AI SEO Statistics: SEO Marketing for Flower Shop (2026-07 edition)

Reading the 45 answers model by model shows how differently the three assistants treat the same seo marketing for flower shop 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 33.3% (Claude), a 13-point gap on an identical question set.

Across the 15 seo marketing for flower shop answers it produced, ChatGPT 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 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 60% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 0%, averaging 652 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 20%, and framed the choice around local proximity in 26.7%; a selection-criteria checklist appeared in 26.7% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 seo marketing for flower shop answers it produced, Claude recommended hiring a professional in 33.3% 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. Claude asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 0%, averaging 313 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 33.3%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 seo marketing for flower shop answers it produced, Gemini recommended hiring a professional in 40% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 13.3% of answers (about 0.5 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 0%, and told the buyer to verify credentials in 0%, averaging 261 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 33.3%; a selection-criteria checklist appeared in 20% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a seo marketing for flower shop buyer to a professional (46.7%) and Claude the least (33.3%). ChatGPT produced the longest answers, at 652 words on average. Specific providers were named most often by Gemini (13.3%) — even there, roughly one answer in 8 carried a name.

On other behaviors the three models move almost in lockstep — the points of near-consensus for seo marketing for flower shop, 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 local proximity: 26.7%–33.3% across all three (a 7-point spread).
• Gives price or cost information: 13.3%–20% across all three (a 7-point spread).

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" (13.3%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the seo marketing for flower shop 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 26.7% of answers on average and a recommendation to gather multiple quotes in 2.2%. The single least-reproduced protective signal for seo marketing for flower shop 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.

The 15 questions behind every percentage on this page form a frozen seo marketing for flower shop (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 seo marketing for flower shop 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.