neuroscience component of the study consisted of four phases, each phase comprising of different methodologies:

a) Traditional explicit (N=200) and implicit (N=100) measures;

b) Eye tracking and biometric measures (N=30);

c) EEG (N=30) and d) fMRI (N=30).

Subjects completed a pre-screening questionnaire to assess familiarity with products used in the study. During all phases, they watched 37 ads across different categories and answered questions about familiarity, liking and purchase intent. They completed recall/recognition tests at the end. For preliminary analysis, we obtained aggregate data across the entire 30s of the commercial for all neurophysiological methods.  We also estimated advertising elasticities for each commercial using time-series data supplied by each project sponsor.

Using a F-test of restriction on the reduced set of variables, we found that set of traditional measures (primarily change in purchase intent) was the most significant predictor of the advertising elasticities (p<0.024).

fMRI measures (ventral striatum and vmPFC activation) explained the most incremental variance beyond the traditional measures (p<0.036), followed by biometrics (p<0.081).

Lugar: Sala de Conferencias del Campus Río Hondo.

Hora: de 13:00 a 14:30.