A practical framework for measuring hotel visibility across ChatGPT, Google AI Overviews, and Perplexity using repeated prompts, citations, and honest uncertainty.
The previous article in this series argued that a single AI search result cannot describe a hotel's visibility. This one is about what to do instead.
Visibility is a pattern. It is how often the property appears across relevant traveler questions, how consistently it appears across repeated runs, what the answer says, which sources support it, and whether the cited path leads to the hotel or an intermediary.
That pattern must be measured separately in ChatGPT, Google AI Overviews, and Perplexity. The surfaces may answer the same traveler question differently because they retrieve, generate, and present information differently.
A useful hotel AI visibility program does not ask, "Did we show up?" It asks, "For which traveler needs did we appear, how often, with what evidence, and how stable was the result?"
What does hotel AI visibility mean?
Hotel AI visibility is the frequency and quality with which a property appears in AI-generated travel answers for relevant traveler needs.
It has at least four parts:
- Presence: Was the hotel mentioned or recommended?
- Fit: Was it associated with the traveler need being tested?
- Evidence: Which pages, listings, reviews, or other sources supported the answer?
- Path: Did the answer give the traveler a usable route to the hotel's own website, or only to an intermediary?
These parts should not be collapsed into one unexplained score.
A hotel can be mentioned frequently but described inaccurately. It can be recommended for the right use case but supported only by third-party pages. It can be cited directly but appear inconsistently. Those are different visibility profiles, and each suggests a different action.
The goal is not to manufacture certainty. It is to make the uncertainty legible enough for a commercial team to act.
Why should each AI search surface be measured separately?
The same prompt does not produce a directly comparable experience everywhere.
Google describes AI Overviews as generated snapshots that appear when its systems determine generative AI may be useful. They include links to supporting information, but an AI Overview does not appear for every search.
OpenAI explains that ChatGPT Search may rewrite a user's question into one or more targeted searches. Responses can include inline citations and a Sources panel, and location may influence local recommendations.
Perplexity explains that it searches the live web and provides numbered citations linking to original sources.
These interfaces expose different evidence and may use different retrieval paths. A hotel that appears in one surface but not another has not produced a contradiction. It has produced a cross-platform difference worth investigating.
Research accepted to ACM SIGIR 2026 compared Google Search, Google AI Overviews, and Gemini across 11,500 queries. The researchers found low overlap among retrieved sources and lower consistency across repeated generative runs than traditional search. The study did not test ChatGPT or Perplexity, so its findings should not be generalized to every platform. It does support one practical rule: one search surface should not be used as a proxy for another.
Measure each surface independently, then compare the patterns.
What traveler questions should a hotel test?
A prompt set should represent real demand, not only the hotel's brand name.
Branded prompts are useful for checking factual accuracy. They show whether the system understands the property's location, room types, amenities, policies, and positioning. They do not show whether the property enters an unbranded traveler's consideration set.
A balanced prompt portfolio should include several intent types:
| Prompt family | Commercial question | Example structure |
|---|---|---|
| Destination discovery | Does the hotel enter the regional consideration set? | "Where should a couple stay in [destination] for a quiet beachfront trip?" |
| Need-based discovery | Is the property associated with a specific traveler need? | "Which hotels in [destination] have connecting rooms and activities for children?" |
| Occasion | Does the hotel appear for high-value trip purposes? | "Best hotels in [destination] for a small destination wedding" |
| Constraint | Can the system verify a practical requirement? | "Hotels near [landmark] with reliable Wi-Fi and late arrival" |
| Comparison | How is the property framed beside alternatives? | "Which is better for [need], [property] or other hotels in [market]?" |
| Branded fact check | Is public information accurate and current? | "Does [property] offer [room, policy, amenity, or experience]?" |
The exact prompts should come from the demand the hotel wants to win. A resort, an airport hotel, and an urban group property should not inherit the same list.
This is also the first point where the two halves of the problem separate. A prompt portfolio built from a marketing team's assumptions tests how the hotel describes itself. A prompt portfolio built from calls, emails, chats, RFPs, and reservation notes tests how guests actually describe what they want. Those are rarely the same language, and only one of them is evidence. The hotel that already structures its own guest interactions starts this exercise with a portfolio it did not have to guess at.
How should the test be controlled?
A useful benchmark preserves enough context to be repeated.
For every run, record:
- The exact prompt.
- The platform used.
- The date and time.
- The full answer, not only a screenshot of the hotel mention.
- Every cited source and destination link.
- The recommended properties and their order.
Session conditions also affect what a surface retrieves, so hold them constant across a baseline and note them in the report.
Do not change the wording halfway through a baseline. Small edits can change what the system retrieves and how it interprets the need.
A peer-reviewed EMNLP 2025 study found substantial sensitivity to meaning-preserving prompt variations and proposed repeated sampling as part of reliable evaluation. It is not a hotel benchmark, but it supports the measurement principle: prompt sensitivity and nondeterminism should be measured rather than ignored.
Keep the baseline prompt fixed. Test wording variants separately when the goal is to understand how travelers may phrase the same need.
How many times should each prompt be run?
There is no universal run count for every hotel, platform, prompt, and decision.
The right sample depends on how unstable the answer is and how consequential the decision will be. A factual check can require less sampling than a broad recommendation prompt. A monthly directional report can tolerate more uncertainty than a claim used to justify a major content or distribution investment.
Begin with several repeated runs per prompt and surface. Add samples when the result changes materially as new runs are included.
The stop condition should not be "we reached a round number." It should be "the distribution is stable enough for the decision we need to make."
Every report should disclose the sample size. A percentage without the underlying number of runs creates false precision.
Which hotel AI visibility metrics matter?
A useful report should separate visibility, evidence, accuracy, and stability.
| Metric | What it answers |
|---|---|
| Mention frequency | In what share of runs was the hotel named? |
| Recommendation frequency | In what share of runs was the hotel presented as a fit? |
| Position distribution | Where did the hotel tend to appear when listed? |
| Need association | Which traveler needs were attached to the property? |
| Citation frequency | Which domains repeatedly supported the answer? |
| Direct-site citation rate | How often did the hotel's own site appear as evidence? |
| Source diversity | Was the answer supported by one source or several source types? |
| Factual accuracy | Were room, policy, location, and amenity claims correct? |
Each of these is easy to misread on its own. A mention is not an endorsement, and a high mention rate can coexist with an unfavorable description. Recommendation frequency measures how a model frames the property, not whether a traveler clicked or booked. Position is a tendency across runs rather than a fixed rank. A need association can be confidently stated and still be wrong. A repeated citation shows which domains were present, not which ones drove the recommendation, and even a strong direct-site citation rate does not mean the hotel controlled the answer. More sources are not automatically better sources. And accuracy, however good, does not oblige any system to recommend the property.
Report the distribution for each prompt family and platform. A single blended score can hide the most useful difference.
For example, a property may be strong in branded fact checks, inconsistent in regional discovery, and absent for a specific family need. The action is not "improve AI visibility." The action is to correct or strengthen the evidence around that particular need.
How should hotels compare results over time?
A visibility baseline should be repeatable before it is frequent.
Use the same core prompt portfolio, platform conditions, run method, and reporting fields from one period to the next. Preserve enough historical detail to distinguish a true shift from normal answer variation.
Compare:
- Mention and recommendation distributions.
- Changes in cited domains.
- New or disappearing factual claims.
- Changes in direct-site citation.
- Changes in the properties appearing beside the hotel.
- Changes in how much the recommendation set moves across repeated runs.
When a result moves, investigate the evidence environment before declaring success or failure. A source page may have changed. A policy may now conflict across channels. A destination article may have been published. The platform itself may have changed how it searches or displays answers.
AI visibility is not a permanent rank. It is a changing information environment.
What should a hotel do with the findings?
Every finding should lead to a specific evidence question.
If the hotel is absent for a relevant need, ask whether the need is clearly documented on authoritative, crawlable pages.
If the hotel appears with an inaccurate policy, find the conflicting source and correct the underlying information.
If intermediaries are cited but the hotel's site is not, examine whether the direct site answers the traveler's question with enough specificity.
If the hotel appears only in unstable runs, treat the result as a lead rather than a win.
If the property is consistently recommended for a need that never appears in direct inquiries, investigate whether the outside perception matches actual demand.
That last comparison is where visibility becomes commercially useful.
What can an AI visibility report still not tell you?
Even a rigorous cross-platform report remains outside-in.
It can show how public systems describe and recommend the hotel. It can show the sources behind those answers and how stable the result is. It cannot show the guests who called, emailed, chatted, requested a proposal, hesitated, or booked.
That second body of evidence already exists inside the hotel, and it is the half nobody else can reconstruct. Every property generates a continuous record of what real travelers ask for, in their own words, before any model is involved. Two hotels can run an identical measurement program against the same prompt portfolio and reach different conclusions from it, because only one of them can check the outside-in picture against its own demand.
An outside-in report tells a hotel what the public information environment says. Its own interaction record tells it what the market actually wants. The gap between those two is the finding worth acting on: needs travelers ask about directly but no model associates with the property, and needs models confidently attach to the property that no guest has ever raised.
Anana measures AI visibility with repeated prompts and source-level evidence, and reads that outside-in result against the guest interactions the hotel already owns.
Measure ChatGPT, Google AI Overviews, and Perplexity separately. Preserve the distribution. Then ask the question no public model can answer on its own: who asked, and who booked?