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

Journal of Marketing Research (2015), 52 (4), 436-452

Posted: 20 Sep 2014 Last revised: 17 Dec 2016

See all articles by Vinod Venkatraman

Vinod Venkatraman

Temple University - Department of Marketing and Supply Chain Management; Temple University - Decision Neuroscience

Angelika Dimoka

Temple University - Department of Marketing and Supply Chain Management; Center for Neural Decision Making, Temple University

Paul A. Pavlou

Temple University - Department of Management Information Systems; Temple University - Department of Strategic Management

Khoi Vo

Center for Neural Decision Making, Temple University

William Hampton

Temple University - Department of Psychology; Decision Neuroscience

Bryan Bollinger

New York University (NYU) - Department of Marketing

Hal Hershfield

University of California, Los Angeles (UCLA) - Marketing Area

Masakazu Ishihara

New York University (NYU) - Leonard N. Stern School of Business

Russell S. Winer

New York University (NYU) - Department of Marketing

Date Written: September 18, 2014

Abstract

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.

Keywords: advertising elasticities, neuroscience, biometrics, implicit measures, market response modeling

Suggested Citation

Venkatraman, Vinod and Dimoka, Angelika and Pavlou, Paul A. and Vo, Khoi and Hampton, William and Bollinger, Bryan and Hershfield, Hal and Ishihara, Masakazu and Winer, Russell S., Predicting Advertising Success Beyond Traditional Measures: New Insights from Neurophysiological Methods and Market Response Modeling (September 18, 2014). Journal of Marketing Research (2015), 52 (4), 436-452. Available at SSRN: https://ssrn.com/abstract=2498095 or http://dx.doi.org/10.2139/ssrn.2498095

Vinod Venkatraman

Temple University - Department of Marketing and Supply Chain Management ( email )

Philadelphia, PA 19122
United States

Temple University - Decision Neuroscience ( email )

Philadelphia, PA 19122
United States

Angelika Dimoka

Temple University - Department of Marketing and Supply Chain Management ( email )

Philadelphia, PA 19122
United States

Center for Neural Decision Making, Temple University ( email )

Philadelphia, PA 19122
United States

HOME PAGE: http://www.fox.temple.edu/minisites/neural/index.html

Paul A. Pavlou

Temple University - Department of Management Information Systems ( email )

1810 N. 13th Street
Floor 2
Philadelphia, PA 19128
United States

Temple University - Department of Strategic Management ( email )

Fox School of Business and Management
Philadelphia, PA 19122
United States

Khoi Vo

Center for Neural Decision Making, Temple University ( email )

1801 Liacouras Walk A502
Marketing Department
Philadelphia, PA 19122
United States

William Hampton

Temple University - Department of Psychology ( email )

Weiss Hall
1701 N. 13th St.
Philadelphia, PA 19122
United States

Decision Neuroscience ( email )

Philadelphia, PA 19122
United States

Bryan Bollinger

New York University (NYU) - Department of Marketing ( email )

40 W 4th St
Tisch 804
New York, NY 10012
United States

Hal Hershfield

University of California, Los Angeles (UCLA) - Marketing Area ( email )

110 Westwood Plaza
Los Angeles, CA 90095-1481
United States

Masakazu Ishihara

New York University (NYU) - Leonard N. Stern School of Business ( email )

44 West 4th Street
Suite 9-160
New York, NY NY 10012
United States

Russell S. Winer (Contact Author)

New York University (NYU) - Department of Marketing ( email )

Henry Kaufman Ctr
44 W 4 St.
New York, NY
United States
212-998-0540 (Phone)
212-995-4006 (Fax)

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