Everything You Should Know About Query Fan Out

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We are effectively engineering relevant if you're doing SEO the right way. To me, this is like the ninety two Olympic dream team. You can't get any stronger than the than the players that are here right now. There's QR codes everywhere, tools that you can really take, study, and take back to your work and implement that right away. The future of content, it's about creating connections that resonate, engage, and endure, and ultimately about building lasting community. It's not just SES. Do not minimize this moment. This is our opportunity to be better, to be more effective, frankly, make more money. The energy feels very collaborative. The curation of people and speakers and everything else that you get here is just out of this world. The future of search requires us to be intentional in order to run the show, not just be the show. Wait a second. Don't meet me here, beat me here because I'm gonna be in the front row.

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WHAT'S IN THE DECK?

Mike King breaks down how Query Fan Out drives every major AI search system today. His Tech SEO Connect presentation shows how Google, ChatGPT, Gemini, and Perplexity expand a single query into many hidden variations, route those variations to different content formats, and decide which passages and which brands get pulled into the final AI answer.

MEAT AND POTATOES

1. Visibility in AI Search is not guaranteed by SEO rankings

Multiple datasets (ZipTie, Profound, Seer) show the chance of appearing in AI results is between 19–25%, even for top-ranking organic pages.
About 62% of LLM citations come from outside the top 10 search results.
Nearly 30% of ChatGPT-cited pages don’t rank for anything in organic search.

2. Query Fan-Out determines who shows up

Google expands a single user query into dozens of rewritten, contextual variations.
These sub-queries route to different content formats, sources, and modalities.
SERP saturation, entity clarity, and passage-level quality drive probability of retrieval and citation.

3. Retrieval > Ranking

Answer generation involves expansion, routing, retrieval, selection, and synthesis.
Content must match the expected modality (text, video, structured, product data) or it’s never even considered.
Relevance Engineering focuses on making content extractable at the passage level, not just rankable.

4. Qforia is the easiest way to model Google’s fan-out

Qforia generates synthetic queries that mirror the expansion stage Google runs.
It now predicts the expected content-type for each sub-query based on routing logic.
Reverse-intersecting AI citations with SERP data reveals “real” fan-out patterns for your category.

5. This shift requires a new SEO skillset

Writing entity-rich, embedding-friendly language.
Structuring content for passage-level extractability.
Using semantic triples to strengthen context and relationships.
Adding exclusive data, insights, or product attributes that LLMs can’t synthesize elsewhere.

TAKEAWAYS

Learn how these systems work so you can discover opportunities.
It takes more than SEO to earn visibility.
Search behavior has changed for good.
Most traditional SEO tools won’t get you where you need to go.
This is an opportunity to define the future.

//.eBook

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