Predicting Advertising Effectiveness by Facial Expressions in Response to Amusing Persuasive Stimuli

We present a psychophysiological study of facial expressions of happiness (FEH) produced by advertisements using the FaceReader system (Noldus, 2013) for automatic analysis of facial expressions of basic emotions (FEBE; Ekman, 1972). 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 (AAD) and attitude toward the brand (AB). Differences were observed between FEH scores in response to high-, medium-, and low-amusing video advertisements (AVAs). 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 (sadness, anger, surprise, fear, and disgust) 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.