AI SEO Statistics: Insurance Agent (2026-07 edition)

Reading the 114 answers model by model shows how differently the three assistants treat the same insurance agent questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 86.8% (ChatGPT) down to 52.6% (Gemini), a 34-point gap on an identical question set.

Across the 38 insurance agent answers it produced, ChatGPT recommended hiring a professional in 86.8% of them and suggested a DIY approach first 18.4% of the time. It named a specific provider in 5.3% of answers (about 0.1 distinct providers per answer) and included price or cost information 0% of the time. ChatGPT asked a clarifying question before answering in 50% of cases, warned about red flags or scams in 21.1%, and told the buyer to verify credentials in 23.7%, averaging 482 words per answer. On the remaining cues it told the buyer to check reviews in 15.8%, pointed to case studies or a portfolio in 7.9%, and framed the choice around local proximity in 18.4%; a selection-criteria checklist appeared in 50% of its answers and a recommendation to gather multiple quotes in 44.7%.

Across the 38 insurance agent answers it produced, Claude recommended hiring a professional in 65.8% of them and suggested a DIY approach first 18.4% of the time. It named a specific provider in 42.1% of answers (about 1.1 distinct providers per answer) and included price or cost information 10.5% of the time. Claude asked a clarifying question before answering in 55.3% of cases, warned about red flags or scams in 21.1%, and told the buyer to verify credentials in 13.2%, averaging 295 words per answer. On the remaining cues it told the buyer to check reviews in 7.9%, pointed to case studies or a portfolio in 2.6%, and framed the choice around local proximity in 18.4%; a selection-criteria checklist appeared in 52.6% of its answers and a recommendation to gather multiple quotes in 26.3%.

Across the 38 insurance agent answers it produced, Gemini recommended hiring a professional in 52.6% of them and suggested a DIY approach first 10.5% of the time. It named a specific provider in 50% of answers (about 1.9 distinct providers per answer) and included price or cost information 18.4% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 18.4%, and told the buyer to verify credentials in 7.9%, averaging 305 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 13.2%; a selection-criteria checklist appeared in 36.8% of its answers and a recommendation to gather multiple quotes in 15.8%.

Taken together, ChatGPT is the assistant most likely to route an insurance agent buyer to a professional (86.8%) and Gemini the least (52.6%). ChatGPT produced the longest answers, at 482 words on average. Specific providers were named most often by Gemini (50%) — 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 insurance agent, where all three landed within a few points of each other:

• Warns about red flags or scams: 18.4%–21.1% across all three (a 3-point spread).
• Mentions local proximity: 13.2%–18.4% across all three (a 5-point spread).
• Suggests a DIY approach first: 10.5%–18.4% across all three (a 8-point spread).
• Mentions case studies or portfolio: 0%–7.9% across all three (a 8-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 89.5% of questions) and least consistently on "asks a clarifying question" (26.3%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the insurance agent buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 7.9% of answers on average. Verifying credentials or certifications appeared in 14.9%. Warning about red flags or scams appeared in 20.2%.

On structuring the decision, a selection-criteria checklist showed up in 46.5% of answers on average and a recommendation to gather multiple quotes in 28.9%. The single least-reproduced protective signal for insurance agent is "tells the buyer to check reviews" at 7.9% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

The 38 questions behind every percentage on this page were drawn from real insurance agent (professional services; 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 insurance agent 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.