Turn Search Console Queries Into an AEO Backlog
If a buyer asks how long implementation takes and your website answers “built for scale”, you have some work to do.
That is the kind of gap I want to find before I start talking about AEO. Not because every buyer question maps neatly to an AI citation, but because answer engines can only work with the answers you have actually made available.
Google Search Console is a useful place to start because it shows where your site is already being surfaced for real searches. It will not tell you what people ask ChatGPT. It will not prove that a content change will earn AI visibility. But combined with the pages themselves and some commercial context, it can give you a practical backlog of buyer questions worth answering properly.
This is buyer research for answer engine optimisation. It gives you questions to investigate and answers to improve. It does not, by itself, tell you what people ask ChatGPT or prove that a change will earn AI citations.
If you want the structural layer behind this workflow, I would pair it with my AI retrieval content checklist and the more hands-on AEO content audit guide. This article is the Search Console input layer: where the buyer questions come from, which pages appear, and how that becomes a backlog.
Here is how I would approach it.
Start with a commercial boundary
Choose one product, audience or buying situation before exporting anything. A whole-site analysis can easily become a very organised list of things that do not matter.
Write a short brief covering your priority audience, the problem you help them solve, the product in scope and the concerns that tend to delay a decision. Include what is out of scope and what evidence you have available: customer examples, implementation documentation, product limitations or research.
This is also the context an AI assistant needs. Otherwise, you are asking it to infer your commercial priorities from search volume.
This boundary is what keeps the work honest. Without it, you are not building an AEO backlog. You are sorting a spreadsheet and hoping importance reveals itself.
The output is not a list of keywords. It is a review queue: buyer question, surfaced page, current answer, evidence gap, recommended action and owner. Search Console helps you find the visible demand. The content review tells you whether the answer deserves to be improved.
1. Export the questions, but do not let regex define the market
In Search Console, open Performance -> Search results, select Web as the search type and choose a completed period. I would start with three months, extending to six if the site has limited data. Keep the same dates, country and device settings across exports, and record them alongside the files.
Under Add filter -> Query -> Custom (regex), select Matches regex and enter:
^(what|how|why|who|when|where|which|can|could|does|do|is|are|should|will|would)\sThis is a starting filter for English questions. Adapt the vocabulary for other languages. It will miss searches such as “implementation timeline”, which is why the next filter matters too. Search Console uses RE2 expressions and matches partially unless you anchor the expression. Google’s filtering guide.
Export the Queries table with Query, Clicks, Impressions, CTR and Average position. Save it as question-queries.csv.
Next, replace the question filter with an evaluation filter:
\b(best|alternatives?|compare|comparison|versus|vs|pricing|prices?|costs?|migration|implementation|integrations?|security|compliance)\bExport the same columns as evaluation-queries.csv. The word boundaries reduce accidental matches inside unrelated words. Add category-specific language where useful, but inspect the matches: a term such as “security” does not automatically mean someone is evaluating a purchase.
I would also inspect an unfiltered query export for the product or section in scope. Regex is useful for finding a subset, but it should not define the whole research field.
Sort by impressions to find visible patterns, then read the actual queries. Impressions indicate that your site appeared. They are not total market search volume or evidence that Google is deliberately testing your content.
2. Find the pages Google is already surfacing
Once you have the queries, resist the temptation to jump straight into content ideas. First find out which pages Google is already associating with those questions.
Separate query and page exports do not contain that relationship, and you cannot reconstruct it by matching row order or similar metrics.
For a small pilot, use the interface. Select a query to apply an exact query filter, open the Pages tab and export the results. Record the selected query alongside each page row. Repeat for the questions you want to investigate. You can also filter to one exact page and inspect its Queries tab. Google’s guide to applying filters.
A Pages export under a broad regex filter shows pages associated with that whole query set. It is useful for orientation, but it does not show individual query-to-page matches.
For a larger analysis, request the query and page dimensions together through the Search Console API. Keep the same period and filters as the query exports, paginate the results and save the output as query-pages.csv. The API supports those dimensions, but does not guarantee every data row. Search Analytics API documentation.
Your working fields are:
| Field | Purpose |
|---|---|
| Query | The recorded search wording |
| Page | The URL associated with that query |
| Clicks and impressions | Performance of that combination |
| CTR and average position | Supporting context for the review |
| Date range and filters | The scope needed to interpret and repeat the export |
Call this the surfaced page, rather than assuming it was a landing page. A row can have impressions without a visit.
3. Bring in the actual page content
Now collect the content for the relevant URLs. A crawl with extracted main text, a CMS export or a manual review can all work. If you use Codex with repository access, check whether the content is actually in the repo. Templates alone will not tell you what a CMS-driven page currently says.
For each URL, include the title, H1, main body text, relevant internal links and CTA. Add supporting documentation and customer evidence where available. Titles and headings can help locate a page, but they are not enough to judge the quality of its answer. Save the material as one document per URL or a structured site export, retaining each source URL and the collection date. Remove repeated navigation and footer text so the review focuses on the main content.
This is the part people skip when they want the analysis to feel fast. I would not. The surfaced page might be a thin template, an outdated CMS page or a page that answers a different question entirely.
The method needs three inputs:
| Input | What you use it for |
|---|---|
| Query data | Identify candidate questions and calculate cluster metrics |
| Query + page data | Find which URLs appear for those questions |
| Actual page content | Assess the answer and its supporting evidence |
Keep an explicit record of pages you could not inspect. “Not found in the supplied content” is a useful finding. “The company has no answer” is a much bigger claim.
4. Clean the data without cleaning away intent
Separate login, careers and irrelevant navigation searches. Keep brand-led evaluation searches such as “[brand] vs [competitor]”, “[brand] pricing” and “[brand] implementation”. These can be some of the most commercially useful rows in the export.
Support queries also deserve a quick look before exclusion. A recurring integration problem may matter to an existing customer and to a buyer evaluating the product. Tag the context instead of assuming it is irrelevant. Keep low-impression queries when they reveal a relevant concern, but mark the evidence as limited rather than treating a single row as a demand trend.
Deduplicate rows that appear in both regex exports before calculating totals. Only deduplicate matching records at the same grain, with the same period, market and device settings, allowing for the different regex filters that selected them. Do not discard distinct dates, countries or query-page combinations because their query text matches.
Use query-level data for the main cluster metrics and query-page data for page mapping. Page-level impressions are not interchangeable with property-level impressions: several URLs can appear in the same search. Google’s explanation of report aggregation.
Also remember that filtered data can omit anonymised queries and be truncated. Treat the export as the visible portion of demand associated with your site, not a complete account of your market. Google’s filtering guide.
5. Group searches by the decision behind them
At this point the spreadsheet usually starts to look more authoritative than it is. The next job is to turn rows into decisions without flattening the buyer’s intent.
“Implementation timeline”, “how long does setup take” and “resources needed for implementation” may belong to related but distinct concerns. Preserve those distinctions if they require different answers.
Start with a prompt like this:
Use the supplied commercial brief and Search Console files.
First check the dates, filters, available columns, aggregation levels and
potential duplicate rows. Flag incompatible inputs before combining them.
Group the query-level data into specific buyer-question clusters. Assign
one primary cluster to each query for totals; use secondary tags if useful.
Group by the underlying concern, decision or context, not just shared words.
Keep branded evaluation queries. Mark ambiguous intent as uncertain.
For each cluster, return:
- The underlying buyer question
- Actual example queries from the data
- Total clicks and impressions from deduplicated query-level rows
- CTR = total clicks / total impressions, expressed as a percentage
- Impression-weighted position = sum(position * impressions) / sum(impressions)
- Associated URLs from query-page data, if supplied
- Likely intent and buying context, labelled as inference
- Relevance to the supplied commercial brief, with a reason
Use code for calculations. Do not average row CTRs or mix query-level and
page-level totals. Return N/A for a zero denominator. Label calculated
metrics as applying to the exported rows, not the whole property.
Do not invent query-page relationships when only separate exports exist.
Do not recommend content changes yet.Check a sample of the assignments, especially the clusters you might prioritise. AI can produce a tidy table while merging questions that require quite different answers.
Position and CTR help you decide where to look. A position of 12 or a low CTR does not diagnose weak content by itself; the search results, device, market and competing pages all affect the context.
6. Check whether the answer is actually good enough
Once the clusters make sense, compare the important ones with the actual pages. I would look for five things: a direct answer, enough detail to apply it, evidence behind the claims, product fit for the buyer’s situation and an appropriate next step.
For evaluation-stage questions, also ask whether the page explains why the product fits this buyer’s situation, including the conditions and limitations. A useful explanation of implementation is not necessarily enough to help a small team without developers decide whether the solution is right for them.
For implementation effort, I would expect the page to say what happens, who does what, what slows the project down and what a buyer needs to prepare internally. If you cannot give one universal timeframe, say that. Give the variables. A made-up number is not a better answer than a vague claim.
Where relevant, check external customer experiences alongside the company’s own evidence. If the website claims the product suits small teams, do relevant reviews or discussions support that claim, qualify it or contradict it? Record the source, date and customer context. Treat this as a credibility check, not proof that an AI system will recommend the product.
A second prompt keeps the review grounded:
Review the selected clusters against the supplied page content and
commercial brief. For each cluster, identify the currently surfaced URL
and any stronger answer elsewhere in the inspected material.
Return:
- Answer status: absent, partial, supported, or not assessable
- Exact URL or source file and a short supporting excerpt
- What is answered and what remains unclear
- For evaluation-stage questions: evidence of product fit for the buyer's situation, including conditions, limitations and unresolved questions
- Available evidence and evidence still needed, including relevant external customer experiences where supplied or accessible; distinguish company claims from third-party accounts
- The smallest useful action, with its rationale
- Uncertainty and the scope of content inspected
Separate observations from recommendations. If content is unavailable,
mark it not assessable. Do not invent product capabilities, customer proof,
timelines or quotes. Do not infer absence across the whole site from a
partial export. Propose changes for review; do not publish them.Check the sources behind the highest-priority findings yourself. This is where access to the website and commercial knowledge make the analysis useful: you can investigate a specific gap and decide whether the proposed answer is both accurate and worth producing.
7. Turn the findings into work someone can finish
Here is an illustrative example for a fictional B2B software company. These are not findings from a real site.
| Buyer question | Observed gap | Next action | Completion check |
|---|---|---|---|
| How much work will implementation take? | Benefits are explained, but responsibilities are unclear | Add project stages, dependencies and customer responsibilities to the existing page | A buyer can understand what their team needs to contribute; a delivery specialist verifies it |
| Which option fits our situation? | A comparison lists features without explaining trade-offs | Add decision criteria, limitations and supporting evidence | The page explains who each option suits and why |
| What will this cost beyond the licence? | Pricing omits the main additional cost categories | Explain cost drivers and link to the next scoping step | Buyers can identify the variables they need to budget for |
| How do I make the internal business case? | No inspected content explains stakeholder concerns | Validate the need with sales, then prepare a business-case guide | The guide addresses the confirmed objections without invented ROI claims |
Add the source queries, current URL, owner, priority and review date to the backlog. Keep evidence gaps visible. Sometimes the next useful task belongs to product, sales or delivery before it belongs to content.
Prioritise by audience fit, product fit, the importance of the buying concern, the weakness of the current answer and your ability to improve it credibly. Use impressions as supporting context. A low-volume question that repeatedly blocks a relevant deal may deserve attention before a broad educational topic.
I would start with three clusters. That is enough to test whether the workflow produces useful work, without pretending the first pass is a website transformation programme.
8. Measure carefully, and keep the blind spots visible
This is also where I would slow down. A tidy backlog can still be built from a narrow view of demand.
Search Console mainly helps you investigate topics where your site already has visibility. Add questions from sales conversations, customer research and product discussions so the backlog does not simply reproduce your current coverage. Label those sources separately; a sales objection is valuable evidence, but it is not a measured search query.
For each implemented change, record the date, the relevant URLs, the question addressed and the expected outcome. After a suitable period, compare the same query clusters and pages using consistent filters. Four to eight weeks can be an initial review point, with longer needed for sparse data. Check seasonality and other changes before attributing movement to the edit. For a quick structural pass before rewriting, I would also run the page through the AI Answer Readiness Checker and compare the tool output with your human review.
Follow relevant on-site behaviour too: do visitors reach the implementation guidance, explore the comparison or take a useful next step? That can help assess whether the new answer supports the buying process.
For AI visibility, keep a separate measurement layer. Google has introduced dedicated Search Console reporting for impressions within its generative AI features, including AI Overviews and AI Mode. Those reports can complement this workflow, but the query filters above do not identify ChatGPT prompts or isolate “LLM queries”. Google’s announcement of the generative AI performance reports.
If you also check answers in AI tools, record the prompt, system and date, and treat the results as a sample. Record source citations, brand mentions and explicit product recommendations separately; they can overlap, but they are not interchangeable. Note whether a relevant link is present, and do not treat a recommendation or link as evidence of a click or conversion. A citation in one answer is not evidence of consistent visibility, just as a rise in Google clicks is not proof of an AEO effect.
A better starting brief
The value of this workflow is not that it proves where AI systems will cite you. It is that it gives the team a concrete place to start.
You can trace each proposed change from a buyer question to a surfaced page, a current answer, an evidence gap and an owner. That makes the work easier to review, easier to prioritise and harder to inflate into vague “AEO activity”.
Clear structure still matters. So does crawlability, documentation, internal linking and whether third-party systems can access enough of your content to use it. This workflow covers the research and prioritisation part of AEO. It is not a complete technical, brand or off-site visibility strategy.
But when the brief is “we need to do something about AEO”, I would rather begin with a question we can answer properly: what do our buyers need to understand, and where does our website leave them guessing?
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