Visual Listening In: Extracting Brand Image Portrayed on Social Media
39 Pages Posted: 6 Jun 2017
Date Written: May 8, 2017
Marketing academics and practitioners recognize the importance of monitoring consumer online conversations about brands. The focus so far has been on user generated content in the form of text. However, images are on their way to surpassing text as the medium of choice for social conversations. In these images, consumers often tag brands. We propose a “visual listening in” approach to measuring how brands are portrayed on social media (Instagram), by mining visual content posted by users. Our approach consists of two stages. We first use two supervised machine learning methods, traditional support vector machine classifiers and deep convolutional neural networks, to measure brand attributes (glamorous, rugged, healthy, fun) from images. We then apply the classifiers to brand-related images posted on social media to measure what consumers are visually communicating about brands. We study 56 brands in the apparel and beverages categories, and compare their portrayal in consumer-created images with images on the firm’s official Instagram account, as well as with consumer brand perceptions measured in a national brand survey. Although the three measures exhibit convergent validity, we find key differences between how consumers and firms portray the brands on visual social media, and how the average consumer perceives the brands.
Keywords: Computer Vision, Machine Learning, Deep Learning, Transfer Learning, Social Media, Visual Marketing, Brand Perceptions
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