AI SEO Statistics: Wine Shop (2026-07 edition)

Reading the 45 answers model by model shows how differently the three assistants treat the same wine shop questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 40% (ChatGPT) down to 20% (Gemini), a 20-point gap on an identical question set.

Across the 15 wine shop answers it produced, ChatGPT 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 40% of answers (about 1.9 distinct providers per answer) and included price or cost information 6.7% of the time. ChatGPT asked a clarifying question before answering in 33.3% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 6.7%, averaging 530 words per answer. On the remaining cues it told the buyer to check reviews in 20%, 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 40% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 wine 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 33.3% of answers (about 2.9 distinct providers per answer) and included price or cost information 20% of the time. Claude asked a clarifying question before answering in 53.3% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 6.7%, averaging 291 words per answer. On the remaining cues it told the buyer to check reviews in 33.3%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 wine shop answers it produced, Gemini recommended hiring a professional in 20% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 46.7% of answers (about 1.3 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 13.3%, and told the buyer to verify credentials in 0%, averaging 225 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 13.3%; a selection-criteria checklist appeared in 26.7% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a wine shop buyer to a professional (40%) and Gemini the least (20%). ChatGPT produced the longest answers, at 530 words on average. Specific providers were named most often by Gemini (46.7%) — even there, roughly one answer in 2 carried a name.

On other behaviors the three models move almost in lockstep — the points of near-consensus for wine shop, where all three landed within a few points of each other:

• Mentions case studies or portfolio: 0% across all three models.
• Suggests a DIY approach first: 13.3%–20% across all three (a 7-point spread).
• Tells the buyer to verify credentials: 0%–6.7% across all three (a 7-point spread).
• Warns about red flags or scams: 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 "mentions case studies or portfolio" (identical coding in 100% of questions) and least consistently on "names a specific provider" (33.3%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the wine shop buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 20% of answers on average. Verifying credentials or certifications appeared in 4.5%. Warning about red flags or scams appeared in 15.5%.

On structuring the decision, a selection-criteria checklist showed up in 40% of answers on average and a recommendation to gather multiple quotes in 6.7%. The single least-reproduced protective signal for wine shop 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.

The 15 questions behind every percentage on this page were drawn from real wine shop (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 wine 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.