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Visual Elicitation of Brand Perception

Author

Listed:
  • Daria Dzyabura

    (New Economic School, Moscow, Russia)

  • Renana Peres

    (Hebrew University of Jerusalem, Israel)

Abstract

Understanding how consumers perceive brands is at the core of effective brand management. In this paper, we present the Brand Visual Elicitation Platform (B-VEP), an electronic tool we developed that allows consumers to create online collages of images that represent how they view a brand. Respondents select images for the collage from a searchable repository of tens of thousands of images. We implement an unsupervised machine-learning approach to analyze the collages and elicit the associations they describe. We demonstrate the platform’s operation by collecting large, unaided, directly elicited data for 303 large US brands from 1,851 respondents. Using machine learning and image-processing approaches to extract from these images systematic content associations, we obtain a rich set of associations for each brand. We combine the collage-making task with well-established brand-perception measures such as brand personality and brand equity, and suggest various applications for brand management.

Suggested Citation

  • Daria Dzyabura & Renana Peres, 2019. "Visual Elicitation of Brand Perception," Working Papers w0260, New Economic School (NES).
  • Handle: RePEc:abo:neswpt:w0260
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    File URL: https://www.nes.ru/files/Preprints-resh/WP260.pdf
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    Cited by:

    1. Zecong Ma & Sergio Palacios, 2021. "Image-mining: exploring the impact of video content on the success of crowdfunding," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(4), pages 265-285, December.
    2. Dariusz Dudek & Marcin Lipowski & Ilona Bondos, 2021. "Changing Energy Supplier on the Market with a Strong Position of Incumbent Suppliers—Polish Example," Energies, MDPI, vol. 14(13), pages 1-16, June.

    More about this item

    Keywords

    Image processing; machine learning; branding; brand associations; brand collages; Latent Dirichlet Allocation;
    All these keywords.

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