We Checked Whether On-Site SEO Predicts AI Citations. The Data Says Mostly No.

Causabi, a generative engine optimization tool, found that on-site SEO scores have almost no correlation with AI citation rates. In a study of 44 domains, the correlation between on-site readiness and citations was near zero, with 86% of domains receiving zero citations regardless of their score. Brand prominence was the primary driver of citations, not technical optimization.

Every GEO "generative engine optimization" tool, including ours until recently, sells some version of the same pitch: fix your robots.txt, add Schema.org markup, write FAQ schema, and AI engines will cite you more. We build one of these tools — Causabi scans sites for AI-crawler readiness and generates fix files robots.txt, llms.txt, JSON-LD, FAQ blocks . As part of validating our own scoring weights, we ran the numbers on whether the score actually predicts getting cited. Short version: it mostly doesn't, once brand prominence is in the picture. What we measured We scored 44 domains on a 6-category on-site readiness algorithm: - robots.txt AI bots allowed or blocked - Schema.org Organization/LocalBusiness JSON-LD completeness - FAQ schema FAQPage markup, 3+ entries - content depth/structure - brand/NAP signals - freshness dateModified, recency Then we checked how often each domain actually got cited by an AI engine Claude, via its web-search tool, one measurement window, a fixed prompt set per domain . What we found - On-site score vs. citation rate: Pearson r ≈ -0.08, Spearman ρ ≈ -0.03. Functionally no correlation — if anything, a very slight negative one, which is more likely noise than a real inverse relationship at this sample size. - 86% of the 44 domains got zero citations in the window, independent of their score. - The domains that did get cited clustered almost entirely by brand prominence — well-known domains got cited at a noticeably higher rate ~0.16 of prompts than everyone else ~0 for the rest of the sample , regardless of how well-optimized their markup was. Why I'm not overselling this n=44 is small. This is an internal validation exercise for our own product, not a peer-reviewed study, and I don't want it read as one. Specific caveats: - Single engine Claude this round. Citation behavior differs meaningfully across ChatGPT, Gemini, Grok, and Perplexity — we haven't run the same check across all four yet. - One time window, no longitudinal before/after. We didn't take a domain, improve its score, and watch citations change over months. That's the actually convincing experiment and we haven't run it yet. - Prompt-domain matching wasn't blind. Some prompts were picked because a domain plausibly related to that topic, which likely biases toward domains that would get mentioned anyway. - "Brand prominence" is a fuzzy variable that probably absorbs some real content-quality signal we're not capturing separately. We can't fully rule out that what looks like "brand wins" is partly "genuinely better/more authoritative content wins," which on-site markup scoring doesn't measure. What we still think is true, with more confidence Some things aren't correlational guesses — they're closer to mechanical facts: - robots.txt blocking is binary. If GPTBot , ClaudeBot , or similar are disallowed, that engine cites you zero times, by construction. About 89% of sites we've scanned block at least one AI crawler by default, usually by accident a blanket Disallow: / that predates AI bots existing . - FAQ schema changes extraction, not inclusion. For content that's already in an engine's consideration set, structuring it as self-contained Q&A chunks seems to affect whether it gets pulled into a RAG-style citation — this lines up with published research on chunking behavior. But that's a "how you're cited" lever, not a "whether you're cited" lever. Where that leaves the product We're rewriting our own copy to say what the score actually measures: AI-crawler readiness and machine-readability, not citation probability. No tool — ours included — can promise the second one. If your on-site work is mostly aimed at "getting cited more," the more binding constraint for most sites is probably brand/mentions elsewhere, not another Schema.org type. The scoring engine and fix generator are open source MIT if you want to see the logic or run it on your own site without touching our SaaS: Repo: https://github.com/SHADRINMMM/causabi-geo https://github.com/SHADRINMMM/causabi-geo Site hosted version + monitoring : https://causabi.com https://causabi.com