Traditional rank tracking still has its place, but it is no longer enough on its own. It can tell you where a page appears in the standard Google results, but it will not accurately tell you whether your content is being cited in Google AI Overviews, ChatGPT, Perplexity or other AI-led search tools.
For website owners and marketers, this is a critical blind spot. Studies now suggest that AI-generated search results can reduce clicks to normal organic listings, while some AI referral traffic may arrive with stronger intent. The numbers vary by source and sector, but the direction is clear enough: rankings, clicks and visibility are starting to separate.
In this guide, I’ll show how I review AI visibility using Google Search Console, GA4 and ChatGPT. It will not replace a dedicated AI visibility platform, but it gives you a practical starting point using data you probably already have.
Why Your Rank Tracker Is Missing Half the Picture
Most SEO tools were built around a fairly simple model. They check a search result, find your URL, and report the position. For many years, that was a sensible way to monitor SEO performance.
The problem is that the search results are no longer just a static list of links. Google AI Overviews, featured snippets, People Also Ask, Reddit discussions, shopping modules, videos and paid results can all sit around or above the standard organic results. On top of that, users are increasingly asking tools like ChatGPT and Perplexity questions directly, rather than searching Google in the traditional way.
AI-generated answers change the relationship between ranking and traffic. Ahrefs found that the presence of an AI Overview correlated with a 58% lower average click-through rate for the top-ranking page in the queries they analysed. Pew Research Center also found that users who saw an AI summary in Google were less likely to click on traditional search result links than users who did not see one.
A traditional rank tracker can tell you that a page ranks third. It cannot tell you whether an AI system has decided to use that page as evidence in its answer, whether a competitor has been cited instead, or whether the answer is satisfying the user before they ever reach your site.
Ranking is about visibility in a list, whereas citation is about being trusted enough to support the generated answer. They overlap, especially where E-E-A-T is concerned, but they are not the same.
For most businesses, the sensible approach is to add another layer of analysis alongside rank tracking. You still need to know where you rank, but also whether your pages are being cited, whether AI results are suppressing clicks, and which pages are attracting AI referral traffic.
Step 1: Export Your GSC Data and Run It Through ChatGPT
Google Search Console is still one of the most useful tools for this kind of analysis. Most people use it to check clicks, impressions and average positions, but the real value is in the raw query data.
When you export the Queries report, you get searches your site appeared for, with impressions, clicks, click-through rate and average position. ChatGPT can help by grouping similar searches, identifying patterns and highlighting areas that deserve closer attention.
How to get the data
- Log in to Google Search Console
- Navigate to Performance then Search Results
- Set the date range to the last three to six months
- Click Export and choose Download CSV (make sure you have the Queries tab selected)
- Open the CSV in a plain text editor like Notepad, TextEdit or VS Code.
- Copy the contents and paste into ChatGPT with one of the prompts below or attach as a file and reference in the prompt.
Prompt 1: Identifying AI Overview triggers
Question-based searches are often the ones most likely to trigger AI Overviews, featured snippets or other answer-led search features.
Analyse this Google Search Console query data. Identify which queries are likely
triggering AI Overviews or featured snippets based on their phrasing (question-based,
definition-based, or comparison-based queries). Group them and list the top 10 most
at-risk queries where I may be losing clicks to AI-generated answers.
[paste CSV data here]
The aim here is not to treat the query export as proof that an AI Overview appeared for every search. That is where Google’s new Generative AI features report now changes the process.
If you have access to it, go to:
Performance > Generative AI
This report shows impressions from Google’s generative AI features, including AI Overviews and AI Mode. In the current version, you can review the data by pages, countries, devices and dates. That is far more useful than guessing from normal GSC data alone, because you can see which URLs are actually appearing in Google’s generative AI results.
There are still limits. At the time of writing, the report does not give the same query-level, click, CTR and average position detail as the standard Search Results report. It is also still being rolled out, so not every site will see it yet.
For that reason, I would now use the two reports together:
- Use Generative AI features to find the URLs already appearing in AI Overviews and AI Mode.
- Use the standard Search Results > Queries export to understand the likely searches and intents behind those URLs.
- Use ChatGPT to group those queries into topics, risks and content opportunities.
Prompt 2: Finding suppressed clicks
High impressions, reasonable rankings and poor click-through rates can still be a useful warning sign. It does not always mean an AI Overview is responsible. Paid results, map packs, featured snippets, Reddit results, shopping modules and brand bias can all affect CTR as well.
From this Google Search Console data, identify queries where my average position
is between 4 and 15 but impressions are high and CTR is unusually low (below 2%).
These may be queries where an AI Overview is appearing above my result and
suppressing clicks. List them ranked by impression volume.
[paste CSV data here]
I would treat this prompt as a diagnostic check rather than a final answer. A page can hold a reasonable average position and still fail to attract the traffic you would normally expect.
You can then cross-check suspicious pages against the Generative AI features report:
- Strong rankings, weak CTR and high Generative AI impressions may mean the page is appearing in AI results but not earning the click.
- Strong rankings, weak CTR and no Generative AI impressions may point to another SERP feature suppressing clicks.
- No Generative AI impressions for an informational topic may mean the page needs clearer structure, better supporting evidence or stronger topical depth.
Prompt 3: Discovering semantic clusters
Semantic clusters show where multiple searches are really asking the same underlying question. This is useful for AI search because AI systems are less interested in exact-match keyword repetition and more interested in whether a page covers the topic clearly and comprehensively.
Analyse this query data and identify semantic clusters. These are groups of queries that
share the same underlying intent. For each cluster, suggest one piece of content
that would address the full cluster rather than individual keywords.
[paste CSV data here]
For example, searches around “how to optimise images for web”, “image compression”, “WebP images”, “lazy loading images” and “image file size SEO” may all belong to the same broader topic.
GSC tells you what people are searching for. The Generative AI features report shows which pages are appearing in Google’s AI-led results. ChatGPT helps group the searches into themes, so you can decide whether to improve an existing page, merge weaker content, or create something new.
Step 2: Use GA4 to Find Your AI Referral Traffic
GSC now gives a direct view of Google generative AI impressions where the report is available, but it still does not tell the whole story. It does not show every AI platform, and impressions alone do not tell you whether users engaged, converted or made an enquiry after landing on the site.
That is where GA4 is still useful. If your content is being used by platforms such as ChatGPT, Perplexity, Copilot, Gemini, or Claude, you may see some of those visits appearing in your acquisition data.
How to get the data
- Log in to Google Analytics 4
- Navigate to Reports then Acquisition then Traffic Acquisition
- Set the date range to the last six to twelve months
- In the Session source / medium dimension, look for entries containing chatgpt.com, perplexity.ai, you.com, copilot.microsoft.com, bing.com, gemini.google.com, claude.ai or other AI/search sources
- Export this data as a CSV using the download icon
- Open the file in a plain text editor and paste it into ChatGPT with the prompt below.
Prompt 4: Analysing AI referral quality
Analyse this GA4 traffic acquisition data. Identify all sessions from AI search
platforms (ChatGPT, Perplexity, Bing Copilot, You.com, or similar). For each
AI source: show total sessions, average engagement rate, and average session
duration. Compare these metrics to my overall organic search traffic. Which
pages are receiving the most AI referral traffic?
[paste CSV data here]
What to look for
First, check whether AI platforms are sending any traffic at all. For many sites, the numbers will still be small, but even a small number of visits can show which pages are being surfaced by AI tools.
It is also worth comparing GA4 with the Generative AI features report:
- A page with high Generative AI impressions but no AI referral traffic may be visible in Google’s AI results but not attracting clicks.
- A page with AI referral traffic from ChatGPT or Perplexity may be performing well outside Google’s own AI features.
- A page with standard organic traffic, no AI impressions and no AI referrals may still be doing well in conventional SEO, but not yet gaining much AI-led visibility.
Step 3: Use the Generative AI Report to Prioritise Pages
Before making technical changes, I would now add one extra step: use the Generative AI features report as a prioritisation layer.
In GSC, review:
- Top pages — which URLs are getting the most generative AI impressions?
- Dates — are impressions increasing, stable or dropping?
- Countries — are impressions coming from the locations you actually target?
- Devices — are mobile or desktop users seeing your content more often in AI features?
If a page is already receiving AI impressions, Google is at least considering it relevant enough to show in generative AI features. That page may only need refinement rather than a complete rewrite.
For each high-impression page, I would then check:
- Does the page answer the likely query clearly near the top?
- Are the headings descriptive enough to stand alone?
- Is there original insight, experience, data or examples?
- Are important claims supported with credible sources?
- Is the page crawlable, indexable and eligible to show a snippet in Google Search?
- Does the page include useful images, video, tables or structured content where relevant?
Google says standard SEO best practice remains relevant for generative AI search because these features are rooted in its core Search ranking and quality systems. It also says content needs to be crawlable, indexable and eligible to show a snippet to be eligible for generative AI features.
The answer is not to create a separate “GEO” version of every page. It is to make the page more useful, clearer and easier to understand.
Step 4: Technical Changes That Can Help AI Visibility
Once you have reviewed GSC, the Generative AI features report and GA4, the next step is to improve the pages most likely to matter. There is no guaranteed formula for AI citation, but there are sensible changes that should make a page easier to understand, extract and reference.
Action 1: Add useful schema to your highest-priority pages
Start with the pages identified in GSC as having high impressions, question-led queries, weak click-through rates or strong Generative AI visibility. If those pages already answer common questions, FAQ schema can help make that content more clearly structured.
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "FAQPage",
"mainEntity": [
{
"@type": "Question",
"name": "What is Generative Engine Optimisation?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Generative Engine Optimisation (GEO) is the practice of optimising content to earn citations and mentions within AI-generated search responses, such as Google AI Overviews, ChatGPT Search, and Perplexity answers."
}
}
]
}
</script>
For important articles and guides, it also makes sense to use Article schema to define the author, publish date and modified date. Organization schema can help connect the content to the business behind it. The schema.org vocabulary uses American spelling for Organization, so that spelling needs to be preserved in the markup.
Google has said structured data is useful for eligible rich results and helping Google understand pages, but it should match visible content and should not be treated as a way to force inclusion in AI features. The broader aim is to make the page clearer and more trustworthy, not to add markup for the sake of it.
Action 2: Restructure high-value content into clearer sections
AI systems tend to work better with content that is clearly structured. Dense prose can still rank, but it is not always easy for an AI system to extract a clean answer from it.
For important pages, I would look at the following:
- Use descriptive H2 and H3 headings
- Answer the main question early in each section
- Keep paragraphs reasonably short
- Use tables or lists where they genuinely help comparison
- Add examples, data and sources where appropriate
- Avoid vague claims that are not backed by anything specific
- Include useful images or video where they help the user understand the topic
If two pages cover the same topic, the more useful page is normally the one that adds something specific, e.g. original data, a better explanation, clearer examples, or practical steps based on real experience. Thin summaries are easier to produce, but they are also easier to ignore.
What I Actually Found
When I ran this analysis on my own site, the results were more striking than I expected. A single blog post on AI and the future of SEO had accumulated 416,000 impressions across 1,000 queries over 16 months — but generated only 528 clicks. That is an overall CTR of 0.068%.
The suppressed clicks prompt made the pattern immediately obvious. Here are the top queries by impression volume:
| Query | Impressions | Clicks | CTR | Avg Position |
|---|---|---|---|---|
| ai and the future of seo | 21,485 | 2 | 0.01% | 3.88 |
| ai impact on seo | 14,111 | 3 | 0.02% | 5.42 |
| future of seo | 13,109 | 8 | 0.06% | 10.61 |
| how is ai changing seo | 12,585 | 3 | 0.02% | 5.65 |
| will ai replace seo | 9,552 | 4 | 0.04% | 5.30 |
| future of seo with ai | 6,980 | 14 | 0.20% | 4.83 |
| will seo be replaced by ai | 4,038 | 4 | 0.10% | 6.19 |
| is seo dead with ai | 2,525 | 6 | 0.24% | 7.30 |
The post was ranking at position 3.88 for "ai and the future of seo", yet that single query produced 21,485 impressions and just 2 clicks. Every high-impression, near-zero-CTR query in the table is question-format or topic-based. These are exactly the queries where Google deploys an AI Overview, meaning the page was ranking well but wasn't getting the traffic.
The semantic cluster prompt added a second layer of insight. These queries were not all ones I had explicitly targeted. ChatGPT grouped them into a single intent cluster around AI disruption to search and flagged that the existing content was already covering the cluster and just needed restructuring to improve its citation likelihood within the AI Overviews.
The GSC data also revealed something else. Several of the URLs appearing in the results were not the page URL itself but anchor links: /#how-is-ai-changing-seo, /#ai-vs-seo-traditional-search, and /#the-evolution-of-seo were each generating over 30,000 impressions with zero clicks. These are section headings being pulled directly into AI Overviews as cited sources. The content was being read and used but wasn't driving visitors back to the site. That is the gap this process helps you find and close.
This has changed how I now approach new content. I am still thinking about rankings, internal links, metadata and the usual SEO basics, but I am also looking at whether each section can stand on its own as a useful answer.
Final Thoughts
Using GSC, GA4 and ChatGPT as outlined here will give you a much better starting point than we had before.
The Generative AI features report shows which pages are appearing in Google’s AI results. The standard GSC report gives the query and ranking context. GA4 shows whether AI platforms are already sending traffic. ChatGPT helps group the data, find patterns and turn exports into something you can actually use.
The key point is that SEO reporting needs to move beyond rankings alone. Rankings are still useful, but they do not tell the full story anymore. For many informational searches, the more useful question is whether your content is visible, trusted and cited where the answer is being generated.
Top comments (1)
Great tips, I didn't even know GSC had a Generative AI section