Visual Elicitation of Brand Perception

Daria Dzyabura*, Renana Peres

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

Understanding consumers’ associations with brands is at the core of brand management. However, measuring associations is challenging because consumers can associate a brand with many objects, emotions, activities, sceneries, and concepts. This article presents an elicitation platform, analysis methodology, and results on consumer associations of U.S. national brands. The elicitation is direct, unaided, scalable, and quantitative and uses the power of visuals to depict a detailed representation of respondents’ relationships with a brand. The proposed brand visual elicitation platform allows firms to collect online brand collages created by respondents and analyze them quantitatively to elicit brand associations. The authors use the platform to collect 4,743 collages from 1,851 respondents for 303 large U.S. brands. Using unsupervised machine-learning and image-processing approaches, they analyze the collages and obtain a detailed set of associations for each brand, including objects (e.g., animals, food, people), constructs (e.g., abstract art, horror, delicious, famous, fantasy), occupations (e.g., musician, bodybuilder, baker), nature (e.g., beach, misty, snowscape, wildlife), and institutions (e.g., corporate, army, school). The authors demonstrate the following applications for brand management: obtaining prototypical brand visuals, relating associations to brand personality and equity, identifying favorable associations per category, exploring brand uniqueness through differentiating associations, and identifying commonalities between brands across categories for potential collaborations.

Original languageAmerican English
Pages (from-to)44-66
Number of pages23
JournalJournal of Marketing
Volume85
Issue number4
DOIs
StatePublished - Jul 2021

Bibliographical note

Publisher Copyright:
© American Marketing Association 2021.

Keywords

  • brand associations
  • brand collages
  • branding
  • image processing
  • latent Dirichlet allocation
  • machine learning

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