From afterthought to first recommendation
- Client
- The Canal House Hotel
- Market
- Amsterdam, Netherlands
A sample audit of The Canal House Hotel, a Leading Hotels of the World member in Amsterdam — Michelin-starred dining, decades of brand authority — measured against 60 real traveler queries across ChatGPT, Claude, Perplexity, and Gemini. Property details anonymized; methodology and findings intact.
GEO Visibility Score: 36.7 / 100
Status: Afterthought. Branded authority is high, but generic recommendation visibility is structurally absent across the major LLMs. AI confidently endorses the hotel to guests who ask about it by name — and almost never surfaces it to guests who don’t.
The four decision moments
| Moment | Score | Pattern |
|---|---|---|
| Discovery | 25.0 | Zero indexing of the hotel’s unique positioning language |
| Recommendation | 25.0 | Absent from generic “where to stay” queries despite stronger credentials than appearing competitors |
| Comparison | 38.3 | Perfect branded performance (100), weak generic (22.9) — known but not recommended |
| Trust | 85.4 | Strong validation authority for guests who already know the brand |
Platform mention rates
- ChatGPT: 20.0% (12 of 60 responses)
- Claude: 33.3% (20 of 60)
- Perplexity: 41.7% (25 of 60)
- Gemini: 63.3% (38 of 60)
The spread is diagnostic. Gemini surfaces the hotel often because Google’s own index picks up structured content; ChatGPT can’t extract the same content because it’s not in the right schema for ChatGPT’s primary data source. The visibility problem isn’t content — it’s structure.
What we found
- A competing flagship captured 57 displacements across 19 territories where the hotel should have been competitive.
- 16 zero-mention queries clustered around recommendation moments — the exact point where booking decisions get made.
- The amenities page covered four gap territories but wasn’t being indexed by AI platforms for recommendation extraction.
- Dining was the exception: fine-dining queries surfaced the hotel on Claude, Perplexity, and Gemini — proof that when the content foundation is right, the platforms cite it correctly.
The core pattern
“Strong validation authority but weak discovery optimization — the hotel validates well but doesn’t surface for initial consideration.”
A structural indexing gap, not a content gap. The 90-day execution plan reorganized existing assets into schema and positioning language AI platforms can extract — and prioritized ChatGPT-specific fixes where the gap was widest.