AI SEO Statistics: Insurance Agent (2026-07 edition)
38 questions · 114/114 expected AI responses · 3 models · measured 2026-07-06
Apply these findings to your insurance agent SEO strategy.
This benchmark explains how AI assistants advise buyers. The related service page turns those findings into the technical, content, authority, and conversion priorities for this market.
The question bank
The questions we tested: a frozen buyer-intent benchmark for insurance agent.
The question set was curated from a predefined buyer-intent taxonomy and held constant for this edition. Each model received the same wording. These are the prompts behind every percentage on this page.
Show all 38 questions
Model by model
24.9% question-level model disagreement.
This rate is the average pairwise disagreement between binary behavior codes across questions and behaviors. It is not the gap between the highest and lowest aggregated model percentages.
Behavior prevalence across 38 insurance agent benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 86.8% | 65.8% | 52.6% | 68.4% |
| Suggests DIY first | 18.4% | 18.4% | 10.5% | 15.8% |
| Names specific providers | 5.3% | 42.1% | 50% | 32.5% |
| Gives price or cost info | 0% | 10.5% | 18.4% | 9.6% |
| Tells to check reviews | 15.8% | 7.9% | 0% | 7.9% |
| Tells to verify credentials | 23.7% | 13.2% | 7.9% | 14.9% |
| Mentions case studies / portfolio | 7.9% | 2.6% | 0% | 3.5% |
| Mentions local proximity | 18.4% | 18.4% | 13.2% | 16.7% |
| Gives selection criteria | 50% | 52.6% | 36.8% | 46.5% |
| Warns about red flags | 21.1% | 21.1% | 18.4% | 20.2% |
| Asks a clarifying question | 50% | 55.3% | 0% | 35.1% |
| Recommends multiple quotes | 44.7% | 26.3% | 15.8% | 28.9% |
Question-level agreement
How often all measured models received the same binary code.
Agreement is calculated question by question for each behavior. A high value can coexist with a low behavior prevalence; it means the models usually agreed on whether the behavior appeared.
All-model binary agreement by behavior across 38 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 60.5% |
| Suggests DIY first | 81.6% |
| Names specific providers | 39.5% |
| Gives price or cost info | 73.7% |
| Tells to check reviews | 81.6% |
| Tells to verify credentials | 65.8% |
| Mentions case studies / portfolio | 89.5% |
| Mentions local proximity | 68.4% |
| Gives selection criteria | 42.1% |
| Warns about red flags | 65.8% |
| Asks a clarifying question | 26.3% |
| Recommends multiple quotes | 57.9% |
By model
How each assistant handled Insurance Agent questions.
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.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 24.9%. This is the average pairwise rate at which models received different binary codes across questions and behaviors. The observed rate spreads below are a separate measure showing where which assistant an insurance agent buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 55.3% (Claude). The spread is 55 points.
- Names a specific provider: from 5.3% (ChatGPT) to 50% (Gemini). The spread is 45 points.
- Recommends hiring a professional: from 52.6% (Gemini) to 86.8% (ChatGPT). The spread is 34 points.
- Recommends multiple quotes: from 15.8% (Gemini) to 44.7% (ChatGPT). The spread is 29 points.
- Gives price or cost information: from 0% (ChatGPT) to 18.4% (Gemini). The spread is 18 points.
The widest single gap concerns asks a clarifying question at 55 points. This means an insurance agent 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 insurance agent market.
Where they agree
The points of near-consensus in Insurance Agent.
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%).
Every behavior, measured
All twelve coded behaviors for Insurance Agent, averaged across the three models.
The behaviors AI models reproduce most often for insurance agent are recommends hiring a professional (68.4% on average), gives selection criteria (46.5%) and asks a clarifying question (35.1%); the rarest are mentions case studies or portfolio (3.5%), tells the buyer to check reviews (7.9%) and gives price or cost information (9.6%). Each figure below is the share of a model's 38 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:
- Recommends hiring a professional: 68.4% on average (ChatGPT 86.8%, Claude 65.8%, Gemini 52.6%). The spread is 34 points.
- Gives selection criteria: 46.5% on average (ChatGPT 50%, Claude 52.6%, Gemini 36.8%). The spread is 16 points.
- Asks a clarifying question: 35.1% on average (ChatGPT 50%, Claude 55.3%, Gemini 0%). The spread is 55 points.
- Names a specific provider: 32.5% on average (ChatGPT 5.3%, Claude 42.1%, Gemini 50%). The spread is 45 points.
- Recommends multiple quotes: 28.9% on average (ChatGPT 44.7%, Claude 26.3%, Gemini 15.8%). The spread is 29 points.
- Warns about red flags or scams: 20.2% on average (ChatGPT 21.1%, Claude 21.1%, Gemini 18.4%). The spread is 3 points.
- Mentions local proximity: 16.7% on average (ChatGPT 18.4%, Claude 18.4%, Gemini 13.2%). The spread is 5 points.
- Suggests a DIY approach first: 15.8% on average (ChatGPT 18.4%, Claude 18.4%, Gemini 10.5%). The spread is 8 points.
- Tells the buyer to verify credentials: 14.9% on average (ChatGPT 23.7%, Claude 13.2%, Gemini 7.9%). The spread is 16 points.
- Gives price or cost information: 9.6% on average (ChatGPT 0%, Claude 10.5%, Gemini 18.4%). The spread is 18 points.
- Tells the buyer to check reviews: 7.9% on average (ChatGPT 15.8%, Claude 7.9%, Gemini 0%). The spread is 16 points.
- Mentions case studies or portfolio: 3.5% on average (ChatGPT 7.9%, Claude 2.6%, Gemini 0%). The spread is 8 points.
Trust signals
How well the models protect the insurance agent buyer.
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. This is 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 Insurance Agent providers?
For service providers the decisive question is whether these systems name anyone at all. Across 114 insurance agent answers, a specific provider was named in 32.5% of responses on average, or roughly 1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for insurance agent: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 38 Insurance Agent questions cover.
The 38 questions behind every percentage on this page form a frozen insurance agent (professional services; 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 insurance agent question set rather than 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 38 answers in which the behavior appeared at least once. It is not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific insurance agent 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.
38 frozen benchmark questions, one expected response per model per question (ChatGPT API (gpt-5-mini), Claude API (claude-sonnet-5), Gemini API (gemini-3-flash-preview)), collected 2026-07-06 and coded against a fixed 12-behavior rubric. The pipeline validates the schema, recomputes aggregates and reports consistency issues. AI outputs vary with model version, location and time, so the figures describe this edition's exact sample and measurement window. Read the full methodology →
Citation
Cite this edition.
Authority Specialist. “AI SEO Statistics: Insurance Agent (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/professional/insurance-agent