AI SEO Statistics: Florist (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 florist.
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
22-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 florist buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 73% | 67% | 47% | 60% |
| Suggests DIY first | 0% | 7% | 0% | 93% |
| Names specific providers | 27% | 47% | 60% | 67% |
| Gives price or cost info | 13% | 20% | 47% | 67% |
| Tells to check reviews | 27% | 13% | 7% | 73% |
| Tells to verify credentials | 7% | 7% | 0% | 93% |
| Mentions case studies / portfolio | 20% | 7% | 0% | 80% |
| Mentions local proximity | 53% | 67% | 53% | 47% |
| Gives selection criteria | 73% | 67% | 53% | 47% |
| Warns about red flags | 13% | 7% | 13% | 80% |
| Asks a clarifying question | 87% | 67% | 0% | 7% |
| Recommends multiple quotes | 7% | 13% | 0% | 87% |
By model
How each assistant handled Florist questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same florist questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 73.3% (ChatGPT) down to 46.7% (Gemini), a 27-point gap on an identical question set.
Across the 15 florist answers it produced, ChatGPT recommended hiring a professional in 73.3% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 26.7% of answers (about 2 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 86.7% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 6.7%, averaging 400 words per answer. On the remaining cues it told the buyer to check reviews in 26.7%, pointed to case studies or a portfolio in 20%, and framed the choice around local proximity in 53.3%; a selection-criteria checklist appeared in 73.3% of its answers and a recommendation to gather multiple quotes in 6.7%.
Across the 15 florist answers it produced, Claude recommended hiring a professional in 66.7% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 46.7% of answers (about 1.6 distinct providers per answer) and included price or cost information 20% of the time. Claude asked a clarifying question before answering in 66.7% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 6.7%, averaging 256 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 66.7%; a selection-criteria checklist appeared in 66.7% of its answers and a recommendation to gather multiple quotes in 13.3%.
Across the 15 florist answers it produced, Gemini recommended hiring a professional in 46.7% 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 46.7% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 0%, averaging 253 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 0%, and framed the choice around local proximity in 53.3%; 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 florist buyer to a professional (73.3%) and Gemini the least (46.7%). ChatGPT produced the longest answers, at 400 words on average. Specific providers were named most often by Gemini (60%) — even there, roughly one answer in 2 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
The divergence index for this study is 22.2 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a florist buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 86.7% (ChatGPT) — a 87-point spread.
- Gives price or cost information: from 13.3% (ChatGPT) to 46.7% (Gemini) — a 33-point spread.
- Names a specific provider: from 26.7% (ChatGPT) to 60% (Gemini) — a 33-point spread.
- Recommends hiring a professional: from 46.7% (Gemini) to 73.3% (ChatGPT) — a 27-point spread.
- Tells the buyer to check reviews: from 6.7% (Gemini) to 26.7% (ChatGPT) — a 20-point spread.
The widest single gap — asks a clarifying question, 87 points — means a florist 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 florist market.
Where they agree
The points of near-consensus in Florist.
On other behaviors the three models move almost in lockstep — the points of near-consensus for florist, where all three landed within a few points of each other:
- Warns about red flags or scams: 6.7%–13.3% across all three (a 7-point spread).
- Suggests a DIY approach first: 0%–6.7% across all three (a 7-point spread).
- Tells the buyer to verify credentials: 0%–6.7% 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 "suggests a DIY approach first" (identical coding in 93.3% of questions) and least consistently on "asks a clarifying question" (6.7%).
Every behavior, measured
All twelve coded behaviors for Florist, averaged across the three models.
The behaviors AI models reproduce most often for florist are gives selection criteria (64.4% on average), recommends hiring a professional (62.2%) and mentions local proximity (57.8%); the rarest are suggests a DIY approach first (2.2%), tells the buyer to verify credentials (4.5%) and recommends multiple quotes (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: 64.4% on average (ChatGPT 73.3%, Claude 66.7%, Gemini 53.3%) — a 20-point spread.
- Recommends hiring a professional: 62.2% on average (ChatGPT 73.3%, Claude 66.7%, Gemini 46.7%) — a 27-point spread.
- Mentions local proximity: 57.8% on average (ChatGPT 53.3%, Claude 66.7%, Gemini 53.3%) — a 13-point spread.
- Asks a clarifying question: 51.1% on average (ChatGPT 86.7%, Claude 66.7%, Gemini 0%) — a 87-point spread.
- Names a specific provider: 44.5% on average (ChatGPT 26.7%, Claude 46.7%, Gemini 60%) — a 33-point spread.
- Gives price or cost information: 26.7% on average (ChatGPT 13.3%, Claude 20%, Gemini 46.7%) — a 33-point spread.
- Tells the buyer to check reviews: 15.6% on average (ChatGPT 26.7%, Claude 13.3%, Gemini 6.7%) — a 20-point spread.
- Warns about red flags or scams: 11.1% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 13.3%) — a 7-point spread.
- Mentions case studies or portfolio: 8.9% on average (ChatGPT 20%, Claude 6.7%, Gemini 0%) — a 20-point spread.
- Recommends multiple quotes: 6.7% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 0%) — a 13-point spread.
- Tells the buyer to verify credentials: 4.5% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 0%) — a 7-point spread.
- Suggests a DIY approach first: 2.2% on average (ChatGPT 0%, Claude 6.7%, Gemini 0%) — a 7-point spread.
Trust signals
How well the models protect the florist buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the florist 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 4.5%. Warning about red flags or scams appeared in 11.1%.
On structuring the decision, a selection-criteria checklist showed up in 64.4% of answers on average and a recommendation to gather multiple quotes in 6.7%. The single least-reproduced protective signal for florist is "tells the buyer to verify credentials" at 4.5% 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 Florist providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 florist answers, a specific provider was named in 44.5% of responses on average — roughly 1.8 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for florist: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Florist questions cover.
The 15 questions behind every percentage on this page were drawn from real florist (ecommerce / online retail; 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 florist 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 florist 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 →