AI SEO Statistics: Toy Stores (2026-07 edition)
15 questions · 45/45 expected AI responses · 3 models · measured 2026-07-06
Apply these findings to your toy stores 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 toy stores.
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 15 questions
Model by model
23.7% 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 prevalence across 15 toy stores benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 6.7% | 6.7% | 6.7% | 6.7% |
| Suggests DIY first | 0% | 20% | 0% | 6.7% |
| Names specific providers | 40% | 60% | 60% | 53.3% |
| Gives price or cost info | 6.7% | 6.7% | 26.7% | 13.4% |
| Tells to check reviews | 46.7% | 53.3% | 0% | 33.3% |
| Tells to verify credentials | 40% | 26.7% | 6.7% | 24.5% |
| Mentions case studies / portfolio | 0% | 0% | 0% | 0% |
| Mentions local proximity | 26.7% | 26.7% | 20% | 24.5% |
| Gives selection criteria | 66.7% | 86.7% | 40% | 64.5% |
| Warns about red flags | 20% | 40% | 6.7% | 22.2% |
| Asks a clarifying question | 60% | 73.3% | 0% | 44.4% |
| Recommends multiple quotes | 13.3% | 6.7% | 0% | 6.7% |
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 15 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 100% |
| Suggests DIY first | 80% |
| Names specific providers | 46.7% |
| Gives price or cost info | 80% |
| Tells to check reviews | 40% |
| Tells to verify credentials | 66.7% |
| Mentions case studies / portfolio | 100% |
| Mentions local proximity | 73.3% |
| Gives selection criteria | 46.7% |
| Warns about red flags | 53.3% |
| Asks a clarifying question | 6.7% |
| Recommends multiple quotes | 80% |
By model
How each assistant handled Toy Stores questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same toy stores questions. On the most consequential behavior, whether to send the buyer to a professional at all, the rate ranged from 6.7% (ChatGPT) down to 6.7% (ChatGPT), a 0-point gap on an identical question set.
Across the 15 toy stores answers it produced, ChatGPT recommended hiring a professional in 6.7% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 40% of answers (about 2.5 distinct providers per answer) and included price or cost information 6.7% of the time. ChatGPT asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 40%, averaging 490 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 0%, and framed the choice around local proximity in 26.7%; a selection-criteria checklist appeared in 66.7% of its answers and a recommendation to gather multiple quotes in 13.3%.
Across the 15 toy stores answers it produced, Claude recommended hiring a professional in 6.7% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 60% of answers (about 4.3 distinct providers per answer) and included price or cost information 6.7% of the time. Claude asked a clarifying question before answering in 73.3% of cases, warned about red flags or scams in 40%, and told the buyer to verify credentials in 26.7%, averaging 278 words per answer. On the remaining cues it told the buyer to check reviews in 53.3%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 26.7%; a selection-criteria checklist appeared in 86.7% of its answers and a recommendation to gather multiple quotes in 6.7%.
Across the 15 toy stores answers it produced, Gemini recommended hiring a professional in 6.7% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 60% of answers (about 1.8 distinct providers per answer) and included price or cost information 26.7% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 6.7%, averaging 224 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 20%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a toy stores buyer to a professional (6.7%) and ChatGPT the least (6.7%). ChatGPT produced the longest answers, at 490 words on average. Specific providers were named most often by Claude (60%). Even there, roughly one answer in 2 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 23.7%. 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 which assistant a toy stores buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 73.3% (Claude). The spread is 73 points.
- Tells the buyer to check reviews: from 0% (Gemini) to 53.3% (Claude). The spread is 53 points.
- Gives selection criteria: from 40% (Gemini) to 86.7% (Claude). The spread is 47 points.
- Tells the buyer to verify credentials: from 6.7% (Gemini) to 40% (ChatGPT). The spread is 33 points.
- Warns about red flags or scams: from 6.7% (Gemini) to 40% (Claude). The spread is 33 points.
The widest single gap concerns asks a clarifying question at 73 points. This means a toy stores 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 toy stores market.
Where they agree
The points of near-consensus in Toy Stores.
On other behaviors the three models move almost in lockstep. The points of near-consensus for toy stores, where all three landed within a few points of each other:
- Recommends hiring a professional: 6.7% across all three models.
- Mentions case studies or portfolio: 0% across all three models.
- Mentions local proximity: 20%–26.7% across all three (a 7-point spread).
- Recommends multiple quotes: 0%–13.3% across all three (a 13-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "recommends hiring a professional" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (6.7%).
Every behavior, measured
All twelve coded behaviors for Toy Stores, averaged across the three models.
The behaviors AI models reproduce most often for toy stores are gives selection criteria (64.5% on average), names a specific provider (53.3%) and asks a clarifying question (44.4%); the rarest are mentions case studies or portfolio (0%), recommends multiple quotes (6.7%) and suggests a DIY approach first (6.7%). 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:
- Gives selection criteria: 64.5% on average (ChatGPT 66.7%, Claude 86.7%, Gemini 40%). The spread is 47 points.
- Names a specific provider: 53.3% on average (ChatGPT 40%, Claude 60%, Gemini 60%). The spread is 20 points.
- Asks a clarifying question: 44.4% on average (ChatGPT 60%, Claude 73.3%, Gemini 0%). The spread is 73 points.
- Tells the buyer to check reviews: 33.3% on average (ChatGPT 46.7%, Claude 53.3%, Gemini 0%). The spread is 53 points.
- Tells the buyer to verify credentials: 24.5% on average (ChatGPT 40%, Claude 26.7%, Gemini 6.7%). The spread is 33 points.
- Mentions local proximity: 24.5% on average (ChatGPT 26.7%, Claude 26.7%, Gemini 20%). The spread is 7 points.
- Warns about red flags or scams: 22.2% on average (ChatGPT 20%, Claude 40%, Gemini 6.7%). The spread is 33 points.
- Gives price or cost information: 13.4% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 26.7%). The spread is 20 points.
- Recommends hiring a professional: 6.7% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 6.7%).
- Suggests a DIY approach first: 6.7% on average (ChatGPT 0%, Claude 20%, Gemini 0%). The spread is 20 points.
- Recommends multiple quotes: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%). The spread is 13 points.
- Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the toy stores buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the toy stores buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 33.3% of answers on average. Verifying credentials or certifications appeared in 24.5%. Warning about red flags or scams appeared in 22.2%.
On structuring the decision, a selection-criteria checklist showed up in 64.5% of answers on average and a recommendation to gather multiple quotes in 6.7%. The single least-reproduced protective signal for toy stores is "recommends multiple quotes" at 6.7% 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 Toy Stores providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 toy stores answers, a specific provider was named in 53.3% of responses on average, or roughly 2.9 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for toy stores: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Toy Stores questions cover.
The 15 questions behind every percentage on this page form a frozen toy stores (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 toy stores 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 15 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 toy stores 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 2026-07-06 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: Toy Stores (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/ecommerce/toy-stores