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Predicting the Personal Appeal of Marketing Images Using Computational Methods
Author(s) -
Matz Sandra C.,
Segalin Cristina,
Stillwell David,
Müller Sandrine R.,
Bos Maarten W.
Publication year - 2019
Publication title -
journal of consumer psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.433
H-Index - 110
eISSN - 1532-7663
pISSN - 1057-7408
DOI - 10.1002/jcpy.1092
Subject(s) - appeal , personality , predictive power , set (abstract data type) , psychology , image (mathematics) , advertising , consumer behaviour , big five personality traits , social psychology , computer science , artificial intelligence , marketing , epistemology , business , philosophy , political science , law , programming language
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).