Hotel AI Search: The Questions Independent Operators Actually Ask

Where ChatGPT gets its information about your property, why the description is so bland, and which fixes are worth your time. Answered with the data.

We published a piece on how AI assistants decide which hotels to recommend and the replies were all variations of the same thing: fine, but where is the machine getting this from, and what do I fix first. Reasonable questions. Here are the answers, with the numbers behind them and a note wherever the sample is thin enough that you should hold the finding loosely.

Where does ChatGPT actually get information about my hotel?

From map data providers, not from your website directly. A study tracking 99,538 hotel entity cards in ChatGPT between December 2025 and March 2026 found four sources: Google Places at 88.8%, Yelp at 10.2%, Foursquare at 0.2% and TripAdvisor at 0.1%.

The aggregate hides the interesting part. In December 2025 Google was the sole source at 100%. By the final tracked week of March 2026 it was down to 70.3%, with Yelp at 22.4%, Foursquare at 3.9% and TripAdvisor at 1.0%. Thirty points of share moved in ninety days. Treat that last week carefully — it was a 508-entity sample cut mid-week, so the direction is solid and the precise number is not.

Your AI presence is not one listing. ChatGPT assembled hotel results from four separate providers in the study window. Fixing Google alone leaves the other three describing you however they like.

Do I need to bother with Yelp?

It depends entirely on where your property sits, and the split is stark. Yelp supplied 40.8% of ChatGPT's hotel entities in Las Vegas, 34.9% in San Francisco, 29.9% in New York and 25.8% in Berlin. In Paris, London, Rome and Barcelona it supplied none at all.

The same study found provider mix is chosen by where the hotel is, not where the traveler is searching from. A guest in Germany and a guest in the U.S. asking about the same Las Vegas property get roughly the same source blend. So the rule for a U.S. operator is simple: if you are in a major American metro, your Yelp profile is doing real work in AI answers and most independents treat it as an afterthought.

Why does the AI describe my property so blandly?

Because Google, which supplies most of the data, almost never includes a description. Only 1.3% of Google Places hotel entities carried one, averaging 97 characters. For the other 98.7%, ChatGPT gets a name, a rating and an address, and has to infer the rest.

TripAdvisor is the opposite: 69.7% of its entities carry a description and they average 855 characters, roughly nine times Google's length. That is why a provider sitting at 0.1% of volume matters more than its share suggests — when it does appear, it brings the only real narrative in the stack. The obvious caveat is that this rests on 66 TripAdvisor entities. It is a direction, not a measurement.

If ChatGPT recommends me, do I get the direct booking?

Usually, and it depends which provider powered the card. Google, Yelp and Foursquare entity cards link to the hotel's own website. TripAdvisor cards link back to a TripAdvisor listing, where the click gets monetized by somebody who is not you. It is the first provider in ChatGPT to route hotel traffic through an intermediary.

Today that is a rounding error. The thing worth watching is the pattern: every new provider in this study went from nothing to meaningful share within weeks. If TripAdvisor lands where Yelp already is, a real slice of AI-driven hotel discovery starts bypassing hotel websites. That is the same squeeze the OTAs already apply, arriving through a channel most operators still think of as free.

What is schema markup, and does my property need it?

Schema markup is a block of structured data in your page source that states plainly what your business is: a hotel, at this address, with this rating, these amenities, these check-in times. Models read it directly rather than guessing from your marketing copy. Yes, you need it, and almost nobody has it done properly.

A March 2026 crawl of 121,425 hotel homepages across seven countries found 36.3% with no structured data at all. Among properties that did have it, 41.1% used the wrong type — declaring themselves a generic Organization or LocalBusiness rather than a Hotel, which throws away every hotel-specific field. Scored out of 100, the average was 14.3 and the median was zero.

The fields that actually drive a recommendation are the ones nobody fills in. Amenities appear on 7.7% of hotel listings with structured data, aggregate rating on 12.5%, coordinates on 18.8%, and a plain description on 21.8%. A traveler asking for somewhere with parking near the stadium is asking a question that 92% of properties have made unanswerable.

What is the single highest-return fix?

Change your schema type from Organization to Hotel. It is one line, it applies to roughly 24,119 of the properties in that crawl, and until you do it every other field you have filled in scores nothing because the type contradicts them. Your web developer can do it in the time it takes to read this paragraph.

• Fix the type first. Organization or LocalBusiness becomes Hotel. Everything downstream depends on it.
• Add coordinates. Missing on 81.2% of listings, and without them a model cannot answer "near the airport" with any confidence.
• Add a description. Missing on 78.2%. This is the field that decides whether you sound like a specific place or a room with a bed in it.
• Add amenities. Missing on 92.3%, and amenities are what travelers actually filter on.
• Then claim and fill the profiles that feed the models. Google Business Profile always, Yelp if you are in a U.S. metro, TripAdvisor everywhere — that description field is worth more than its traffic implies.

How often should I be checking any of this?

Monthly, and that is not a consultant padding a retainer. Google went from supplying every hotel card in ChatGPT to roughly seven in ten over a single quarter. An audit you ran in January described a system that no longer existed by April.

The cheap version costs you twenty minutes. Write down eight questions a guest would really ask — the constraint-shaped ones, price and parking and pets and distance — run them through ChatGPT and Google's AI Mode on the first of the month, and record whether you got named and what the model said about you. That log is worth more than any visibility score, because it tells you which specific answer you are losing. If you would rather have it done properly and tracked against competitors, that is what our answer engine optimization work is.

One habit to carry over from the home-services side: whatever the model sends you, answer it fast. The speed-to-lead research holds regardless of where the enquiry originated, and an AI-referred guest is comparing you against two other properties inside the same conversation.