Tomer Tagrin, Co-founder and CEO of Yotpo, recently shared a fascinating, real-world analysis of 127 beauty brands across a custom GEO framework designed to measure visibility in AI-driven product discovery. Brands were scored on five beauty-specific parameters: ingredient transparency, use case specificity, visual user content richness, expert validation, and catalog fragmentation. These factors reveal how well AI models like ChatGPT and Perplexity can understand and recommend products based on natural language queries.
Top performers were predominantly clinical, science-driven skincare brands. Paula’s Choice scored the highest because of extensive research-backed content and a deep ingredient dictionary. The Ordinary excels by naming products with ingredient-first conventions and clear concentration data. The Inkey List stood out for personalized skincare routines, and Naturium impressed with molecular details and educational content.
Legacy brands such as Revlon, Nivea, and Innisfree scored much lower due to generic descriptions, weak educational content, and fragmented product sets that confuse discovery. Certain professional or dermatologist-led brands like SkinCeuticals and EltaMD also scored high due to strong clinical authority, even with limited direct-to-consumer presence.
Skincare as a category strongly outperformed makeup and fragrance. Skincare products inherently require explanation of actives like hyaluronic acid or retinol, offering AI models more structured signals to rank, while makeup and fragrance lacked semantic depth and struggled to articulate meaningful text-based benefits. Fragrance in particular scored poorly because smell cannot be easily expressed in search-friendly language.
The findings show that AI discovery rewards clarity, depth, and expert validation far more than traditional SEO factors or broad marketing narratives.
