Abstract
Large language models (LLMs) are becoming a major way for consumers to find products, but we do not yet understand how brands compete in this new channel. We study brand dynamics in LLM recommendations using skincare products—a category where consumers cannot easily judge quality before buying and must rely on brand reputation—across three commercial LLMs (GPT-4o-mini, Claude Sonnet, Gemini 3 Flash), with a robustness check on search goods.
In three experiments, we find:
- Conditional Monopoly: Well-known brands get recommended 100% of the time (IAI = 10.0) when all products have the same specifications, but this dominance disappears with less than a +0.1-star rating advantage for a competitor.
- Authority-style Marketing Language: Including fabricated clinical-evidence claims, breaks this monopoly at a Bias Surplus Value equal to +0.17 rating points, with each model responding differently.
- Social Dilemma in Multi-brand GEO Competition: When all brands adopt the same optimization strategy, individual payoff falls from +0.802 to +0.007 in our payoff proxy, and non-participating brands receive zero recommendations in our tests.
Our results suggest that generative engine optimization (GEO) should be studied not only as a security risk but also as an emerging marketing practice that shapes market competition.
Blogger's Review: This article delves into the complex relationship between brands and consumers within LLM recommendation systems, particularly how cognitive biases influence consumer decisions. It provides valuable insights for future marketing strategies, warranting attention from researchers and practitioners in related fields.