Original research · 2026-07 edition

AI SEO Statistics: Ivf Clinic SEO Marketing (2026-07 edition)

40 questions · 120/120 expected AI responses · 3 models · measured 2026-07-06

The question bank

The questions we tested — a frozen buyer-intent benchmark for ivf clinic seo marketing.

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.

Why is my fertility clinic's website not ranking for IVF near me anymore?
How long does it typically take to see an increase in new patient inquiries from SEO?
Is it better to hire a general SEO agency or one that only works with medical clinics?
What specific keywords should an IVF clinic target to attract patients looking for egg freezing?
How do I know if my current marketing agency is actually doing SEO or just running ads?
What is a realistic monthly budget for SEO for a multi-location fertility center?
Can I handle my own blog posts for the clinic or do I need a medical writer for SEO?
How does HIPAA compliance affect what we can do with SEO and patient reviews?
Show all 40 questions
Should we focus on cheap IVF keywords or high-intent phrases like best success rates?
How do I improve my clinic's Google Business Profile to show up in the map pack?
What are the red flags when interviewing a digital marketing agency for a healthcare practice?
How do we compete with big national fertility networks in local search results?
Does having patient testimonials on our site help our search engine rankings?
What's the difference between medical SEO and standard business SEO?
How much of our marketing budget should be allocated to organic search vs social media?
We just opened a new satellite clinic how do we get it to rank quickly in that new city?
How can we rank for specific procedures like ICSI or PGT-A testing?
Is it worth paying for a premium SEO audit before hiring a full-time agency?
How do we track if a patient actually came from an organic search result?
Why is our bounce rate so high on our cost of IVF page?
What kind of backlinks are most valuable for a healthcare and medical niche?
Can SEO help us attract more international patients looking for fertility treatments?
Do we need to change our website platform like WordPress to something else for better SEO?
How often should we be publishing new content to stay competitive in the fertility space?
What questions should I ask a marketing agency about their experience with E-E-A-T for medical sites?
Is it better to focus on video content or written blogs for IVF SEO this year?
How do I fix a sudden drop in organic traffic to our clinic's website?
Should we hire an in-house marketing person or outsource to a specialized IVF SEO agency?
What are the most common SEO mistakes fertility clinics make on their service pages?
How do we optimize our site for voice search queries like where is the best IVF doctor?
Can an SEO agency help us manage our online reputation and negative reviews?
How do I compare the ROI of SEO against the cost per lead of Google Ads for IVF?
What metrics should be in a monthly SEO report for a healthcare provider?
Does page load speed really impact how many patients book a consultation?
How can we use SEO to promote our upcoming free fertility seminars?
What is the process for migrating our clinic's website without losing all our current rankings?
How do we rank for fertility clinic when there are so many hospital-based competitors?
Is content about LGBTQ+ family building good for SEO or is it too niche?
How do I know if an agency is using black hat techniques that might get my clinic banned?
What should be the first priority for a brand new IVF clinic's digital marketing strategy?

Model by model

16% 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 matrixModel-by-model evidence
Measured

Behavior prevalence across 40 ivf clinic seo marketing benchmark questions, 2026-07 edition. Last column: equal-model mean.

Behavior prevalence across 40 ivf clinic seo marketing benchmark questions, 2026-07 edition. Last column: equal-model mean.
BehaviorChatGPTClaudeGeminiEqual-model mean
Recommends hiring a professional27.5%12.5%15%18.3%
Suggests DIY first55%25%20%33.3%
Names specific providers5%5%5%5%
Gives price or cost info10%10%7.5%9.2%
Tells to check reviews2.5%2.5%2.5%2.5%
Tells to verify credentials5%2.5%5%4.2%
Mentions case studies / portfolio15%7.5%0%7.5%
Mentions local proximity22.5%32.5%35%30%
Gives selection criteria20%20%17.5%19.2%
Warns about red flags15%15%12.5%14.2%
Asks a clarifying question45%62.5%0%35.8%
Recommends multiple quotes5%2.5%0%2.5%

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.

Behavior matrixModel-by-model evidence
Measured

All-model binary agreement by behavior across 40 benchmark questions.

All-model binary agreement by behavior across 40 benchmark questions.
BehaviorAll-model agreement
Recommends hiring a professional80%
Suggests DIY first55%
Names specific providers87.5%
Gives price or cost info85%
Tells to check reviews92.5%
Tells to verify credentials87.5%
Mentions case studies / portfolio82.5%
Mentions local proximity57.5%
Gives selection criteria82.5%
Warns about red flags82.5%
Asks a clarifying question25%
Recommends multiple quotes95%

By model

How each assistant handled Ivf Clinic SEO Marketing questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same ivf clinic seo marketing questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 27.5% (ChatGPT) down to 12.5% (Claude), a 15-point gap on an identical question set.

Across the 40 ivf clinic seo marketing answers it produced, ChatGPT recommended hiring a professional in 27.5% of them and suggested a DIY approach first 55% of the time. It named a specific provider in 5% of answers (about 0.3 distinct providers per answer) and included price or cost information 10% of the time. ChatGPT asked a clarifying question before answering in 45% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 5%, averaging 739 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 15%, and framed the choice around local proximity in 22.5%; a selection-criteria checklist appeared in 20% of its answers and a recommendation to gather multiple quotes in 5%.

Across the 40 ivf clinic seo marketing answers it produced, Claude recommended hiring a professional in 12.5% of them and suggested a DIY approach first 25% of the time. It named a specific provider in 5% of answers (about 0.1 distinct providers per answer) and included price or cost information 10% of the time. Claude asked a clarifying question before answering in 62.5% of cases, warned about red flags or scams in 15%, 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 7.5%, and framed the choice around local proximity in 32.5%; a selection-criteria checklist appeared in 20% of its answers and a recommendation to gather multiple quotes in 2.5%.

Across the 40 ivf clinic seo marketing answers it produced, Gemini recommended hiring a professional in 15% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 5% of answers (about 0.1 distinct providers per answer) and included price or cost information 7.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 5%, averaging 236 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 0%, and framed the choice around local proximity in 35%; a selection-criteria checklist appeared in 17.5% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route an ivf clinic seo marketing buyer to a professional (27.5%) and Claude the least (12.5%). ChatGPT produced the longest answers, at 739 words on average. Specific providers were named most often by ChatGPT (5%) — even there, roughly one answer in 20 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

Question-level model disagreement is 16% — 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 which assistant an ivf clinic seo marketing buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 62.5% (Claude) — a 63-point spread.
  • Suggests a DIY approach first: from 20% (Gemini) to 55% (ChatGPT) — a 35-point spread.
  • Recommends hiring a professional: from 12.5% (Claude) to 27.5% (ChatGPT) — a 15-point spread.
  • Mentions case studies or portfolio: from 0% (Gemini) to 15% (ChatGPT) — a 15-point spread.
  • Mentions local proximity: from 22.5% (ChatGPT) to 35% (Gemini) — a 13-point spread.

The widest single gap — asks a clarifying question, 63 points — means an ivf clinic seo marketing 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 ivf clinic seo marketing market.

Where they agree

The points of near-consensus in Ivf Clinic SEO Marketing.

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

  • Names a specific provider: 5% across all three models.
  • Tells the buyer to check reviews: 2.5% across all three models.
  • Gives price or cost information: 7.5%–10% across all three (a 3-point spread).
  • Tells the buyer to verify credentials: 2.5%–5% across all three (a 3-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" (25%).

Every behavior, measured

All twelve coded behaviors for Ivf Clinic SEO Marketing, averaged across the three models.

The behaviors AI models reproduce most often for ivf clinic seo marketing are asks a clarifying question (35.8% on average), suggests a DIY approach first (33.3%) and mentions local proximity (30%); the rarest are recommends multiple quotes (2.5%), tells the buyer to check reviews (2.5%) and tells the buyer to verify credentials (4.2%). 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:

  • Asks a clarifying question: 35.8% on average (ChatGPT 45%, Claude 62.5%, Gemini 0%) — a 63-point spread.
  • Suggests a DIY approach first: 33.3% on average (ChatGPT 55%, Claude 25%, Gemini 20%) — a 35-point spread.
  • Mentions local proximity: 30% on average (ChatGPT 22.5%, Claude 32.5%, Gemini 35%) — a 13-point spread.
  • Gives selection criteria: 19.2% on average (ChatGPT 20%, Claude 20%, Gemini 17.5%) — a 3-point spread.
  • Recommends hiring a professional: 18.3% on average (ChatGPT 27.5%, Claude 12.5%, Gemini 15%) — a 15-point spread.
  • Warns about red flags or scams: 14.2% on average (ChatGPT 15%, Claude 15%, Gemini 12.5%) — a 3-point spread.
  • Gives price or cost information: 9.2% on average (ChatGPT 10%, Claude 10%, Gemini 7.5%) — a 3-point spread.
  • Mentions case studies or portfolio: 7.5% on average (ChatGPT 15%, Claude 7.5%, Gemini 0%) — a 15-point spread.
  • Names a specific provider: 5% on average (ChatGPT 5%, Claude 5%, Gemini 5%).
  • Tells the buyer to verify credentials: 4.2% on average (ChatGPT 5%, Claude 2.5%, Gemini 5%) — a 3-point spread.
  • Tells the buyer to check reviews: 2.5% on average (ChatGPT 2.5%, Claude 2.5%, Gemini 2.5%).
  • Recommends multiple quotes: 2.5% on average (ChatGPT 5%, Claude 2.5%, Gemini 0%) — a 5-point spread.

Trust signals

How well the models protect the ivf clinic seo marketing buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the ivf clinic seo marketing 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 4.2%. Warning about red flags or scams appeared in 14.2%.

On structuring the decision, a selection-criteria checklist showed up in 19.2% of answers on average and a recommendation to gather multiple quotes in 2.5%. The single least-reproduced protective signal for ivf clinic seo marketing is "tells the buyer to check reviews" at 2.5% 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 Ivf Clinic SEO Marketing providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 ivf clinic seo marketing answers, a specific provider was named in 5% of responses on average — roughly 0.2 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for ivf clinic seo marketing: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Ivf Clinic SEO Marketing questions cover.

The 40 questions behind every percentage on this page form a frozen ivf clinic seo marketing (healthcare 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 ivf clinic seo marketing 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 ivf clinic seo marketing 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: Ivf Clinic SEO Marketing (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/health/ivf-clinic-seo-marketing