Query fan-out

What is query fan-out?

Query fan-out is when an AI search feature takes one question, breaks it into smaller related searches, runs them all and builds one answer from what it finds. Google uses the term for how AI Overviews and AI Mode work. Its AI features documentation says they may issue "multiple related searches across subtopics and data sources" to develop a response.

Let's say you sell employee onboarding software. An HR lead asks AI Mode: "what's the best onboarding software for a company hiring 30 people a month in three countries?" Behind the scenes, the AI may search for onboarding software with multi-country support, right-to-work checks in each country, pricing per new hire, integrations with HRIS tools like BambooHR and recent reviews. Then it writes one answer and cites the pages it used.

That means your page doesn't have to rank for the full question to be used. It has to be the best answer to one of the smaller searches.

Why query fan-out matters for B2B SaaS

B2B buying questions are long and specific. They mention team size, location, budget, tools and rules. Fan-out turns each of those details into its own search, so more of your pages get a chance to be picked.

The data points the same way, though it shows correlation only. In a Surfer study of 10,000 keywords, pages that ranked for the main search and its fan-out searches were 161% more likely to be cited in AI Overviews.

Here's why I think it changes how you plan content:

• Narrow pages get used: A page on "right-to-work checks for remote hires in Germany" may never rank for "best onboarding software". It can still be the page AI Mode cites for that part of the answer.
• Depth on one topic pays off: The more sub-questions your site answers well, the more often you show up across the fan-out. That's topical authority in practice.
• Specific facts win: Sub-searches often look for prices, limits, integrations and setup steps. Pages that state those plainly are easy to lift into an answer.
• The bar keeps going up: At Google I/O in May 2025, Google said Deep Search in AI Mode takes the same technique further and can run hundreds of searches for one question.

How to write for query fan-out

You can't see the exact searches Google runs. But you can make a good guess and cover them, the same way you'd build topical authority on a subject. Here's my process:

• List the sub-questions: Take your main buying searches and write down every detail a buyer adds, like company size, region, stack and budget. Sales calls are the best source, and People Also Ask boxes and related searches in Google fill in the rest.
• Read what AI already cites: Run your main questions in AI Mode and note the sources it links. Those pages show you which sub-questions it searched and what kind of page it picked for each.
• Give each sub-question a home: Answer it in a clear section of a pillar page or on its own page if there's enough to say. A topic cluster is a natural way to organize this.
• Put the answer first: Start each section with one or two sentences that answer the sub-question directly, then add the detail. A section that takes four paragraphs to get to the point is hard to use.
• State the hard facts: Write the price, the limit, the supported tools and the date you last checked them. Vague claims like "flexible pricing" give an AI nothing to quote.
• Stay eligible: Google says a page must be indexed and eligible to show with a snippet to appear as a supporting link. Check that no nosnippet tag or blocked crawl keeps your best pages out.

A query fan-out example

Here's how the onboarding question could fan out, and the page a hypothetical onboarding software company would need for each part:

Only one of those five is a classic product page. The rest are guides, integration pages and third-party reviews, and they're the pages that decide whether you show up.

How to measure query fan-out

You can't measure fan-out directly, so measure the pages and answers it touches. Here's what I track:

• Queries per page: In Google Search Console, open a page and look at all the queries it shows up for. A page that appears for many long, specific queries is covering sub-questions well. Google counts AI Mode clicks and impressions in the regular Web search totals.
• Citations for the full question: Run your main buying questions in AI Mode and AI Overviews through a prompt tracking tool, and count how often your pages get cited.
• Which pages get cited: Note whether the cited pages are your product pages, your guides or third-party sites. That tells you where to add depth.
• Coverage gaps: Keep a sheet of sub-questions per main search and mark which ones have a page. The empty rows are your content plan.

Common query fan-out mistakes

• Stuffing everything into one page: A 6,000-word page that tries to answer every sub-question in passing is hard to use for any of them. Give the bigger sub-questions their own sections or pages.
• Guessing the sub-questions: Writing from a keyword tool alone misses the details buyers really add. Start from sales calls and the sources AI already cites.
• Ignoring other sites: Some sub-searches look for reviews and comparisons. If your brand isn't on G2, Capterra or the roundups in your category, those parts of the answer go to someone else.
• Hiding facts in PDFs and images: Pricing in a PDF or a feature list in a screenshot is hard for search to read. Put the facts in the page text.
• Treating it as a trick: Fan-out rewards sites that answer buyer questions well. There's no shortcut that makes a thin page look deep.

Here's a simple test for this week. Take your most important buying search and write down every detail a real buyer adds to it. How many of those details does your site answer on a page of its own?