AI SEO Statistics: Delivery Service (2026-07 edition)
40 questions · 120/120 expected AI responses · 3 models · measured 2026-07-06
Apply these findings to your delivery service SEO strategy.
This benchmark explains how AI assistants advise buyers. The related service page turns those findings into the technical, content, authority, and conversion priorities for this market.
The question bank
The questions we tested: a frozen buyer-intent benchmark for delivery service.
The question set was curated from a predefined buyer-intent taxonomy and held constant for this edition. Each model received the same wording. These are the prompts behind every percentage on this page.
Show all 40 questions
Model by model
23.2% question-level model disagreement.
This rate is the average pairwise disagreement between binary behavior codes across questions and behaviors. It is not the gap between the highest and lowest aggregated model percentages.
Behavior prevalence across 40 delivery service benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 62.5% | 62.5% | 57.5% | 60.8% |
| Suggests DIY first | 22.5% | 15% | 7.5% | 15% |
| Names specific providers | 37.5% | 57.5% | 62.5% | 52.5% |
| Gives price or cost info | 20% | 20% | 30% | 23.3% |
| Tells to check reviews | 15% | 20% | 0% | 11.7% |
| Tells to verify credentials | 27.5% | 17.5% | 7.5% | 17.5% |
| Mentions case studies / portfolio | 0% | 2.5% | 0% | 0.8% |
| Mentions local proximity | 50% | 47.5% | 40% | 45.8% |
| Gives selection criteria | 55% | 55% | 30% | 46.7% |
| Warns about red flags | 12.5% | 10% | 5% | 9.2% |
| Asks a clarifying question | 62.5% | 67.5% | 2.5% | 44.2% |
| Recommends multiple quotes | 27.5% | 15% | 0% | 14.2% |
Question-level agreement
How often all measured models received the same binary code.
Agreement is calculated question by question for each behavior. A high value can coexist with a low behavior prevalence; it means the models usually agreed on whether the behavior appeared.
All-model binary agreement by behavior across 40 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 82.5% |
| Suggests DIY first | 75% |
| Names specific providers | 50% |
| Gives price or cost info | 70% |
| Tells to check reviews | 70% |
| Tells to verify credentials | 75% |
| Mentions case studies / portfolio | 97.5% |
| Mentions local proximity | 45% |
| Gives selection criteria | 47.5% |
| Warns about red flags | 87.5% |
| Asks a clarifying question | 15% |
| Recommends multiple quotes | 67.5% |
By model
How each assistant handled Delivery Service questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same delivery service questions. On the most consequential behavior, whether to send the buyer to a professional at all, the rate ranged from 62.5% (ChatGPT) down to 57.5% (Gemini), a 5-point gap on an identical question set.
Across the 40 delivery service answers it produced, ChatGPT recommended hiring a professional in 62.5% of them and suggested a DIY approach first 22.5% of the time. It named a specific provider in 37.5% of answers (about 2 distinct providers per answer) and included price or cost information 20% of the time. ChatGPT asked a clarifying question before answering in 62.5% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 27.5%, averaging 505 words per answer. On the remaining cues it told the buyer to check reviews in 15%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 50%; a selection-criteria checklist appeared in 55% of its answers and a recommendation to gather multiple quotes in 27.5%.
Across the 40 delivery service answers it produced, Claude recommended hiring a professional in 62.5% of them and suggested a DIY approach first 15% of the time. It named a specific provider in 57.5% of answers (about 2.6 distinct providers per answer) and included price or cost information 20% of the time. Claude asked a clarifying question before answering in 67.5% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 17.5%, averaging 272 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 47.5%; a selection-criteria checklist appeared in 55% of its answers and a recommendation to gather multiple quotes in 15%.
Across the 40 delivery service answers it produced, Gemini recommended hiring a professional in 57.5% of them and suggested a DIY approach first 7.5% of the time. It named a specific provider in 62.5% of answers (about 3 distinct providers per answer) and included price or cost information 30% of the time. Gemini asked a clarifying question before answering in 2.5% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 7.5%, averaging 264 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 30% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a buyer researching delivery service toward professional help (62.5%) and Gemini the least (57.5%). ChatGPT produced the longest answers, at 505 words on average. Specific providers were named most often by Gemini (62.5%). Even there, roughly one answer in 2 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 23.2%. This is the average pairwise rate at which models received different binary codes across questions and behaviors. The observed rate spreads below are a separate measure showing where the choice of assistant matters most for a buyer researching delivery service:
- Asks a clarifying question: from 2.5% (Gemini) to 67.5% (Claude). The spread is 65 points.
- Recommends multiple quotes: from 0% (Gemini) to 27.5% (ChatGPT). The spread is 28 points.
- Names a specific provider: from 37.5% (ChatGPT) to 62.5% (Gemini). The spread is 25 points.
- Gives selection criteria: from 30% (Gemini) to 55% (ChatGPT). The spread is 25 points.
- Tells the buyer to check reviews: from 0% (Gemini) to 20% (Claude). The spread is 20 points.
The widest single gap concerns asks a clarifying question at 65 points. This means a buyer researching delivery service 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 delivery service market.
Where they agree
The points of near-consensus in Delivery Service.
On other behaviors the three models move almost in lockstep. The points of near-consensus for delivery service, where all three landed within a few points of each other:
- Mentions case studies or portfolio: 0%–2.5% across all three (a 3-point spread).
- Recommends hiring a professional: 57.5%–62.5% across all three (a 5-point spread).
- Warns about red flags or scams: 5%–12.5% across all three (a 8-point spread).
- Gives price or cost information: 20%–30% across all three (a 10-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 97.5% of questions) and least consistently on "asks a clarifying question" (15%).
Every behavior, measured
All twelve coded behaviors for Delivery Service, averaged across the three models.
The behaviors AI models reproduce most often for delivery service are recommends hiring a professional (60.8% on average), names a specific provider (52.5%) and gives selection criteria (46.7%); the rarest are mentions case studies or portfolio (0.8%), warns about red flags or scams (9.2%) and tells the buyer to check reviews (11.7%). 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: 60.8% on average (ChatGPT 62.5%, Claude 62.5%, Gemini 57.5%). The spread is 5 points.
- Names a specific provider: 52.5% on average (ChatGPT 37.5%, Claude 57.5%, Gemini 62.5%). The spread is 25 points.
- Gives selection criteria: 46.7% on average (ChatGPT 55%, Claude 55%, Gemini 30%). The spread is 25 points.
- Mentions local proximity: 45.8% on average (ChatGPT 50%, Claude 47.5%, Gemini 40%). The spread is 10 points.
- Asks a clarifying question: 44.2% on average (ChatGPT 62.5%, Claude 67.5%, Gemini 2.5%). The spread is 65 points.
- Gives price or cost information: 23.3% on average (ChatGPT 20%, Claude 20%, Gemini 30%). The spread is 10 points.
- Tells the buyer to verify credentials: 17.5% on average (ChatGPT 27.5%, Claude 17.5%, Gemini 7.5%). The spread is 20 points.
- Suggests a DIY approach first: 15% on average (ChatGPT 22.5%, Claude 15%, Gemini 7.5%). The spread is 15 points.
- Recommends multiple quotes: 14.2% on average (ChatGPT 27.5%, Claude 15%, Gemini 0%). The spread is 28 points.
- Tells the buyer to check reviews: 11.7% on average (ChatGPT 15%, Claude 20%, Gemini 0%). The spread is 20 points.
- Warns about red flags or scams: 9.2% on average (ChatGPT 12.5%, Claude 10%, Gemini 5%). The spread is 8 points.
- Mentions case studies or portfolio: 0.8% on average (ChatGPT 0%, Claude 2.5%, Gemini 0%). The spread is 3 points.
Trust signals
How well the models protect the delivery service buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the delivery service buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 11.7% of answers on average. Verifying credentials or certifications appeared in 17.5%. Warning about red flags or scams appeared in 9.2%.
On structuring the decision, a selection-criteria checklist showed up in 46.7% of answers on average and a recommendation to gather multiple quotes in 14.2%. The single least-reproduced protective signal for delivery service is "warns about red flags or scams" at 9.2% on average. This is 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 Delivery Service providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 delivery service answers, a specific provider was named in 52.5% of responses on average, or roughly 2.5 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for delivery service: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Delivery Service questions cover.
The 40 questions behind every percentage on this page form a frozen delivery service (professional services; buyer hiring decisions for this specific service) buyer-intent benchmark. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact delivery service question set rather than 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. It is not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific delivery service 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 frozen benchmark questions, one expected response per model per question (ChatGPT API (gpt-5-mini), Claude API (claude-sonnet-5), Gemini API (gemini-3-flash-preview)), collected 2026-07-06 and coded against a fixed 12-behavior rubric. The pipeline validates the schema, recomputes aggregates and reports consistency issues. AI outputs vary with model version, location and time, so the figures describe this edition's exact sample and measurement window. Read the full methodology →
Citation
Cite this edition.
Authority Specialist. “AI SEO Statistics: Delivery Service (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/professional/delivery-service