AI SEO Statistics: Bakery (2026-07 edition)
15 questions · 45 AI responses · 3 models · measured 2026-07-04
The question bank
The questions we tested — sampled from real buyer journeys in bakery.
Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.
Show all 15 questions
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 bakery buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 60% | 20% | 27% | 47% |
| Suggests DIY first | 20% | 20% | 13% | 80% |
| Names specific providers | 0% | 7% | 20% | 80% |
| Gives price or cost info | 27% | 27% | 20% | 93% |
| Tells to check reviews | 20% | 20% | 7% | 80% |
| Tells to verify credentials | 20% | 20% | 7% | 80% |
| Mentions case studies / portfolio | 7% | 13% | 13% | 93% |
| Mentions local proximity | 47% | 47% | 20% | 67% |
| Gives selection criteria | 53% | 53% | 53% | 40% |
| Warns about red flags | 13% | 27% | 20% | 73% |
| Asks a clarifying question | 60% | 67% | 0% | 13% |
| Recommends multiple quotes | 0% | 7% | 0% | 93% |
By model
How each assistant handled Bakery questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same bakery questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 60% (ChatGPT) down to 20% (Claude), a 40-point gap on an identical question set.
Across the 15 bakery answers it produced, ChatGPT recommended hiring a professional in 60% 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 26.7% of the time. ChatGPT asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 20%, averaging 492 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 46.7%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 15 bakery answers it produced, Claude recommended hiring a professional in 20% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 6.7% of answers (about 0.2 distinct providers per answer) and included price or cost information 26.7% of the time. Claude asked a clarifying question before answering in 66.7% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 20%, averaging 276 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 13.3%, and framed the choice around local proximity in 46.7%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 6.7%.
Across the 15 bakery answers it produced, Gemini recommended hiring a professional in 26.7% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 20% of answers (about 0.9 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 6.7%, averaging 255 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, pointed to case studies or a portfolio in 13.3%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a bakery buyer to a professional (60%) and Claude the least (20%). ChatGPT produced the longest answers, at 492 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 20 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a bakery buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 66.7% (Claude) — a 67-point spread.
- Recommends hiring a professional: from 20% (Claude) to 60% (ChatGPT) — a 40-point spread.
- Mentions local proximity: from 20% (Gemini) to 46.7% (ChatGPT) — a 27-point spread.
- Names a specific provider: from 0% (ChatGPT) to 20% (Gemini) — a 20-point spread.
- Warns about red flags or scams: from 13.3% (ChatGPT) to 26.7% (Claude) — a 13-point spread.
The widest single gap — asks a clarifying question, 67 points — means a bakery 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 bakery market.
Where they agree
The points of near-consensus in Bakery.
On other behaviors the three models move almost in lockstep — the points of near-consensus for bakery, where all three landed within a few points of each other:
- Gives selection criteria: 53.3% across all three models.
- Mentions case studies or portfolio: 6.7%–13.3% across all three (a 7-point spread).
- Suggests a DIY approach first: 13.3%–20% across all three (a 7-point spread).
- Gives price or cost information: 20%–26.7% across all three (a 7-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "gives price or cost information" (identical coding in 93.3% of questions) and least consistently on "asks a clarifying question" (13.3%).
Every behavior, measured
All twelve coded behaviors for Bakery, averaged across the three models.
The behaviors AI models reproduce most often for bakery are gives selection criteria (53.3% on average), asks a clarifying question (42.2%) and mentions local proximity (37.8%); the rarest are recommends multiple quotes (2.2%), names a specific provider (8.9%) and mentions case studies or portfolio (11.1%). 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: 53.3% on average (ChatGPT 53.3%, Claude 53.3%, Gemini 53.3%).
- Asks a clarifying question: 42.2% on average (ChatGPT 60%, Claude 66.7%, Gemini 0%) — a 67-point spread.
- Mentions local proximity: 37.8% on average (ChatGPT 46.7%, Claude 46.7%, Gemini 20%) — a 27-point spread.
- Recommends hiring a professional: 35.6% on average (ChatGPT 60%, Claude 20%, Gemini 26.7%) — a 40-point spread.
- Gives price or cost information: 24.5% on average (ChatGPT 26.7%, Claude 26.7%, Gemini 20%) — a 7-point spread.
- Warns about red flags or scams: 20% on average (ChatGPT 13.3%, Claude 26.7%, Gemini 20%) — a 13-point spread.
- Suggests a DIY approach first: 17.8% on average (ChatGPT 20%, Claude 20%, Gemini 13.3%) — a 7-point spread.
- Tells the buyer to check reviews: 15.6% on average (ChatGPT 20%, Claude 20%, Gemini 6.7%) — a 13-point spread.
- Tells the buyer to verify credentials: 15.6% on average (ChatGPT 20%, Claude 20%, Gemini 6.7%) — a 13-point spread.
- Mentions case studies or portfolio: 11.1% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 13.3%) — a 7-point spread.
- Names a specific provider: 8.9% on average (ChatGPT 0%, Claude 6.7%, Gemini 20%) — a 20-point spread.
- Recommends multiple quotes: 2.2% on average (ChatGPT 0%, Claude 6.7%, Gemini 0%) — a 7-point spread.
Trust signals
How well the models protect the bakery buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the bakery buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 15.6% of answers on average. Verifying credentials or certifications appeared in 15.6%. Warning about red flags or scams appeared in 20%.
On structuring the decision, a selection-criteria checklist showed up in 53.3% of answers on average and a recommendation to gather multiple quotes in 2.2%. The single least-reproduced protective signal for bakery is "recommends multiple quotes" at 2.2% 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 Bakery providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 bakery answers, a specific provider was named in 8.9% of responses on average — roughly 0.4 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for bakery: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Bakery questions cover.
The 15 questions behind every percentage on this page were drawn from real bakery (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 bakery 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 2026-07-04, the figures describe this specific bakery 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 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 2026-07-04, 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 →