AI SEO Statistics: Tableau Development (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 tableau development.
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
18-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 tableau development buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 53% | 35% | 38% | 63% |
| Suggests DIY first | 15% | 10% | 8% | 78% |
| Names specific providers | 3% | 8% | 13% | 85% |
| Gives price or cost info | 15% | 15% | 20% | 75% |
| Tells to check reviews | 0% | 3% | 5% | 93% |
| Tells to verify credentials | 13% | 3% | 5% | 90% |
| Mentions case studies / portfolio | 30% | 20% | 13% | 68% |
| Mentions local proximity | 13% | 10% | 5% | 90% |
| Gives selection criteria | 43% | 55% | 40% | 50% |
| Warns about red flags | 13% | 20% | 10% | 83% |
| Asks a clarifying question | 60% | 65% | 0% | 13% |
| Recommends multiple quotes | 5% | 0% | 0% | 95% |
By model
How each assistant handled Tableau Development questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same tableau development questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 52.5% (ChatGPT) down to 35% (Claude), a 18-point gap on an identical question set.
Across the 40 tableau development answers it produced, ChatGPT recommended hiring a professional in 52.5% of them and suggested a DIY approach first 15% 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 15% of the time. ChatGPT asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 12.5%, averaging 677 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 30%, and framed the choice around local proximity in 12.5%; a selection-criteria checklist appeared in 42.5% of its answers and a recommendation to gather multiple quotes in 5%.
Across the 40 tableau development answers it produced, Claude recommended hiring a professional in 35% of them and suggested a DIY approach first 10% 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 15% of the time. Claude asked a clarifying question before answering in 65% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 2.5%, averaging 333 words per answer. On the remaining cues it told the buyer to check reviews in 2.5%, pointed to case studies or a portfolio in 20%, and framed the choice around local proximity in 10%; a selection-criteria checklist appeared in 55% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 40 tableau development answers it produced, Gemini recommended hiring a professional in 37.5% of them and suggested a DIY approach first 7.5% of the time. It named a specific provider in 12.5% of answers (about 0.6 distinct providers per answer) and included price or cost information 20% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 5%, averaging 264 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 12.5%, and framed the choice around local proximity in 5%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a tableau development buyer to a professional (52.5%) and Claude the least (35%). ChatGPT produced the longest answers, at 677 words on average. Specific providers were named most often by Gemini (12.5%) — even there, roughly one answer in 8 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
The divergence index for this study is 17.8 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a tableau development buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 65% (Claude) — a 65-point spread.
- Recommends hiring a professional: from 35% (Claude) to 52.5% (ChatGPT) — a 18-point spread.
- Mentions case studies or portfolio: from 12.5% (Gemini) to 30% (ChatGPT) — a 18-point spread.
- Gives selection criteria: from 40% (Gemini) to 55% (Claude) — a 15-point spread.
- Names a specific provider: from 2.5% (ChatGPT) to 12.5% (Gemini) — a 10-point spread.
The widest single gap — asks a clarifying question, 65 points — means a tableau development 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 tableau development market.
Where they agree
The points of near-consensus in Tableau Development.
On other behaviors the three models move almost in lockstep — the points of near-consensus for tableau development, where all three landed within a few points of each other:
- Gives price or cost information: 15%–20% across all three (a 5-point spread).
- Tells the buyer to check reviews: 0%–5% across all three (a 5-point spread).
- Recommends multiple quotes: 0%–5% across all three (a 5-point spread).
- Suggests a DIY approach first: 7.5%–15% across all three (a 8-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "recommends multiple quotes" (identical coding in 95% of questions) and least consistently on "asks a clarifying question" (12.5%).
Every behavior, measured
All twelve coded behaviors for Tableau Development, averaged across the three models.
The behaviors AI models reproduce most often for tableau development are gives selection criteria (45.8% on average), recommends hiring a professional (41.7%) and asks a clarifying question (41.7%); the rarest are recommends multiple quotes (1.7%), tells the buyer to check reviews (2.5%) and tells the buyer to verify credentials (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:
- Gives selection criteria: 45.8% on average (ChatGPT 42.5%, Claude 55%, Gemini 40%) — a 15-point spread.
- Recommends hiring a professional: 41.7% on average (ChatGPT 52.5%, Claude 35%, Gemini 37.5%) — a 18-point spread.
- Asks a clarifying question: 41.7% on average (ChatGPT 60%, Claude 65%, Gemini 0%) — a 65-point spread.
- Mentions case studies or portfolio: 20.8% on average (ChatGPT 30%, Claude 20%, Gemini 12.5%) — a 18-point spread.
- Gives price or cost information: 16.7% on average (ChatGPT 15%, Claude 15%, Gemini 20%) — a 5-point spread.
- Warns about red flags or scams: 14.2% on average (ChatGPT 12.5%, Claude 20%, Gemini 10%) — a 10-point spread.
- Suggests a DIY approach first: 10.8% on average (ChatGPT 15%, Claude 10%, Gemini 7.5%) — a 8-point spread.
- Mentions local proximity: 9.2% on average (ChatGPT 12.5%, Claude 10%, Gemini 5%) — a 8-point spread.
- Names a specific provider: 7.5% on average (ChatGPT 2.5%, Claude 7.5%, Gemini 12.5%) — a 10-point spread.
- Tells the buyer to verify credentials: 6.7% on average (ChatGPT 12.5%, Claude 2.5%, Gemini 5%) — a 10-point spread.
- Tells the buyer to check reviews: 2.5% on average (ChatGPT 0%, Claude 2.5%, Gemini 5%) — a 5-point spread.
- Recommends multiple quotes: 1.7% on average (ChatGPT 5%, Claude 0%, Gemini 0%) — a 5-point spread.
Trust signals
How well the models protect the tableau development buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the tableau development buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 2.5% of answers on average. Verifying credentials or certifications appeared in 6.7%. Warning about red flags or scams appeared in 14.2%.
On structuring the decision, a selection-criteria checklist showed up in 45.8% of answers on average and a recommendation to gather multiple quotes in 1.7%. The single least-reproduced protective signal for tableau development is "recommends multiple quotes" at 1.7% 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 Tableau Development providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 tableau development answers, a specific provider was named in 7.5% of responses on average — roughly 0.3 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for tableau development: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Tableau Development questions cover.
The 40 questions behind every percentage on this page were drawn from real tableau development (technology / SaaS; 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 tableau development 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 tableau development 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 →