Which platforms matter most
Start with where your guests read reviews before booking, then add where AI engines get their information. For most hotels the shortlist is the same three or four: your Google Business Profile, the OTA that sends you the most bookings (often Booking.com in Europe), Tripadvisor, and Expedia if it is a significant channel for you.
Google matters more than its share of review volume suggests, because Google's AI products draw on Maps data. Google's own trip-planning post says Gemini gets locations, reviews and prices from Google Maps, Flights and Hotels. Reviews on your Business Profile feed that directly.
Look at your own booking mix and your source markets before adding more. A spa hotel with a large German clientele may need to watch a German-language platform that a city hotel can ignore. Our guide to how reviews affect AI hotel recommendations covers which sources engines lean on in more detail.
What you can skip: small review aggregators nobody in your market reads, and platforms where you get a handful of reviews a year. Spreading the same effort across ten platforms usually means replying late on the three that matter.
Response process, owners and timing
Reputation work fails most often because nobody owns it. Name one person, usually the GM, the front office manager or a guest relations lead, and make replies part of their week, not something done when there is time.
Set a response target and keep it. For most independent hotels, replying within two working days on the main platforms is realistic and noticeably better than the weeks-late replies that are common. Faster matters most for negative reviews, where a prompt, calm answer is visible to every future reader of the complaint.
Agree an escalation route. A review that mentions safety, discrimination, a health issue, theft or a legal threat goes to the GM before anyone replies. Some complaints should be answered privately first, with a short public reply saying you have been in touch.
Use the platforms' own tools. Google Business Profile requires a verified business before you can reply, posts replies under the business name, and checks them against its content policies before they appear. Replies can be edited or deleted later. The OTA extranets and Tripadvisor each have their own reply rules and moderation, so check the current guidelines on each before you write a standard process around them.
Do not argue in public. You can correct a factual error, politely, once. Beyond that, the reply is for the next guest reading it, not for the reviewer.
Templates for positive, mixed and negative reviews
Templates are a starting point. Change the bracketed parts every time, and add one sentence that could only be about this review. That specific sentence is what makes a reply read as written by a person, and it is also the part an AI assistant can quote.
Positive review Thank you, [name], for staying with us and for taking the time to write. We're glad [the specific thing they mentioned, e.g. the breakfast terrace] made the stay. [One detail of your own: who was on duty, what's new, or a tip for next time.] We hope to welcome you back. [First name], [role]
Mixed review Thank you, [name], for the detailed review. We're pleased you enjoyed [the positive point], and sorry that [the specific problem] fell short. [What you have done or are doing about it, stated plainly.] If you visit again, please ask for me at reception. [First name], [role]
Negative review Dear [name], thank you for telling us about [the specific problem], and I'm sorry it affected your stay. [One sentence of fact or context, without excuses.] [What has changed, or what you will do.] I'd like to hear more; please contact me directly at [email]. [First name], [role]
Signing with a real name and role helps. So does keeping the reply shorter than the review. Avoid copying the same opening line into every reply: a page of identical replies looks automated, and it is.
Turning repeated complaints into operational fixes
Most hotels have two or three complaints that come up again and again: the street noise in front-facing rooms, the slow lift, the breakfast queue at 9am, the shower pressure on the top floor. Replying to each one individually treats a recurring cause as a series of bad days.
Once a month, read the last 30 days of reviews across your main platforms and tally the complaints by theme. Anything that appears three or more times goes on a list with an owner and a decision: fix it, mitigate it, or set expectations about it honestly on your website and OTA listings.
Setting expectations is a legitimate fix. If front-facing rooms are noisy and you cannot change the street, say so in the room description and offer courtyard rooms to light sleepers. A guest who was told is less likely to complain, and the complaint stops piling up.
There is evidence that the effort pays. Chris Anderson's 2012 Cornell study of hotel review data found that a one-point increase in a hotel's review score on a five-point scale allowed it to raise prices by 11.2% while keeping the same occupancy or market share. A study by Davide Proserpio and Georgios Zervas found that hotels that started replying to Tripadvisor reviews saw their ratings rise by an average of 0.12 stars afterwards, mainly because guests with a poor experience became less likely to leave a negative review. Both are about a correlation in a specific dataset, not a promise for your hotel, but they point the same way.
Checking how AI summarises your reviews
AI assistants do not read your reviews one at a time the way a guest does. They summarise. Ask ChatGPT or Gemini what guests say about your hotel and you will usually get a few strengths and one or two weaknesses, drawn from patterns across many reviews and whatever else the engine has read.
That summary is worth checking every month, for three reasons. It shows which complaints have stuck: if an assistant mentions noise, noise is in enough reviews to be part of your public description. It can be out of date: a problem you fixed last year may still appear because older reviews and articles still say so. And it can be wrong: assistants sometimes state facts about a hotel that are not true, such as a pool you do not have or a restaurant that closed. Our guide to AI hallucinations about your hotel covers why that happens and how to correct it.
To check by hand, ask two or three assistants the same questions: what guests like and dislike about your hotel, whether it is good for a particular kind of trip, and whether there are any common complaints. Note anything wrong or stale. Correct factual errors at the source: your website, your Business Profile and your OTA listings. Old complaints fade as new reviews say something different, which is one more reason to fix the cause rather than reply around it.
Owner replies are part of what assistants can read. A specific, factual reply saying the lift was replaced in March is text an engine can find. A generic thank-you is not.
A monthly hotel reputation management report
Keep it to one page, so it gets read. The figures to track each month:
1. Review count and average score on each main platform, against the same month last year.
2. Response rate and median response time, for positive and negative reviews separately.
3. The top three complaint themes and the top three praise themes from the month's reviews, with counts.
4. Status of each open operational fix from previous months.
5. What two AI assistants say about your hotel's reviews this month, and anything they got wrong.
6. One action for next month, with an owner.
Most of this comes straight from the platforms' own dashboards. The AI check takes ten minutes by hand. If you want it tracked across engines and checked against your own fact base automatically, Staylight's sentiment and hallucination analysis on the Essential plan does that every scan.