AI SEO Statistics: Outdoor Industry (2026-07 edition)
40 questions · 120/120 expected AI responses · 3 models · measured 2026-07-06
Apply these findings to your outdoor industry SEO strategy.
This benchmark explains how AI assistants advise buyers. The related service page turns those findings into the technical, content, authority, and conversion priorities for this market.
The question bank
The questions we tested: a frozen buyer-intent benchmark for outdoor industry.
The question set was curated from a predefined buyer-intent taxonomy and held constant for this edition. Each model received the same wording. These are the prompts behind every percentage on this page.
Show all 40 questions
Model by model
25.8% question-level model disagreement.
This rate is the average pairwise disagreement between binary behavior codes across questions and behaviors. It is not the gap between the highest and lowest aggregated model percentages.
Behavior prevalence across 40 outdoor industry benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 95% | 75% | 65% | 78.3% |
| Suggests DIY first | 10% | 10% | 2.5% | 7.5% |
| Names specific providers | 0% | 5% | 10% | 5% |
| Gives price or cost info | 30% | 22.5% | 25% | 25.8% |
| Tells to check reviews | 22.5% | 22.5% | 0% | 15% |
| Tells to verify credentials | 40% | 45% | 20% | 35% |
| Mentions case studies / portfolio | 35% | 20% | 2.5% | 19.2% |
| Mentions local proximity | 45% | 42.5% | 15% | 34.2% |
| Gives selection criteria | 52.5% | 50% | 47.5% | 50% |
| Warns about red flags | 25% | 30% | 20% | 25% |
| Asks a clarifying question | 52.5% | 57.5% | 0% | 36.7% |
| Recommends multiple quotes | 30% | 17.5% | 0% | 15.8% |
Question-level agreement
How often all measured models received the same binary code.
Agreement is calculated question by question for each behavior. A high value can coexist with a low behavior prevalence; it means the models usually agreed on whether the behavior appeared.
All-model binary agreement by behavior across 40 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 62.5% |
| Suggests DIY first | 87.5% |
| Names specific providers | 85% |
| Gives price or cost info | 75% |
| Tells to check reviews | 65% |
| Tells to verify credentials | 52.5% |
| Mentions case studies / portfolio | 57.5% |
| Mentions local proximity | 52.5% |
| Gives selection criteria | 40% |
| Warns about red flags | 65% |
| Asks a clarifying question | 27.5% |
| Recommends multiple quotes | 65% |
By model
How each assistant handled Outdoor Industry questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same outdoor industry questions. On the most consequential behavior, whether to send the buyer to a professional at all, the rate ranged from 95% (ChatGPT) down to 65% (Gemini), a 30-point gap on an identical question set.
Across the 40 outdoor industry answers it produced, ChatGPT recommended hiring a professional in 95% of them and suggested a DIY approach first 10% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 30% of the time. ChatGPT asked a clarifying question before answering in 52.5% of cases, warned about red flags or scams in 25%, and told the buyer to verify credentials in 40%, averaging 618 words per answer. On the remaining cues it told the buyer to check reviews in 22.5%, pointed to case studies or a portfolio in 35%, and framed the choice around local proximity in 45%; a selection-criteria checklist appeared in 52.5% of its answers and a recommendation to gather multiple quotes in 30%.
Across the 40 outdoor industry answers it produced, Claude recommended hiring a professional in 75% of them and suggested a DIY approach first 10% of the time. It named a specific provider in 5% of answers (about 0.2 distinct providers per answer) and included price or cost information 22.5% of the time. Claude asked a clarifying question before answering in 57.5% of cases, warned about red flags or scams in 30%, and told the buyer to verify credentials in 45%, averaging 311 words per answer. On the remaining cues it told the buyer to check reviews in 22.5%, pointed to case studies or a portfolio in 20%, and framed the choice around local proximity in 42.5%; a selection-criteria checklist appeared in 50% of its answers and a recommendation to gather multiple quotes in 17.5%.
Across the 40 outdoor industry answers it produced, Gemini recommended hiring a professional in 65% of them and suggested a DIY approach first 2.5% of the time. It named a specific provider in 10% of answers (about 0.2 distinct providers per answer) and included price or cost information 25% 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 20%, averaging 251 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 15%; a selection-criteria checklist appeared in 47.5% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a buyer researching outdoor industry toward professional help (95%) and Gemini the least (65%). ChatGPT produced the longest answers, at 618 words on average. Specific providers were named most often by Gemini (10%). Even there, roughly one answer in 10 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 25.8%. This is the average pairwise rate at which models received different binary codes across questions and behaviors. The observed rate spreads below are a separate measure showing where the choice of assistant matters most for a buyer researching outdoor industry:
- Asks a clarifying question: from 0% (Gemini) to 57.5% (Claude). The spread is 58 points.
- Mentions case studies or portfolio: from 2.5% (Gemini) to 35% (ChatGPT). The spread is 33 points.
- Recommends hiring a professional: from 65% (Gemini) to 95% (ChatGPT). The spread is 30 points.
- Mentions local proximity: from 15% (Gemini) to 45% (ChatGPT). The spread is 30 points.
- Recommends multiple quotes: from 0% (Gemini) to 30% (ChatGPT). The spread is 30 points.
The widest single gap concerns asks a clarifying question at 58 points. This means a buyer researching outdoor industry 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 outdoor industry market.
Where they agree
The points of near-consensus in Outdoor Industry.
On other behaviors the three models move almost in lockstep. The points of near-consensus for outdoor industry, where all three landed within a few points of each other:
- Gives selection criteria: 47.5%–52.5% across all three (a 5-point spread).
- Suggests a DIY approach first: 2.5%–10% across all three (a 8-point spread).
- Gives price or cost information: 22.5%–30% across all three (a 8-point spread).
- Names a specific provider: 0%–10% across all three (a 10-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 87.5% of questions) and least consistently on "asks a clarifying question" (27.5%).
Every behavior, measured
All twelve coded behaviors for Outdoor Industry, averaged across the three models.
The behaviors AI models reproduce most often for outdoor industry are recommends hiring a professional (78.3% on average), gives selection criteria (50%) and asks a clarifying question (36.7%); the rarest are names a specific provider (5%), suggests a DIY approach first (7.5%) and tells the buyer to check reviews (15%). 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:
- Recommends hiring a professional: 78.3% on average (ChatGPT 95%, Claude 75%, Gemini 65%). The spread is 30 points.
- Gives selection criteria: 50% on average (ChatGPT 52.5%, Claude 50%, Gemini 47.5%). The spread is 5 points.
- Asks a clarifying question: 36.7% on average (ChatGPT 52.5%, Claude 57.5%, Gemini 0%). The spread is 58 points.
- Tells the buyer to verify credentials: 35% on average (ChatGPT 40%, Claude 45%, Gemini 20%). The spread is 25 points.
- Mentions local proximity: 34.2% on average (ChatGPT 45%, Claude 42.5%, Gemini 15%). The spread is 30 points.
- Gives price or cost information: 25.8% on average (ChatGPT 30%, Claude 22.5%, Gemini 25%). The spread is 8 points.
- Warns about red flags or scams: 25% on average (ChatGPT 25%, Claude 30%, Gemini 20%). The spread is 10 points.
- Mentions case studies or portfolio: 19.2% on average (ChatGPT 35%, Claude 20%, Gemini 2.5%). The spread is 33 points.
- Recommends multiple quotes: 15.8% on average (ChatGPT 30%, Claude 17.5%, Gemini 0%). The spread is 30 points.
- Tells the buyer to check reviews: 15% on average (ChatGPT 22.5%, Claude 22.5%, Gemini 0%). The spread is 23 points.
- Suggests a DIY approach first: 7.5% on average (ChatGPT 10%, Claude 10%, Gemini 2.5%). The spread is 8 points.
- Names a specific provider: 5% on average (ChatGPT 0%, Claude 5%, Gemini 10%). The spread is 10 points.
Trust signals
How well the models protect the outdoor industry buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the outdoor industry buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 15% of answers on average. Verifying credentials or certifications appeared in 35%. Warning about red flags or scams appeared in 25%.
On structuring the decision, a selection-criteria checklist showed up in 50% of answers on average and a recommendation to gather multiple quotes in 15.8%. The single least-reproduced protective signal for outdoor industry is "tells the buyer to check reviews" at 15% on average. This is 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 Outdoor Industry providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 outdoor industry answers, a specific provider was named in 5% of responses on average, or roughly 0.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for outdoor industry: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Outdoor Industry questions cover.
The 40 questions behind every percentage on this page form a frozen outdoor industry (professional services; buyer hiring decisions for this specific service) buyer-intent benchmark. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact outdoor industry question set rather than 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. It is not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific outdoor industry 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 frozen benchmark questions, one expected response per model per question (ChatGPT API (gpt-5-mini), Claude API (claude-sonnet-5), Gemini API (gemini-3-flash-preview)), collected 2026-07-06 and coded against a fixed 12-behavior rubric. The pipeline validates the schema, recomputes aggregates and reports consistency issues. AI outputs vary with model version, location and time, so the figures describe this edition's exact sample and measurement window. Read the full methodology →
Citation
Cite this edition.
Authority Specialist. “AI SEO Statistics: Outdoor Industry (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/professional/outdoor-industry