AI SEO Statistics: Wedding Pros (2026-07 edition)
40 questions · 120 AI responses · 3 models · measured 2026-07-06
The question bank
The questions we tested — sampled from real buyer journeys in wedding pros.
Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.
Show all 40 questions
Model by model
21-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 wedding pros buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 58% | 43% | 30% | 60% |
| Suggests DIY first | 20% | 18% | 15% | 78% |
| Names specific providers | 3% | 8% | 10% | 85% |
| Gives price or cost info | 35% | 25% | 33% | 55% |
| Tells to check reviews | 10% | 18% | 0% | 80% |
| Tells to verify credentials | 10% | 5% | 5% | 83% |
| Mentions case studies / portfolio | 10% | 5% | 5% | 83% |
| Mentions local proximity | 23% | 20% | 8% | 78% |
| Gives selection criteria | 48% | 48% | 33% | 45% |
| Warns about red flags | 13% | 18% | 23% | 78% |
| Asks a clarifying question | 65% | 60% | 0% | 25% |
| Recommends multiple quotes | 18% | 13% | 0% | 80% |
By model
How each assistant handled Wedding Pros questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same wedding pros questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 57.5% (ChatGPT) down to 30% (Gemini), a 28-point gap on an identical question set.
Across the 40 wedding pros answers it produced, ChatGPT recommended hiring a professional in 57.5% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 2.5% of answers (about 0.1 distinct providers per answer) and included price or cost information 35% of the time. ChatGPT asked a clarifying question before answering in 65% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 10%, averaging 578 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 10%, and framed the choice around local proximity in 22.5%; a selection-criteria checklist appeared in 47.5% of its answers and a recommendation to gather multiple quotes in 17.5%.
Across the 40 wedding pros answers it produced, Claude recommended hiring a professional in 42.5% of them and suggested a DIY approach first 17.5% of the time. It named a specific provider in 7.5% of answers (about 0.3 distinct providers per answer) and included price or cost information 25% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 17.5%, and told the buyer to verify credentials in 5%, averaging 292 words per answer. On the remaining cues it told the buyer to check reviews in 17.5%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 47.5% of its answers and a recommendation to gather multiple quotes in 12.5%.
Across the 40 wedding pros answers it produced, Gemini recommended hiring a professional in 30% of them and suggested a DIY approach first 15% of the time. It named a specific provider in 10% of answers (about 0.3 distinct providers per answer) and included price or cost information 32.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 22.5%, and told the buyer to verify credentials in 5%, averaging 266 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 7.5%; a selection-criteria checklist appeared in 32.5% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a wedding pros buyer to a professional (57.5%) and Gemini the least (30%). ChatGPT produced the longest answers, at 578 words on average. Specific providers were named most often by Gemini (10%) — even there, roughly one answer in 10 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
The divergence index for this study is 20.7 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a wedding pros buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 65% (ChatGPT) — a 65-point spread.
- Recommends hiring a professional: from 30% (Gemini) to 57.5% (ChatGPT) — a 28-point spread.
- Tells the buyer to check reviews: from 0% (Gemini) to 17.5% (Claude) — a 18-point spread.
- Recommends multiple quotes: from 0% (Gemini) to 17.5% (ChatGPT) — a 18-point spread.
- Mentions local proximity: from 7.5% (Gemini) to 22.5% (ChatGPT) — a 15-point spread.
The widest single gap — asks a clarifying question, 65 points — means a wedding pros 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 wedding pros market.
Where they agree
The points of near-consensus in Wedding Pros.
On other behaviors the three models move almost in lockstep — the points of near-consensus for wedding pros, where all three landed within a few points of each other:
- Suggests a DIY approach first: 15%–20% across all three (a 5-point spread).
- Tells the buyer to verify credentials: 5%–10% across all three (a 5-point spread).
- Mentions case studies or portfolio: 5%–10% across all three (a 5-point spread).
- Names a specific provider: 2.5%–10% across all three (a 8-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "names a specific provider" (identical coding in 85% of questions) and least consistently on "asks a clarifying question" (25%).
Every behavior, measured
All twelve coded behaviors for Wedding Pros, averaged across the three models.
The behaviors AI models reproduce most often for wedding pros are recommends hiring a professional (43.3% on average), gives selection criteria (42.5%) and asks a clarifying question (41.7%); the rarest are mentions case studies or portfolio (6.7%), tells the buyer to verify credentials (6.7%) and names a specific provider (6.7%). Each figure below is the share of a model's 40 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: 43.3% on average (ChatGPT 57.5%, Claude 42.5%, Gemini 30%) — a 28-point spread.
- Gives selection criteria: 42.5% on average (ChatGPT 47.5%, Claude 47.5%, Gemini 32.5%) — a 15-point spread.
- Asks a clarifying question: 41.7% on average (ChatGPT 65%, Claude 60%, Gemini 0%) — a 65-point spread.
- Gives price or cost information: 30.8% on average (ChatGPT 35%, Claude 25%, Gemini 32.5%) — a 10-point spread.
- Suggests a DIY approach first: 17.5% on average (ChatGPT 20%, Claude 17.5%, Gemini 15%) — a 5-point spread.
- Warns about red flags or scams: 17.5% on average (ChatGPT 12.5%, Claude 17.5%, Gemini 22.5%) — a 10-point spread.
- Mentions local proximity: 16.7% on average (ChatGPT 22.5%, Claude 20%, Gemini 7.5%) — a 15-point spread.
- Recommends multiple quotes: 10% on average (ChatGPT 17.5%, Claude 12.5%, Gemini 0%) — a 18-point spread.
- Tells the buyer to check reviews: 9.2% on average (ChatGPT 10%, Claude 17.5%, Gemini 0%) — a 18-point spread.
- Names a specific provider: 6.7% on average (ChatGPT 2.5%, Claude 7.5%, Gemini 10%) — a 8-point spread.
- Tells the buyer to verify credentials: 6.7% on average (ChatGPT 10%, Claude 5%, Gemini 5%) — a 5-point spread.
- Mentions case studies or portfolio: 6.7% on average (ChatGPT 10%, Claude 5%, Gemini 5%) — a 5-point spread.
Trust signals
How well the models protect the wedding pros buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the wedding pros buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 9.2% of answers on average. Verifying credentials or certifications appeared in 6.7%. Warning about red flags or scams appeared in 17.5%.
On structuring the decision, a selection-criteria checklist showed up in 42.5% of answers on average and a recommendation to gather multiple quotes in 10%. The single least-reproduced protective signal for wedding pros is "tells the buyer to verify credentials" at 6.7% 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 Wedding Pros providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 wedding pros answers, a specific provider was named in 6.7% of responses on average — roughly 0.2 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for wedding pros: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Wedding Pros questions cover.
The 40 questions behind every percentage on this page were drawn from real wedding pros (hospitality; 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 wedding pros 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 40 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific wedding pros 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.
40 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 2026-07-06, 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 →