Generative Engine Optimization Statistics: Every Figure Sourced & Graded

Most generative engine optimization statistics you will find are secondhand, and only a few trace to a study with a stated sample and date. Two hold up. Ahrefs found about 12% of AI-cited links also rank in Google's top 10 across 15,000 prompts, and Conductor measured roughly 1.08% of site visits coming from AI referrals.

Both figures describe the periods and samples their publishers state, so they fit a planning deck as long as the deck carries the source, date and sample beside each number.

Neither study measures revenue or the effect of GEO work. They show how AI search behaves, not what GEO spend returns.

GEO Statistics At A Glance

Here is every figure on this page that we could trace to a named source, with the sample, the period and a grade beside it.

The three Ahrefs rows come from one overlap study of 15,000 prompts. The Conductor row comes from a benchmark of 13,770 domains. We did not record a publication date for the Ahrefs study, so check the date on its page before you cite it.

The last row is ours. It is one client, one engagement and a result we produced, which is why it carries the vendor-reported grade and sits below the two independent studies.

How To Grade A GEO Statistic Before You Cite It

A GEO statistic is only as good as the information printed next to it. Before any number goes into a deck or an article, give it one of three grades.

• Measured: A figure from a named publisher that states its sample, its period and exactly what it counted.
• Vendor-Reported: A figure a company publishes about its own users, its own product or its own client, where the sample or method cannot be checked from outside.
• Unverified: A figure that appears in roundups with no traceable publisher, sample or date.

The grade comes from four checks. Run them in this order.

1. Source: Find the publisher that first produced the number. If the page you are reading links to another roundup, keep following the links until you reach a study or give up.
2. Date: Find the period the data covers and the date it was published. A figure with no period cannot be compared with anything.
3. Sample: Find how many prompts, domains, sessions or users sit behind the number. A percentage without a sample size is a slogan.
4. What was measured: Check what the number actually counted. A share of citations, a share of visits and a change in revenue are three different things, and roundups blur them constantly.

Most roundup figures fail at the first step. The top-ranking GEO statistics pages are mostly roundups by tool vendors and agencies that repeat figures from aggregators, and many give no sample size or date for each figure. Several of the most repeated numbers, such as a conversion multiplier for AI-referred visitors and a claim that nearly every AI Overview cites top-10 pages, appear with no source at all. We could not trace them, so they are not on this page.

If you need the term defined before you start grading, read what AI search visibility means. The short version is that it covers how often and how prominently AI answers mention your brand, which is why citation and referral figures are the ones worth grading first.

A study published by a company that sells in this space deserves the same four checks as any other. The publisher's interest does not make the number wrong. It means you should read the sample and the method before you quote it.

AI Search Adoption And Referral Traffic Statistics

AI referrals were about 1.08% of all web traffic across 13,770 domains and 3.3 billion sessions from May to September 2025, according to Search Engine Land's report on Conductor's benchmark. That is the one referral-traffic figure on this page that we could trace to a stated sample.

The scope behind it is short.

• Domains: 13,770 websites.
• Sessions: 3.3 billion.
• Industries: 10.
• Period: May to September 2025.

For a planning deck, the number does one useful job. It gives you a reference point for your own AI referral share, so you can say whether your site sits above or below a measured benchmark. A site that gets 3% of its visits from AI assistants is doing something different from a site that gets 0.2%, and 1.08% tells you roughly where the middle sits.

It does not tell you three things. A share of visits is not a share of conversions, so it says nothing about whether those visitors buy. It is an average across 10 industries, so your own sector can sit well away from it. And it covers five months in 2025, so it describes a past period and promises nothing about the next one.

Adoption counts for individual assistants are a different case. The user and weekly-active figures that circulate for ChatGPT, Gemini and Google's AI Mode are published by the companies themselves, and we could not confirm them against a primary source. We do not grade them as measured and we do not print them here. If a deck needs one, cite the company's own announcement, name it as company-reported on the slide, and do not stack it next to independent figures as if the two were the same kind of evidence.

Click Decline And Zero-Click Figures: What The Data Does Not Show

Click-decline figures are the least reliable group of GEO statistics. A usable claim has to state four things, and most published figures state none of them.

• Panel: Whose browsing or search behaviour was counted, and how many people or queries that was.
• Period: The start and end dates, because a before-and-after comparison is meaningless without them.
• Query set: Which searches were included. A figure built on informational queries cannot be applied to branded or commercial ones.
• Definition of a click: What counted as a click, what counted as a zero-click search, and whether a click on an AI answer's citation counted at all.

That is why the numbers on this topic conflict. Different pages use different panels, different baselines and different definitions, then print the result as if it were one settled fact. Some click-decline figures conflict between pages, and the gap between them is usually a gap in method rather than a gap in reality.

We found no click-decline or zero-click figure that passed all four checks, so this page prints none. A number we could not defend would do more damage in your deck than an honest gap.

Here is what to say in a deck instead. State that search results increasingly answer the question on the page, that the measurement of the resulting click loss varies by method, and that you will track your own click-through rate and your own AI referral share from your analytics. That is a claim a sceptical finance lead can accept, because every part of it can be checked against your own data.

AI Citation Statistics: How Often Cited Links Rank In Google

Across 15,000 long-tail prompts, about 12% of links cited by ChatGPT, Gemini and Copilot also appear in Google's top 10 for the same prompt, according to Ahrefs' overlap study. Perplexity is the exception. It cites Google top 10 pages most closely, at about 28.6% of its cited URLs.

The same study found an average overlap of about 10.96% between AI-cited links and the Google and Bing top 10, again across 15,000 prompts. Put plainly, roughly nine in ten of the links these assistants cite sit outside the pages you would find by checking Google's first page.

The study reports ChatGPT, Gemini and Copilot as a group, so we cannot split that 12% by engine. We would rather say that than invent a per-engine breakdown.

The consequence is the part to put in front of your team. Ranking in Google is not a proxy for being cited. A page can sit in the top 10 and never be cited, and a page can be cited without ranking there. Engines also differ from each other, which is why a tracker that watches only one of them gives you a partial picture. Before you choose one, compare AI visibility tools by engine coverage.

So what do you do with pages that rank but do not get cited? Start with the page itself. Answer the question in the first lines, name your entities in full, and put a figure and its qualifier in the same sentence. Our guide to structuring content for AI answers goes through that in detail, and our answer engine optimization service is the version of that work we do for clients.

What GEO Research Proves And What It Does Not

A citation study, a traffic study and a revenue study answer three different questions. The figures on this page answer the first two. Ahrefs tells you how often cited links overlap with search rankings. Conductor tells you what share of web visits came from AI referrals. Neither tells you what a pound or a dollar of GEO spend returned.

That gap matters because the three measures get swapped in conversation. Someone says "GEO works" and cites a citation study. Someone else hears "GEO drives revenue" and puts it in a budget. A rise in citations does not have to mean a rise in clicks, and a rise in clicks does not have to mean a rise in sales. Each step needs its own measurement.

Two other limits apply to every study here.

• Sample: A study of 15,000 long-tail prompts describes long-tail prompts. It says less about short, high-volume queries.
• Dating: AI products change often. A result from a past period describes that period, and you should re-check it before you build a long plan on it.

Be equally careful with who ran the study. A benchmark published by a company that sells related tools can still be sound, but it should be read with its method open beside it. An independent study with a clear method earns more trust, and one with neither earns none.

We found no verified effect-size figure for any specific GEO tactic, so this page states none. If you want to know what GEO work involves in practice, the generative engine optimization service page sets out how we approach it.

Build Your Own GEO Baseline Instead Of Borrowing One

A borrowed benchmark tells you how other sites behave. Your own baseline tells you whether your work is moving anything. Build it in four steps.

1. Measure AI referral share: Divide AI-referred sessions by total sessions, using the same analytics source each time. Record it monthly.
2. Compare it with the benchmark: Set your share beside the 1.08% Conductor figure, and remember that figure is an average across 10 industries.
3. Measure citation rate: Divide the number of prompts where an engine cites your brand by the number of prompts you tested. Keep the prompt list fixed and run it on a fixed schedule, weekly or monthly.
4. Log rankings beside it: Record your Google positions for the same topics on the same schedule, so a change in citations can be read next to a change in rankings.

For the Google side of the baseline, we use Semrush Position Tracking to log the same keywords on a fixed schedule, so ranking changes can be read next to your citation log.

Here is a worked example. Every input below is illustrative and invented for the calculation. None of it comes from a real site.

• Illustrative total sessions: 40,000 in the month.
• Illustrative AI-referred sessions: 520.
• AI referral share: 520 divided by 40,000, which is 1.3%.
• Illustrative prompts tested: 50.
• Illustrative prompts that cited the brand: 9.
• Citation rate: 9 divided by 50, which is 18%.

In this made-up case, a 1.3% referral share sits slightly above the 1.08% benchmark, and an 18% citation rate becomes the number to beat next month. The value is in the second reading, not the first. One month of data is a starting point, and the trend over several months is the finding.

Do not skip the logging. A baseline that was never recorded cannot be compared with anything later. Our post on AI search visibility metrics and KPIs covers which metrics to log, and our list of tools for tracking brand visibility in AI search covers what can run the prompt list for you.

For a real example of a measured referral change, read TRM's ChatGPT referral case study. It reports ChatGPT referral growth of 8,337% in 90 days after Generative Engine Optimization work. It is our own result from one client engagement, so it is one example and no forecast.

How To Use These Statistics In A Planning Deck

A budget case lands when each slide makes one claim the audience can check. Build it like this.

1. Write one claim per slide: State the finding in a single sentence, such as "About 12% of AI-cited links also rank in Google's top 10."
2. Add the footer: Put the source, date, sample and grade at the foot of the slide in the same format every time.
3. State the limit: Add one line saying what the figure does not show, so nobody reads more into it than it holds.
4. Pair it with your own number: Put your baseline beside the benchmark, so the audience sees the gap you want to close.

The footer is the part people skip, and it is the part that protects you in the room. Copy this template.

• Source: The publisher and the page it came from.
• Date: The period the data covers.
• Sample: How many prompts, domains or sessions sit behind it.
• Grade: Measured, vendor-reported or unverified.
• Limit: The one thing the figure does not show.

Once the case is made, the next question is what to do with the budget. Our guide to AI search visibility strategy for B2B SaaS is where we send leaders after they have the numbers approved.

Where No Reliable GEO Figure Exists Yet

Our research found four topics where we could not source and grade a usable figure. We list them so you know where to rely on your own data and not on a roundup.

• GEO market size: The figures in circulation have no traceable method or sample behind them.
• GEO return on investment: No study we could verify links GEO spend to revenue, and a citation study cannot stand in for one.
• AI-referral conversion against organic: The multiplier that gets repeated has no traceable source, so it stays out.
• B2B GEO adoption: We found no verified count of how many B2B companies run GEO programmes.

For all four, the honest approach is to use your own analytics. Track your AI referral share, your citation rate and your conversion by channel, and report them with their own source and date. A small number you measured beats a large one you borrowed.