AI SEO Statistics: Luxury Travel (2026-07 edition)
40 questions · 120 AI responses · 3 models · measured 2026-07-06
The question bank
The questions we tested — sampled from real buyer journeys in luxury travel.
Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.
Show all 40 questions
Model by model
22-point average divergence: which AI you ask changes the answer.
The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about luxury travel buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 50% | 45% | 23% | 50% |
| Suggests DIY first | 15% | 20% | 8% | 78% |
| Names specific providers | 43% | 65% | 63% | 50% |
| Gives price or cost info | 25% | 18% | 18% | 75% |
| Tells to check reviews | 20% | 18% | 3% | 70% |
| Tells to verify credentials | 20% | 13% | 8% | 73% |
| Mentions case studies / portfolio | 15% | 0% | 0% | 85% |
| Mentions local proximity | 20% | 25% | 13% | 78% |
| Gives selection criteria | 43% | 58% | 23% | 38% |
| Warns about red flags | 8% | 8% | 5% | 85% |
| Asks a clarifying question | 50% | 73% | 3% | 23% |
| Recommends multiple quotes | 3% | 5% | 0% | 95% |
By model
How each assistant handled Luxury Travel questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same luxury travel questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 50% (ChatGPT) down to 22.5% (Gemini), a 28-point gap on an identical question set.
Across the 40 luxury travel answers it produced, ChatGPT recommended hiring a professional in 50% of them and suggested a DIY approach first 15% of the time. It named a specific provider in 42.5% of answers (about 2 distinct providers per answer) and included price or cost information 25% of the time. ChatGPT asked a clarifying question before answering in 50% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 20%, averaging 555 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 15%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 42.5% of its answers and a recommendation to gather multiple quotes in 2.5%.
Across the 40 luxury travel answers it produced, Claude recommended hiring a professional in 45% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 65% of answers (about 3.2 distinct providers per answer) and included price or cost information 17.5% of the time. Claude asked a clarifying question before answering in 72.5% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 12.5%, averaging 283 words per answer. On the remaining cues it told the buyer to check reviews in 17.5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 25%; a selection-criteria checklist appeared in 57.5% of its answers and a recommendation to gather multiple quotes in 5%.
Across the 40 luxury travel answers it produced, Gemini recommended hiring a professional in 22.5% of them and suggested a DIY approach first 7.5% of the time. It named a specific provider in 62.5% of answers (about 1.8 distinct providers per answer) and included price or cost information 17.5% of the time. Gemini asked a clarifying question before answering in 2.5% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 7.5%, averaging 191 words per answer. On the remaining cues it told the buyer to check reviews in 2.5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 12.5%; a selection-criteria checklist appeared in 22.5% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a luxury travel buyer to a professional (50%) and Gemini the least (22.5%). ChatGPT produced the longest answers, at 555 words on average. Specific providers were named most often by Claude (65%) — even there, roughly one answer in 2 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
The divergence index for this study is 22.4 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a luxury travel buyer happens to ask matters most:
- Asks a clarifying question: from 2.5% (Gemini) to 72.5% (Claude) — a 70-point spread.
- Gives selection criteria: from 22.5% (Gemini) to 57.5% (Claude) — a 35-point spread.
- Recommends hiring a professional: from 22.5% (Gemini) to 50% (ChatGPT) — a 28-point spread.
- Names a specific provider: from 42.5% (ChatGPT) to 65% (Claude) — a 23-point spread.
- Tells the buyer to check reviews: from 2.5% (Gemini) to 20% (ChatGPT) — a 18-point spread.
The widest single gap — asks a clarifying question, 70 points — means a luxury travel buyer can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the luxury travel market.
Where they agree
The points of near-consensus in Luxury Travel.
On other behaviors the three models move almost in lockstep — the points of near-consensus for luxury travel, where all three landed within a few points of each other:
- Warns about red flags or scams: 5%–7.5% across all three (a 3-point spread).
- Recommends multiple quotes: 0%–5% across all three (a 5-point spread).
- Gives price or cost information: 17.5%–25% across all three (a 8-point spread).
- Suggests a DIY approach first: 7.5%–20% across all three (a 13-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "recommends multiple quotes" (identical coding in 95% of questions) and least consistently on "asks a clarifying question" (22.5%).
Every behavior, measured
All twelve coded behaviors for Luxury Travel, averaged across the three models.
The behaviors AI models reproduce most often for luxury travel are names a specific provider (56.7% on average), asks a clarifying question (41.7%) and gives selection criteria (40.8%); the rarest are recommends multiple quotes (2.5%), mentions case studies or portfolio (5%) and warns about red flags or scams (6.7%). Each figure below is the share of a model's 40 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:
- Names a specific provider: 56.7% on average (ChatGPT 42.5%, Claude 65%, Gemini 62.5%) — a 23-point spread.
- Asks a clarifying question: 41.7% on average (ChatGPT 50%, Claude 72.5%, Gemini 2.5%) — a 70-point spread.
- Gives selection criteria: 40.8% on average (ChatGPT 42.5%, Claude 57.5%, Gemini 22.5%) — a 35-point spread.
- Recommends hiring a professional: 39.2% on average (ChatGPT 50%, Claude 45%, Gemini 22.5%) — a 28-point spread.
- Gives price or cost information: 20% on average (ChatGPT 25%, Claude 17.5%, Gemini 17.5%) — a 8-point spread.
- Mentions local proximity: 19.2% on average (ChatGPT 20%, Claude 25%, Gemini 12.5%) — a 13-point spread.
- Suggests a DIY approach first: 14.2% on average (ChatGPT 15%, Claude 20%, Gemini 7.5%) — a 13-point spread.
- Tells the buyer to check reviews: 13.3% on average (ChatGPT 20%, Claude 17.5%, Gemini 2.5%) — a 18-point spread.
- Tells the buyer to verify credentials: 13.3% on average (ChatGPT 20%, Claude 12.5%, Gemini 7.5%) — a 13-point spread.
- Warns about red flags or scams: 6.7% on average (ChatGPT 7.5%, Claude 7.5%, Gemini 5%) — a 3-point spread.
- Mentions case studies or portfolio: 5% on average (ChatGPT 15%, Claude 0%, Gemini 0%) — a 15-point spread.
- Recommends multiple quotes: 2.5% on average (ChatGPT 2.5%, Claude 5%, Gemini 0%) — a 5-point spread.
Trust signals
How well the models protect the luxury travel buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the luxury travel buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 13.3% of answers on average. Verifying credentials or certifications appeared in 13.3%. Warning about red flags or scams appeared in 6.7%.
On structuring the decision, a selection-criteria checklist showed up in 40.8% of answers on average and a recommendation to gather multiple quotes in 2.5%. The single least-reproduced protective signal for luxury travel is "recommends multiple quotes" at 2.5% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.
Referral behavior
Do AI models name Luxury Travel providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 luxury travel answers, a specific provider was named in 56.7% of responses on average — roughly 2.3 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for luxury travel: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Luxury Travel questions cover.
The 40 questions behind every percentage on this page were drawn from real luxury travel (hospitality; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact luxury travel question set — not a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.
How to read this
A note on the numbers.
A percentage here is the share of a model's 40 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific luxury travel question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.
Methodology
A controlled snapshot, documented end to end.
40 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-06, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →