AI SEO Statistics: Wedding Planner (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 wedding planner.
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
20-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 planner buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 73% | 67% | 73% | 80% |
| Suggests DIY first | 0% | 13% | 0% | 87% |
| Names specific providers | 0% | 0% | 7% | 93% |
| Gives price or cost info | 13% | 33% | 40% | 67% |
| Tells to check reviews | 13% | 13% | 0% | 73% |
| Tells to verify credentials | 27% | 7% | 0% | 73% |
| Mentions case studies / portfolio | 13% | 27% | 13% | 80% |
| Mentions local proximity | 33% | 20% | 7% | 73% |
| Gives selection criteria | 40% | 60% | 47% | 47% |
| Warns about red flags | 13% | 47% | 20% | 60% |
| Asks a clarifying question | 47% | 60% | 0% | 33% |
| Recommends multiple quotes | 20% | 20% | 0% | 73% |
By model
How each assistant handled Wedding Planner questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same wedding planner questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 73.3% (ChatGPT) down to 66.7% (Claude), a 7-point gap on an identical question set.
Across the 15 wedding planner answers it produced, ChatGPT recommended hiring a professional in 73.3% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 26.7%, averaging 545 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 13.3%, 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 20%.
Across the 15 wedding planner answers it produced, Claude recommended hiring a professional in 66.7% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 33.3% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 46.7%, and told the buyer to verify credentials in 6.7%, averaging 295 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 26.7%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 20%.
Across the 15 wedding planner answers it produced, Gemini recommended hiring a professional in 73.3% of them and suggested a DIY approach first 0% 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 20%, and told the buyer to verify credentials in 0%, averaging 273 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 13.3%, and framed the choice around local proximity in 6.7%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a wedding planner buyer to a professional (73.3%) and Claude the least (66.7%). ChatGPT produced the longest answers, at 545 words on average. Specific providers were named most often by Gemini (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 20 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a wedding planner buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 60% (Claude) — a 60-point spread.
- Warns about red flags or scams: from 13.3% (ChatGPT) to 46.7% (Claude) — a 33-point spread.
- Gives price or cost information: from 13.3% (ChatGPT) to 40% (Gemini) — a 27-point spread.
- Tells the buyer to verify credentials: from 0% (Gemini) to 26.7% (ChatGPT) — a 27-point spread.
- Mentions local proximity: from 6.7% (Gemini) to 33.3% (ChatGPT) — a 27-point spread.
The widest single gap — asks a clarifying question, 60 points — means a wedding planner 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 planner market.
Where they agree
The points of near-consensus in Wedding Planner.
On other behaviors the three models move almost in lockstep — the points of near-consensus for wedding planner, where all three landed within a few points of each other:
- Recommends hiring a professional: 66.7%–73.3% across all three (a 7-point spread).
- Names a specific provider: 0%–6.7% across all three (a 7-point spread).
- Suggests a DIY approach first: 0%–13.3% across all three (a 13-point spread).
- Tells the buyer to check reviews: 0%–13.3% across all three (a 13-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 93.3% of questions) and least consistently on "asks a clarifying question" (33.3%).
Every behavior, measured
All twelve coded behaviors for Wedding Planner, averaged across the three models.
The behaviors AI models reproduce most often for wedding planner are recommends hiring a professional (71.1% on average), gives selection criteria (48.9%) and asks a clarifying question (35.6%); the rarest are names a specific provider (2.2%), suggests a DIY approach first (4.4%) and tells the buyer to check reviews (8.9%). 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:
- Recommends hiring a professional: 71.1% on average (ChatGPT 73.3%, Claude 66.7%, Gemini 73.3%) — a 7-point spread.
- Gives selection criteria: 48.9% on average (ChatGPT 40%, Claude 60%, Gemini 46.7%) — a 20-point spread.
- Asks a clarifying question: 35.6% on average (ChatGPT 46.7%, Claude 60%, Gemini 0%) — a 60-point spread.
- Gives price or cost information: 28.9% on average (ChatGPT 13.3%, Claude 33.3%, Gemini 40%) — a 27-point spread.
- Warns about red flags or scams: 26.7% on average (ChatGPT 13.3%, Claude 46.7%, Gemini 20%) — a 33-point spread.
- Mentions local proximity: 20% on average (ChatGPT 33.3%, Claude 20%, Gemini 6.7%) — a 27-point spread.
- Mentions case studies or portfolio: 17.8% on average (ChatGPT 13.3%, Claude 26.7%, Gemini 13.3%) — a 13-point spread.
- Recommends multiple quotes: 13.3% on average (ChatGPT 20%, Claude 20%, Gemini 0%) — a 20-point spread.
- Tells the buyer to verify credentials: 11.1% on average (ChatGPT 26.7%, Claude 6.7%, Gemini 0%) — a 27-point spread.
- Tells the buyer to check reviews: 8.9% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 0%) — a 13-point spread.
- Suggests a DIY approach first: 4.4% on average (ChatGPT 0%, Claude 13.3%, Gemini 0%) — a 13-point spread.
- Names a specific provider: 2.2% on average (ChatGPT 0%, Claude 0%, Gemini 6.7%) — a 7-point spread.
Trust signals
How well the models protect the wedding planner buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the wedding planner buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 8.9% of answers on average. Verifying credentials or certifications appeared in 11.1%. Warning about red flags or scams appeared in 26.7%.
On structuring the decision, a selection-criteria checklist showed up in 48.9% of answers on average and a recommendation to gather multiple quotes in 13.3%. The single least-reproduced protective signal for wedding planner is "tells the buyer to check reviews" at 8.9% 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 Planner providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 wedding planner answers, a specific provider was named in 2.2% 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 wedding planner: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Wedding Planner questions cover.
The 15 questions behind every percentage on this page were drawn from real wedding planner (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 wedding planner 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 wedding planner 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 →