Abstract
Our faces are said to be windows into the soul. But can they also reflect who we are as consumers? Can facial images predict brand preferences? To answer these questions, we analyze a unique dataset of over 100,000 single-face Twitter profile pictures linked with brand followership data for 444 brands across categories and brand personality metrics. Using advanced machine learning for automated face analysis, we demonstrate that consumers’ social media profile faces can reveal their preferences between rival brands (study 1). We further show that consumers who follow brands with similar brand personality traits (e.g., glamorous, rugged) tend to look alike (study 2). Finally, we identify facial appearance characteristics associated with brand personality traits; for example, followers of “smart” brands like Barnes & Noble are likely to wear reading glasses, while “edgy” brands like Diesel attract bearded men (study 3). Together, our findings show that brands can extract actionable insights from consumers’ facial images, both to address the “cold start” problem by predicting preferences before any consumption actions are observed and to better understand the brand’s identity through the aggregated traits of its followers. This work is among the first to demonstrate large-scale associations between faces and brand preferences.