Original research · 2026-07 edition

AI SEO Statistics: Travel Agency (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

See how AI talks about YOUR businessFull data table

The question bank

The questions we tested — sampled from real buyer journeys in travel agency.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

Is it actually cheaper to use a travel agent for a honeymoon or should I just book everything myself on a travel site?
What kind of service fees do travel agents typically charge for planning a multi-country European itinerary?
I'm overwhelmed by all the resort options in the Caribbean; can a travel advisor help me find a place that isn't a tourist trap?
What are the specific benefits of booking through a travel agent versus a credit card portal?
How can I tell if a travel agent is actually an expert in a specific region like Southeast Asia or just reading from a brochure?
Are there travel agents who specialize in finding accessible hotels and transportation for travelers with mobility issues?
Do travel agents get access to room upgrades and free breakfasts that I can't get by booking directly with the hotel?
Is it worth hiring a travel planner for a trip with a $5,000 budget or are they only for luxury travelers?
Show all 15 questions
What happens if my flight gets cancelled and I booked through an agent—do they handle the rebooking or do I still have to call the airline?
What are some red flags that a travel agency might be a scam or just a middleman with no real experience?
I'm planning a destination wedding for 40 people; should I hire a group travel specialist or try to manage the room blocks myself?
How do I find a travel agent who is part of a major network like Virtuoso to get extra perks?
Can a travel agent help me get a refund on a non-refundable hotel booking if there's a family emergency?
What questions should I ask during a consultation to see if a travel advisor is the right fit for my family's style?
Is it better to use a local travel agency I can visit in person or a highly-rated one that operates entirely online?

Model by model

26-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 travel agency buyers.

Behavior rates across 15 travel agency buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional53%80%73%73%
Suggests DIY first13%7%0%80%
Names specific providers33%47%33%47%
Gives price or cost info7%27%40%60%
Tells to check reviews47%33%0%53%
Tells to verify credentials40%27%7%53%
Mentions case studies / portfolio27%13%13%73%
Mentions local proximity33%7%7%73%
Gives selection criteria53%53%33%47%
Warns about red flags27%13%20%73%
Asks a clarifying question47%47%0%33%
Recommends multiple quotes27%0%0%73%

By model

How each assistant handled Travel Agency questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same travel agency questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 80% (Claude) down to 53.3% (ChatGPT), a 27-point gap on an identical question set.

Across the 15 travel agency answers it produced, ChatGPT recommended hiring a professional in 53.3% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 33.3% of answers (about 1.1 distinct providers per answer) and included price or cost information 6.7% of the time. ChatGPT asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 40%, averaging 527 words per answer. On the remaining cues it told the buyer to check reviews in 46.7%, pointed to case studies or a portfolio in 26.7%, and framed the choice around local proximity in 33.3%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 26.7%.

Across the 15 travel agency answers it produced, Claude recommended hiring a professional in 80% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 46.7% of answers (about 1.1 distinct providers per answer) and included price or cost information 26.7% of the time. Claude asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 26.7%, averaging 298 words per answer. On the remaining cues it told the buyer to check reviews in 33.3%, pointed to case studies or a portfolio in 13.3%, and framed the choice around local proximity in 6.7%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 travel agency answers it produced, Gemini recommended hiring a professional in 73.3% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 33.3% of answers (about 0.9 distinct providers per answer) and included price or cost information 40% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 6.7%, averaging 246 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 13.3%, and framed the choice around local proximity in 6.7%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, Claude is the assistant most likely to route a travel agency buyer to a professional (80%) and ChatGPT the least (53.3%). ChatGPT produced the longest answers, at 527 words on average. Specific providers were named most often by Claude (46.7%) — 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 25.6 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a travel agency buyer happens to ask matters most:

  • Tells the buyer to check reviews: from 0% (Gemini) to 46.7% (ChatGPT) — a 47-point spread.
  • Asks a clarifying question: from 0% (Gemini) to 46.7% (ChatGPT) — a 47-point spread.
  • Gives price or cost information: from 6.7% (ChatGPT) to 40% (Gemini) — a 33-point spread.
  • Tells the buyer to verify credentials: from 6.7% (Gemini) to 40% (ChatGPT) — a 33-point spread.
  • Recommends hiring a professional: from 53.3% (ChatGPT) to 80% (Claude) — a 27-point spread.

The widest single gap — tells the buyer to check reviews, 47 points — means a travel agency 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 travel agency market.

Where they agree

The points of near-consensus in Travel Agency.

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

  • Suggests a DIY approach first: 0%–13.3% across all three (a 13-point spread).
  • Names a specific provider: 33.3%–46.7% across all three (a 13-point spread).
  • Mentions case studies or portfolio: 13.3%–26.7% across all three (a 13-point spread).
  • Warns about red flags or scams: 13.3%–26.7% across all three (a 13-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "suggests a DIY approach first" (identical coding in 80% of questions) and least consistently on "asks a clarifying question" (33.3%).

Every behavior, measured

All twelve coded behaviors for Travel Agency, averaged across the three models.

The behaviors AI models reproduce most often for travel agency are recommends hiring a professional (68.9% on average), gives selection criteria (46.6%) and names a specific provider (37.8%); the rarest are suggests a DIY approach first (6.7%), recommends multiple quotes (8.9%) and mentions local proximity (15.6%). Each figure below is the share of a model's 15 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:

  • Recommends hiring a professional: 68.9% on average (ChatGPT 53.3%, Claude 80%, Gemini 73.3%) — a 27-point spread.
  • Gives selection criteria: 46.6% on average (ChatGPT 53.3%, Claude 53.3%, Gemini 33.3%) — a 20-point spread.
  • Names a specific provider: 37.8% on average (ChatGPT 33.3%, Claude 46.7%, Gemini 33.3%) — a 13-point spread.
  • Asks a clarifying question: 31.1% on average (ChatGPT 46.7%, Claude 46.7%, Gemini 0%) — a 47-point spread.
  • Tells the buyer to check reviews: 26.7% on average (ChatGPT 46.7%, Claude 33.3%, Gemini 0%) — a 47-point spread.
  • Gives price or cost information: 24.5% on average (ChatGPT 6.7%, Claude 26.7%, Gemini 40%) — a 33-point spread.
  • Tells the buyer to verify credentials: 24.5% on average (ChatGPT 40%, Claude 26.7%, Gemini 6.7%) — a 33-point spread.
  • Warns about red flags or scams: 20% on average (ChatGPT 26.7%, Claude 13.3%, Gemini 20%) — a 13-point spread.
  • Mentions case studies or portfolio: 17.8% on average (ChatGPT 26.7%, Claude 13.3%, Gemini 13.3%) — a 13-point spread.
  • Mentions local proximity: 15.6% on average (ChatGPT 33.3%, Claude 6.7%, Gemini 6.7%) — a 27-point spread.
  • Recommends multiple quotes: 8.9% on average (ChatGPT 26.7%, Claude 0%, Gemini 0%) — a 27-point spread.
  • Suggests a DIY approach first: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%) — a 13-point spread.

Trust signals

How well the models protect the travel agency buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the travel agency buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 26.7% of answers on average. Verifying credentials or certifications appeared in 24.5%. Warning about red flags or scams appeared in 20%.

On structuring the decision, a selection-criteria checklist showed up in 46.6% of answers on average and a recommendation to gather multiple quotes in 8.9%. The single least-reproduced protective signal for travel agency is "recommends multiple quotes" at 8.9% 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 Travel Agency providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 travel agency answers, a specific provider was named in 37.8% of responses on average — roughly 1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for travel agency: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Travel Agency questions cover.

The 15 questions behind every percentage on this page were drawn from real travel agency (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 travel agency 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 15 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-04, the figures describe this specific travel agency 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.

15 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 2026-07-04, 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 →