Publication Tag: Marketing

We present a psychophysiological study of facial expressions of happiness produced by advertisements using the FaceReader system for automatic analysis of facial expressions of basic emotions . FaceReader scores were associated with self-reports of the advertisement’s effectiveness. Building on work describing the role of emotions in marketing research, we examined the relationship between the patterns of the FEBE and the perceived amusement of the advertisements, attitude toward the advertisement and attitude toward the brand . Differences were observed between FEH scores in response to high-, medium-, and low-amusing video advertisements . Positive correlations were found between FEH and AAD and FEH and AB in high- and medium- but not in low-AVAs. As hypothesized, other basic emotions did not predict advertisement amusement or advertisements’ effectiveness. FaceReader enabled a detailed analysis of more than 120,000 frames of video-recordings contributing to an identification of global patterns of facial reactions to amusing persuasive stimuli. For amusing commercials, context-specific FEH features were found to be the major indicators of advertisement effectiveness. The study used video-recordings of participants in their natural environments obtained through a crowd-sourcing platform. The naturalistic design of the study strengthened its ecological validity and demonstrated the robustness of the software algorithms even under austere conditions. Our findings provide first evidence for the applicability of FaceReader methodology in the basic consumer science research.

There has been little focus on nonverbal communication in social media advertising campaigns. We propose that specific patterns of facial expressions predict the popularity of YouTube videos among users of social media. To test that proposition, we used a neuromarketing tool—FaceReader—to code facial videos of professional speakers who participated in the YouTube social media campaigns of 2 large commercial banks. We analyzed more than 25,000 video frames of 16 speakers’ 6 basic facial expressions. We found that less incidence of affiliative facial emotions and more incidence of nonemotional expressions explained an additional 25% of variance in the video’s popularity after 8 months in t2 , in comparison to t1 as the only baseline predictor. We further showed that the disaffiliative facial emotions of the speakers did not contribute as an indicator of the future performance of social media content. We hope that these findings will open new lines of research in corporate communication by incorporating neuromarketing and nonverbal communication to understand not only what content is effective but how it should be presented.