Original research · 2026-07 edition

AI SEO Statistics: Marketing Agency (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

See how AI talks about YOUR businessFull data table

The question bank

The questions we tested — sampled from real buyer journeys in marketing agency.

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

I've been trying to run Facebook ads for my local bakery but I'm just burning money; how do I know if it's time to hire a professional instead of doing it myself?
What is the typical price range for a small business SEO audit and a six-month strategy plan?
How can I tell if a marketing agency actually has experience in the B2B SaaS space or if they're just using a generic template?
Should I hire a freelancer for social media management or go with a full-service agency if I want to scale quickly?
What are the biggest red flags to look for when reviewing a marketing agency's portfolio or case studies?
My current agency isn't giving me clear reports on conversions; what specific metrics should I be demanding in our monthly meetings?
Is it better to pay a marketing agency a flat monthly retainer or a percentage of my total ad spend?
How long does it usually take to see actual sales results after hiring a digital marketing firm for a brand-new e-commerce site?
Show all 15 questions
I need to rebrand my construction company before a big trade show in three weeks; is that a realistic timeline for an agency to handle?
What's the difference between a lead generation agency and a general brand awareness agency, and which one do I need for a service-based business?
How do I verify the references of a marketing agency to make sure their past clients were actually satisfied?
If I have a marketing budget of $5,000 a month, how much of that should go toward the agency's fee versus the actual ad spend?
Does it matter if my marketing agency is local to my city, or is it fine to work with a remote firm for content creation?
What are some common hidden costs in marketing agency contracts that I should look out for before signing?
How do I transition my accounts and data away from my current agency if I'm not happy with their performance?

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 marketing agency buyers.

Behavior rates across 15 marketing agency buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional87%33%40%40%
Suggests DIY first27%20%7%73%
Names specific providers0%7%0%93%
Gives price or cost info27%40%33%80%
Tells to check reviews7%7%0%87%
Tells to verify credentials0%0%0%100%
Mentions case studies / portfolio20%13%13%60%
Mentions local proximity7%13%7%80%
Gives selection criteria40%60%53%27%
Warns about red flags20%27%33%53%
Asks a clarifying question27%47%0%53%
Recommends multiple quotes7%7%0%87%

By model

How each assistant handled Marketing Agency questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same marketing agency questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 86.7% (ChatGPT) down to 33.3% (Claude), a 53-point gap on an identical question set.

Across the 15 marketing agency answers it produced, ChatGPT recommended hiring a professional in 86.7% of them and suggested a DIY approach first 26.7% 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 20%, and told the buyer to verify credentials in 0%, averaging 674 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, 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 6.7%.

Across the 15 marketing agency answers it produced, Claude recommended hiring a professional in 33.3% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 6.7% of answers (about 0.1 distinct providers per answer) and included price or cost information 40% of the time. Claude asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 0%, averaging 333 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, pointed to case studies or a portfolio in 13.3%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 marketing agency answers it produced, Gemini recommended hiring a professional in 40% 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 33.3% 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 0%, averaging 245 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 53.3% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a marketing agency buyer to a professional (86.7%) and Claude the least (33.3%). ChatGPT produced the longest answers, at 674 words on average. Specific providers were named most often by Claude (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 20.4 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a marketing agency buyer happens to ask matters most:

  • Recommends hiring a professional: from 33.3% (Claude) to 86.7% (ChatGPT) — a 53-point spread.
  • Asks a clarifying question: from 0% (Gemini) to 46.7% (Claude) — a 47-point spread.
  • Suggests a DIY approach first: from 6.7% (Gemini) to 26.7% (ChatGPT) — a 20-point spread.
  • Gives selection criteria: from 40% (ChatGPT) to 60% (Claude) — a 20-point spread.
  • Gives price or cost information: from 26.7% (ChatGPT) to 40% (Claude) — a 13-point spread.

The widest single gap — recommends hiring a professional, 53 points — means a marketing agency 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 marketing agency market.

Where they agree

The points of near-consensus in Marketing Agency.

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

  • Tells the buyer to verify credentials: 0% across all three models.
  • Mentions local proximity: 6.7%–13.3% across all three (a 7-point spread).
  • Names a specific provider: 0%–6.7% across all three (a 7-point spread).
  • Tells the buyer to check reviews: 0%–6.7% across all three (a 7-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 "gives selection criteria" (26.7%).

Every behavior, measured

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

The behaviors AI models reproduce most often for marketing agency are recommends hiring a professional (53.3% on average), gives selection criteria (51.1%) and gives price or cost information (33.3%); the rarest are tells the buyer to verify credentials (0%), names a specific provider (2.2%) and recommends multiple quotes (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: 53.3% on average (ChatGPT 86.7%, Claude 33.3%, Gemini 40%) — a 53-point spread.
  • Gives selection criteria: 51.1% on average (ChatGPT 40%, Claude 60%, Gemini 53.3%) — a 20-point spread.
  • Gives price or cost information: 33.3% on average (ChatGPT 26.7%, Claude 40%, Gemini 33.3%) — a 13-point spread.
  • Warns about red flags or scams: 26.7% on average (ChatGPT 20%, Claude 26.7%, Gemini 33.3%) — a 13-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: 17.8% on average (ChatGPT 26.7%, Claude 20%, Gemini 6.7%) — a 20-point spread.
  • Mentions case studies or portfolio: 15.5% on average (ChatGPT 20%, Claude 13.3%, Gemini 13.3%) — a 7-point spread.
  • Mentions local proximity: 8.9% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 6.7%) — a 7-point spread.
  • Tells the buyer to check reviews: 4.5% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 0%) — a 7-point spread.
  • Recommends multiple quotes: 4.5% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 0%) — a 7-point spread.
  • Names a specific provider: 2.2% on average (ChatGPT 0%, Claude 6.7%, Gemini 0%) — a 7-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 marketing agency buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the marketing agency buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 4.5% of answers on average. Verifying credentials or certifications appeared in 0%. Warning about red flags or scams appeared in 26.7%.

On structuring the decision, a selection-criteria checklist showed up in 51.1% of answers on average and a recommendation to gather multiple quotes in 4.5%. The single least-reproduced protective signal for marketing agency 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 Marketing Agency providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 marketing agency answers, a specific provider was named in 2.2% of responses on average — roughly 0 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for marketing agency: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Marketing Agency questions cover.

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