Cluster fanout queries by the task your reader needs to finish
Cluster fanout queries by the decision a reader needs to make. Similar words are a clue, but do not establish that questions belong on the same page. The worksheet keeps the question, origin, evidence needed and page action together.
Use this with an existing query selection or modelled question list. The worked example is an editorial teaching exercise, with no provider output, customer results or search-volume data.
Start with a clearly labelled query listUse an existing export or run a short topic. Keep native observations and modelled ideas separate.Open the toolPrepare the list without losing its evidence
Start by preserving the exact question. Label whether it came from a native API run, a modelled idea or your own editorial work. A useful grouping does not change that origin. Keep run metadata in a separate file if the worksheet does not carry all of it.
For the complete scope, use the guide to query export.
Use query_id as a stable connection to the input. Do not silently delete a wording variant from the export: it can receive the same cluster_id in the worksheet while its original phrase remains intact. This lets you trace a later page decision back to the questions that prompted it.
The blank CSV has nine columns. Fill reader_job and evidence_needed beside the question first. A reader may need to select suitable software, calculate the usable plan cost or import a file. These verbs tell you more than a cluster label such as SEO or AI.
The example paths belong to a fictional buying-guide website. They are not pages of this tool. Download the blank file for your work and the completed example for reference; replace the origins and targets before adding your own data.
Seven questions, three tasks and one exclusion
The original example asks about SEO software for a small team. q01 and q02 use team and business wording but lead to the same selection decision. They belong in the selection cluster. Two nearly identical buying guides would send the same reader through the same review twice.
q03 and q04 ask about five-user cost and included seats. They need different evidence: current prices, extra users and contract conditions. The example assigns a cost cluster while keeping those questions as sections of the same buying guide. An additional reader question does not automatically need another URL.
q05 and q06 concern CSV import and UTF-8. Their outcome is a correctly imported file. That operation can support a separate tutorial with a tested example; encoding belongs inside it as an error case. q07 concerns SQL fanout and is excluded because its reader task is unrelated to software selection.
For a mixed intent, write down both reader jobs and review each one. Do not merge everything because it shares words. Equally, do not split questions that need the same evidence and next step. The actions below belong to this exercise, not to a universal URL rule.
Decisions in the example
Original editorial questions; no observed queries or demand metrics.
| Questions | Reader task | Page action |
|---|---|---|
| q01–q02 | Select a tool | One buying guide; merge wording variants |
| q03–q04 | Calculate usable cost | Cost section within the buying guide |
| q05–q06 | Import a file correctly | Separate tested tutorial with an encoding case |
| q07 | Tune a database | Exclude for this audience |
Check the cluster against your website
For each remaining cluster, review the strongest page already on your site. If it can cover the task, evidence and next step clearly, improve it. A different reader, output or independent verification process may instead justify a separate page.
The worksheet calculates no demand or ranking chance. Before setting priorities, add observable search data, business relevance and the effort needed to fill evidence gaps. Unknown values stay unknown. A recurring fanout branch is neither monthly search volume nor a quality judgement about your page.
Check visible search data with the Search Console worksheet.
Write a reviewable sentence in reason: the same buying decision, different evidence within one article, or a separate import task. That is more useful later than an unexplained automatic cluster score. Pass the worksheet and relevant source files to the person who will write the brief.
The next work product is a content brief from fanout.
Sources used
These sources support the functions and limits described here. They do not prove claims beyond that scope.
- Original query-clustering, content-brief and GSC-mapping worksheetsOpen source
Blank worksheets and completed teaching examples. Seven illustrative questions and fictional website paths support the walkthrough only. No real provider output, client result, GSC measurement or keyword volume is published in these files.
- Optimizing for generative AI features on Google SearchOpen source
Normal SEO foundations remain relevant; pages made primarily for fan-out variations are not the accepted content model.