Predicting the Personal Appeal of Marketing Images Using Computational Methods

Abstract

Images play a central role in digital marketing. They attract attention, trigger emotions, and shape consumers' first impressions of products and brands. We propose that the shift from one‐to‐many mass communication to highly personalized one‐to‐one communication requires an understanding of image appeal at a personal level. Instead of asking "How appealing is this image?" we ask "How appealing is this image to this particular consumer?" Using the well‐established five‐factor model of personality, we apply machine learning algorithms to predict an image's personality appeal—the personality of consumers to which the image appeals most—from a set of 89 automatically extracted image features (Study 1). We subsequently apply the same algorithm on new images to predict consequential outcomes from the fit between consumer and image personality. We show that image‐person fit adds incremental predictive power over the images' general appeal when predicting (a) consumers' liking of new images (Study 2) and (b) consumers' attitudes and purchase intentions (Study 3).

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