Original research · 2026-07 edition

AI SEO Statistics: Awning (2026-07 edition)

40 questions · 120 AI responses · 3 models · measured 2026-07-06

See how AI talks about YOUR businessFull data table

The question bank

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

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

How much does it cost to install a retractable awning on a 12x10 deck?
Is it worth getting a motorized awning or should I just stick with a manual crank?
My west-facing patio gets way too hot in the afternoon, what kind of awning provides the most shade?
Can I install a retractable awning myself if I buy a kit from a big box store?
What are the pros and cons of acrylic vs. polyester fabric for outdoor awnings?
How do I know if my house siding can support the weight of a heavy retractable awning?
Do I need a permit to put a permanent metal awning over my front door?
What wind speed rating should I look for if I live in a gusty area?
Show all 40 questions
Are there awnings that automatically retract when it gets too windy?
How much cooler will my house stay if I install window awnings on the south side?
What's the average lifespan of a high-end fabric awning before it needs a replacement cover?
Is it cheaper to repair a broken arm on a retractable awning or just replace the whole unit?
What should I ask an awning contractor during the initial consultation to make sure they're legit?
Can an awning be mounted directly onto a roof or does it have to be on the wall?
I have a $2,000 budget, what are my best options for shading a medium-sized patio?
How do I clean bird droppings and mildew off a fixed fabric awning without damaging it?
Does a dark-colored awning fabric trap more heat underneath than a light-colored one?
What are some red flags when looking at a quote from a local awning company?
Are metal awnings better than fabric ones for snow loads in the winter?
How long does the typical installation take once the awning is actually delivered?
Will adding a retractable awning increase my home's resale value?
Can I get an awning that has built-in LED lights and speakers?
My HOA has strict rules about exterior colors, how do I find a contractor who handles the approval process?
What's the difference between a lateral arm awning and a drop arm awning?
Is there a way to add shade to my deck without drilling into the brick of my house?
How do I measure my patio correctly to figure out what size awning I need to buy?
Are there smart home awnings that I can control with my phone or Alexa?
Why is one company quoting me $1,500 and another $4,000 for the same size awning?
What happens if the motor on my retractable awning dies, can I still close it manually?
I live near the ocean, what materials should I avoid so my awning hardware doesn't rust?
Can I put an awning over my grill or is that a fire hazard?
What's the best time of year to buy an awning to get a discount?
Does a waterproof awning exist or are they all just water-resistant?
Should I get a valance on my awning or does that just make it look dated?
How much extra does it cost for a professional to haul away my old, rusted metal awning?
Can I change just the fabric on my existing retractable frame if I want a new color?
Are there portable awnings that are sturdy enough to stay out all summer?
What kind of maintenance is required for the moving parts of a retractable awning?
Is it better to get a freestanding awning or one attached to the house for a very large pool deck?
Do retractable awnings come with sensors that close them when it starts to rain?

Model by model

20-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 awning buyers.

Behavior rates across 40 awning buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional55%40%10%40%
Suggests DIY first23%15%23%88%
Names specific providers8%18%18%80%
Gives price or cost info20%20%20%83%
Tells to check reviews5%10%0%88%
Tells to verify credentials10%5%3%88%
Mentions case studies / portfolio5%3%3%93%
Mentions local proximity30%23%5%60%
Gives selection criteria50%43%23%48%
Warns about red flags8%8%3%90%
Asks a clarifying question75%68%0%13%
Recommends multiple quotes15%13%0%80%

By model

How each assistant handled Awning questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same awning questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 55% (ChatGPT) down to 10% (Gemini), a 45-point gap on an identical question set.

Across the 40 awning answers it produced, ChatGPT recommended hiring a professional in 55% of them and suggested a DIY approach first 22.5% of the time. It named a specific provider in 7.5% of answers (about 0.4 distinct providers per answer) and included price or cost information 20% of the time. ChatGPT asked a clarifying question before answering in 75% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 10%, averaging 488 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 30%; a selection-criteria checklist appeared in 50% of its answers and a recommendation to gather multiple quotes in 15%.

Across the 40 awning answers it produced, Claude recommended hiring a professional in 40% of them and suggested a DIY approach first 15% of the time. It named a specific provider in 17.5% of answers (about 0.6 distinct providers per answer) and included price or cost information 20% of the time. Claude asked a clarifying question before answering in 67.5% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 5%, averaging 273 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 22.5%; a selection-criteria checklist appeared in 42.5% of its answers and a recommendation to gather multiple quotes in 12.5%.

Across the 40 awning answers it produced, Gemini recommended hiring a professional in 10% of them and suggested a DIY approach first 22.5% of the time. It named a specific provider in 17.5% of answers (about 0.5 distinct providers per answer) and included price or cost information 20% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 2.5%, and told the buyer to verify credentials in 2.5%, averaging 285 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 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 an awning buyer to a professional (55%) and Gemini the least (10%). ChatGPT produced the longest answers, at 488 words on average. Specific providers were named most often by Claude (17.5%) — even there, roughly one answer in 6 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 19.6 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant an awning buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 75% (ChatGPT) — a 75-point spread.
  • Recommends hiring a professional: from 10% (Gemini) to 55% (ChatGPT) — a 45-point spread.
  • Gives selection criteria: from 22.5% (Gemini) to 50% (ChatGPT) — a 28-point spread.
  • Mentions local proximity: from 5% (Gemini) to 30% (ChatGPT) — a 25-point spread.
  • Recommends multiple quotes: from 0% (Gemini) to 15% (ChatGPT) — a 15-point spread.

The widest single gap — asks a clarifying question, 75 points — means an awning 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 awning market.

Where they agree

The points of near-consensus in Awning.

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

  • Gives price or cost information: 20% across all three models.
  • Mentions case studies or portfolio: 2.5%–5% across all three (a 3-point spread).
  • Warns about red flags or scams: 2.5%–7.5% across all three (a 5-point spread).
  • Suggests a DIY approach first: 15%–22.5% across all three (a 8-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 92.5% of questions) and least consistently on "asks a clarifying question" (12.5%).

Every behavior, measured

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

The behaviors AI models reproduce most often for awning are asks a clarifying question (47.5% on average), gives selection criteria (38.3%) and recommends hiring a professional (35%); the rarest are mentions case studies or portfolio (3.3%), tells the buyer to check reviews (5%) and warns about red flags or scams (5.8%). 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:

  • Asks a clarifying question: 47.5% on average (ChatGPT 75%, Claude 67.5%, Gemini 0%) — a 75-point spread.
  • Gives selection criteria: 38.3% on average (ChatGPT 50%, Claude 42.5%, Gemini 22.5%) — a 28-point spread.
  • Recommends hiring a professional: 35% on average (ChatGPT 55%, Claude 40%, Gemini 10%) — a 45-point spread.
  • Suggests a DIY approach first: 20% on average (ChatGPT 22.5%, Claude 15%, Gemini 22.5%) — a 8-point spread.
  • Gives price or cost information: 20% on average (ChatGPT 20%, Claude 20%, Gemini 20%).
  • Mentions local proximity: 19.2% on average (ChatGPT 30%, Claude 22.5%, Gemini 5%) — a 25-point spread.
  • Names a specific provider: 14.2% on average (ChatGPT 7.5%, Claude 17.5%, Gemini 17.5%) — a 10-point spread.
  • Recommends multiple quotes: 9.2% on average (ChatGPT 15%, Claude 12.5%, Gemini 0%) — a 15-point spread.
  • Tells the buyer to verify credentials: 5.8% on average (ChatGPT 10%, Claude 5%, Gemini 2.5%) — a 8-point spread.
  • Warns about red flags or scams: 5.8% on average (ChatGPT 7.5%, Claude 7.5%, Gemini 2.5%) — a 5-point spread.
  • Tells the buyer to check reviews: 5% on average (ChatGPT 5%, Claude 10%, Gemini 0%) — a 10-point spread.
  • Mentions case studies or portfolio: 3.3% on average (ChatGPT 5%, Claude 2.5%, Gemini 2.5%) — a 3-point spread.

Trust signals

How well the models protect the awning buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the awning buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 5% of answers on average. Verifying credentials or certifications appeared in 5.8%. Warning about red flags or scams appeared in 5.8%.

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 9.2%. The single least-reproduced protective signal for awning is "tells the buyer to check reviews" at 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 Awning providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 awning answers, a specific provider was named in 14.2% of responses on average — roughly 0.5 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for awning: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Awning questions cover.

The 40 questions behind every percentage on this page were drawn from real awning (home services; 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 awning 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 awning 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 →