GEO for Hotels: Get Your Hotel Recommended by AI

Example query: "best boutique hotels in central Lisbon under 180 euros a night". An assistant typically returns a handful of names with a reason for each. Run the equivalent query for your own city and price band and note whether your hotel appears.

Some travelers will then check Booking, Google Maps or Tripadvisor and may still find you. Others will go straight to the options the answer suggested. Either way, the answer has shaped which hotels they consider first.

Accommodation decisions involve many variables — location, price, style, atmosphere, reviews, breakfast, flexible cancellation — and some travelers now use AI assistants to narrow them down. The assistant doesn't replace Booking or Google Maps; it can add a step before them that influences which hotels get considered.

A concrete first task: run the same set of queries by city, price range and hotel type, and for each answer record which hotels are named, in what order, and which sources are cited. Repeat the set later to see whether your hotel appears and how the answers change.

How Travelers Use AI Before Booking

Alongside metasearch, Booking and Google Maps, some travelers now start with a conversation with an AI assistant. Typical questions fall into four groups:

City and area discovery. "Which neighborhood should I stay in for a family trip?" or "safest area for tourists in a given city?". Before picking a hotel, the traveler asks where to stay. If the assistant doesn't recommend your area for that kind of trip, you start at a disadvantage.

Style and need filter. "Charming hotels for a romantic getaway" or "hotels with good breakfast and wifi for working remotely". The traveler is looking for a hotel that fits their situation. If public information about your hotel doesn't connect it with a style or need, it's less likely to appear in these narrower answers.

Validation before booking. "How good is Hotel X?" or "is it worth paying 200 euros a night at this specific hotel?". The traveler already has candidates and wants an outside view. What the assistant says here can reinforce or weaken their choice.

Alternatives and comparisons. "Compare Hotel A and Hotel B in this city". The answer lists differences drawn from reviews, articles and mentions. If it uses outdated or incorrect information about your hotel, you may lose the comparison without knowing why.

Why Being on Booking Isn't Enough on Its Own

Many hoteliers reason: "we're already on Booking with good reviews, that's enough." It's a reasonable starting point, but assistants recommend hotels, not booking platforms, and they draw on many sources.

Booking can be one of those sources, alongside Tripadvisor, travel blogs, editorial guides, press articles, forums like Reddit and hotels' own websites. Which sources an answer uses varies by product, question and whether the assistant searched the web. For broad questions like "charming hotels" or "best boutiques", answers often reflect editorial lists and guides as well as OTA listings, so it helps to be well described in more than one place.

OTA commissions also matter. Rates vary by platform, country and contract, so check your own. A reservation made directly on your website avoids that commission. If assistants send more travelers to your own site, that can support direct bookings, but measure it rather than assume it.

Five Factors Worth Working On

Model providers don't publish how they choose which hotels to mention, and the process varies between models and between answers that search the web and those that don't. These common failure patterns point to five factors hotels can influence:

Editorial presence in travel media. Coverage in travel publications, newspaper travel sections and well-known travel blogs creates independent descriptions of your hotel that assistants can find or may have learned from. Treat it as one important signal alongside reviews, not a replacement for them.

Consistency of information across sources. If your website says "boutique hotel with 12 rooms in the historic center", Booking says "three-star hotel in central area" and Tripadvisor lists you as a "guesthouse", an answer built from those sources may describe you inconsistently. Keep the core facts the same wherever you appear.

Reviews with text, not just stars. Review text describes what kind of guest enjoys your hotel. Reviews that mention specifics like "ideal for couples, excellent breakfast, personal service" give an answer about "romantic hotels" something concrete to draw on; generic "all good" reviews don't.

Mentions in forums and communities. Reddit threads (r/travel, r/solotravel, city-specific subreddits), travel forums and Facebook groups contain first-hand recommendations. Answers that search the web may pick them up, and they carry weight with readers because they come from travelers.

Answer-oriented owned content. Pages like "what to do in the neighborhood" or "our guide to local restaurants" can become a citable source for the AI when an assistant searches the web. They also help your guests.

Common Mistakes in Hotel AI Visibility

These patterns come up often. All are fixable once you spot them:

Website machine-translated from another language. Some hotels keep literal translations for foreign-language markets. Awkward copy is less useful to travelers and to any system summarizing the page, and it can miss the words people use in that language. Native copy for your key markets is worth reviewing.

All information on OTAs, almost nothing on your own website. The hotel website is just a booking engine with two photos. When an assistant looks for an official source, it finds little, so answers rely on third parties and may get details wrong.

Reviews answered with generic templates. "Thank you for your stay, we hope to see you soon" says nothing about the hotel. Replies that mention specifics (the breakfast, the neighborhood, the room type) add useful public information and show guests that you pay attention.

No content about the surroundings. The hotel describes its rooms and little else. For questions like "where to stay near a specific landmark", answers need location information. If your website doesn't describe the area, assistants have less to connect your hotel with those searches.

Relying on stars instead of style. "4-star hotel" says little about who the hotel suits. "Boutique hotel with 14 rooms, adults only, focused on slow travel and gastronomy" gives an assistant the facts it needs to match you with the right questions.

A Starting Plan for a Small Team

Steps you or your front-desk team can take over the next few weeks:

Do the basic test. Open ChatGPT, Gemini and Perplexity and ask as a typical traveler would: "boutique hotels in this city with character", "where to stay in this neighborhood", "romantic hotels in the area under 150 euros", "how good is Hotel [your name]?". Note what comes up and whether your hotel appears.

Rewrite the "about us" section of your website. Describe what kind of hotel you are (boutique, family, business, romantic getaway), how many rooms, what type of guest fits well, what's special about the area. No empty marketing language. The AI needs concrete data, not adjectives.

Create 3 or 4 neighborhood guides. "What to do in the neighborhood in a weekend". "The five restaurants we recommend to our guests". "How to get from the airport to downtown". These are articles that help your actual guest and are a citable source for the AI.

Answer reviews with context. For example: "We're glad you enjoyed breakfast on the terrace. Our chef makes the eggs benedict with eggs from a nearby farm." A reply like this adds specific, public detail about the hotel that generic replies never provide.

Take part in travel forums honestly. If someone on Reddit asks for hotels in your city, you can reply, but say clearly that you work at or own the hotel, follow the community's rules on self-promotion, and give a genuinely useful answer that includes other options. Hidden promotion breaks forum rules and can harm your reputation; transparent, helpful participation doesn't.

How to Know If AI Recommends Your Hotel Today

A manual test is fine for a first diagnosis. Answers can change as models are updated, new reviews appear and articles are published, so repeat the test periodically with the same questions and compare.

Mentio analyzes your hotel with questions adapted to your hotel type, city and market. The report shows your visibility by model, your position relative to local competitors, what the AI says about you and which competitors are recommended ahead of you. Which models are included depends on the plan.

For a boutique hotel, the questions shouldn't be generic: "best boutiques in the central district", "charming hotels in the old town", "where to stay during peak season", "romantic hotel under 200 euros". Use the questions your potential guests actually ask.

Compare what you find with your direct booking data over time before attributing changes to AI answers.

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