Development of an AI-driven branding platform integrating persona/scenario methods for fashion startups

Abstract As the fashion industry undergoes rapid digital transformation, designer brands have gained increasing visibility through expanded creative and communication opportunities. However, many still face significant challenges in brand development, including limited resources and insufficient consumer understanding. This study proposes an AI-driven branding framework that integrates the Persona/Scenario (P/S) methodology to assist designers in identifying visual style features, understanding target consumer profiles, and formulating strategic brand positioning. The framework incorporates multimodal AI components, including mixture-of-experts convolutional neural networks (MoE-CNNs) for visual style classification, large language models (LLMs) for consumer text analysis, and k-nearest neighbors (KNN) for brand similarity mapping in semantic space. Based on in-depth interviews and simulation-based evaluation procedures, the study demonstrates the potential of AI to enhance the traditional P/S approach and support brand strategy development for early-stage designer brands. The framework provides a scalable and flexible pathway to facilitate systematic and consumer-oriented brand growth. By proposing a structured yet adaptable decision-support system, this research contributes to the interdisciplinary integration of AI technologies and fashion branding and enables a strategic branding path that fuses user orientation with design-driven thinking.