Personalized Fashion Advertising with Large Language Models: A Case Study on Fine-Tuning for Marketing Copy Generation

The rapid digitalization of the fashion industry has transformed marketing strategies, emphasizing the need for personalized and adaptive advertising content. This paper presents a case study on fine-tuning Large Language Models (LLMs) for fashion advertising, focusing on OVS, a major Italian fashion retailer. By leveraging real-world marketing data from OVS's newsletters and social media campaigns, we developed a fine-tuned model capable of generating engaging and stylistically coherent promotional content. To evaluate the effectiveness of this approach, we introduced a novel brand compliance index, measuring the alignment of AI-generated text with key branding requirements, such as audience targeting, event specificity, and platform appropriateness. Experimental results show that the fine-tuned model achieved a compliance score of 0.82, significantly outperforming the baseline model (0.63). Although this approach introduces a minor increase in generation latency, the enhanced alignment with brand identity justifies its use in marketing automation. Our findings highlight the potential of fine-tuned LLMs to streamline advertising content generation while maintaining brand consistency, offering valuable insights for the future of AI-driven digital marketing.