Source: zyppy-ai-citation-ranking-factors-2026-05-07.md — Cyrus Shepard, Signal by Zyppy (Substack). Published 2026-05-07.
Cyrus Shepard (Zyppy SEO) synthesized 54 published AI-citation experiments, patents, and case studies into a single ranked factor list. The output is a 23-factor table with evidence-based 0-10 scores for ChatGPT, Gemini, and Perplexity. The strongest signal: AI citation engines re-rank on top of classical search relevance, so winning organic SEO is the precondition. The most-hyped 2025 tactics — schema and LLMs.txt — score near the bottom (5.6 and 2.0 respectively). Shepard’s thesis: “win SEO, win AI citations (most of the time, with extra steps).”
Key Takeaways
- Top tier (9+ across all engines): URL Accessibility (can the bot reach the page?), Search Rank (does it already rank on Google?), Fan-out Rank (does it rank for query-expansions, not just the head term?), Preview Control (does your title/meta-description match what the engine is asked?), Query-Answer Match (does the page literally answer the question?), Intent-Format Match (does the format — listicle, table, definition — match the intent?).
- Mid tier (7-8): Direct Quote Density, Statistic / Number Specificity, Citation by Other Cited Sources (the “second-order citation” effect), Recency.
- Low-scoring tactics (under 5): Backlinks (4.8 — present but indirect), Long-Form Content (4.2 — past 1500 words, returns diminish), Brand Mentions Without Link (3.9), LLMs.txt (2.0 — the most overhyped tactic of 2025-26 by Shepard’s read).
- Structured Data scores 5.6 (#20 of 23). Helps with parseability and rich snippets but doesn’t independently bump citation odds. Empirically corroborated by the Ahrefs schema causal study (2026-05-11) which found no statistically meaningful AI citation lift from adding JSON-LD schema.
- Per-engine variation matters. Gemini weights Search Rank more than ChatGPT (Gemini is more tightly Google-Search-backed). ChatGPT weights Preview Control and Recency more (RAG over fresh web). Perplexity weights Citation-by-Cited-Sources more (its retrieval is more cluster-based).
- The single-best-explanation hypothesis. Search Rank, Fan-out Rank, and Query-Answer Match all sit at 9+. These three together describe ~75% of AI citation outcomes per Shepard’s read of the 54 studies. The remaining 25% is engine-specific tweaks (recency for ChatGPT, etc).
- Why this matters more than individual case studies. Each of the 54 underlying studies has small-sample or selection-bias issues. Aggregating them with weighted scores removes some noise and surfaces the factors that consistently matter across studies, surfaces, and methodologies.
The 23 Ranking Factors (Top-to-Bottom)
| # | Factor | Avg Score | ChatGPT | Gemini | Perplexity |
|---|---|---|---|---|---|
| 1 | URL Accessibility | 10.0 | 10 | 10 | 10 |
| 2 | Search Rank | 9.7 | 9 | 10 | 10 |
| 3 | Fan-out Rank | 9.3 | 9 | 10 | 9 |
| 4 | Preview Control | 9.0 | 10 | 8 | 9 |
| 5 | Query-Answer Match | 9.0 | 9 | 9 | 9 |
| 6 | Intent-Format Match | 9.0 | 9 | 9 | 9 |
| 7 | Direct Quote Density | 8.0 | 8 | 8 | 8 |
| 8 | Statistic / Number Specificity | 7.7 | 8 | 7 | 8 |
| 9 | Citation by Other Cited Sources | 7.3 | 7 | 7 | 8 |
| 10 | Recency | 7.0 | 8 | 7 | 6 |
| 11 | Internal Linking | 6.7 | 7 | 7 | 6 |
| 12 | Topical Authority | 6.7 | 7 | 7 | 6 |
| 13 | Domain Authority | 6.3 | 6 | 7 | 6 |
| 14 | Page Speed | 6.0 | 6 | 6 | 6 |
| 15 | Mobile UX | 6.0 | 6 | 6 | 6 |
| 16 | Anchor Text | 5.8 | 6 | 6 | 5 |
| 17 | Entity Mentions | 5.7 | 6 | 6 | 5 |
| 18 | Heading Structure | 5.7 | 6 | 6 | 5 |
| 19 | Image Alt Text | 5.7 | 6 | 6 | 5 |
| 20 | Structured Data | 5.6 | 6 | 5 | 6 |
| 21 | Backlinks | 4.8 | 5 | 5 | 4 |
| 22 | Long-Form Content (1500+ words) | 4.2 | 4 | 4 | 5 |
| 23 | LLMs.txt | 2.0 | 2 | 2 | 2 |
Scores are averaged across the 54 underlying studies, weighted by study quality (sample size, control design, peer review). Methodology described in full in the source.
Tactical Implications
- Win SEO first. Search Rank is #2 (9.7 avg). If you aren’t ranking organically, AI citations are a near-impossibility regardless of every other factor. This collapses the “AEO is separate from SEO” narrative.
- Optimize for query fan-out. Fan-out Rank is #3. AI engines expand the query into sub-queries (the Google AI Mode query fan-out pattern); pages ranking for the head term and its expansions get cited more. Practical move: cluster your content around topical concepts, not just exact-match keywords.
- Control your preview. Preview Control (#4) means your title and meta description literally appear in the engine’s source-attribution UI. A clear, query-matching preview gets clicked / cited more than a clever-but-vague one.
- Format-match the intent. A “how to” query wants a numbered listicle. A “what is” query wants a definition + example. A “compare” query wants a table. Wrong format = no citation even if your content is correct.
- Skip LLMs.txt. Score 2.0 (#23). The most overhyped 2025 tactic. Engines aren’t actually using it as a retrieval signal at meaningful rates.
- Schema for the right reasons. Score 5.6 (#20). Add schema because Google still rewards it on classical surfaces (rich snippets, knowledge panel) — not because of AI-citation lift. The Ahrefs causal study is the independent empirical confirmation.
Per-Engine Subtleties
- ChatGPT. Highest weight on Preview Control and Recency. RAG-heavy retrieval over fresh web means your title/preview matters more and stale content gets dropped faster.
- Gemini. Highest weight on Search Rank. Gemini is the most tightly integrated with Google Search, so winning classical SEO maps almost-directly into Gemini citations.
- Perplexity. Highest weight on Citation-by-Other-Cited-Sources. Perplexity’s retrieval clusters around already-cited-by-others sources, so the second-order citation effect is strongest here. Implication: getting cited somewhere in the Perplexity-citation graph creates compounding returns.
Open Questions
- The exact 54 underlying studies. Shepard lists representative examples but the full study list lives in his methodology appendix. Worth pulling for citation-chain verification. ^[inferred]
- How scores decay over time. AI engine retrieval changes monthly. The 2.0 for LLMs.txt assumes engines aren’t using it as of May 2026 — if any engine starts honoring it, this score would jump.
- Weighting by traffic / vertical. All 23 factors are averaged across studies. Per-vertical (e.g., medical, legal, e-commerce) weights would likely shift the rankings — the consensus is that medical content gets cited more conservatively, so factors like Topical Authority and Brand Authority probably weight higher there.
Related
- Ahrefs Schema → AI Citations Causal Study — Independent causal-inference study (1,885 pages DiD) that empirically confirms Shepard’s 5.6 score for Structured Data: no statistically meaningful AI citation lift from adding schema.
- AirOps + Kevin Indig Fan-Out Effect ChatGPT Study — Largest single-engine empirical dataset. AirOps’s retrieval-rank finding (rank-1 cited 58.4% vs rank-10 14.2%) is the deepest empirical backing for Shepard’s 2-3-5 weights (Search Rank, Fan-out Rank, Query-Answer Match).
- Digital Applied 1,000 AIO Citation Pattern Study — Concrete AIO-only data on schema lift (2.3×-2.8× after DA control). Magnitude varies between Shepard’s meta-analytic 5.6 and Digital Applied’s much-larger correlational lift — see Open Questions about meta-analysis weighting.
- GEO-16 Framework (arXiv 2509.10762v1) — Academic cross-sectional study. Likely one of the 54 underlying studies in Shepard’s meta-analysis. ^[inferred]
- FLUQs Framework — The “core SEO first” thesis. Shepard’s 23 factors are the empirical backing.
- Google’s Generative AI Search Optimization Guide — Google’s official position aligns: AI Overviews + AI Mode use the same Search index, so AI search is still SEO.
- Similarweb Most-Cited Domains in LLMs — What domains LLMs actually cite. The factor ranking is how you become a cited domain; Similarweb’s data is who the cited domains are.
- GSC Autonomous SEO Engine — Operationalizes factors 2-6 (Search Rank, Fan-out Rank, Preview Control, Query-Answer Match, Intent-Format Match) via GSC query data.
Try It
- Pull your own top-10 ranking pages from GSC. Cross-reference against the 23 factors. Identify the 1-2 lowest-scoring factors on your pages — those are your highest-leverage improvements.
- Audit your title tags and meta descriptions. Preview Control (#4) is one of the cheapest wins. Rewrite any preview that doesn’t literally answer the page’s primary query.
- Check fan-out coverage. Run your head term through Google AI Mode and capture the 5-10 expanded sub-queries it generates. Does your page address all of them? If not, expand sections or add an FAQ block.
- Stop new LLMs.txt projects. Score 2.0. Reallocate the engineering hours to factors 2-6.
- Re-frame schema work. Keep your existing schema (still helps classical Google surfaces). Don’t expand schema in pursuit of AI citations — the Ahrefs causal study + Shepard’s #20 ranking both say it’s not there.