Image Analytics in Marketing

Daria Dzyabura, Siham El Kihal*, Renana Peres

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Scopus citations

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.

Original languageAmerican English
Title of host publicationHandbook of Market Research
PublisherSpringer International Publishing
Pages665-692
Number of pages28
ISBN (Electronic)9783319574134
ISBN (Print)9783319574110
DOIs
StatePublished - 1 Jan 2021

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2022.

Keywords

  • Color histograms
  • Consumer images
  • Deep neural networks
  • Feature extraction
  • Firm images
  • Gabor filters
  • High-level features
  • Human-coded features
  • Image analytics
  • Image processing
  • Image tagging
  • Low-level features
  • Visual information

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