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	Facial Coding in Advertising Research	Implicit Reaction-Time Brand Measures
Odbor	Marketing	Marketing
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	1978	1986
Tvorca	Paul Ekman & Wallace Friesen (FACS); Daniel McDuff & Rana el Kaliouby (automated ad coding)	Russell Fazio (affective priming); Anthony Greenwald, Brian Nosek & Mahzarin Banaji (D-score scoring)
Typ	Facial-expression measurement pipeline for emotional ad response	Response-latency measurement pipeline for implicit brand associations
Pôvodný zdroj	Ekman, P., & Friesen, W. V. (1978). Facial Action Coding System: A Technique for the Measurement of Facial Movement. Palo Alto, CA: Consulting Psychologists Press. ISBN: 9780931835018	Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50(2), 229-238.
Ďalšie názvy	Facial Expression Analysis, Automated Facial Coding, Emotion AI for Ads, Facial Action Coding for Marketing	Implicit Brand Association Measures, Response-Latency Brand Testing, Affective Priming for Brands, Implicit Brand Attitude Measurement
Príbuzné	3	3
Zhrnutie	Facial coding measures consumers' emotional responses to advertising by analyzing the movements of their faces while they watch. It rests on Paul Ekman and Wallace Friesen's Facial Action Coding System (FACS), which decomposes any expression into elemental action units, the contractions of individual facial muscles such as the lip-corner pull of a smile or the brow lowering of a frown. Manual FACS coding is precise but slow, so the field has shifted to automated facial coding, in which computer-vision models detect landmarks and action units frame by frame and map them to emotions and to continuous valence and arousal. Daniel McDuff, Rana el Kaliouby, and colleagues showed at scale that these automatically measured facial responses to ads predict ad liking and even changes in purchase intent. Aggregated across viewers, the result is a second-by-second emotional response curve over the ad, revealing where it amuses, surprises, bores, or repels. Facial coding thus turns spontaneous, fleeting expressions into a quantitative, time-resolved index of how an ad makes people feel.	Implicit reaction-time brand measures use how fast people respond, rather than what they say, to gauge the associations a brand automatically triggers. The logic comes from Russell Fazio's demonstration that strong attitudes are activated automatically: when a brand acts as a prime, it speeds responses to evaluatively congruent targets and slows responses to incongruent ones, and the size of that facilitation indexes the brand's implicit evaluation. Building on this, response-latency tasks pair brands with positive or negative words, with attribute categories, or with competing brands, and read off implicit associations from millisecond differences in reaction time. Anthony Greenwald, Brian Nosek, and Mahzarin Banaji's improved scoring algorithm turns these latency differences into a standardized D-score that is comparable across people and tasks. Because the measures tap associations that operate before deliberate editing, they capture brand equity that consumers may be unwilling or unable to report. The result is a behaviorally grounded, hard-to-fake complement to survey-based brand tracking.