A solo founder in r/ClaudeAI shared the weekly routine behind 30,500 organic clicks in three months. Every Monday they exported two CSV files from Search Console, queries and pages, and gave them to an AI with one prompt. In week three it spotted five articles competing for the same query, and merging them into one moved it from position 14 to 3, now worth about 460 clicks a month.
That routine works as well in ChatGPT as in Claude, and it's the most useful way I know to use ChatGPT for SEO. The model is unreliable when you ask it for search volumes or rankings it can't see. It's very good at reading a spreadsheet you hand it and spotting patterns a tired human skims past. This guide gives you seven prompts that run on your own Search Console data, plus the one rule that keeps the answers honest.
I use Claude to analyse my Google Search Console data every week. 30K clicks in 3 months. Here's the exact workflow
I'm a non-technical solo founder. I run a marketplace where developers sell skills to freelancers and small businesses who use AI agents but struggle to get good output from them. I don't code. Claude does everything. 30.5K organic clicks....
Why your own data beats a blank chat
Ask a fresh ChatGPT chat for keyword ideas and you get plausible lists with made-up volumes. We covered why in our guide to ChatGPT keyword research prompts, and the short answer is that it has no live search data. Your Search Console export is different. Every row is a real query that showed one of your pages in Google, with real impressions, clicks and positions.
So the job changes from inventing to sorting. ChatGPT reads a few thousand rows in seconds and groups, filters and compares them the way you'd do in a spreadsheet over an afternoon.
Getting the data out of Search Console
In Search Console, open Performance, then Search results, set the date range and use the export button to download the tables. You get separate tables for queries, pages, countries, devices and dates, and the queries and pages tables are the ones these prompts use.
One limit matters. The standard export lists queries and pages separately, so it can't tell you which page earned which query. For that, filter the report by one query and export its Pages table, or pull query and page pairs through the Search Console API, which Google says returns up to 50,000 rows a day per search type.
Upload the CSV files into a ChatGPT chat or a Project. If you use Projects, add a short note about your site, your product and who buys it, so every prompt starts with that context.
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The rule that keeps the answers honest
The best reply in that r/ClaudeAI thread wasn't about a prompt. It suggested making the AI separate what it observed from what it guessed. "Impressions dropped" is in the data. "Google can't render the page" is a theory until you check the page yourself.
So end every prompt below with the same line, and you'll get a table you can act on instead of a confident story.
Answer in a table with three columns: what the data shows, what might explain it, and the action to take. Only use numbers that are in my files. Mark anything you're guessing as a guess.
Prompt 1: queries with impressions but no clicks
Upload the queries table, then run this.
From the queries file, list queries with at least 100 impressions and a CTR below 1%, sorted by impressions. For each one, say which of my pages most likely ranks for it, and suggest a page title that matches what the searcher wants.
This finds titles that show up but don't get chosen. The founder in that thread found 36 similar articles getting impressions and no clicks, rewrote the titles to lead with the outcome, and saw CTR double across the batch.
Prompt 2: pages stuck on page two
Upload the pages table.
List pages with an average position between 8 and 20 and at least 200 impressions. Sort by impressions. For each, suggest one change to the page that would most likely help it reach the first page, and say why.
Pages at positions 8 to 20 are where Google already thinks you're relevant, which makes them the cheapest wins on the site. Rewrite the title and opening paragraph first, since those are what change click-through.
Prompt 3: queries you rank for without a page
Upload both tables. This one comes from a workflow shared in r/Agentic_SEO.
Compare the queries file with the pages file. List queries where I get impressions but no page is clearly about that topic. Group similar queries together and suggest one new page or one new section of an existing page for each group.
Before writing anything new, check whether an existing page could answer the query with a stronger section, which another commenter in that thread rightly pointed out is often enough.
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Prompt 4: two pages competing for one query
This needs query and page pairs, so use the API export or filter the report by query first.
From this query and page data, find queries where two or more of my pages each get at least 10% of impressions. For each, list the pages, their positions and clicks, and recommend which page to keep as the main one and what to do with the others.
Merging or redirecting the weaker page is the usual fix. That's the move that took the Reddit founder's merged article from position 14 to 3.
Prompt 5: long, question-style queries
Upload the queries table.
List every query with seven or more words, plus any query of five or more words that starts with what, how, why, which, can, does or should. Group them by topic and show total impressions and clicks per group.
These are the searches people type the way they talk to an AI tool, and they're the ones AI answers tend to cover. Each group is a candidate for a page that answers the question in its first two sentences, which our LLM SEO strategies explain in more detail.
Prompt 6: what dropped since last month
Export the same tables for two periods, for example the last 28 days and the 28 days before.
Compare these two periods. List the 20 pages and 20 queries that lost the most clicks. For each, show clicks and position in both periods, and say whether the drop looks like lost position, lost click-through or lower demand.
The three causes need different fixes, so this is worth splitting before you touch anything. Lost position usually means a stronger page appeared, lost click-through usually means the result changed, and lower demand is often seasonal.
Prompt 7: a refresh brief for one page
Filter the report to one page and export its queries.
These are the queries this page gets impressions for. Write a refresh brief listing the questions the page should answer that it doesn't today, the queries that deserve their own section, and a new title and meta description.
Then edit the page yourself, and keep the date of the change so next month's export can show whether it worked.
Three mistakes that skew the answers
Most bad output from these prompts comes from the export, not the model. Three are worth checking every time.
The first is position. Google's help page defines average position as the position of your topmost result, averaged across every time it showed. A page averaging 8.4 might sit at 3 on some days and 15 on others, so ask ChatGPT to treat small differences as noise, especially on queries with few impressions.
The second is mixing date ranges. If one file covers 28 days and the other three months, every comparison ChatGPT makes between them is wrong, and it won't notice unless you tell it. Export everything for the same period and say which period it is in the prompt.
The third is acting on a single week. Weekly numbers on a small site swing a lot, so a query that lost half its clicks may simply have had a quiet week. Look for changes that hold across two or three exports before you rewrite or merge anything.
What ChatGPT still can't tell you
Search Console only covers Google. It won't show whether ChatGPT, Perplexity or Gemini cite your pages, and ChatGPT can't tell you that about its own answers from inside a chat either. For that you need to check a fixed set of buyer questions in each engine on a schedule.
RankControl's AI visibility tracking does that weekly for 50 questions across six engines, and its analytics syncs Search Console daily so you can see which queries each article earns. Our guide to ChatGPT SEO workflows for small teams covers the writing side if you want ChatGPT drafting as well as analyzing.
What this costs in hours
Exporting and running all seven prompts takes about an hour. Acting on the results is the real work. Rewriting ten titles takes an hour or two, refreshing five page-two pages is 10 to 15 hours, and merging competing pages needs redirects and a careful edit.
You can run this loop by hand every month. Or RankControl's agents can do much of it for you, finding the questions to write about, writing and publishing the pages, and checking Google and six AI engines every week, while you focus on your product.
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RankControl credits every visit to the assistant that sent it: ChatGPT, Perplexity, Claude, Gemini, Copilot, or Grok. Full source attribution, next to your Google traffic.
