AI SEO Statistics: Hospitality (2026-07 edition)

Reading the 81 answers model by model shows how differently the three assistants treat the same hospitality questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 22.2% (ChatGPT) down to 3.7% (Claude), a 19-point gap on an identical question set.

Across the 27 hospitality answers it produced, ChatGPT recommended hiring a professional in 22.2% of them and suggested a DIY approach first 22.2% of the time. It named a specific provider in 40.7% of answers (about 2.9 distinct providers per answer) and included price or cost information 18.5% of the time. ChatGPT asked a clarifying question before answering in 40.7% of cases, warned about red flags or scams in 3.7%, and told the buyer to verify credentials in 7.4%, averaging 604 words per answer. On the remaining cues it told the buyer to check reviews in 3.7%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 18.5%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 11.1%.

Across the 27 hospitality answers it produced, Claude recommended hiring a professional in 3.7% of them and suggested a DIY approach first 40.7% of the time. It named a specific provider in 55.6% of answers (about 3.7 distinct providers per answer) and included price or cost information 22.2% of the time. Claude asked a clarifying question before answering in 55.6% of cases, warned about red flags or scams in 7.4%, and told the buyer to verify credentials in 7.4%, averaging 284 words per answer. On the remaining cues it told the buyer to check reviews in 29.6%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 33.3%; a selection-criteria checklist appeared in 55.6% of its answers and a recommendation to gather multiple quotes in 7.4%.

Across the 27 hospitality answers it produced, Gemini recommended hiring a professional in 11.1% of them and suggested a DIY approach first 22.2% of the time. It named a specific provider in 63% of answers (about 2 distinct providers per answer) and included price or cost information 22.2% of the time. Gemini asked a clarifying question before answering in 3.7% of cases, warned about red flags or scams in 3.7%, and told the buyer to verify credentials in 0%, averaging 185 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 7.4%; a selection-criteria checklist appeared in 25.9% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a hospitality buyer to a professional (22.2%) and Claude the least (3.7%). ChatGPT produced the longest answers, at 604 words on average. Specific providers were named most often by Gemini (63%) — even there, roughly one answer in 2 carried a name.

On other behaviors the three models move almost in lockstep — the points of near-consensus for hospitality, where all three landed within a few points of each other:

• Mentions case studies or portfolio: 0% across all three models.
• Gives price or cost information: 18.5%–22.2% across all three (a 4-point spread).
• Warns about red flags or scams: 3.7%–7.4% across all three (a 4-point spread).
• Tells the buyer to verify credentials: 0%–7.4% across all three (a 7-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "mentions case studies or portfolio" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (37%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the hospitality buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 11.1% of answers on average. Verifying credentials or certifications appeared in 4.9%. Warning about red flags or scams appeared in 4.9%.

On structuring the decision, a selection-criteria checklist showed up in 38.3% of answers on average and a recommendation to gather multiple quotes in 6.2%. The single least-reproduced protective signal for hospitality is "tells the buyer to verify credentials" at 4.9% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

The 27 questions behind every percentage on this page were drawn from real hospitality (hotels, restaurants, travel, venues, catering) buyer journeys, expanded from 3 seed prompts. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact hospitality 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.