How to turn Search Console queries into a content plan using AI
- Level: Beginner
- A few hours to set up
The short answer
Export a year of queries with their landing pages, ask a model to group them by what the searcher wants rather than by shared words, compare each group with the page that should answer it, and let an editor decide which gaps need a new page, an update or nothing.
On this page
At a glance
- The marketing problem
- Search Console holds thousands of queries, but a query list is not a content plan. Similar searches need grouping by the need behind them, with a decision about which page should serve it.
- Who this is for
- SEO and content teams deciding what to improve or write next.
- Tools involved
- Google Search Console
- Claude API
- Screaming Frog
Where I have run it
1,000+ ranking keywords, average position 40 to 11
View case study ↗What you need
Before you start
- Search Console access for the site
- A list of your existing pages and what each is for
- An understanding of the buyer’s journey, so clusters can be mapped to stages
What goes in
- Queries with their landing pages, impressions, clicks and average position, over a comparable period
- Queries with impressions but few clicks: where you appear but do not satisfy
- Questions sales and support hear, to check the clusters against
How it works
- 01 · ToolExport queries with their pages
- 02 · AI stepGroup queries by intent
- 03 · AI stepCompare clusters with existing pages
- 04 · You decideEditor decides the priorities
- 05 · OutputContent plan with a reason attached
- Tool
- AI step
- You decide
- Output
Step by step
01 Tool
Export queries with their pages
Export queries and pages for the last twelve months, keeping the query-to-page relationship. The interface export stops at 1,000 rows, so use the Search Console API, Looker Studio or a Sheets add-on for more. Keep queries with impressions and no clicks: that is where the gaps are.
02 AI step
Group queries by intent
Ask the model only for a cluster name per query, then total clicks and impressions yourself with a pivot table. Models are unreliable at arithmetic over long lists.
Prompt to copy
Below are search queries. Group them by what the searcher is trying to achieve, not by shared words. For example, “how much does X cost” and “X pricing” belong together. Return a table with two columns: query, cluster name. Then list each cluster with a one-line description of the need and its buying stage (learning, comparing, ready to buy). Do not drop queries; put any that fit nowhere in a cluster called “unclustered”. Queries: [paste]
03 AI step
Compare clusters with existing pages
For each cluster, find the page that should answer it and judge whether it does.
Prompt to copy
Here are query clusters, the page that currently ranks for each query, and a list of our pages with their purpose. For each cluster say: which page best serves it, whether that page answers the need (yes, partly, no) and what is missing. Suggest “update page”, “new page” or “no action”, with one sentence of reasoning. Clusters: [paste] Our pages: [paste]
04 You decide
Editor decides the priorities
Check overlap, business relevance and whether a new page is really justified. Most clusters do not deserve a page; saying so is the job.
05 Output
Content plan with a reason attached
Each planned piece lists the queries it exists for, the page it creates or updates and the outcome it should affect.
Where a person decides
An editor decides what gets written or updated; a cluster is an argument for a page, not a commission.
What to watch for
- Search Console hides some low-volume queries, so the export is not all demand.
- A cluster is a suggestion, not proof of a new page opportunity; check existing coverage first.
- A weak title and description is a different problem from missing content.
How to tell it is working
- A sample of clusters is checked by hand for sensible grouping.
- Proposed pages are checked against existing ones for overlap.
- Affected pages are tracked over time, separately from seasonal or site-wide changes.
An illustrative example
Input
Illustrative: several queries about the cost of software development land on a broad services page.
Output
A “pricing questions” cluster with its queries, a note that the services page never mentions cost, and a recommendation to add a pricing section before considering a separate guide.
Want this running in your team?
AI workflow implementation. Turn a repetitive marketing task into a workflow your team can use with confidence.
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