Original research · 2026-07 edition

AI SEO Statistics: Retail (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 retail.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

I'm struggling to get traffic to my new online boutique, should I hire an SEO person or an ads specialist first?
What's the average monthly retainer for a full-service e-commerce management agency in 2024?
How do I know if a web developer is actually good or just using basic templates for my store?
My conversion rate is under 1% and I can't figure out why, who do I hire to audit my site and user experience?
Is it worth paying $5,000 for a custom store design or should I just stick with a premium pre-made theme?
What are some red flags to look out for when interviewing a third-party logistics provider for a growing small business?
I want to move from a marketplace to my own independent site, how much would a migration expert cost for about 200 products?
How do I compare the ROI of hiring a social media influencer agency versus a traditional PR firm for a retail brand?
Show all 15 questions
What specific questions should I ask a retail consultant about scaling from $10k to $100k in monthly revenue?
Our checkout keeps crashing during high-traffic product drops, do we need a dedicated technical specialist or just better hosting?
Can I realistically manage my own digital ad campaigns with a $2k budget or is it better to pay a freelancer to do it?
What are the pros and cons of hiring a local e-commerce agency versus an offshore team for a complex site build?
I need to integrate my physical store's inventory with my website, what kind of specialist handles POS and online syncing?
How do I vet an agency that claims they can help me expand onto multiple global marketplaces and handle international shipping?
My current marketing agency isn't hitting the return on spend they promised, at what point should I fire them and look for someone new?

Model by model

19-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 retail buyers.

Behavior rates across 15 retail buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional80%60%53%53%
Suggests DIY first7%47%13%53%
Names specific providers0%7%13%80%
Gives price or cost info27%33%33%53%
Tells to check reviews0%13%0%87%
Tells to verify credentials0%0%0%100%
Mentions case studies / portfolio7%20%7%73%
Mentions local proximity0%7%7%87%
Gives selection criteria27%40%47%60%
Warns about red flags7%20%20%80%
Asks a clarifying question27%47%0%53%
Recommends multiple quotes13%13%0%80%

By model

How each assistant handled Retail questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same retail questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 80% (ChatGPT) down to 53.3% (Gemini), a 27-point gap on an identical question set.

Across the 15 retail answers it produced, ChatGPT recommended hiring a professional in 80% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 26.7% of the time. ChatGPT asked a clarifying question before answering in 26.7% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 0%, averaging 669 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 26.7% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 retail answers it produced, Claude recommended hiring a professional in 60% of them and suggested a DIY approach first 46.7% of the time. It named a specific provider in 6.7% of answers (about 0.3 distinct providers per answer) and included price or cost information 33.3% of the time. Claude asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 0%, averaging 336 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 20%, and framed the choice around local proximity in 6.7%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 retail answers it produced, Gemini 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 13.3% of answers (about 0.7 distinct providers per answer) and included price or cost information 33.3% 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 258 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 6.7%, 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 retail buyer to a professional (80%) and Gemini the least (53.3%). ChatGPT produced the longest answers, at 669 words on average. Specific providers were named most often by Gemini (13.3%) — 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 18.9 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a retail buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 46.7% (Claude) — a 47-point spread.
  • Suggests a DIY approach first: from 6.7% (ChatGPT) to 46.7% (Claude) — a 40-point spread.
  • Recommends hiring a professional: from 53.3% (Gemini) to 80% (ChatGPT) — a 27-point spread.
  • Gives selection criteria: from 26.7% (ChatGPT) to 46.7% (Gemini) — a 20-point spread.
  • Names a specific provider: from 0% (ChatGPT) to 13.3% (Gemini) — a 13-point spread.

The widest single gap — asks a clarifying question, 47 points — means a retail 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 retail market.

Where they agree

The points of near-consensus in Retail.

On other behaviors the three models move almost in lockstep — the points of near-consensus for retail, where all three landed within a few points of each other:

  • Tells the buyer to verify credentials: 0% across all three models.
  • Gives price or cost information: 26.7%–33.3% across all three (a 7-point spread).
  • Mentions local proximity: 0%–6.7% across all three (a 7-point spread).
  • Names a specific provider: 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 "tells the buyer to verify credentials" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (53.3%).

Every behavior, measured

All twelve coded behaviors for Retail, averaged across the three models.

The behaviors AI models reproduce most often for retail are recommends hiring a professional (64.4% on average), gives selection criteria (37.8%) and gives price or cost information (31.1%); the rarest are tells the buyer to verify credentials (0%), tells the buyer to check reviews (4.4%) and mentions local proximity (4.5%). 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: 64.4% on average (ChatGPT 80%, Claude 60%, Gemini 53.3%) — a 27-point spread.
  • Gives selection criteria: 37.8% on average (ChatGPT 26.7%, Claude 40%, Gemini 46.7%) — a 20-point spread.
  • Gives price or cost information: 31.1% on average (ChatGPT 26.7%, Claude 33.3%, Gemini 33.3%) — a 7-point spread.
  • Asks a clarifying question: 24.5% on average (ChatGPT 26.7%, Claude 46.7%, Gemini 0%) — a 47-point spread.
  • Suggests a DIY approach first: 22.2% on average (ChatGPT 6.7%, Claude 46.7%, Gemini 13.3%) — a 40-point spread.
  • Warns about red flags or scams: 15.6% on average (ChatGPT 6.7%, Claude 20%, Gemini 20%) — a 13-point spread.
  • Mentions case studies or portfolio: 11.1% on average (ChatGPT 6.7%, Claude 20%, Gemini 6.7%) — a 13-point spread.
  • Recommends multiple quotes: 8.9% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 0%) — a 13-point spread.
  • Names a specific provider: 6.7% on average (ChatGPT 0%, Claude 6.7%, Gemini 13.3%) — a 13-point spread.
  • Mentions local proximity: 4.5% on average (ChatGPT 0%, Claude 6.7%, Gemini 6.7%) — a 7-point spread.
  • Tells the buyer to check reviews: 4.4% on average (ChatGPT 0%, Claude 13.3%, Gemini 0%) — a 13-point spread.
  • Tells the buyer to verify credentials: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the retail buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the retail buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 4.4% of answers on average. Verifying credentials or certifications appeared in 0%. Warning about red flags or scams appeared in 15.6%.

On structuring the decision, a selection-criteria checklist showed up in 37.8% of answers on average and a recommendation to gather multiple quotes in 8.9%. The single least-reproduced protective signal for retail is "tells the buyer to verify credentials" at 0% 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 Retail providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 retail answers, a specific provider was named in 6.7% 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 retail: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Retail questions cover.

The 15 questions behind every percentage on this page were drawn from real retail (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 retail 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 retail 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 →