Image Analytics in Marketing

Daria Dzyabura, Siham El Kihal () and Renana Peres ()
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Siham El Kihal: Frankfurt School of Finance and Management
Renana Peres: Hebrew University of Jerusalem

A chapter in Handbook of Market Research, 2022, pp 665-692 from Springer

Abstract: Abstract Recent technical advances and the rise of digital platforms enhanced consumers’ abilities to take and share images and led to a tremendous increase in the importance of visual communication. The abundance of visual data, together with the development of image processing tools and advanced modeling techniques, provides unique opportunities for marketing researchers, in both academia and practice, to study the relationship between consumers and firms in depth and to generate insights which can be generalized across a variety of people and contexts. However, with the opportunity come challenges. Specifically, researchers interested in using image analytics for marketing are faced with a triple challenge: (1) To which type of research questions can image analytics add insights that cannot be obtained otherwise? (2) Which visual data should be used to answer the research questions, and (3) which method is the right one? In this chapter, the authors provide a guidance on how to formulate a worthy research question, select the appropriate data source, and apply the right method of analysis. They first identify five relevant areas in marketing that would benefit greatly from image analytics. They then discuss different types of visual data and explain their merits and drawbacks. Finally, they describe methodological approaches to analyzing visual data and discuss issues such as feature extraction, model training, evaluation, and validation as well as application to a marketing problem.

Keywords: Image analytics; Visual information; Image processing; Image tagging; Firm images; Consumer images; Feature extraction; Deep neural networks; High-level features; Low-level features; Human-coded features; Color histograms; Gabor filters (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-319-57413-4_38

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