# nicolassitter.com — Full Content Index

> AI Search & Hotel Tech Research by Nicolas Sitter

## Author

Nicolas Sitter is an AI search and hotel tech researcher. He reverse-engineers how AI search engines like ChatGPT, Perplexity, Google AI Mode, and Gemini discover, rank, and cite hotel content. He analyzes AI models daily and runs experiments on a database of over 1M prompts, citations, and mentions.

## Published Research

### 1. The AI Hotel Landscape 2026
- URL: https://nicolassitter.com/research/ai-hotel-landscape-2026
- Date: January 2026
- Summary: The most comprehensive study of AI hotel recommendations. Analysis of 1.2M+ citations across ChatGPT, Gemini, Perplexity, Claude, Copilot, and Grok. Covers 12,500+ prompts across 25 cities.
- Key findings: OTAs dominate AI citations, Booking.com leads, independent hotels struggle for visibility.

### 2. Google AI Mode: Where Do Hotel Clicks Go?
- URL: https://nicolassitter.com/research/google-ai-mode-hotel-study-2026
- Date: February 2026
- Summary: Analysis of 4,000 hotel queries in Google AI Mode. 79% of hotel clicks go to Google Business Profiles. 84K+ references analyzed across 1,146 hotels.
- Key findings: GBP dominates click distribution, hotel websites get minimal direct traffic from AI Mode.

### 3. Do French Hotels Blog? A 15,000-Hotel Study
- URL: https://nicolassitter.com/research/french-hotel-blog-study-2026
- Date: January 2026
- Summary: Study of 15,155 French hotel websites measuring blog adoption. 49.3% have blogs, but only 1 in 4 are actively maintained.
- Key findings: Blog presence correlates with higher AI visibility, but most hotel blogs are dormant.

### 4. Anatomy of a ChatGPT Hotel Search
- URL: https://nicolassitter.com/research/anatomy-chatgpt-hotel-search-2026
- Date: March 2026
- Summary: Technical teardown of how ChatGPT builds hotel recommendations. 12 systems, 7 data providers, 424 A/B tests analyzed.
- Key findings: ChatGPT uses a complex multi-provider pipeline including Yelp, TripAdvisor, and Google Places.

### 5. How Consistent Are AI Hotel Rankings?
- URL: https://nicolassitter.com/research/ai-hotel-rankings-consistency-study-2026
- Date: February 2026
- Summary: Replicating SparkToro's consistency methodology for hotels. Only 50.5% position stability across reruns of 4,000 queries.
- Key findings: AI hotel rankings are volatile — the same query produces different results each time.

### 6. Hotel Schema.org Adoption Study
- URL: https://nicolassitter.com/research/hotel-schema-adoption-study-2026
- Date: March 2026
- Summary: Scanned 121,425 hotel websites across 7 countries for structured data. 36.3% have no schema at all, 41% use the wrong type.
- Key findings: Most hotels are invisible to AI due to missing or incorrect structured data.

### 7. Yelp in ChatGPT: Hotel Data Study
- URL: https://nicolassitter.com/research/yelp-chatgpt-hotels-study-2026
- Date: February 2026
- Summary: How Yelp is integrated into ChatGPT hotel queries. 14 destinations analyzed, 33% Yelp integration rate in US markets.
- Key findings: Yelp integration is US-focused, European hotels rarely appear via Yelp in ChatGPT.

### 8. What Hotels Are Actually Called: A Naming Study
- URL: https://nicolassitter.com/research/hotel-naming-study-2026
- Date: March 2026
- Summary: Analysis of naming conventions across 121,425 hotels in 7 countries. 8 analysis angles including word frequency, length, and star-rating patterns.
- Key findings: "Hotel" is the most common word, luxury properties use longer names, regional naming patterns vary significantly.

## Endpoints

- Website: https://nicolassitter.com
- Research hub: https://nicolassitter.com/research
- Blog: https://nicolassitter.com/blog
- API (JSON): https://nicolassitter.com/api/posts
- RSS: https://nicolassitter.com/rss.xml
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- Identity: https://nicolassitter.com/llms.txt

## Contact

- LinkedIn: https://www.linkedin.com/in/nicolassitter/
- X: https://x.com/nicolassitter
- Email: contact@nicolassitter.com