Is LinkedIn useful forAI visibility in hotel search?
TL;DR: Across 105,377 hotel-search captures with citation data, LinkedIn shows up in just 197 of them (0.19%) — but that number hides three very different stories. ChatGPT cites LinkedIn roughly 60% of the times it even sees it, almost always a hotel's own company page. Grok sees LinkedIn far more often (122 captures) but mostly as social posts it retrieves and then ignores. Perplexity, Copilot, and Gemini barely touch it — Gemini never once in this dataset. LinkedIn's occasional usefulness depends entirely on which model, which content type, and whether you're counting what the model saw or what it actually cited.
Executive Summary
Rare overall. Not rare everywhere. Not the same thing once it shows up.
We added a dedicated linkedin_count column to our AI hotel-search capture pipeline and backfilled it across every stored capture: 105,377 hotel-search answers with citation data, going back to December 2025. LinkedIn appears in 197 of them — a real signal, not zero, but thin enough that a single small sample would have missed most of the story (an earlier 400-row-per-engine check found essentially nothing; the full backfill found six times that in absolute count once every engine and every historical week was included).
The headline 0.19% number is the least interesting part. What matters is that it is not one phenomenon: it is a ChatGPT phenomenon (company pages, cited more often than not), a Grok phenomenon (social posts, retrieved constantly and cited rarely), and a non-event on three other engines. Anyone deciding whether a hotel's LinkedIn presence is worth maintaining for AI visibility needs the breakdown, not the average.
“LinkedIn shows up” and “LinkedIn gets cited” are different claims
Two engines in this corpus (ChatGPT, Google AI Mode) expose a broader retrieved pool separately from the sources that actually make it into the visible answer. Grok exposes the same split, structurally. Perplexity and Copilot don't — everything their API surfaces to us is already labeled cited, so there's no way to tell what they saw and rejected. Treat their 100% conversion rate below as a measurement ceiling, not a behavior.
| Engine | Retrieved, not cited | Cited | Conversion rate |
|---|---|---|---|
| ChatGPT | 49 | 73 | 59.8% |
| Grok | 147 | 27 | 15.5% |
| Google AI Mode | 21 | 3 | 12.5% |
| Copilot | not exposed | 9 | 100% (structural ceiling) |
| Perplexity | not exposed | 3 | 100% (structural ceiling) |
| Gemini | 0 | 0 | — |
It depends which engine — and which LinkedIn
LinkedIn is not one kind of page. Company pages, long-form “Pulse” articles, and ordinary social posts get pulled in at completely different rates depending on the engine.
| Engine | Company pages | Pulse articles | Social posts | Total rows |
|---|---|---|---|---|
| ChatGPT | 96 | 25 | 1 | 122 |
| Grok | 60 | 33 | 79 | 174 |
| Google AI Mode | 5 | 13 | 5 (+1 personal profile) | 24 |
| Copilot | 1 | 0 | 8 | 9 |
| Perplexity | 0 | 0 | 3 | 3 |
ChatGPT: company-page citer
96 of ChatGPT's 122 LinkedIn rows (79%) are company pages — a specific hotel's own LinkedIn profile. Example: a “luxury hotels in Zermatt” query cited linkedin.com/company/cervo-mountain-resort directly. ChatGPT treats LinkedIn mostly as a business directory, not a content source, and almost never cites a bare social post (1 out of 122).
Grok: social-feed miner
79 of Grok's 174 LinkedIn rows (45%) are ordinary posts — hotel brand announcements, guest reviews, even a stranger's opinion post on “Michelin vs. Forbes Travel Guide” that surfaced twice for unrelated hotel queries. Grok retrieves LinkedIn like a social platform (which tracks with how it treats Reddit and X/Twitter elsewhere in this corpus), but converts it to an actual citation only 1 time in 6.
What kind of query surfaces LinkedIn
Every LinkedIn citation we found traces back to a broad, destination-level discovery prompt — never a query naming a specific hotel. Representative examples pulled directly from the data:
| Query | Engine | Cited? |
|---|---|---|
| luxury hotels in Zermatt | ChatGPT | Yes |
| best hotels in Amalfi Coast | ChatGPT | Yes |
| hotels near Matterhorn, Zermatt | Copilot | Yes |
| luxury hotels in Beverly Hills | Grok | Yes |
| design hotels in SoHo New York | ChatGPT | No (retrieved only) |
| best hotels in Maldives for business travelers | Google AI Mode | No (retrieved only) |
Methodology
Corpus. 105,377 AI hotel-search captures with non-null citation data, all-time (December 2025 onward), across six engines: ChatGPT, Perplexity, Copilot, Gemini, Google AI Mode, and Grok. Every prompt is a destination-level discovery query — “best/luxury/affordable hotels in [city]”, optionally modified by traveler type or amenity.
Column. A new linkedin_count column was added to the capture table (mirroring existing youtube_count/reddit_count/facebook_count columns), matching citations against the linkedin.com domain, and backfilled across every stored capture with citation data. 197 captures have a non-zero count.
Sources vs. citations detail. The per-engine retrieved-vs-cited split in Section 1 comes from the underlying flattened citation table, which additionally records a cited boolean per source row for engines whose raw payload distinguishes a retrieval pool from the final answer (ChatGPT, Google AI Mode, Grok). Perplexity and Copilot's APIs only ever expose the already-cited subset, so their conversion rate is reported as a structural ceiling, not a measured behavior.
Content-type classification. Each LinkedIn URL was classified by path pattern: /company/ (business page), /pulse/ (long-form article), /posts/ (social post), /in/ (personal profile), /jobs/ (job listing). 183 distinct URLs across 332 citation rows; a handful of URLs recur across multiple captures or multiple positions within the same capture.
Access. The column addition and backfill were reviewed before being applied; the backfill itself was a straightforward re-read of already-stored citation data (no re-scraping, no new captures).
FAQ
Every number here is from our own AI hotel-search captures
105,377 captures, all-time, all engines — CC-BY-4.0. Using this data? A citation or link back to nicolassitter.com is always appreciated.