A Combination of Self-Reported Data and Social-Related Neural Measures Forecasts Viral Marketing Success on Social Media. Issue 1 (November 2020)

• Record Type: Journal Article
• Authors: Motoki, Kosuke; Suzuki, Shinsuke; Kawashima, Ryuta; Sugiura, Motoaki
• Is Part Of: Journal of interactive marketing. Volume 52:Issue 1(2020)
• Page Start: 99
• Page End: 117
• Publication Date: 2020-11
• Subjects: Viral marketing -- fMRI -- Neuroforecasting -- Social media -- Video ads; Direct marketing -- Periodicals; Internet marketing -- Periodicals; 658.87
• DOI: 10.1016/j.intmar.2020.06.003
• Languages: English
• ISSNs: 1094-9968

Abstract

Consumers often share product-related content (e.g., advertising), and highly shared advertising has a huge impact on consumer behavior. Despite its apparent effectiveness, prediction of whether such advertising will be highly shared is a poorly understood area of marketing. Advances in brain imaging techniques may allow researchers to forecast aggregate consumer behavior beyond subjective reports. Using neuroimaging techniques, previous research has established models showing that expectations of self-related outcomes (potential for self-enhancement) and the social impact of sharing (potential for social approval) contribute to the likelihood of users sharing non-commercial static content (i.e., text-based health news). However, whether this finding can be applied to forecasting the virality of dynamic commercial stimuli, which is more relevant to interactive marketing (i.e., video ads), remains unknown. Combining brain imaging techniques, cross-validation methods, and real-world data regarding sharing on social media, the present study investigated whether brain data can be used to forecast the viral marketing success of video ads. We used neuroimaging (functional magnetic resonance imaging: fMRI) to measure neural activity during three sets of theory-driven neural measures implicated in value, self, and social (mentalizing) processes while 40 participants viewed video ads that brands had posted on Facebook. Contrary to previous findings regarding value-related virality in non-commercial static content, our results indicate that social-related neural activity contributes significantly to forecasting the virality of dynamic marketing-related content. The model that included both social-related neural measures and subjective intentions to share forecasted viral marketing success better than the model that included only social-related neural measures. The model that included only subjective intention to share did not forecast viral marketing success. Overall, these findings provide a novel connection between neurophysiological measures and real-world dynamic commercial content. Contrary to previous neuroforecasting findings, social-related but not value-related neural measures can significantly improve our ability to predict market-level sharing of video ads.