Abstract
Smartphones have made it nearly effortless to share images of branded experiences. This research classifies social media brand imagery and studies user response. Aside from packshots (standalone product images), two types of brand-related selfie images appear online: consumer selfies (featuring brands and consumers’ faces) and an emerging phenomenon the authors term “brand selfies” (invisible consumers holding a branded product). The authors use convolutional neural networks to identify these archetypes and train language models to infer social media response to more than a quarter-million brand-image posts (185 brands on Twitter and Instagram). They find that consumer-selfie images receive more sender engagement (i.e., likes and comments), whereas brand selfies result in more brand engagement, expressed by purchase intentions. These results cast doubt on whether conventional social media metrics are appropriate indicators of brand engagement. Results for display ads are consistent with this observation, with higher click-through rates for brand selfies than for consumer selfies. A controlled lab experiment suggests that self-reference is driving the differential response to selfie images. Collectively, these results demonstrate how (interpretable) machine learning helps extract marketing-relevant information from unstructured multimedia content and that selfie images are a matter of perspective in terms of actual brand engagement.