{"@context":"https://schema.org","@type":"Blog","name":"Nicolas Sitter","url":"https://nicolassitter.com","author":{"@type":"Person","name":"Nicolas Sitter","url":"https://nicolassitter.com/about","sameAs":["https://www.linkedin.com/in/nicolassitternolleau/","https://github.com/Nicositter88","https://hotelrank.ai"]},"blogPosts":[{"@type":"BlogPosting","headline":"Does Language Change ChatGPT’s Hotel Picks? The Hotels Shuffle, the Sources Don’t (2026)","description":"4,875 live ChatGPT searches for Paris hotels across 5 languages × 5 countries. ~Half the hotels change by language, but the sources almost never do — it cites the same English web (Reddit, Time Out, oyster.com). Japanese-from-Japan = 0.2% Japanese sources.","datePublished":"2026-06-11T00:00:00.000Z","dateModified":"2026-06-11T00:00:00.000Z","url":"https://nicolassitter.com/research/chatgpt-hotel-language-ip-2026","category":"research"},{"@type":"BlogPosting","headline":"Are Hotels in Common Crawl? 39% Are Missing From AI Training Data (2026)","description":"108,109 hotel websites checked against the May 2026 Common Crawl snapshot: 60.6% are in it, 39.4% absent. Independents (61%) beat chains (45.9%); local-market TLDs are present but shallow; .es lags at 37%. Includes an interactive coverage map and a free checker.","datePublished":"2026-06-09T00:00:00.000Z","dateModified":"2026-06-09T00:00:00.000Z","url":"https://nicolassitter.com/research/hotels-in-common-crawl-2026","category":"research"},{"@type":"BlogPosting","headline":"AI Hotel Memory 2026: What Chatbots Remember About Hotels Without Searching","description":"A parametric-recall study with web search OFF. Three cheap models (GPT-5.4-nano, GPT-5.4-mini, Gemini 3.1 Flash-Lite) named hotels in JSON, returning each hotel's website so it could be verified by DNS. ~1,400 generations across global chains and Paris/Dubai/London/New York. Findings: hotel chains are known cold (~99% correct websites everywhere); individual-hotel websites resolve only 47%–97% of the time depending on model and city; the failure mode is the model knowing a real hotel but inventing its web address (Le Bristol Paris → dead bristolparis.com vs real oetkercollection.com); Paris is the hardest city (independent palaces on collection domains); and the cheapest model, Gemini 3.1 Flash-Lite, had the most accurate hotel memory.","datePublished":"2026-06-09T00:00:00.000Z","dateModified":"2026-06-09T00:00:00.000Z","url":"https://nicolassitter.com/research/ai-hotel-memory-2026","category":"research"},{"@type":"BlogPosting","headline":"AI Search for Specialty Coffee in Marseille (2026)","description":"Marseille breaks the entity-engine pattern. Across Paris yoga, Berlin yoga and Amsterdam bikes, ChatGPT cited shop/studio websites ~32% of the time. For Marseille specialty coffee it's only 10% — instead 31% Reddit + 32% review-aggregators + 14% French local blogs = 77% third-party. 27 prompts × 5 AI engines × EN/FR (9/10 platform-proxy batches; AI Mode × FR rejected at the Bright Data trigger), 413 captures, 3,442 citations, 786 map entities against 280 specialty cafés. Three Marseille-only findings: Instagram at 237 cites (the highest social signal we've measured in any city/vertical), Gemini swinging to global specialty press (baristamagazine.com = 34% of its citations) when local trade press is absent, and a two-metric leaderboard split — Deep is the text-mention consensus winner (216 mentions across all five engines, 61.5% of ChatGPT) while Nua tops the cite-counted score (356) only because its brand_key is instagram.com and every Instagram cite attributes to it. EN vs FR control prompt = 11% overlap, the most language-divergent result so far.","datePublished":"2026-05-28T00:00:00.000Z","dateModified":"2026-05-28T00:00:00.000Z","url":"https://nicolassitter.com/research/specialty-coffee-marseille-ai-search-2026","category":"research"},{"@type":"BlogPosting","headline":"AI Search for Yoga Studios in Berlin (2026)","description":"Direct replication of the Paris yoga study in Berlin: 27 prompts × 5 AI engines × EN/DE, 540 captures, 5,293 citations matched to 631 studios. The three engine personalities replicate almost to the percentage point (Copilot 95% entity, ChatGPT 32% studios + 19% Reddit, AI Mode 59% google.com) — strong evidence they're structural, not city-specific. The Berlin twist: booking platforms (Urban Sports Club, Eversports, ClassPass) climb to 31% of Perplexity citations, with blog.urbansportsclub.com and classpass.com the two most-cited domains overall.","datePublished":"2026-05-27T00:00:00.000Z","dateModified":"2026-05-27T00:00:00.000Z","url":"https://nicolassitter.com/research/yoga-studios-berlin-ai-search-2026","category":"research"},{"@type":"BlogPosting","headline":"AI Search for Bike Shops in Amsterdam (2026)","description":"27 prompts × 5 AI engines × EN/NL against 228 Amsterdam bike shops, 378 captures, 3,010 citations. The cleanest cross-engine entity consensus in the study: Copilot 97% shop websites, Reddit the single most-cited domain anywhere (198 cites, 4 platforms), AI Mode 82% google.com self-citation. Perplexity's exposed search query (fanout_count=1, prompt language preserved) is the mechanism behind 0% EN/NL overlap on repair and commuter queries.","datePublished":"2026-05-26T00:00:00.000Z","dateModified":"2026-05-26T00:00:00.000Z","url":"https://nicolassitter.com/research/bike-shops-amsterdam-ai-search-2026","category":"research"},{"@type":"BlogPosting","headline":"AI Search for Bookstores in Tokyo (2026)","description":"22 prompts × 4 AI engines (Perplexity returned no usable Tokyo data), EN + JA, US/JP proxies, matched to 584 Tokyo bookstores. Tokyo is where AI search stops looking Western: Gemini cites store sites just 5% of the time and runs on a 58% local-guide web (whenin.tokyo, Tokyo Weekender, GaijinPot), Japanese prompts cite .jp domains 5× more than English (the sharpest language→TLD coupling in the study), and DAIKANYAMA T-SITE / Kinokuniya tie at the top (122 each).","datePublished":"2026-05-26T00:00:00.000Z","dateModified":"2026-05-26T00:00:00.000Z","url":"https://nicolassitter.com/research/bookstores-tokyo-ai-search-2026","category":"research"},{"@type":"BlogPosting","headline":"AI Search for Yoga Studios in Paris (2026)","description":"First non-hotel field test of the AI-search methodology. 27 prompts across 5 AI engines (ChatGPT, Perplexity, Gemini, Copilot, Google AI Mode), EN + FR, matched to 369 Paris yoga studios. Copilot cites studio sites 96% of the time, ChatGPT leans on Reddit (its #1 source at 17%), Google AI Mode cites its own SERP back 52%. The per-engine citation split from the hotel studies generalises intact.","datePublished":"2026-05-24T00:00:00.000Z","dateModified":"2026-05-24T00:00:00.000Z","url":"https://nicolassitter.com/research/yoga-studios-paris-ai-search-2026","category":"research"},{"@type":"BlogPosting","headline":"The ChatGPT Direct-Traffic Explosion for Hotels (May 2026)","description":"On May 7, 2026, ChatGPT started embedding hotel-brand URLs inline in answers. Across The Hotels Network's panel of 17,000+ hotels, daily AI referrer sessions jumped +62% (31,688 → 51,282/day) and held through May 25. A skewed tail: 286 hotels now draw 5%+ of new sessions from AI, 43 get 10%+. Perplexity and Claude lost share — it's a ChatGPT-only story.","datePublished":"2026-05-21T00:00:00.000Z","dateModified":"2026-05-29T00:00:00.000Z","url":"https://nicolassitter.com/research/chatgpt-hotel-direct-traffic-explosion-2026","category":"research"},{"@type":"BlogPosting","headline":"The Schema.org Debate (2026): Why It Still Matters for Hotels","description":"AEO oversold schema as an LLM unlock. The pushback that transformers read tokens, not JSON-LD, is right for the general case. For hotels, every major AI still grounds against Places / KG / OTA aggregators — surfaces that sit downstream of schema. The four fields that actually move the needle: Hotel, sameAs, starRating, alternateName.","datePublished":"2026-05-13T00:00:00.000Z","dateModified":"2026-05-13T00:00:00.000Z","url":"https://nicolassitter.com/research/schema-org-grounding-loop-2026","category":"research"},{"@type":"BlogPosting","headline":"How Mistral Searches Hotels","description":"Captured Le Chat event streams. One Brave web_search call per entity (parallelised for brand-vs-brand prompts), prompt-language preserved with per-term rewrites, the current year injected as a freshness anchor, snippet paraphrase. Niche queries surface real specialists; generic queries surface SEO-spam aggregators. Authority-laundering patterns turn single reviews and self-marketing into asserted features — and one hallucinated a hotel that doesn’t exist.","datePublished":"2026-05-05T00:00:00.000Z","dateModified":"2026-05-05T00:00:00.000Z","url":"https://nicolassitter.com/research/how-mistral-searches-hotels-2026","category":"research"},{"@type":"BlogPosting","headline":"How Claude Searches Hotels","description":"Captured event streams across several Claude hotel conversations. With Connector Discovery off (the default), almost everything goes through one Google Places call. Turn it on and Claude branches into a small curated OTA-connector picker (Booking.com / Tripadvisor / Trivago and a few others) — no ads by design, so the curation logic itself becomes the product.","datePublished":"2026-05-01T00:00:00.000Z","dateModified":"2026-05-04T00:00:00.000Z","url":"https://nicolassitter.com/research/how-claude-searches-hotels-2026","category":"research"},{"@type":"BlogPosting","headline":"ChatGPT Hotel Ads Are Live — CPC Pivot, Ads Manager, $50K Entry","description":"Sponsored ads in 20-35% of hotel queries. Booking.com at 43.5%. April 29 update: OpenAI launched a self-serve ads manager, moved from CPM to CPC, dropped entry to $50K, ~$100M annualised revenue six weeks in.","datePublished":"2026-04-29T00:00:00.000Z","url":"https://nicolassitter.com/research/chatgpt-hotel-ads-live-2026","category":"research"},{"@type":"BlogPosting","headline":"ChatGPT 5.3 Halved Its Hotel Sources — March 5, 2026 Cutover","description":"Daily ChatGPT UI runs of 140 world hotel prompts from 4 locales. On Mar 5, URLs per answer fell 49% (24→12). Booking −82%, Expedia −76%, Reddit −93%.","datePublished":"2026-04-17T00:00:00.000Z","url":"https://nicolassitter.com/research/chatgpt-hotel-source-shift-2026","category":"research"},{"@type":"BlogPosting","headline":"Hotel YouTube Channels — Activity Study 2026","description":"We analyzed YouTube channels linked from 9,889 hotel websites. 43.7% are ghost channels. Only 11.3% post monthly.","datePublished":"2026-04-16T00:00:00.000Z","url":"https://nicolassitter.com/research/youtube-hotel-visibility-2026","category":"research"},{"@type":"BlogPosting","headline":"ChatGPT Hotel Index vs Live Web — What Changes When Search Goes Offline","description":"400 hotel queries, 2 models, 2 search modes. 83% of cited domains differ between live and cached.","datePublished":"2026-04-09T00:00:00.000Z","url":"https://nicolassitter.com/research/chatgpt-hotel-index-vs-live-web-2026","category":"research"},{"@type":"BlogPosting","headline":"How Dirty Is Google Maps Hotel Data? 179K Study","description":"17% of Google Maps hotel listings fail QA. 8,167 OYO vacation rentals. Belgium loses 54% after cleaning.","datePublished":"2026-04-01T00:00:00.000Z","url":"https://nicolassitter.com/research/google-maps-hotel-data-quality-2026","category":"research"},{"@type":"BlogPosting","headline":"ChatGPT Hotel Data Sources: 100K Entity Study","description":"Google dropped from 100% to 70.3% in 90 days. TripAdvisor descriptions are 8.8x longer.","datePublished":"2026-03-24T00:00:00.000Z","url":"https://nicolassitter.com/research/tripadvisor-chatgpt-hotels-study-2026","category":"research"},{"@type":"BlogPosting","headline":"What Hotel Footers Reveal — 98K Study","description":"Instagram is in 40.8% of hotel footers. 24% of copyright years are 3+ years stale. 10% link to OTAs.","datePublished":"2026-03-23T00:00:00.000Z","url":"https://nicolassitter.com/research/hotel-footer-analysis-study-2026","category":"research"},{"@type":"BlogPosting","headline":"Hotel llms.txt Adoption Study 2026","description":"105,002 hotel websites scanned for llms.txt. Only 6.3% have one. US leads at 12.4%.","datePublished":"2026-03-21T00:00:00.000Z","url":"https://nicolassitter.com/research/hotel-llms-txt-adoption-study-2026","category":"research"},{"@type":"BlogPosting","headline":"Hotel robots.txt & AI Blocking Study 2026","description":"105,002 hotel robots.txt files parsed. Only 3.3% block any AI crawler. France leads at 7.5%.","datePublished":"2026-03-20T00:00:00.000Z","url":"https://nicolassitter.com/research/hotel-robots-ai-blocking-study-2026","category":"research"},{"@type":"BlogPosting","headline":"What Hotels Are Actually Called: A Naming Study","description":"Analysis of naming conventions across 121,425 hotels in 7 countries","datePublished":"2026-03-10T00:00:00.000Z","url":"https://nicolassitter.com/research/hotel-naming-study-2026","category":"research"},{"@type":"BlogPosting","headline":"Hotel Schema.org Adoption Study 2026","description":"121,425 hotels scanned — 36.3% have no schema at all","datePublished":"2026-03-05T00:00:00.000Z","url":"https://nicolassitter.com/research/hotel-schema-adoption-study-2026","category":"research"},{"@type":"BlogPosting","headline":"Anatomy of a ChatGPT Hotel Search","description":"12 systems, 7 providers, 424 A/B tests — a technical teardown","datePublished":"2026-03-01T00:00:00.000Z","url":"https://nicolassitter.com/research/anatomy-chatgpt-hotel-search-2026","category":"research"},{"@type":"BlogPosting","headline":"Yelp in ChatGPT: Hotel Data Study","description":"33% Yelp integration rate in US hotel queries across 14 destinations","datePublished":"2026-02-20T00:00:00.000Z","url":"https://nicolassitter.com/research/yelp-chatgpt-hotels-study-2026","category":"research"},{"@type":"BlogPosting","headline":"How Consistent Are AI Hotel Rankings?","description":"Only 50.5% position stability across query reruns","datePublished":"2026-02-15T00:00:00.000Z","url":"https://nicolassitter.com/research/ai-hotel-rankings-consistency-study-2026","category":"research"},{"@type":"BlogPosting","headline":"Google AI Mode: Where Do Hotel Clicks Actually Go?","description":"79% of hotel clicks in Google AI Mode go to Google Business Profiles","datePublished":"2026-02-10T00:00:00.000Z","url":"https://nicolassitter.com/research/google-ai-mode-hotel-study-2026","category":"research"},{"@type":"BlogPosting","headline":"Do French Hotels Blog? A 15,000-Hotel Study","description":"49.3% have blogs but only 1 in 4 are active","datePublished":"2026-01-20T00:00:00.000Z","url":"https://nicolassitter.com/research/french-hotel-blog-study-2026","category":"research"},{"@type":"BlogPosting","headline":"The AI Hotel Landscape 2026","description":"How 6 AI Models Rank 12,500+ Hotels Across 1.2 Million Citations","datePublished":"2026-01-15T00:00:00.000Z","url":"https://nicolassitter.com/research/ai-hotel-landscape-2026","category":"research"}]}