Visual Elicitation of Brand Perception

Daria Dzyabura, New Economic School, Moscow and Renana Peres, Hebrew University of Jerusalem, 2019, 19-132

Understanding how consumers perceive brands is at the core of brand management. It helps managers develop and position new products, understand the competitive landscape, and create effective marketing communications.

Given its importance, measuring brand perception is also a central topic for marketing academics. Over the years, methods for eliciting brand perceptions have included surveys, where consumers are asked to rank brands on a pre-defined set of attributes (e.g., brand personality, brand equity) and qualitative, open-ended approaches where consumers use free associations or visual collages.

More recently, user-generated content (UGC) on social media platforms has enabled scalable brand tracking. These data have the advantage that they are unaided, and consumers can freely discuss any topic related to the brand.

Researchers have mined UGC, such as reviews blogs, microblogs, discussion forums, and visual images, to identify topics frequently discussed with brands. However, for understanding consumer brand perceptions, UGC suffers from shortcomings: It is available only for certain categories; it is difficult to control the characteristics of content contributors; and consumers may post strategically to signal about themselves rather than provide their opinions about the brand.   

Here, Daria Dzyabura and Renana Peres suggest a method for direct, unaided, large-scale quantitative elicitation of brand perceptions by asking consumers to create online collages of images. Building on the theory of offline direct-elicitation collage methods, which emphasizes the power of unaided visual elicitation in retrieving deep brand metaphors, they develop a digital platform called Brand Visual Elicitation Platform (B-VEP). The platform allows consumers to create collages of images that represent how they view a brand, using a searchable repository of hundreds of thousands of images.

Dzyabura and Peres demonstrate the platform’s operation by collecting large, unaided, directly elicited data for 302 large U.S. brands from 1,851 respondents. Using machine learning and image-processing approaches to extract from these images systematic content associations, they obtain a rich set of associations for each brand. Further, by combining the collage-making task with well-established brand-perception measures, they are able to map perceptual dimensions such as brand personality and brand equity.

Put into Practice

These method and results can help brand managers to gain a comprehensive, quantitatively derived set of objects, concepts, emotions, and activities consumers associate with their brand, opening a new window into dimensions of brand associations. Brand managers can evaluate these associations against their desired positioning goals, aim their marketing communications to enhance the associations that fit to this positioning, and repress the undesirable associations. Creative advertising teams can use the method as a guide to increase the effectiveness of marketing communications by using visual elements that capture desired brand associations.  

Daria Dzyabura is Assistant Professor of Marketing at the New Economic School, Moscow. Renana Peres is Associate Professor, Marketing Department School of Business Administration, Hebrew University of Jerusalem.



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