A new publication in the Journal of Business Research introduces the CTV-CBBE model linking computer vision to brand equity.
April 2025 — In an era where millions of images are uploaded to digital platforms every day, visual content has become central to how brands are built, perceived, and engaged with. A new study published in the Journal of Business Research offers a pioneering framework for understanding this shift—bringing together cutting-edge computer vision technology and established theories of brand equity.
The article, Computer vision in branding: A conceptual framework and future research agenda, is co-authored by Yaqiu Li, PhD student at ESCP’s Paris campus, Associate Professor Hsin-Hsuan Meg Lee (ESCP London), and Professor Lorena Blasco-Arcas (ESCP Madrid). Together, the authors propose a new conceptual model—CTV-CBBE—that links the technical processes of computer vision with consumer-facing outcomes in branding. As Professor Lorena Blasco-Arcas pinpoints, “This project represents a first step in understanding how technical aspects of computer vision can effectively contribute to brand strategy and support communication that resonates with human values.”
Understanding Brand Equity through Visual Data
From packaging design to user-generated content, visual communication plays a powerful role in shaping consumer perceptions and decisions. Yet most branding theories were developed in an era of text-heavy advertising and static media. Today’s brand interactions are increasingly visual, dynamic, and algorithmically distributed—requiring new tools for analysis and interpretation.
Computer vision, a branch of artificial intelligence that enables automated image recognition and interpretation, provides marketers with the ability to analyse visual content at scale. However, applying these tools effectively requires a theoretical foundation—one that connects image features with brand identity, meaning, response, and engagement.
Introducing the CTV-CBBE Framework
The authors' core contribution is the CTV-CBBE framework, which integrates two influential theoretical models:
- Computational Theory of Vision (CTV): A foundational model from cognitive science that explains how visual information can be broken down and interpreted at multiple levels—computational, algorithmic, and implementational.
- Customer-Based Brand Equity (CBBE): A marketing framework developed by Kevin Lane Keller, which defines brand equity in terms of consumers’ awareness, associations, responses, and loyalty.
By combining these perspectives, the CTV-CBBE framework provides a structured approach to understanding how specific visual features—such as layout, colour, emotion, or context—influence consumer perception and brand value. It also outlines how different computer vision tasks (e.g. facial expression recognition, image clustering, generative design) can support strategic branding decisions.
A Strategic Agenda for Future Research
The paper also sets out a research agenda to guide the next wave of work in this space. The authors identify three key areas for development:
- Underexplored visual features: Moving beyond obvious elements like logos to consider visual harmony, background context, or brand storytelling in images.
- Generative AI for branding: Exploring how tools such as image generation models can support brand development and testing.
- Multimodal analysis: Combining image data with text, audio, and emotion tracking to better understand the full spectrum of consumer-brand interaction.
This agenda reflects a growing need for interdisciplinary thinking—blending marketing insight, technological fluency, and consumer psychology to keep pace with evolving brand ecosystems.
“Understanding the role of visuals in brand communication has become paramount for marketing strategy in digital platforms and social media” comments Professor Lorena Blasco-Arcas, “the Transformative Research on AI for Companies, Individuals, and Society centre (TRACIS) has developed several projects in collaboration with other experts in the area to understand better how consumers use visuals to engage with brands, how consumers perceive skin color, and also how emerging technologies like computer vision and AI leverage branding and marketing strategy”.
About the Authors
Professor Lorena Blasco-Arcas is a Full Professor of Marketing at ESCP Business School (Madrid campus) and Co-Founder of TRACIS (Transformative Research on AI for Companies, Individuals, and Society). Her research focuses on the societal and organisational impact of emerging technologies and has received international recognition.
Associate Professor Hsin-Hsuan Meg Lee teaches Marketing Analytics and Marketing for Good at ESCP’s London campus. Her work explores consumer identity, technology and well-being, and has been published in leading journals. She is actively engaged in executive education and data-driven decision-making for industry.
Yaqiu Li is a doctoral researcher at ESCP’s Paris campus. Her work focuses on visual marketing, computer vision, and unstructured data. She holds a Master’s degree in Data Analytics and Digital Management from EDHEC Business School.
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