Retail

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).

Major life transitions, such as pregnancy and childbirth, reshape lifestyles and purchasing priorities, yet causal evidence on how consumers reallocate spending across product categories remains limited. We quantify the effects of first-time parenthood by linking a large-scale transactional panel to verified birth records. To identify causal effects, we implement a difference-in-differences design augmented with causal forests, enabling flexible comparisons between households entering parenthood and carefully matched controls. We uncover a pronounced and dynamic behavioral trajectory.

Counterfeiting is a negative phenomenon, bearing undesirable consequences for both companies and consumers of the original brands. Yet some consumers, while acknowledging the immorality of counterfeiting, still have positive predispositions toward such fake products. Why? We investigate consumers’ reactions to counterfeits as a function of consumers’ subjective knowledge in the domain of fashion and luxury goods.