If you run an independent hotel and you have never actually asked ChatGPT “where should I stay in [your town],” you are flying blind on the fastest-growing slice of travel search. I do this for a living, and the number of hoteliers who assume they show up in AI answers, then find out they do not, is genuinely high.
So this is the post I wish someone had handed me two years ago: the exact monthly routine I run to measure what AI engines say about a hotel. Not vibes. A fixed prompt set, four engines, a tracker, and a sentiment column. It turns “are we visible in AI?” from a shrug into a number you can watch move.
Let me walk you through it the way I actually do it.
Why a routine, not a one-off check
The first time most people test this, they type one question into ChatGPT, see their hotel (or not), and form a permanent opinion. That is a mistake. AI answers are not stable. The models get retrained. Your Google reviews shift. A competitor publishes a great neighborhood guide and suddenly the model starts citing them instead of you.
A single check is a photograph. What you want is a time-lapse. The only way to know whether your AEO and GEO work is paying off is to run the same prompts, the same way, on a schedule, and log the results so you can compare March to April to May.
There is real demand behind this, by the way. “AEO” alone does around 27,100 US searches a month, “AI SEO” about 8,100, and “generative engine optimization” roughly 5,400. The travelers and the industry are both moving here at once. You want measurement in place before your competitors do.
A single AI answer is a photograph. A monthly routine is a time-lapse. You cannot manage AI visibility you only check on a whim, because you will never know whether a change you made helped, hurt, or did nothing at all.
Step 1: Build a fixed prompt set (and never quietly change it)
The whole routine lives or dies on consistency. If you reword your prompts every month, your data is garbage, because you cannot tell whether a change came from the model or from your typing. So I build a fixed set of 12 to 20 prompts per property and freeze them.
I group them into four buckets:
- Branded prompts. “Tell me about [Hotel Name] in [City].” “Is [Hotel Name] a good place to stay?” “What do people say about [Hotel Name]?” This tells you whether the engine even knows you exist and what tone it takes.
- Category prompts. “What are the best boutique hotels in [City]?” “Where should I stay in [Neighborhood] for a weekend?” This is the big one. It is the question a real traveler types, and the one where you are competing to be named at all.
- Attribute prompts. “Which hotels in [City] are good for couples?” “Pet-friendly boutique hotels near [landmark]?” “Hotels in [City] with free parking and a pool?” These surface whether the model knows your actual amenities.
- Comparison prompts. “Is [Your Hotel] or [Competitor] better for a romantic weekend?” Uncomfortable, useful, and very revealing about how you are positioned.
Write them in a doc, number them, and treat that list as sacred. If you must add a prompt, add it as a new row, do not edit an old one. Future-you, comparing six months of data, will thank present-you.
Step 2: Run the same prompts across all four engines
I run every prompt through ChatGPT, Gemini, Perplexity, and Copilot. They are not interchangeable, and that is exactly why you test all of them.
- ChatGPT leans on its training plus live browsing. Phrasing matters a lot here.
- Gemini pulls heavily from Google’s ecosystem, so your Google Business Profile and reviews carry real weight. If your Google Business Profile is a mess, Gemini notices.
- Perplexity is the honest one for our purposes because it cites its sources inline. That citation list is a gift. It tells you exactly which pages the model trusts about your market.
- Copilot sits on Bing’s index, which behaves differently from Google’s. Properties that look invisible in Gemini sometimes show up fine here, and vice versa.
A practical note: use fresh chats or temporary chats for each run so your own history does not bias the answers. I also avoid logging in to a personalized account where I can, because I want the model’s default answer, not the answer it tailors to my browsing. Run the prompt, read the full response, and capture it before moving on.
Step 3: Log mentions and sentiment in a tracker
Here is the part that turns this from a curiosity into a management tool. Every result goes into a tracker. Prompts down the rows, engines across the columns, one tab per month (or a date column if you prefer one long sheet).
For each prompt-and-engine cell I record three things:
- Mentioned? Yes or no. Did your hotel get named at all?
- Position. Were you first in the list, buried at number seven, or mentioned only as an afterthought? “Named” and “named first” are very different outcomes.
- Sentiment. Positive, neutral, or negative. Did it call you “a charming boutique stay” or “a basic budget option with dated rooms”? Sentiment is where the real story hides.
Here is the shape of the tracker I use:
| Prompt | Engine | Mentioned | Position | Sentiment | Notes / Sources |
|---|---|---|---|---|---|
| Best boutique hotels in [City] | ChatGPT | Yes | 3rd | Positive | Cited our site + a travel blog |
| Best boutique hotels in [City] | Gemini | No | n/a | n/a | Listed 2 OTAs, not us |
| Pet-friendly hotels near [landmark] | Perplexity | Yes | 1st | Positive | Cited our amenities page |
| Is [Hotel] a good place to stay? | Copilot | Yes | n/a | Neutral | Vague, pulled from old reviews |
Those numbers are illustrative, not a real audit. But the structure is exactly what I use. After two or three months you stop reading individual cells and start reading patterns, and the patterns are where the money is.
Step 4: Read the patterns, not the cells
Once you have a few months logged, step back and look at the whole grid. A few things I look for every single time:
Where am I invisible across the board? If every engine names competitors for “best boutique hotels in [City]” and never you, that is not an AI problem, that is a content-and-authority problem. The models are reading the open web and not finding enough about you to feel confident recommending you. This is usually the single most valuable thing the routine surfaces.
Where is sentiment wrong? Sometimes you get mentioned but the model describes you with a stale or unfair frame. “Dated rooms” three years after a renovation means the model is reading old reviews and old third-party descriptions. That is a content and reputation fix, and it is fixable.
Where do the engines disagree? If Perplexity loves you and Gemini ignores you, the source list usually explains why. Perplexity is citing your site; Gemini is leaning on your Google Business Profile, which may be thin. The disagreement points straight at the gap.
What sources keep getting cited? This is gold. If the same regional blog or “best of” list shows up again and again in the citations, you now have a target. Getting mentioned on the pages the models already trust is one of the most direct ways to improve your odds, which is exactly the logic behind earning brand mentions in LLMs and PR and authority links.
The single most useful column in my whole tracker is not “mentioned” or “sentiment.” It is the source list from Perplexity. The pages the model cites about your town are a literal to-do list of where you need to show up next.
Step 5: Tie the data to actual work
Measurement without action is just anxiety with a spreadsheet. Each month, after I read the patterns, I pull out two or three concrete moves. Not twenty. Two or three. For example:
- The model keeps citing a “best hotels in [City]” listicle you are not on. Action: pitch to be added or get a credible mention near it.
- Sentiment is stale. Action: refresh the on-site descriptions and amenities pages, and work the reputation angle so newer reviews carry the weight.
- You are invisible for an attribute you actually offer (say, pet-friendly). Action: make that explicit on your site and in your Google Business Profile so the models have something to read.
Then you log the action with a date, and next month you watch the relevant cells. Did the “pet-friendly” prompts start naming you after you fixed the amenities page? That feedback loop is the entire point. It is also how this connects to the wider goal: better AI and search visibility feeds the direct-booking engine that claws back margin from the OTAs.
A realistic word on timelines and promises
I am not going to tell you to run this routine for one month and watch ChatGPT crown you the best hotel in town. That is not how any of this works, and anyone promising a guaranteed AI ranking is lying to you.
What is true: AI models read the same web that search engines do, plus reviews, plus structured data, plus third-party mentions. When you improve those inputs steadily, your odds of being recommended go up. But models retrain on their own schedule, citations shift, and competitors are working too. Expect to see meaningful movement over a few months of consistent effort, not days. The routine does not control the output. It makes the inputs visible so you can improve them deliberately instead of guessing.
And to be clear about scope: this work helps you win back a healthier share of direct bookings and reduce how dependent you are on the OTAs taking their 15 to 25 percent cut. It does not let you walk away from the OTAs entirely, and I would be suspicious of anyone who frames it that way. A better OTA mix is the realistic, durable win.
The whole routine in one breath
Here is the compressed version you can pin above your desk:
- Freeze a set of 12 to 20 prompts across branded, category, attribute, and comparison buckets.
- Run every prompt through ChatGPT, Gemini, Perplexity, and Copilot in fresh chats.
- Log mentioned, position, sentiment, and sources in a tracker, one snapshot per month.
- Read patterns, not cells: where you are invisible, where sentiment is wrong, where engines disagree, which sources keep getting cited.
- Pick two or three concrete actions, date them, and check the result next cycle.
That is it. An hour or two a month gives you something most independent hotels simply do not have: a measurable, honest read on how the AI engines describe you, and a list of what to fix next. If you want the deeper context on why this matters, my pieces on whether your hotel is invisible to ChatGPT and the 2026 hotel SEO starter guide pair nicely with this one.
Want me to run the first one with you?
If setting up a 20-prompt tracker across four engines sounds like a project you will start and never finish, that is exactly the kind of thing I do for properties day in and day out. Book a free intro call at /book and I will run a first AI-visibility audit on your hotel, show you the tracker live, and tell you the two or three things actually dragging your odds down. No guaranteed rankings, just an honest baseline and a plan.