Did you know? Fan-out questions already bring Aiso citations
17.1%
of citations in our Bing grounding-query export came from queries containing “fan-out.”
60,740 / 354,388 × 100 = 17.14%. Source: getaiso.com Bing AI Search Queries export, October 25, 2026. The 29 matching rows are included in the downloadable source extract.
This is a first-party snapshot of citations for our site in the exported reporting window. It is not a share of people, clicks or all AI searches. The match uses the literal phrase “fan-out,” so spelling variants can be missed. The full query export contains 608 rows; its total differs from the separate daily overview. We use the query export as the denominator throughout this calculation.
That observation gives us a reason to deepen a topic already being retrieved. It does not prove that adding a workflow will increase citations. Microsoft explains how grounding queries and opportunity ranges should be interpreted.
How to integrate query fan-out analysis into an AEO workflow
- Step 1Capture the buyer question
- Step 2Inspect searches and sources
- Step 3Choose the page and action
- Step 4Repeat and measure
1. Capture the question and its context
Save the exact prompt, any relevant follow-up, assistant, date, language and target market. Separate an observed buyer conversation from a test prompt created by your team. A short keyword label loses constraints such as budget, location and company size.
2. Inspect the actual searches and citations
Use the manual fan-out walkthrough or a tool that exposes captured searches. Save query strings and cited URLs together. A predicted fan-out is useful for brainstorming but belongs in a separate column.
3. Group the decision, then choose the smallest useful update
Queries about “model coverage,” “data accuracy” and “historical data” can be sections of one evaluation page. An implementation question can need a different page from a product comparison. Check the existing site before creating another URL.
| What the answer shows | What to inspect | Useful next action |
|---|---|---|
| Competitor page cited; yours absent | Whether your page answers the same specific question | Add missing evidence, definitions or decision criteria to the relevant page |
| Your page cited; your brand absent | Whether the page explains your relevant product or service fit | Add a clear, factual connection between the evidence and what you provide |
| Third-party sources dominate | Which reviews, directories or publications answer the question | Check whether your public information is accurate and whether you have useful evidence to contribute |
| Your evidence cannot be retrieved | Status codes, access controls and readable page content | Fix the retrieval issue before expanding the copy |
For an illustrative example, “tools to identify AI visibility gaps” and “AI visibility tools with pros and cons” can share a buying guide. “How do I fix a missing brand?” needs diagnostic steps. The wording alone should not create three nearly identical listicles.
A content brief your editor can act on
Record the exact query, existing page, missing answer, evidence source, proposed change, owner and review date. Add the original answer and competitor sources so the editor can see the gap without recreating the research.
Download the blank fan-out content brief. The template contains no invented performance results.
Ask the editor to state what the page adds: a worked calculation, original screenshot, product limitation, source comparison or a documented method. Repeating a definition that already exists on ten pages adds little practical depth.
How to measure fan-out content performance
Keep the original prompt cohort and model settings for a before-and-after comparison. Record whether your page is cited, whether your brand is named, whether it is recommended and whether a visitor takes a useful action. Keep new discovery prompts in a separate cohort so an expanded denominator does not look like a decline.
In Bing, compare citation share for the same query alongside total citations. Fewer citations with stable share can have a different explanation from falling share. In an answer tracker, inspect repeated runs per model. Neither a crawler visit nor a single new citation proves a lead.
Continue with the fan-out and prompt-tracking benchmark, the measurement guide and the retrieval audit.
Questions to settle before you start
Should every fan-out query become an article?
No. Group queries that ask for the same decision and map them to an existing page first. Create a new page when the reader needs a different answer or a different buying task, not simply because the wording changes.
Can I use predicted fan-outs to plan content?
Yes, as hypotheses. Label predicted queries separately from searches captured in an actual answer. Validate the query and cited sources before treating it as observed demand.
Does ranking for a fan-out guarantee an AI recommendation?
No. Search retrieval, source citation and brand recommendation are separate outcomes. An assistant can cite a page without recommending its publisher, so record all three separately.