AI SEO Statistics: Jewelry Websites (2026-07 edition)
15 questions · 45 AI responses · 3 models · measured 2026-07-05
The question bank
The questions we tested — sampled from real buyer journeys in jewelry websites.
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
32-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 jewelry websites buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 53% | 47% | 20% | 33% |
| Suggests DIY first | 13% | 20% | 7% | 87% |
| Names specific providers | 40% | 53% | 67% | 67% |
| Gives price or cost info | 27% | 47% | 67% | 47% |
| Tells to check reviews | 33% | 33% | 0% | 47% |
| Tells to verify credentials | 53% | 53% | 13% | 40% |
| Mentions case studies / portfolio | 7% | 0% | 0% | 93% |
| Mentions local proximity | 47% | 27% | 13% | 40% |
| Gives selection criteria | 67% | 73% | 47% | 27% |
| Warns about red flags | 33% | 20% | 33% | 67% |
| Asks a clarifying question | 67% | 60% | 0% | 13% |
| Recommends multiple quotes | 13% | 27% | 0% | 73% |
By model
How each assistant handled Jewelry Websites questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same jewelry websites questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 53.3% (ChatGPT) down to 20% (Gemini), a 33-point gap on an identical question set.
Across the 15 jewelry websites answers it produced, ChatGPT recommended hiring a professional in 53.3% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 40% of answers (about 1.8 distinct providers per answer) and included price or cost information 26.7% of the time. ChatGPT asked a clarifying question before answering in 66.7% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 53.3%, averaging 534 words per answer. On the remaining cues it told the buyer to check reviews in 33.3%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 46.7%; a selection-criteria checklist appeared in 66.7% of its answers and a recommendation to gather multiple quotes in 13.3%.
Across the 15 jewelry websites answers it produced, Claude recommended hiring a professional in 46.7% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 53.3% of answers (about 2.5 distinct providers per answer) and included price or cost information 46.7% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 53.3%, averaging 284 words per answer. On the remaining cues it told the buyer to check reviews in 33.3%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 26.7%; a selection-criteria checklist appeared in 73.3% of its answers and a recommendation to gather multiple quotes in 26.7%.
Across the 15 jewelry websites answers it produced, Gemini recommended hiring a professional in 20% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 66.7% of answers (about 2.5 distinct providers per answer) and included price or cost information 66.7% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 13.3%, averaging 221 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 13.3%; 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 jewelry websites buyer to a professional (53.3%) and Gemini the least (20%). ChatGPT produced the longest answers, at 534 words on average. Specific providers were named most often by Gemini (66.7%) — even there, roughly one answer in 1 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
The divergence index for this study is 31.5 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a jewelry websites buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 66.7% (ChatGPT) — a 67-point spread.
- Gives price or cost information: from 26.7% (ChatGPT) to 66.7% (Gemini) — a 40-point spread.
- Tells the buyer to verify credentials: from 13.3% (Gemini) to 53.3% (ChatGPT) — a 40-point spread.
- Mentions local proximity: from 13.3% (Gemini) to 46.7% (ChatGPT) — a 33-point spread.
- Recommends hiring a professional: from 20% (Gemini) to 53.3% (ChatGPT) — a 33-point spread.
The widest single gap — asks a clarifying question, 67 points — means a jewelry websites 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 jewelry websites market.
Where they agree
The points of near-consensus in Jewelry Websites.
On other behaviors the three models move almost in lockstep — the points of near-consensus for jewelry websites, where all three landed within a few points of each other:
- Mentions case studies or portfolio: 0%–6.7% across all three (a 7-point spread).
- Suggests a DIY approach first: 6.7%–20% across all three (a 13-point spread).
- Warns about red flags or scams: 20%–33.3% across all three (a 13-point spread).
- Gives selection criteria: 46.7%–73.3% across all three (a 27-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 93.3% of questions) and least consistently on "asks a clarifying question" (13.3%).
Every behavior, measured
All twelve coded behaviors for Jewelry Websites, averaged across the three models.
The behaviors AI models reproduce most often for jewelry websites are gives selection criteria (62.2% on average), names a specific provider (53.3%) and gives price or cost information (46.7%); the rarest are mentions case studies or portfolio (2.2%), recommends multiple quotes (13.3%) and suggests a DIY approach first (13.3%). 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:
- Gives selection criteria: 62.2% on average (ChatGPT 66.7%, Claude 73.3%, Gemini 46.7%) — a 27-point spread.
- Names a specific provider: 53.3% on average (ChatGPT 40%, Claude 53.3%, Gemini 66.7%) — a 27-point spread.
- Gives price or cost information: 46.7% on average (ChatGPT 26.7%, Claude 46.7%, Gemini 66.7%) — a 40-point spread.
- Asks a clarifying question: 42.2% on average (ChatGPT 66.7%, Claude 60%, Gemini 0%) — a 67-point spread.
- Recommends hiring a professional: 40% on average (ChatGPT 53.3%, Claude 46.7%, Gemini 20%) — a 33-point spread.
- Tells the buyer to verify credentials: 40% on average (ChatGPT 53.3%, Claude 53.3%, Gemini 13.3%) — a 40-point spread.
- Mentions local proximity: 28.9% on average (ChatGPT 46.7%, Claude 26.7%, Gemini 13.3%) — a 33-point spread.
- Warns about red flags or scams: 28.9% on average (ChatGPT 33.3%, Claude 20%, Gemini 33.3%) — a 13-point spread.
- Tells the buyer to check reviews: 22.2% on average (ChatGPT 33.3%, Claude 33.3%, Gemini 0%) — a 33-point spread.
- Suggests a DIY approach first: 13.3% on average (ChatGPT 13.3%, Claude 20%, Gemini 6.7%) — a 13-point spread.
- Recommends multiple quotes: 13.3% on average (ChatGPT 13.3%, Claude 26.7%, Gemini 0%) — a 27-point spread.
- Mentions case studies or portfolio: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.
Trust signals
How well the models protect the jewelry websites buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the jewelry websites buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 22.2% of answers on average. Verifying credentials or certifications appeared in 40%. Warning about red flags or scams appeared in 28.9%.
On structuring the decision, a selection-criteria checklist showed up in 62.2% of answers on average and a recommendation to gather multiple quotes in 13.3%. The single least-reproduced protective signal for jewelry websites is "recommends multiple quotes" at 13.3% 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 Jewelry Websites providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 jewelry websites answers, a specific provider was named in 53.3% of responses on average — roughly 2.3 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for jewelry websites: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Jewelry Websites questions cover.
The 15 questions behind every percentage on this page were drawn from real jewelry websites (ecommerce / online retail; 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 jewelry websites 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 2026-07-05, the figures describe this specific jewelry websites 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 2026-07-05, 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 →