AI SEO Statistics: Funeral Home (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 funeral home.
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
24-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 funeral home buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 67% | 60% | 27% | 53% |
| Suggests DIY first | 27% | 20% | 7% | 67% |
| Names specific providers | 7% | 7% | 7% | 87% |
| Gives price or cost info | 33% | 27% | 40% | 53% |
| Tells to check reviews | 7% | 0% | 0% | 93% |
| Tells to verify credentials | 20% | 13% | 0% | 80% |
| Mentions case studies / portfolio | 0% | 0% | 0% | 100% |
| Mentions local proximity | 67% | 53% | 40% | 27% |
| Gives selection criteria | 53% | 53% | 20% | 53% |
| Warns about red flags | 7% | 20% | 13% | 87% |
| Asks a clarifying question | 73% | 80% | 0% | 13% |
| Recommends multiple quotes | 40% | 13% | 13% | 53% |
By model
How each assistant handled Funeral Home questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same funeral home questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 66.7% (ChatGPT) down to 26.7% (Gemini), a 40-point gap on an identical question set.
Across the 15 funeral home answers it produced, ChatGPT recommended hiring a professional in 66.7% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 6.7% of answers (about 0.1 distinct providers per answer) and included price or cost information 33.3% of the time. ChatGPT asked a clarifying question before answering in 73.3% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 20%, averaging 617 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 66.7%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 40%.
Across the 15 funeral home answers it produced, Claude recommended hiring a professional in 60% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 6.7% of answers (about 0.1 distinct providers per answer) and included price or cost information 26.7% of the time. Claude asked a clarifying question before answering in 80% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 13.3%, averaging 311 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 53.3%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 13.3%.
Across the 15 funeral home answers it produced, Gemini recommended hiring a professional in 26.7% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 6.7% of answers (about 0.2 distinct providers per answer) and included price or cost information 40% 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 256 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 40%; a selection-criteria checklist appeared in 20% of its answers and a recommendation to gather multiple quotes in 13.3%.
Taken together, ChatGPT is the assistant most likely to route a funeral home buyer to a professional (66.7%) and Gemini the least (26.7%). ChatGPT produced the longest answers, at 617 words on average. Specific providers were named most often by ChatGPT (6.7%) — even there, roughly one answer in 15 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
The divergence index for this study is 24.1 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a funeral home buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 80% (Claude) — a 80-point spread.
- Recommends hiring a professional: from 26.7% (Gemini) to 66.7% (ChatGPT) — a 40-point spread.
- Gives selection criteria: from 20% (Gemini) to 53.3% (ChatGPT) — a 33-point spread.
- Mentions local proximity: from 40% (Gemini) to 66.7% (ChatGPT) — a 27-point spread.
- Recommends multiple quotes: from 13.3% (Claude) to 40% (ChatGPT) — a 27-point spread.
The widest single gap — asks a clarifying question, 80 points — means a funeral home 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 funeral home market.
Where they agree
The points of near-consensus in Funeral Home.
On other behaviors the three models move almost in lockstep — the points of near-consensus for funeral home, where all three landed within a few points of each other:
- Names a specific provider: 6.7% across all three models.
- Mentions case studies or portfolio: 0% across all three models.
- Tells the buyer to check reviews: 0%–6.7% across all three (a 7-point spread).
- Gives price or cost information: 26.7%–40% across all three (a 13-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 "asks a clarifying question" (13.3%).
Every behavior, measured
All twelve coded behaviors for Funeral Home, averaged across the three models.
The behaviors AI models reproduce most often for funeral home are mentions local proximity (53.3% on average), recommends hiring a professional (51.1%) and asks a clarifying question (51.1%); the rarest are mentions case studies or portfolio (0%), tells the buyer to check reviews (2.2%) and names a specific provider (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:
- Mentions local proximity: 53.3% on average (ChatGPT 66.7%, Claude 53.3%, Gemini 40%) — a 27-point spread.
- Recommends hiring a professional: 51.1% on average (ChatGPT 66.7%, Claude 60%, Gemini 26.7%) — a 40-point spread.
- Asks a clarifying question: 51.1% on average (ChatGPT 73.3%, Claude 80%, Gemini 0%) — a 80-point spread.
- Gives selection criteria: 42.2% on average (ChatGPT 53.3%, Claude 53.3%, Gemini 20%) — a 33-point spread.
- Gives price or cost information: 33.3% on average (ChatGPT 33.3%, Claude 26.7%, Gemini 40%) — a 13-point spread.
- Recommends multiple quotes: 22.2% on average (ChatGPT 40%, Claude 13.3%, Gemini 13.3%) — a 27-point spread.
- Suggests a DIY approach first: 17.8% on average (ChatGPT 26.7%, Claude 20%, Gemini 6.7%) — a 20-point spread.
- Warns about red flags or scams: 13.3% on average (ChatGPT 6.7%, Claude 20%, Gemini 13.3%) — a 13-point spread.
- Tells the buyer to verify credentials: 11.1% on average (ChatGPT 20%, Claude 13.3%, Gemini 0%) — a 20-point spread.
- Names a specific provider: 6.7% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 6.7%).
- Tells the buyer to check reviews: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.
- Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the funeral home buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the funeral home buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 2.2% of answers on average. Verifying credentials or certifications appeared in 11.1%. Warning about red flags or scams appeared in 13.3%.
On structuring the decision, a selection-criteria checklist showed up in 42.2% of answers on average and a recommendation to gather multiple quotes in 22.2%. The single least-reproduced protective signal for funeral home is "tells the buyer to check reviews" at 2.2% 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 Funeral Home providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 funeral home answers, a specific provider was named in 6.7% of responses on average — roughly 0.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for funeral home: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Funeral Home questions cover.
The 15 questions behind every percentage on this page were drawn from real funeral home (home 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 funeral home 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 funeral home 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 →