How to Get Your Hotel Recommended by ChatGPT: Do Reviews Decide?

I ask an assistant for a family resort with a shallow pool near the beach, it gives me a handful of names, and then I read the reviews anyway. So here is the honest answer to how to get your hotel recommended by ChatGPT. Nobody outside OpenAI knows exactly how it picks, and OpenAI says placement is not guaranteed ^[1]. The largest published test I could find, a September 2026 vendor study of 998 US hotels by Local Falcon, found ChatGPT named 84% of hotels with 2,500 or more Google reviews and 52% of those with fewer than 50 ^[2]. Above a 4.0 rating, the share stopped rising. For the tools side, start with what hotel review management software should handle.

That is correlation from one vendor, not proof. Read beside the rest of the evidence, it points to a plain routine: get above about 4.0, then earn a steady stream of honest reviews by asking every guest the same way.

More travelers plan this way now. Phocuswright's July 2026 research put generative AI use for trip research at 33%, with summarized reviews the second strongest trigger for acting on an AI recommendation, at 33%, after price comparisons at 44% ^[3]. So it stings when you ask ChatGPT about your town from your own lobby and the resort down the road comes up instead. Most vendors selling "AI visibility" say reviews are the lever. Few show evidence.

Do reviews decide whether ChatGPT recommends your hotel?

They are part of it, by the platforms' own account, though no one has shown how much. In the one large test of real hotels that looked at review counts, count tracked being named more closely than rating did once a hotel cleared 4.0. Local Falcon's September 2026 study searched "best hotel near me" and "best hotels near me" from nine points within two miles of each hotel, then matched the results to each hotel's Google listing ^[2]. ChatGPT named 52% of hotels with fewer than 50 Google reviews, and the share rose with every band, to 84% at 2,500 or more ^[2].

Rating looked different. ChatGPT named 42% of hotels rated under 3.5 and 71% of those at 4.0 to 4.4. After that the line went flat: 75% at 4.5 to 4.7, then 71% at 4.8 or above. In Local Falcon's words, "Hotels rated 4.8 or above were named slightly less often than hotels rated 4.5 to 4.7" ^[2].

Hold all of this loosely. The top band is just 34 hotels, the study does not say when the searches ran, and it is one vendor's correlation. Independents and chains were named at nearly the same rate, 64% against 63% ^[2]. Reviews Are Revenue, the fourth ResortConcierge AI Research report reads this study beside the platform rules, in its chapter on volume and how to ask ^[4].

Where do AI assistants get what they know about your reviews?

Mostly from the sites where guests already read them. In Local Falcon's 2026 test, Tripadvisor supplied 27% of ChatGPT's citations and Booking.com 23%. Review platforms as a group made up 29%, booking sites 35% and hotel chain websites 7% ^[2].

The big platforms have also wired themselves in. Tripadvisor's app inside ChatGPT, for logged-in US users, says it will sift through its "millions of reviews and ratings to recommend the best hotels" ^[5]. Booking.com and Expedia arrived as ChatGPT apps in October 2025 ^[6]. In January 2025, Engadget reported that Perplexity would show Tripadvisor summaries on hotel listings ^[7]. And Google's AI Mode, which began rolling out hotel booking in the US in August 2026, returns "a visual list of options, complete with guest reviews" ^[8].

None of them says how much reviews count. Google's Business Profile help says "More reviews and positive ratings can help your business's local ranking" ^[9]. Google Search Central says no "special optimizations" are needed to appear in AI Mode ^[10]. OpenAI says ChatGPT ranks results using "multiple factors" ^[1].

At the desk, that means your Tripadvisor and Booking.com pages may be what an assistant repeats about your pool, not your website, and those pages need fresh, honest reviews.

How strong is the evidence, really?

Thin, and mixed. Lighthouse, a hotel data vendor, ran 4,545 ChatGPT prompts across nine destinations and reported in June 2026 that "The correlation between a hotel's Booking.com score and its ChatGPT visibility was weak" ^[11]. It published no coefficient, model version or test dates. Of the sources ChatGPT cited, 82% were online travel agency (OTA), metasearch or editorial sites, and in Indianapolis, independents got about 7% of hotel mentions ^[11].

That seems to contradict Local Falcon's near tie, but they counted different things: hotels named in searches around each hotel, against all mentions in prompts about a whole destination.

The one controlled test is a June 2026 preprint by Baig, Gillani and Ali ^[12]. They had 12 AI models choose among five invented hotels with details shuffled at random. Hotels shown with a 4.7 rating were picked about 32 percentage points more often than those at 3.9, and hotels shown with 2,100 reviews instead of 45 about 8 points more often. Showing management replies made no measurable difference, and chains were picked about 2 points less often than independents.

My read: getting found and getting chosen may be two steps. Volume and presence on the cited sites went with the first in the vendor tests; in the preprint, rating counted most once an assistant had a list. But the vendor studies describe their own samples, the preprint is not peer reviewed, and none of it shows that adding reviews to a real hotel makes an assistant more likely to name it.

Why are reviews worth this much to an independent resort?

Set the assistants aside and reviews still pay. In a seminal working paper on 2005 to 2014 data from 4,477 hotels in five western states, Lewis and Zervas (2019) found that a hotel rated one star higher across platforms had 28% higher demand ^[13]. The gain was larger for independents, 10% per star against 4% for chains, and the effect was close to zero below about 3.5 stars. The lift came through demand: higher-rated hotels did earn higher average prices, but ratings had a "negligible" effect on how hotels set their prices.

Hollenbeck's seminal 2018 study in the Journal of Marketing Research tells the same story from the other side ^[14]. In small Texas markets, meaning places under 150,000 people and more than 20 miles from a major population center, the revenue premium chains held over independents fell from 25% to 7% between 2000 and 2015. Online reviews were at least part of the reason.

A brand used to tell a traveler what to expect. Reviews do that job now, and independents gain the most. For the revenue math to take to an owner, read why a better review score beats a rate increase.

Why does steady volume matter more than a perfect score?

Because there is a floor, then a flow. Below about 4.0, the score is the job. Local Falcon's test named far fewer hotels rated under 3.5 ^[2], and Lewis and Zervas found almost no demand effect below about 3.5 stars ^[13]. If a main platform shows you under the floor, start with how negative guest reviews hit revenue and the complaints that show up most.

Above the floor, the next tenth of a point is slow to earn, and in the vendor test it went with little extra visibility. A steady stream does quieter work. Tripadvisor says its Popularity Ranking "is based on the quality, recency and quantity of reviews," while warning that "Just having more reviews doesn't mean that a business will be ranked higher than its competitors" ^[15]. New reviews every month keep a profile recent on the same sites assistants cite most.

Replies help volume, if not visibility. In a seminal 2017 study, Proserpio and Zervas found hotels that started responding received 12% more reviews, and their ratings rose 0.12 stars on average ^[16].

A 40 room resort cannot out-volume a 400 room one. It can make sure every guest is asked, every month.

How do you ask for more reviews without breaking the rules?

Ask everyone the same way, offer nothing, screen no one, and keep staff out of it. The Federal Trade Commission (FTC) rule on consumer reviews "became fully effective on October 21, 2024" ^[17]. It bans fake reviews, incentives conditioned on a review being positive or negative, undisclosed insider reviews and review suppression, and a court can impose civil penalties of up to USD 53,088 per violation (January 2025 adjustment) ^[18]. The FTC's 2022 guidance is blunter: "Don't ask for reviews only from customers you think will leave positive ones" ^[19].

The platforms go further. Google prohibits businesses that "selectively solicit positive reviews from customers" and bans incentives "in exchange for posting any review" ^[20]. Asking guests to "visit a Google link or scan a QR code" is fine ^[21]. Tripadvisor bars offering anything for a review "irrespective of rating," prohibits selectively soliciting positive content, and bans incentives that reward employees for encouraging reviews, on pain of ranking penalties ^[22].

This is general information, not legal advice. Check the current policy text before you build a process, and for timing and channel, see when to ask for a review, on which channel, and when not to.

Counseling taught me that people tell you the truth when the question is plain and nothing rides on the answer. Guests are no different, and that happens to be exactly what the rules ask for.

How to get your hotel recommended by ChatGPT: where do you start?

Nothing here guarantees a mention. It is what the evidence and the platforms point to, run as a routine.

1. Note the score each main platform displays. Under about 4.0 anywhere, start there: the top two complaint categories, and problems fixed during the stay.
2. Count reviews from the last three months on Google, Tripadvisor, Booking.com and Expedia. The goal is new reviews every month on each, not a target number.
3. List every way you ask for reviews. Remove any reward and any "how was your stay?" screen in front of the review link. Every guest gets the same ask.
4. Check staff plans: no bonus tied to reviews, and nobody who works for you posts reviews.
5. Reply to negative reviews that describe a real problem, written for that review. Start with replying to a negative review without sounding canned.
6. Update your Google Business Profile and OTA listings so the facts guests ask about, like the pool, the beach and family rooms, are current ^[10].
7. Make sure your site can be indexed and does not block OpenAI's crawler, OAI-SearchBot ^[1].
8. Each quarter, from near the property, ask two assistants for the best hotels nearby. Note whether you appear and which sites they cite.

The full version is the one-hour review audit in the report.

Where does ResortConcierge AI fit?

Fixing a problem while the guest is still on property is the part of reputation a resort controls most. ResortConcierge shows a sentiment marker on every guest conversation, so a problem raised mid-stay is visible to the team while there is still time to fix it.

Specific replies to negative reviews take time a small team rarely has. ResortConcierge drafts replies to Google and Tripadvisor reviews that a person edits and approves; approved replies publish to Google directly, and Tripadvisor replies are copied and pasted.

Check your floor this week. Then ask every guest the same way, and next quarter, stand in your own lobby, ask two assistants where to stay, and see what changed. Whatever they say, the next parent reading your reviews at 10pm will find a fuller, fresher picture.