AI Hotel Landscape
How leading AI assistants recommend hotels — 616 prompts across 56 destinations, refreshed every Monday.
Named hotels in Bangkok — ChatGPT
ChatGPT's top picks specifically in Bangkok, TH. Pick a different destination above, or clear to see worldwide.
Most-mentioned hotel by ChatGPT this week.
Wasn't on ChatGPT's radar last week. Now it is.
When ChatGPT hands out a hotel link, where does it go?Worldwide
Every URL the AI returned for a named hotel, classified into direct (the hotel's own site), OTA (Booking / Expedia / …), chain page (marriott.com / hilton.com / …), or other. Chain folds into direct in the headline number because chain pages are still brand-controlled.
Sponsored placementsWorldwide
OpenAI is rolling out paid sponsor placements in the US, Australia, New Zealand, and Canada — testing may expand. We detect real single-advertiser ad units (organic shopping cards excluded) and pull the brand.
| # | Advertiser | Captures |
|---|---|---|
| 1 | Expedia | 53 |
| 2 | Booking.com | 28 |
| 3 | Preferred Hotels & Resorts | 22 |
| 4 | Airbnb | 13 |
| 5 | Priceline | 7 |
| 6 | GetYourGuide | 2 |
| 7 | ALL Accor | 2 |
| 8 | Hilton | 1 |
| 9 | trivago | 1 |
| 10 | Centara Hotels & Resorts | 1 |
Where ChatGPT pulls hotel information from
Every URL ChatGPT cited classified into 12 buckets: OTAs, editorial, chain sites, direct hotel pages, social, meta-search, AI tools, community, government, directory consortia, and other.
Editorial vogue.com 35% · thetimes.co.uk 33% · timeout.com 33% | 35.5% | 11 |
Other hotelsforkings.com 37% · royalhotelguide.com 32% · thecollegegreenhotel.com 32% | 22.6% | 7 |
Community hotelierschoice.com 78% · luxuryhotel.guide 13% · therooftopguide.com 9% | 16.1% | 5 |
Social Channel reddit.com 100% · youtube.com 0% | 16.1% | 5 |
Review Site tripadvisor.com 73% · oyster.com 25% · tripadvisor.co.uk 2% | 9.7% | 3 |
Top hotel parent groups by ChatGPT mentionsWorldwide
Each ChatGPT mention matched against Google Places, then rolled up to parent group (Marriott = Ritz-Carlton + Westin + Sheraton + EDITION + …, etc.). WoW delta vs last week. Detection via 175+ brand-domain rules.
| 1 | Lungarno | 6 → 12 | +100% |
| 2 | Ovolo | 6 → 12 | +100% |
| 3 | Belmond | 11 → 16 | +45% |
| 4 | Design Hotels | 11 → 16 | +45% |
| 5 | Meliá | 14 → 19 | +36% |
| 1 | Barceló | 13 → 9 | -31% |
| 2 | Generator Hostels | 12 → 9 | -25% |
| 3 | Peninsula | 23 → 19 | -17% |
| 4 | Rocco Forte | 12 → 11 | -8% |
| 5 | Wyndham | 14 → 13 | -7% |
| # | Brand | Mentions | Hotels | W/W | |
|---|---|---|---|---|---|
| 1 | Marriott | 303 | 137 | ↑ 7.8% | |
| 2 | Accor | 169 | 97 | ↑ 15.0% | |
| 3 | Hyatt | 118 | 55 | ↓ 2.5% | |
| 4 | Hilton | 97 | 59 | ↑ 21.3% | |
| 5 | Four Seasons | 89 | 26 | ↑ 17.1% | |
| 6 | IHG | 77 | 53 | ↑ 16.7% | |
| 7 | Mandarin Oriental | 48 | 19 | ↑ 14.3% | |
| 8 | Shangri-La | 43 | 18 | ↑ 19.4% | |
| 9 | Taj | 40 | 13 | ↑ 17.6% | |
| 10 | Radisson | 35 | 18 | ↑ 29.6% | |
| 11 | Minor | 34 | 22 | ↑ 13.3% | |
| 12 | Kempinski | 27 | 8 | · 0% | |
| 13 | Langham | 24 | 7 | ↑ 26.3% | |
| 14 | Meliá | 19 | 13 | ↑ 35.7% | |
| 15 | Peninsula | 19 | 5 | ↓ 17.4% | |
| 16 | Oberoi | 18 | 4 | ↑ 5.9% | |
| 17 | Rosewood | 17 | 7 | · 0% | |
| 18 | Belmond | 16 | 4 | ↑ 45.5% | |
| 19 | Design Hotels | 16 | 5 | ↑ 45.5% | |
| 20 | Wyndham | 13 | 12 | ↓ 7.1% |
Of the hotels named this week: how many resolved to a chain (Marriott / Accor / …), an independent property, a vacation rental, or stayed unmatched.
The 616 prompts we ask ChatGPT every week
56 destinations × 11 templates. Each prompt has structured dimensions: city, country, region, persona (couples / families / solo / business), budget (luxury / mid / budget), and location zoom (wide city vs neighborhood vs landmark). Control prompts are unmodified baselines.
Every destination below gets the same 11 question types — just with the city name swapped in. That's how 56 destinations × 11 templates = 616 prompts per platform per week.
- luxury hotels in <city>
- family friendly hotels in <city>
- hotels in <city> with rooftop pool
- hotels near <neighborhood>, <city>
- boutique hotels in <neighborhood>, <city>
- best hotels in <city> for solo travelers
- best hotels in <city>
- affordable hotels in <city> under $200
- best hotels in <city> for couples
- best hotels in <city> for business travelers
- best hotels in <neighborhood>, <city>
- hotels near <city> National Park
africa4 destinations · 44 prompts
americas14 destinations · 154 prompts
Each destination is asked the 11 prompt patterns above (11 prompts × 14 cities = 154 prompts in this region).
asia16 destinations · 176 prompts
europe17 destinations · 187 prompts
oceania5 destinations · 55 prompts
Each destination is asked the 11 prompt patterns above (11 prompts × 5 cities = 55 prompts in this region).
What every metric means
Plain-English definitions for each number on this page. For deeper visuals + examples, see the annual landscape report linked at the bottom.
- Captures
- Number of AI responses we collected this week. Target = 616 per platform (one per prompt). Less when there are upstream errors.
- Web search
- Did the response trigger a live web fetch? Detected from the underlying response stream’s search-result events, not the unreliable top-level flag. We do NOT force web search — this is organic model behavior.
- Map widget
- Did the response render a hotel-card map (the Google-Maps-style widget ChatGPT shows for travel queries)? Each card is a "map entity" with its own provider.
- Sponsored placements
- Paid sponsor placements. Detected from real single-advertiser ad units in the response stream. Excludes ChatGPT’s organic shopping cards (which are unpaid product carousels). US/AU/NZ/CA only as of May 2026.
- Sources / response
- Distinct URLs the model consulted while answering — the URLs that show up in its retrieval log, regardless of whether it cited them inline. Computed only over web-search responses (otherwise the answer is from training data, no sources to count).
- Citations / response
- Subset of sources that the model rendered as inline footnote pills in the answer. A source becomes a citation when the model explicitly references it.
- Fanouts / response
- Number of sub-queries the model spun up internally to answer the prompt (e.g. "best hotels Paris" might fan out to "hotels Paris Marais", "luxury hotels Paris", "rooftop hotels Paris", …).
- Map entities / response
- Hotel cards inside the map widget. Each carries a provider (Google Places, TripAdvisor, Yelp, Foursquare, SERP) and a place ID where applicable.
- OTA
- Online travel agency (Booking, Expedia, Hotels.com, Agoda, Trip.com, Priceline, etc.) — commission-based booking sites.
- Direct
- A hotel’s own website. Computed at request time by matching the cited domain against Google Places.
- Chain
- A hotel chain’s brand website (hilton.com, marriott.com, ihg.com, …). 175+ chain-domain rules.
- Editorial
- Travel media (Condé Nast Traveler, Time Out, Lonely Planet, Forbes Travel Guide, NYT, etc.) and city-specific travel blogs (santorinidave.com, theurbanlist.com, etc.).
- Directory
- Multi-property hotel consortia / brand collectives (Small Luxury Hotels of the World, Design Hotels, Preferred Hotels, Virtuoso, …). Aggregate hotels under one umbrella but aren’t a single OTA.
- Review
- TripAdvisor, Oyster, Yelp, Trustpilot, Holidaycheck — review-first platforms.