The Power of Brand Selfies

Abstract

Smartphones have made sharing images of branded experiences nearly effortless. 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 we term brand selfies (invisible consumers holding a branded product). We 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). We find 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 self-reference is driving the differential response to selfie images. Collectively, these results demonstrate how (interpretable) machine learning helps extracting marketing-relevant information from unstructured multimedia content and that selfie images are a matter of perspective in terms of actual brand engagement.

Keywords: User-Generated Content, Social Media, Image Analysis, Deep Learning, Natural Language Processing, Interpretable Machine Learning