Listen to the authors present their findings (source: January 2021 JM Webinar)
Last year BMW went through a rebranding process, aiming to change the company’s brand image to become a “relationship brand” associated with openness and clarity. The toy company Fisher Price also went through a rebranding process to enhance its associations as “fun, action, play, celebration, silliness, and joy.” The success of such rebranding activities depends on the ability to measure brand associations. Measuring brand associations is challenging because consumers can associate a brand with any number of objects, emotions, activities, sceneries, and concepts.
Many methods have been developed to elicit brand associations. Survey methods request respondents to rate brands on well-established dimensions. The Brand Personality scale rates brands on five personality traits—sincerity, excitement, competence, sophistication, and ruggedness. The Brand Asset Valuator (BAV) brand equity scale rates brands on four dimensions—differentiation, relevance, esteem, and knowledge. Other methods are qualitative and use verbal and visual stimuli to encourage respondents to share their thoughts about the brand in writing or in a 1-1 interview. While survey methods are quantitative and efficient to administer, they are limited to the pre-defined attributes. Qualitative methods allow for unaided elicitation of associations, but the results are difficult to quantify and scale to a large number of brands.
Online social media platforms make it possible to conduct scalable, quantitative, and unaided brand tracking by mining this user-generated content. However, tapping user-generated content for understanding consumer associations also suffers from some shortcomings. First, it is available for only certain categories. For example, the brand Nike generates a lot of social media commentary, while social media posts on the brand Colgate, for instance, are less abundant. Second, it is difficult to control the characteristics of the content contributors. For example, users with a stronger relationship with the brand, or who hold a particularly strong positive or negative opinion, may contribute more. Finally, any given consumer who contributes brand content may not contribute her true opinion of the brand: Consumers may post strategically to signal about themselves to the public and serve their self-presentation needs.
A new study in the Journal of Marketing proposes a direct, unaided, scalable, and quantitative elicitation method that uses the power of visuals to reflect the emotions, cultural experiences, and attitudes that constitute the consumer associations. Inspired by qualitative elicitation approaches in psychology and marketing that use collages of images, our research team developed an online Brand Visual Elicitation Platform (B-VEP) that asks respondents to create an online collage of images representing their relationship with the brand. Participants can choose photos for their collages from a broad repository of tens of thousands of photos. We analyze the collages using a machine-learning back end to derive brand associations at the individual-respondent level.
We use the platform to elicit associations of 303 major national U.S. brands from nine product and service categories, using 4,743 collages from 1,851 respondents. We retrieve 150 brand associations relating to objects, actions, adjectives, characters, places, sceneries, concepts, and metaphors, on which all these brands are mapped, to form the equivalent of a very high-dimensional perceptual map. For example, the deodorant brand AXE is associated with fashion, urban youth, astronomy, and bodybuilding. Starbucks is associated with coffee, power energy, computer, baking, and dining. We show how our visual-based method provides a richer set of associations than verbal-based methods.
Our methodology and findings can revolutionize the decision-making processes of brand management creative and strategic teams. We demonstrate its power through five examples. First, we show how to create prototypical collages by indexing photo repositories according to their fit to the associations of a brand. These collages serve as mood boards to help graphic designers generate visual brand content and to visually communicate the brand associations.
Second, we relate the associations to the well-established brand personality and brand equity, so each metric has a clear, specific set of related associations. For example, the brand personality trait “Wholesome” is associated with herbs, baby, winter, happy nature, and insects; “Masculine” is associated with bicycle, military, heavy vehicle, auto racing, and photography. The brand equity characteristic “Innovative” is positively correlated with hand, religion, painting, cityscape, and light, and negatively correlated with patriotism, chest, ruin, symbol, and cowboy.
Third, for each of the nine product categories, we relate the associations to brand favorability to identify desirable and undesirable associations in each category. For example, for cars, the associations alcoholic drink, cityscapes, house, fashion, and suit occur more frequently in collages for high quality car brands.
Fourth, we show how to measure brand uniqueness relative to its category, namely, what consumers associate with the brand significantly more/less than with other brands in its category. For example, Old Spice is more associated with bodybuilding, heavy vehicle, running, sports, and military than the rest of the beauty category.
Fifth, we show how to use the similarity and distance in the association space to find brands in different categories with similar associations to suggest potential collaborations (e.g., Kitchen Aid and Cheesecake Factory share the associations of baking, dining, candle, family).
These applications just scratch the surface of the potential of using visual elicitation. B-VEP allows researchers and firms to gather and harvest visual brand-related data directly from consumers, which complements existing brand metrics, as well as the rapidly growing field of visual social media monitoring. Modern software and image-processing tools open many new opportunities for better understanding brand perceptions and for strengthening the relationships between consumers and brands.
Go to the Journal of Marketing