Build Keyword Clusters from Google Search Console
Build keyword clusters from Search Console query and page data in LLMIC. Understand shared queries, crawl coverage and useful next actions.
Google can send several different searches to the same page. It can also show several of your pages for one search. Search Console keyword clusters bring those relationships together so you can investigate where pages overlap and decide what each page should do next.
What this guide helps you do
Keyword clustering groups related Search Console queries so teams can review themes instead of isolated rows. This workflow explains the selected property and date range, clustering evidence and the limits of similarity-based groups.
Build availability: This guide describes the updated workflow checked against the September 2026 development build. Some controls may not yet be included in the public installer. Check the release notes and your app version before following those steps.
The workflow at a glance
- Choose the property
Use a property your connection can access. - Fetch query and page rows
Keep the date range visible. - Build related groups
Connect pages through shared queries. - Review page evidence
Separate search performance from content placement.
Workflow illustration. These steps explain the process; they are not measured performance results.
Connect the right website
Use a plan with Google integrations and follow the Search Console connection guide. Confirm that the selected property covers the URLs in your crawl. A domain property and a URL-prefix property can describe different scopes. The app must have permission to read the property you select.
Fetch and build the clusters
Open Keyword Content Mapping, choose Search Console, select the property and date range, then choose Fetch & Build. Downloading rows and checking local page text are separate stages. In the updated build, the progress message shows checked rows. A large dataset can take longer than a small plan import.
Understand what connects a cluster
The overlap-based grouping connects pages through queries they share. For example, a course page and an admissions page may both appear for the same university-related query. Connected groups can grow through a chain of overlaps. A large group is not proof that every page has the same intent or should be merged.
Choose a page before choosing a fix
Open a cluster and compare its queries, pages and keyword-placement evidence. Impressions show appearances in Google results; clicks show visits reported by Search Console. Average position summarizes observations in the selected slice. Review the intended primary page alongside content purpose, rather than simply choosing whichever URL has the most impressions.
When a request seems stuck
Read the current stage first. A permission error needs a connection check, an empty response needs a property/date review, and a local analysis delay needs a different investigation. Try a narrower period to diagnose the issue. Search Console query rows do not necessarily include every search, and missing rows must not be treated as zero demand.
Quick reference
| What you see | What to do next |
|---|---|
| Rows fetched | Google returned data; local analysis may still be running. |
| Shared queries | Inspect page intent before calling the overlap harmful. |
| Page absent from crawl | Crawl the page before interpreting placement gaps. |
| No rows returned | Check property, period, access and available traffic. |
Your next step
Continue with connect Google services, investigate overlapping pages, check a planned keyword list. Return to the documentation home for the full workflow. Before sharing an export, check its website, dates and filters, and remove private information. If a control is missing or a result looks wrong, contact support with your app version and a sanitized example.
Frequently asked questions
Build keyword clusters from Search Console query and page data in LLMIC. Understand shared queries, crawl coverage and useful next actions.
Load a completed crawl that includes the pages you need to inspect. Confirm the crawl scope, capture time, rendering choice, and any filters before interpreting the result.
Review the affected URL, measured field, source or rendered evidence, and the rule that raised the item. Inspect nearby pages and templates because one shared component can create the same pattern across many URLs.
Create a task with the affected URL, observed evidence, intended outcome, smallest safe change, owner, and verification method. Keep the measured finding separate from the proposed fix.
Publish the approved change, run a fresh compatible crawl, and compare the new evidence with the saved result. A resolved local finding confirms the implementation changed; search performance requires separate observation over time.
The check cannot guarantee rankings, indexing, traffic, conversions, inclusion in an AI answer, or a future search-engine action. Interpret it with site context, search performance, and editorial judgment.
Save or export the evidence, assign the approved action, and use the related guides linked on the page for the next check. After implementation, repeat the same workflow against a fresh crawl so the result is comparable.