Travelers no longer just 'search' for hotels — they 'ask AI'. When a guest asks ChatGPT 'a boutique hotel near the Bund in Shanghai for couples', does your property get mentioned, get recommended first, and get described accurately? Hotel AI Visibility turns that question into a metric you can measure, track and improve.
Definition
Scoring dimensions
| Dimension | Weight | What it measures |
|---|---|---|
| D1 Brand authority | 12% | Overall brand recognition and consistency online |
| D2 Entity accuracy | 14% | Name / address / geo / type consistency and resolvability |
| D3 Citation coverage | 10% | How many credible sources cite you where AI can retrieve |
| D4 Review signal | 14% | Rating, review volume and sentiment |
| D5 Geo relevance | 16% | Geographic match to the queried landmark |
| D6 Intent fit | 12% | Fit under intents like couples / family / business |
| D7 Language coverage | 14% | Visibility under multilingual (CN / EN) queries |
| D8 Recency | 8% | How fresh your information is |
Weights are the default config of Hospitality Methodology v1.0, published and auditable.
Comparison table (sample, demo data)
| Hotel | AI Visibility | Share of Voice | Strongest intent |
|---|---|---|---|
| Your hotel (sample) | 37 | 46% | Family 67% |
| Competitor A (sample) | 64 | 92% | Luxury 88% |
| Competitor B (sample) | 62 | 77% | Business 81% |
| Competitor C (sample) | 64 | 100% | Couples / Romantic 90% |
Above is V1 demo data (deterministic market-level probe) to show the scoring model and comparison structure. Enter a real hotel name + city to generate your own snapshot.
Related reading
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