---
id: "claim-llm-processing-styles-vary"
type: "claim"
source_timestamps: ["§ How to Market to LLMs"]
source_url: "https://hbr.org/2025/06/forget-what-you-know-about-seo-heres-how-to-optimize-your-brand-for-llms"
source_title: "Forget What You Know About Search. Optimize Your Brand for LLMs."
tags: ["llm-behavior", "model-variance"]
related: ["concept-share-of-model", "action-tailor-to-llm-processing-styles"]
confidence: "high"
testable: true
speakers: ["David Dubois", "John Dawson", "Akansh Jaiswal"]
sources: ["geo"]
sourceVaultSlug: "hbr-seg-geo"
originDay: 3
articleStem: "hbr-ext-10-optimize-brand-for-llms"
sourceUrl: "https://hbr.org/2025/06/forget-what-you-know-about-seo-heres-how-to-optimize-your-brand-for-llms"
sourceTitle: "Forget What You Know About Search. Optimize Your Brand for LLMs."
---
# Different LLMs apply unique algorithmic lenses to the same category

The authors assert that [[concept-share-of-model-d10|Share of Model]] is **not monolithic** — it varies significantly across AI platforms due to unique processing styles and training weights. Using the US travel industry as an example, they found that when evaluating **Airbnb**, **Llama focuses on 'uniqueness'** of offerings, **ChatGPT indexes on 'local options,'** and **Perplexity values 'flexibility.'** This requires marketers to balance overarching solution-oriented messaging with model-specific tailoring (see [[action-tailor-to-llm-processing-styles]]).

**Confidence: high (testable).**

**Enrichment / validation nuance:** *Well-supported that models differ* in outputs, brand citations, and source types, due to different training sets, RAG connectors, and update cadences — SOM practitioners explicitly recommend measuring **per model, not as a single blended score** ('visibility in one model says little about another') and choosing specific 'battlegrounds' (ChatGPT, Gemini, Perplexity, Claude). **However**, the specific Airbnb behavioral example (uniqueness / local options / flexibility) is **article-specific observational data**, not an established general rule — treat it as a case study, not a universal law. Balance model-specific tuning against brand consistency and operational complexity (see counter-perspective in [[action-tailor-to-llm-processing-styles]]).


## Related across articles
- [[concept-ai-model-segmentation]]
- [[claim-ai-visibility-fragmented]]
- [[claim-model-idiosyncrasy]]