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Query Fan-Out: Why One Buyer Question Becomes 15 Sub-Queries
One buyer question, and the sub-questions the engine generates before answering. Concept: Aleyda Solís.
Your buyer asks one question. The engine asks itself ten to fifteen. Before it writes a word, it decomposes the question into sub-queries, retrieves against each, and assembles an answer from whatever it found. This is query fan-out, and Aleyda Solís named it.
It changes what "covering a topic" means. You are not being scored against the question your buyer typed. You are being scored against every sub-question the engine generated on the way to answering it.
Miss four of those sub-questions and a competitor fills them. The engine cites them for those parts, and once it is citing them, it tends to name them in the summary too.
- -> What CRM features matter most for early-stage B2B sales?
- -> Which CRMs integrate with Slack, email, and common B2B tools?
- -> What does a startup-friendly CRM cost per seat?
- -> How does [product A] compare to [product B] for startups?
- -> What CRMs offer a free tier or trial for early teams?
- -> What do G2 and Capterra reviews say about each option?
- -> Which CRMs scale from 5 to 50 sales reps without re-platforming?
- -> What migration support do CRM vendors offer?
- -> Is [specific product] good for outbound vs. Inbound-heavy teams?
- -> What implementation time should a startup plan for?
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The fan-out audit
The method is unglamorous. Take one buyer question. Write down every sub-question a thorough analyst would need answered before they could responsibly recommend a vendor. Then score your content against that list.
1 | Choose one high-priority buyer question Pick the single most important question your target buyer asks at the research stage — the question that, if answered with your brand cited, would put you on the shortlist. Example: “What is the best AI visibility platform for B2B SaaS marketing teams?” Start with one question. The process is repeatable once you understand it. |
2 | Generate the full sub-query tree Open an AI tool and ask: “What are all the sub-questions you would need to answer fully before responding to: [your question]?” The AI will typically surface 10–20 sub-queries. Group them into categories: product capabilities, comparison and alternatives, use case fit, pricing and terms, trust and social proof, implementation. This is your fan-out tree. |
3 | Map your existing content to the tree For each sub-query in the tree, identify which of your existing pages addresses it — and whether the answer appears in the first 150 words. Create a spreadsheet: sub-query in column A, your page URL in column B, answer in first 150 words (yes/no) in column C. You are looking for gaps (no page addresses this) and depth failures (the page exists but the answer is buried). |
4 | Score your coverage Count the sub-queries you address vs. The total generated. A score of 2/12 = 17% coverage — typical for B2B sites that haven't done AEO work. A score of 9/12 = 75% — the threshold where AI tools begin to see you as a comprehensive source on the topic. Note which gaps represent new content to create vs. Existing content to restructure. |
5 | Check whether a competitor fills your gaps For each sub-query your content doesn't address, ask the AI tool that sub-question directly and record which source it cites. This tells you exactly which competitor is capturing the citation you're missing — and gives you a concrete content creation brief: write a better, more specific, more data-rich version with a direct answer in the opening paragraph. |
6 | Build the content gap list and prioritize From your audit, you now have two lists: (1) existing pages that need restructuring to move answers to the first 150 words, and (2) new content to create for uncovered sub-queries. Prioritize by sub-query frequency — sub-queries in the “product capabilities” and “comparison” clusters typically appear in more fan-out trees. Fix those first. |
Most B2B content answers the root question and stops. That is why so many well-written pages never get cited. They are complete as articles and incomplete as answers.
 | Aleyda Solís Orainti · The AI Search Optimization Roadmap |