Why the checklist matters
Free AI visibility audits for hotels are now common — from GEO platforms, marketing agencies, and email vendors alike. They are not equivalent. Some are single-run, unscored, qualitative reviews delivered days later; others are repeatable measurements you can hold a vendor accountable to. Before you act on any audit (or pay for what follows it), check what it actually contains.
The test that separates them is simple: could you re-run this audit next quarter and get a comparable number? If the answer is no, you have a set of observations, not a measurement — and no way to prove whether the work you paid for moved anything.
The checklist
- Repeat sampling — the same question asked multiple times per engine, with the variation measured. AI answers change between askings; a single-run audit cannot tell you whether what it saw was signal or luck.
- A scored result with rank and share of AI voice — a number on a defined scale, your position in your competitive set, and your share of mentions. Qualitative pattern notes cannot be tracked over time or compared against competitors.
- Verbatim evidence — the raw AI answers, quoted in full and timestamped, so every claim in the audit is checkable against the answer that produced it. Summaries of what the models 'tend to say' are not evidence.
- A per-engine breakdown — being the top answer in ChatGPT while invisible in Gemini is a different problem from being weak everywhere. Blended-only numbers hide exactly the finding you need.
- First-pick rate — how often each engine names you first, not just whether you appear. Travelers act on the first name far more than the fifth.
- The engines your guests actually use — for properties with Asian source markets, that includes DeepSeek, Qwen, GLM and Kimi. An audit that only tests Western engines is measuring someone else's guests.
- A confidence rating — an honest audit states what it could not measure (too few citations, no checkable claims, no repeats) instead of quietly estimating it. If every dimension always has a number, ask how.
- Cited-source analysis — which domains the engines leaned on for your market's answers. This is what turns a score into an action list: those domains are where presence must be earned.
- A published methodology and versioned scoring — so you can check how the number is computed, and so a model change is never silently presented as a visibility change.
- A re-audit path — a fixed question set and deterministic scoring that make next quarter's audit comparable to this one. The delta is the proof that the work paid.
Questions that expose a weak audit
Five questions, askable in one email, that separate measurement from theater:
- "How many times did you ask each question, and what was the variation between runs?" — single-run audits have no answer.
- "What is my score, my rank, and my share of AI voice — and against which competitive set?" — unscored audits have no answer.
- "Can I see the full verbatim answers, with timestamps?" — audits built on summaries have no answer.
- "Which dimensions could you not measure for my property, and how does that affect confidence in the number?" — audits that estimate missing data will dodge this.
- "If I re-run this in 90 days, what exactly is held constant so the two results are comparable?" — audits without a versioned method have no answer.
How Salience's audit maps to this checklist
Salience's free hotel audit was built to this standard: repeat sampling with a volatility measure, a six-dimension GEO Score with cohort rank and share of AI voice, verbatim timestamped answers stored immutably, per-engine scores with first-pick rates, coverage of eight engines including DeepSeek, Qwen, GLM and Kimi, a high/medium/low measurement-confidence rating on every score, cited-source analysis, and a published, versioned methodology so re-audits are comparable by construction.
The full specification — dimensions, weights, point ladders, confidence rules, and the score's stated limits — is public at salienceco.com/methodology.