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Google Ranking Factors Expert Survey 2026 + Google AI Ranking Factors (Zyppy, 131 SEOs)

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Source: raw/newsletter-zyppy-signal-5d239dcd63.md (“Google Ranking Factors Expert Survey 2026”, 2026-09-09) · raw/newsletter-zyppy-signal-25843c6d40.md (“Google AI Ranking Factors”, 2026-09-16) · raw/newsletter-zyppy-signal-fe3dcbdffe.md (“The Full 2026 Google Ranking Factors Chart”, 2026-09-24). Author: Cyrus Shepard, Zyppy Signal. URLs: https://signal.zyppy.com/p/google-ranking-factors-expert-survey · https://signal.zyppy.com/p/google-ai-ranking-factors · https://signal.zyppy.com/p/full-2026-google-ranking-factors

This is an expert-opinion survey, not a measurement. Zyppy asked 131 SEOs how much they believe each of 103 factors affects Google’s organic rankings, then asked the same pool about Google’s AI Overviews and AI Mode. The survey was designed to reflect newer factors from AI, the Google API leak and the antitrust trials, but the scores record what experienced practitioners think; one respondent to the AI survey said many answers “should really be ‘don’t know!’” Read it next to Zyppy’s evidence-scored May 2026 meta-analysis, which ranks factors by study evidence instead of opinion.

Key Takeaways

  • Method (organic survey). 131 respondents rated 103 factors on a 7-point scale from +3 (strongly positive) to −3 (strongly negative), for 13,665 data points, then named their “three most important Google ranking factors” in an open question.
  • Top-3 selection rates (organic):
    1. Relevance 57.1%
    2. Backlinks 54.8%
    3. Content Quality 47.6%
    4. Authority & Trust 36.5%
    5. Behavior / Click Signals 29.4%
    6. Brand Signals 27.0%
    7. User Satisfaction 19.8%
    8. Technical SEO Health 17.5%
    9. Topical Authority 14.3%
    10. Internal Links 11.1%
  • Search Intent Match is the single highest-rated factor of the 100+. The meta description was rated so ineffective that most respondents do not believe it has any impact.
  • Backlinks are not dead. Links from highly trusted domains and from topically relevant pages were two of the highest-rated factors in the whole survey. Spam links were rated one of the biggest negatives, even though Google says it ignores most of them.
  • Content quality means non-commodity content. Original Research and First-Party Data top the content-quality factors. AI-generated content was rated negatively only when scaled with little added value.
  • Brand is how Google learns whom to trust. Online Reputation/Trust and Branded Search Volume rank among the most important factors; Google Ad Spend is barely positive.
  • User signals jumped this year, credited to the antitrust-trial and API-leak evidence. Satisfaction/Task Completion is the top user signal; Return to SERP is rated negative. See Google click signals.
  • Technical SEO is “table stakes.” Bad technical SEO can hurt, but good technical SEO will not lift mediocre content. Mobile usability matters; site speed and Core Web Vitals were among the most contested factors.
  • Google AI (AIO / AI Mode) survey, 13 factors, mean score:
    1. AI Crawl Access & Snippet Eligibility +2.20
    2. Query-Answer Match +2.15
    3. Brand / Entity in LLM Memory +2.08
    4. Citable / Specific Facts +2.07
    5. Organic Fan-Out Coverage Rankings +1.91
    6. Organic Search Ranking +1.89
    7. Unique / First-Party Information +1.85
    8. Cross-Web Consensus & Corroboration +1.81
    9. Source / Publisher Reputation +1.78
    10. Extractable Content Structure +1.69
    11. Answer Prominence +1.65
    12. Structured Data +0.80
    13. llms.txt File +0.05
  • Zyppy’s headline reading of the AI survey: brand and trust outweigh AI-specific tactics such as “chunking” or llms.txt; parametric memory (what the model already knows about you) is one of the biggest factors and one of the hardest to influence quickly; specific evidence beats generic information; and Google AI visibility overlaps heavily with traditional SEO.
  • Shepard’s three-step summary for Google AI answers: make your content eligible (crawl access, query-answer match, organic and fan-out rankings) → give Google specific facts it can use (specific facts, unique first-party information) → make Google trust you (entity prominence in LLM memory, cross-web consensus, publisher reputation).

Details worth keeping

On the AI-specific tactics.

  • “Chunking” (splitting content into pieces for AI) came up 8 times in open answers; almost all respondents said you do not need to chunk for Google to understand content. Clearly organised content (headers, tables, lists, proper sentences) still matters.
  • Structured data scored low overall (+0.80). The exception, per Andy Chadwick: product and merchant data “feeds the Shopping Graph and does real work in AI Mode shopping results.” See AI shopping and agentic commerce.
  • llms.txt scored +0.05. Przemysław Charchan: “I collected 7 independent studies on llms.txt, covering nearly 500,000 domains, and all of them found that AI bots do not check this file on their own.” (Respondent’s claim; the studies are not named.) This adds a third, independent signal to the llms.txt resolution recorded in the AI-SEO pre-publish checklist: skip it for ranking or citation ROI.

On memory. Several respondents called parametric memory the hardest lever. Dan Petrovic (DEJAN): “Entering the model’s training data is hard to do on your own, much harder than link building.” Jan-Willem Bobbink: “You first need to be considered to be included in the fan-outs. That’s memory-based.”

Anecdotes from unnamed respondents (unverified):

  • A client mentioned and linked in a Reuters article saw its ChatGPT visibility rise 4× over the following week.
  • Fixing canonicals that pointed at dissimilar pages produced “an almost immediate positive change” in impressions and clicks, “around +60%.”

The chart. Dawn Shepard’s 103-factor chart is free to reuse with attribution; a high-resolution version is at zyppy.com/assets/2026-full-ranking-factors-chart.png.

Tension with Zyppy’s May 2026 meta-analysis

The same author scored overlapping factors two ways, and they mostly agree. The exception is brand and entity knowledge.

FactorMeta-analysis (May 2026, 54 studies, evidence-scored 0–10, ChatGPT/Gemini/Perplexity)Expert survey (Sept 2026, 131 SEOs, opinion −3..+3, Google AIO/AI Mode)
Crawl accessURL Accessibility 9.5 (#1)AI Crawl Access 2.20 (#1)
Query-answer match9.2 (#5)2.15 (#2)
Fan-out rank9.3 (#3)1.91 (#5)
Organic rankSearch Rank 9.4 (#2)1.89 (#6)
Brand / entity knowledgeBrand / Entity Trust 6.8 (#16); Known Source, “URL already known to the engine via training data”, 5.4 (#21)Brand / Entity in LLM Memory 2.08 (#3)
Publisher reputationDomain Authority 5.0 (#22)Source / Publisher Reputation 1.78 (#9)
Structured data5.6 (#20)0.80 (#12)
llms.txt2.0 (#23)0.05 (#13)
  • This is a tension, not a contradiction. The two methods measure different things: the meta-analysis scores how consistently published studies found an effect; the survey records practitioners’ beliefs. They also cover different engines (the meta-analysis spans ChatGPT, Gemini and Perplexity; the survey asks only about Google’s AI features). “Known Source” is about a URL being in training data, while the survey’s factor is about an entity being in model memory, so even the closest pair is not the same construct.
  • Two readings, neither established here: experts may over-weight brand because it is the lever they already sell; or brand memory is hard to study experimentally, so an evidence-scored ranking under-rates it.
  • Practical consequence: everything at the top of both lists (crawlability, direct answers, ranking for the query and its fan-outs) is agreed. Treat brand-in-memory as a slow, long-horizon investment rather than a proven short-term citation lever.

Try It

  1. Check the agreed top of both lists first. Confirm AI crawlers and snippet eligibility (no accidental nosnippet), answer the query directly near the top, and check rankings for the likely fan-out subqueries, using the audit in the Zyppy AI citation playbook.
  2. Publish one piece of first-party data per important topic: an original stat, test or firsthand observation. The AI survey rates specific facts (#4) and unique first-party information (#7) highly, and the organic survey puts original research and first-party data at the top of content quality.
  3. Audit brand-fact consistency across the web. Cross-web consensus scored +1.81; conflicting facts about your business undercut the entity you want the model to remember.
  4. For a local service business (a dental practice, for example): keep name, services and credentials identical on every profile, and publish local data competitors do not have, such as appointment wait times or procedure counts.
  5. Drop llms.txt and content chunking from the priority list for Google AI visibility; keep clean headings, tables and lists.
  6. Do not chase meta descriptions or Core Web Vitals for rankings; treat both as hygiene.

Open Questions

  • Respondent pool bias. The 131 are SEOs Zyppy invited (it contacted over 200); their opinions are not checked against ranking data. In the AI survey, Will Critchlow (SearchPilot) and Victor Pan (HubSpot) both warned that many scores are guesses (Pan: “This will likely age poorly”).
  • Full 103-factor scores are only in the chart image; the newsletter text gives the top-10 selection rates and qualitative placement, not the mean score for every organic factor.
  • The Charchan “7 studies, ~500,000 domains” llms.txt claim does not name the studies.
  • Does brand-in-memory hold up empirically for Google AI features specifically? No study in this wiki isolates it.

Graph View

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