AI SEO Statistics: Pastry Shops (2026-07 edition)
40 questions · 120 AI responses · 3 models · measured 2026-07-06
The question bank
The questions we tested — sampled from real buyer journeys in pastry shops.
Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.
Show all 40 questions
Model by model
17-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 pastry shops buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 35% | 35% | 28% | 65% |
| Suggests DIY first | 5% | 3% | 3% | 93% |
| Names specific providers | 5% | 5% | 20% | 80% |
| Gives price or cost info | 20% | 13% | 25% | 83% |
| Tells to check reviews | 10% | 20% | 5% | 78% |
| Tells to verify credentials | 8% | 5% | 5% | 88% |
| Mentions case studies / portfolio | 13% | 13% | 5% | 88% |
| Mentions local proximity | 40% | 33% | 25% | 68% |
| Gives selection criteria | 50% | 55% | 38% | 50% |
| Warns about red flags | 10% | 10% | 8% | 93% |
| Asks a clarifying question | 53% | 60% | 5% | 23% |
| Recommends multiple quotes | 8% | 15% | 0% | 85% |
By model
How each assistant handled Pastry Shops questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same pastry shops questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 35% (ChatGPT) down to 27.5% (Gemini), a 8-point gap on an identical question set.
Across the 40 pastry shops answers it produced, ChatGPT recommended hiring a professional in 35% of them and suggested a DIY approach first 5% 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 20% of the time. ChatGPT asked a clarifying question before answering in 52.5% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 7.5%, averaging 435 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 12.5%, and framed the choice around local proximity in 40%; a selection-criteria checklist appeared in 50% of its answers and a recommendation to gather multiple quotes in 7.5%.
Across the 40 pastry shops answers it produced, Claude recommended hiring a professional in 35% of them and suggested a DIY approach first 2.5% 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 12.5% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 5%, averaging 258 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 12.5%, and framed the choice around local proximity in 32.5%; a selection-criteria checklist appeared in 55% of its answers and a recommendation to gather multiple quotes in 15%.
Across the 40 pastry shops answers it produced, Gemini recommended hiring a professional in 27.5% of them and suggested a DIY approach first 2.5% of the time. It named a specific provider in 20% of answers (about 0.6 distinct providers per answer) and included price or cost information 25% of the time. Gemini asked a clarifying question before answering in 5% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 5%, averaging 263 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 25%; a selection-criteria checklist appeared in 37.5% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a pastry shops buyer to a professional (35%) and Gemini the least (27.5%). ChatGPT produced the longest answers, at 435 words on average. Specific providers were named most often by Gemini (20%) — even there, roughly one answer in 5 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
The divergence index for this study is 17.2 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a pastry shops buyer happens to ask matters most:
- Asks a clarifying question: from 5% (Gemini) to 60% (Claude) — a 55-point spread.
- Gives selection criteria: from 37.5% (Gemini) to 55% (Claude) — a 18-point spread.
- Names a specific provider: from 5% (ChatGPT) to 20% (Gemini) — a 15-point spread.
- Tells the buyer to check reviews: from 5% (Gemini) to 20% (Claude) — a 15-point spread.
- Mentions local proximity: from 25% (Gemini) to 40% (ChatGPT) — a 15-point spread.
The widest single gap — asks a clarifying question, 55 points — means a pastry shops 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 pastry shops market.
Where they agree
The points of near-consensus in Pastry Shops.
On other behaviors the three models move almost in lockstep — the points of near-consensus for pastry shops, where all three landed within a few points of each other:
- Suggests a DIY approach first: 2.5%–5% across all three (a 3-point spread).
- Tells the buyer to verify credentials: 5%–7.5% across all three (a 3-point spread).
- Warns about red flags or scams: 7.5%–10% across all three (a 3-point spread).
- Recommends hiring a professional: 27.5%–35% across all three (a 8-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 92.5% of questions) and least consistently on "asks a clarifying question" (22.5%).
Every behavior, measured
All twelve coded behaviors for Pastry Shops, averaged across the three models.
The behaviors AI models reproduce most often for pastry shops are gives selection criteria (47.5% on average), asks a clarifying question (39.2%) and recommends hiring a professional (32.5%); the rarest are suggests a DIY approach first (3.3%), tells the buyer to verify credentials (5.8%) and recommends multiple quotes (7.5%). 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:
- Gives selection criteria: 47.5% on average (ChatGPT 50%, Claude 55%, Gemini 37.5%) — a 18-point spread.
- Asks a clarifying question: 39.2% on average (ChatGPT 52.5%, Claude 60%, Gemini 5%) — a 55-point spread.
- Recommends hiring a professional: 32.5% on average (ChatGPT 35%, Claude 35%, Gemini 27.5%) — a 8-point spread.
- Mentions local proximity: 32.5% on average (ChatGPT 40%, Claude 32.5%, Gemini 25%) — a 15-point spread.
- Gives price or cost information: 19.2% on average (ChatGPT 20%, Claude 12.5%, Gemini 25%) — a 13-point spread.
- Tells the buyer to check reviews: 11.7% on average (ChatGPT 10%, Claude 20%, Gemini 5%) — a 15-point spread.
- Names a specific provider: 10% on average (ChatGPT 5%, Claude 5%, Gemini 20%) — a 15-point spread.
- Mentions case studies or portfolio: 10% on average (ChatGPT 12.5%, Claude 12.5%, Gemini 5%) — a 8-point spread.
- Warns about red flags or scams: 9.2% on average (ChatGPT 10%, Claude 10%, Gemini 7.5%) — a 3-point spread.
- Recommends multiple quotes: 7.5% on average (ChatGPT 7.5%, Claude 15%, Gemini 0%) — a 15-point spread.
- Tells the buyer to verify credentials: 5.8% on average (ChatGPT 7.5%, Claude 5%, Gemini 5%) — a 3-point spread.
- Suggests a DIY approach first: 3.3% on average (ChatGPT 5%, Claude 2.5%, Gemini 2.5%) — a 3-point spread.
Trust signals
How well the models protect the pastry shops buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the pastry shops buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 11.7% of answers on average. Verifying credentials or certifications appeared in 5.8%. Warning about red flags or scams appeared in 9.2%.
On structuring the decision, a selection-criteria checklist showed up in 47.5% of answers on average and a recommendation to gather multiple quotes in 7.5%. The single least-reproduced protective signal for pastry shops is "tells the buyer to verify credentials" at 5.8% 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 Pastry Shops providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 pastry shops answers, a specific provider was named in 10% of responses on average — roughly 0.3 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for pastry shops: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Pastry Shops questions cover.
The 40 questions behind every percentage on this page were drawn from real pastry shops (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 pastry shops 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 pastry shops 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 →