Original research · 2026-07 edition

AI SEO Statistics: On-Page SEO Ecommerce (2026-07 edition)

15 questions · 45/45 expected AI responses · 3 models · measured 2026-07-05

The question bank

The questions we tested — a frozen buyer-intent benchmark for on-page seo ecommerce.

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.

Why are my product pages not showing up on Google even though I have high-quality photos and descriptions?
Can I handle the on-page SEO for a 500-item Shopify store myself or is it time to hire a professional?
What is the average cost per product for a consultant to optimize metadata and header tags for a large online catalog?
I'm seeing a lot of 'duplicate content' errors in my search console because of product variations; how does an expert fix this?
What specific questions should I ask an SEO agency to make sure they actually understand eCommerce and not just blogging?
Is it better to pay for a one-time SEO audit of my online store or a monthly retainer for ongoing product page updates?
How do I find an SEO specialist who knows how to optimize category pages to outrank big-box retailers?
My organic traffic plummeted after I changed my site's theme; can an on-page SEO specialist pinpoint what broke?
Show all 15 questions
What are the red flags to watch out for when hiring someone to rewrite my product descriptions for SEO?
I have a $3,000 budget for SEO; should I spend it all on my top 20 best-sellers or spread it across the whole site?
How long does it typically take to see a jump in rankings after optimizing the H1 tags and image alt text on my store?
Should I hire a copywriter who knows SEO or an SEO specialist who can write for my eCommerce brand?
Does an on-page SEO service usually include fixing internal linking structures between my related products?
I'm migrating my store from Etsy to my own website; what on-page SEO mistakes should I avoid to keep my existing customers?
Is there a difference in how on-page SEO is handled for a luxury boutique versus a discount bulk-buy site?

Model by model

16.7% 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 matrixModel-by-model evidence
Measured

Behavior prevalence across 15 on-page seo ecommerce benchmark questions, 2026-07 edition. Last column: equal-model mean.

Behavior prevalence across 15 on-page seo ecommerce benchmark questions, 2026-07 edition. Last column: equal-model mean.
BehaviorChatGPTClaudeGeminiEqual-model mean
Recommends hiring a professional46.7%46.7%26.7%40%
Suggests DIY first20%33.3%26.7%26.7%
Names specific providers0%6.7%6.7%4.5%
Gives price or cost info13.3%13.3%20%15.5%
Tells to check reviews0%0%0%0%
Tells to verify credentials0%0%0%0%
Mentions case studies / portfolio13.3%13.3%13.3%13.3%
Mentions local proximity6.7%0%0%2.2%
Gives selection criteria26.7%33.3%46.7%35.6%
Warns about red flags0%6.7%20%8.9%
Asks a clarifying question53.3%53.3%0%35.5%
Recommends multiple quotes0%0%0%0%

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.

Behavior matrixModel-by-model evidence
Measured

All-model binary agreement by behavior across 15 benchmark questions.

All-model binary agreement by behavior across 15 benchmark questions.
BehaviorAll-model agreement
Recommends hiring a professional60%
Suggests DIY first60%
Names specific providers86.7%
Gives price or cost info80%
Tells to check reviews100%
Tells to verify credentials100%
Mentions case studies / portfolio66.7%
Mentions local proximity93.3%
Gives selection criteria53.3%
Warns about red flags80%
Asks a clarifying question20%
Recommends multiple quotes100%

By model

How each assistant handled On-Page SEO Ecommerce questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same on-page seo ecommerce questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 46.7% (ChatGPT) down to 26.7% (Gemini), a 20-point gap on an identical question set.

Across the 15 on-page seo ecommerce answers it produced, ChatGPT recommended hiring a professional in 46.7% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 53.3% of cases, warned about red flags or scams in 0%, and told the buyer to verify credentials in 0%, averaging 728 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 26.7% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 on-page seo ecommerce answers it produced, Claude recommended hiring a professional in 46.7% of them and suggested a DIY approach first 33.3% of the time. It named a specific provider in 6.7% of answers (about 0.3 distinct providers per answer) and included price or cost information 13.3% of the time. Claude asked a clarifying question before answering in 53.3% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 0%, averaging 327 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 0%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 on-page seo ecommerce answers it produced, Gemini recommended hiring a professional in 26.7% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 6.7% of answers (about 0.3 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 20%, and told the buyer to verify credentials in 0%, averaging 250 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 0%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route an on-page seo ecommerce buyer to a professional (46.7%) and Gemini the least (26.7%). ChatGPT produced the longest answers, at 728 words on average. Specific providers were named most often by Claude (6.7%) — even there, roughly one answer in 15 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

Question-level model disagreement is 16.7% — 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 which assistant an on-page seo ecommerce buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 53.3% (ChatGPT) — a 53-point spread.
  • Recommends hiring a professional: from 26.7% (Gemini) to 46.7% (ChatGPT) — a 20-point spread.
  • Gives selection criteria: from 26.7% (ChatGPT) to 46.7% (Gemini) — a 20-point spread.
  • Warns about red flags or scams: from 0% (ChatGPT) to 20% (Gemini) — a 20-point spread.
  • Suggests a DIY approach first: from 20% (ChatGPT) to 33.3% (Claude) — a 13-point spread.

The widest single gap — asks a clarifying question, 53 points — means an on-page seo ecommerce 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 on-page seo ecommerce market.

Where they agree

The points of near-consensus in On-Page SEO Ecommerce.

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

  • Tells the buyer to check reviews: 0% across all three models.
  • Tells the buyer to verify credentials: 0% across all three models.
  • Mentions case studies or portfolio: 13.3% across all three models.
  • Recommends multiple quotes: 0% across all three models.

Measured question by question, the three assistants coded a response the same way most consistently on "tells the buyer to check reviews" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (20%).

Every behavior, measured

All twelve coded behaviors for On-Page SEO Ecommerce, averaged across the three models.

The behaviors AI models reproduce most often for on-page seo ecommerce are recommends hiring a professional (40% on average), gives selection criteria (35.6%) and asks a clarifying question (35.5%); the rarest are recommends multiple quotes (0%), tells the buyer to verify credentials (0%) and tells the buyer to check reviews (0%). 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: 40% on average (ChatGPT 46.7%, Claude 46.7%, Gemini 26.7%) — a 20-point spread.
  • Gives selection criteria: 35.6% on average (ChatGPT 26.7%, Claude 33.3%, Gemini 46.7%) — a 20-point spread.
  • Asks a clarifying question: 35.5% on average (ChatGPT 53.3%, Claude 53.3%, Gemini 0%) — a 53-point spread.
  • Suggests a DIY approach first: 26.7% on average (ChatGPT 20%, Claude 33.3%, Gemini 26.7%) — a 13-point spread.
  • Gives price or cost information: 15.5% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 20%) — a 7-point spread.
  • Mentions case studies or portfolio: 13.3% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 13.3%).
  • Warns about red flags or scams: 8.9% on average (ChatGPT 0%, Claude 6.7%, Gemini 20%) — a 20-point spread.
  • Names a specific provider: 4.5% on average (ChatGPT 0%, Claude 6.7%, Gemini 6.7%) — a 7-point spread.
  • Mentions local proximity: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.
  • Tells the buyer to check reviews: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
  • Tells the buyer to verify credentials: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
  • Recommends multiple quotes: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the on-page seo ecommerce buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the on-page seo ecommerce buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 0% of answers on average. Verifying credentials or certifications appeared in 0%. Warning about red flags or scams appeared in 8.9%.

On structuring the decision, a selection-criteria checklist showed up in 35.6% of answers on average and a recommendation to gather multiple quotes in 0%. The single least-reproduced protective signal for on-page seo ecommerce is "tells the buyer to check reviews" at 0% 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 On-Page SEO Ecommerce providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 on-page seo ecommerce answers, a specific provider was named in 4.5% of responses on average — roughly 0.2 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for on-page seo ecommerce: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 On-Page SEO Ecommerce questions cover.

The 15 questions behind every percentage on this page form a frozen on page seo ecommerce (ecommerce / online retail; 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 on-page seo ecommerce 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 2026-07-05, the figures describe this specific on-page seo ecommerce 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 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 2026-07-05 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: On-Page SEO Ecommerce (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/ecommerce/on-page-seo-ecommerce