GEO Statistics (2026 Guide)

Why I keep a running file of GEO statistics

<p>I keep a dated file because figures I cannot trace to a date and a URL are unusable in a client brief. Slide decks recycle round figures with no method attached. I need a log I can still stand behind six months later: the source, the date I pulled it, what the figure actually measures, and the original wording. That habit is why later sections of this guide stay citable rather than ornamental, and why I still open the file every week.</p>

What I refuse to treat as a statistic

<p>I refuse to treat a number as a statistic unless I can open a primary page, see the figure in context, and record the date I fetched it. Vendor one-pagers that announce a percentage of queries now going through AI, with no survey size, no geography, and no methodology note, stay out of the sheet. So do screenshots of dashboards I cannot reproduce, anonymous industry averages, and any claim that bundles ranking, citation, and traffic into one unlabeled percentage. If the source is a keynote and the slide is not archived with a public URL, I do not log it. I also skip compound metrics that mix engines without saying so. A sourced number can be small and still useful. An unsourced round number is a talking point. The line is boring on purpose: if I cannot cite it, I do not keep it, and I do not let it leak into a rewrite brief.</p>

How I date-stamp every number I keep

<p>Every row in the file gets four timestamps, not one. I record the date the source published or last updated the figure, the date I fetched the page, the date I last re-checked the URL, and the date the figure entered a brief. Those four dates stop me from treating an older blog post as if it still described the current year. I paste a short excerpt of the original sentence so I can see if the publisher later rewrote the claim. I store the full URL, not a homepage. When a report is PDF-only, I note the page number. If a number is revised, I do not overwrite the old row; I add a new one and mark the earlier row as superseded. Week-over-week comparison only works if I know which week each cell belongs to. The stamp is clerical. Without it, the file is a pile of undated claims I will not chart.</p>

Where this file sits next to my AEO work

<p>GEO and AEO share sources and they do not share columns. Answer-engine work tracks whether a brand is cited inside a model answer. Generative-engine work tracks whether a page is cited or used inside a generated search result. I keep both in the same notebook and I split the sheets. When a Google report covers AI Overviews, that row can inform both practices, but I still label the metric by what it measures, not by the team that asked for it. Readers who want the answer-engine side should see more on aeo statistics before they mix the two files. I do not average an AEO citation rate with a GEO impression count. Overlap is real. Collapse is not. The first natural place this file sits is beside the AEO log, with a shared date stamp and separate definitions I keep intact. That split is how I send an editor one page without mixing two practices.</p>

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How I separate GEO statistics from adjacent metrics

<p>I separate these figures from ranking, AEO, and visibility numbers because they answer different questions. Rank is position on a results page. AEO is citation inside a model answer. Visibility is a looser blend of presence across surfaces. GEO, in my sheet, is whether and how a page is used or cited inside a generated search experience. Mixing those into one “AI performance” cell is how briefs go wrong. I keep adjacent metrics nearby on purpose. I do not let them overwrite the GEO column.</p>

GEO versus classic ranking reports

<p>Classic rank tracking tells me where a URL sat for a query on a given engine and device. It does not tell me whether a generated answer used that URL, named the brand, or skipped the page entirely. I still run ranking reports because they explain click-through on the blue links that remain. I do not treat a number-one organic position as a GEO win. A page can hold rank and never appear as a citation. A page can lose rank and still be quoted in an overview. When I log GEO, I record citation presence, the prompt or query class, the engine, and the date. When I log rank, I record position, URL, and SERP features. Those are two rows. If a ranking tool adds an “AI” column, I still check what that column actually counted before I copy it into the GEO sheet. Blank is better than an undefined blend.</p>

Where AI visibility numbers overlap

<p>AI visibility numbers overlap with GEO when both count whether a brand or URL appeared in a generated surface. They diverge when visibility tools score presence, sentiment, or share of voice across chat and search without saying which engine produced the mention. I use visibility figures as a scan, not as the GEO record. If a visibility report and my prompt log disagree, I trust the prompt log for citation work and I keep the visibility number in its own column. Readers who need that adjacent set can find more on ai visibility statistics without treating those charts as a substitute for engine-by-engine citation counts. Overlap is useful for spotting a missing surface. It is not a reason to merge definitions. I annotate the overlap in a notes cell so a later reader can see why two similar percentages sit on the same week. That note has saved me from briefing a rewrite on the wrong metric more than once.</p>

What I log as generative engine optimization statistics

<p>The bucket I actually put in the spreadsheet is narrow. I log citation presence or absence for a fixed prompt set, the engine that produced the answer, the date of the run, whether the citation was a linked source or a named mention, and any Search Console generative-AI impressions I can tie to a URL. Those rows are the generative engine optimization statistics I will later compare week over week. I do not log classic rank, click-through, or model-only chat answers in that bucket. I do not log share of AI unless I defined the denominator that week. The tab name is the filing rule. Visibility scores stay on another sheet. Mixing them is how week-over-week movement stops meaning anything I can brief. When a new engine appears, I add a row with the same columns instead of inventing a new score. That is the whole bucket: named, dated, and boring enough to reuse. I keep it narrow.</p>

Platform-scale generative engine optimization statistics I cite

<p>I cite platform-scale geo statistics only when the publisher names the product, the time window, and the metric. For Google, that means the Search I/O posts I re-read after each event, not a recap thread. Scale tells me the surface is large enough to deserve a checklist. It does not tell me my brand’s share of citations. I file those Google numbers in a platform tab, separate from page-level logs, so a billion-user headline cannot sit in a content KPI cell.</p>

AI Mode user and query growth I cite

<p>The two platform figures I actually put in the 2026 file both come from Google’s Search I/O write-up. In that post, Google reported that AI Mode surpassed one billion monthly users approximately one year after launch. The same post reported that queries in AI Mode were more than doubling every quarter. I keep both lines next to the canonical URL: Google’s Search I/O 2026 product post. I copy the wording as published. I do not turn “surpassed one billion” into a precise headcount I was not given, and I do not chart “more than doubling every quarter” as a homemade curve. I stamp the date I fetched the page. Those two sentences are enough to justify treating AI Mode as a surface I measure on a schedule, not a curiosity I mention in passing during a planning meeting. If Google later revises either figure, I add a new row rather than editing the old one. A missing URL is a missing statistic.</p>

Why scale numbers are not citation share

<p>A billion monthly users is a distribution fact. It is not my citation rate. I have watched teams paste the user figure into a slide titled “our AI opportunity” and then skip the prompt log. Scale says the surface has an audience. Citation share says whether a given URL or brand appeared in answers I sampled. I keep them on different tabs so a quarterly-doubling query line cannot be mistaken for a doubling of mentions. When I brief an editor, I state the Google scale figure as context and then show the citation counts from my fixed prompts. If the prompt sample is small, I say so. I do not divide brand mentions by one billion. That quotient would look like a statistic and would not be one. Platform usage belongs in the context paragraph of a brief. Brand mention rates belong in the results table. I do not let the first number dress up the second.</p>

How I map scale stats onto a GEO checklist

<p>Once I accept that AI Mode is large and still growing on Google’s published terms, the practical question is what I check on each URL. I do not add a task for every billion users. I add tasks that make a page easier to cite: a clear answer block, dated sources, and schema I can defend. The scale stats tell me the work is not optional for pages that already rank. They do not tell me which heading to rewrite. For that I use a closer look at geo checklist as the next step after the platform tab, so the file of figures has a place to land in production. I map “one billion monthly users” to “this surface is in the weekly prompt run.” I map “queries more than doubling every quarter” to “re-check the prompt set when Google ships a post, not when a recap thread spikes.” That is the only use I give scale in a meeting.</p>

Audience behavior that reshapes my GEO statistics

<p>I keep audience numbers next to platform scale and I never collapse them. Scale is engine size. Surveys tell me whether people look at the generated block. That is why I still ship classic long-form alongside citation-ready pages. The Pew figures below are U.S. adult survey results, dated, and they sit in a column I do not add to Search Console impressions. Mixing those series is how a file stops being citable. I log skip-readers in the same sheet.</p>

Who actually reads AI search summaries

<p>I file one Pew result as a readership baseline, not as a conversion rate. Six-in-ten U.S. adults said they read AI search-engine summaries, according to that 2026 survey. I copy the universe (U.S. adults), the report date, and the exact wording into the sheet. I do not treat “read” as “clicked a citation” or “trusted the answer.” I treat it as evidence that a majority of that sample encounters the generated block.</p>

<p>I do not apply the figure to every market I publish in. The survey is U.S. adults. A German-query page does not inherit that six-in-ten. When someone asks me to weight the U.S. number globally, I leave the cell blank and note the geography. I then mark which URLs I still expect to be read inside summaries, and I keep pages that assume the reader skipped the block and opened the article. That is an editorial split, not a ranking change.</p>

What non-readers mean for my content bets

<p>The same Pew report is why I still budget for people who never open the generated block. Three-in-ten U.S. adults said they do not read AI search-engine summaries. I log that as a content-bet constraint. If three in ten skip the summary, a page that only works as a cited snippet is incomplete for that share of the sample.</p>

<p>I keep a full-article path, headings, a standalone lede, sources in the body, even when I also write for citation. I do not cut the classic page because a majority reads summaries. I do not treat the three-in-ten as a click-through rate; it is a self-reported skip. I date it, keep the U.S. adult universe, and refuse a global traffic forecast. When a rewrite would make the page unreadable without the AI block, I send it back. That skip share still funds the classic page.</p>

How I weight survey stats against server logs

<p>I keep survey percentages and on-site numbers in two columns that never sum. A Pew share is a self-report from a sample of U.S. adults. A log line is a request my server actually served. I date both. I do not convert six-in-ten into an impressions forecast, and I do not treat a traffic drop as proof that people stopped reading summaries.</p>

<p>When the two series disagree, I write the disagreement in a notes cell. I do not pick a winner. I use the survey to decide whether a page must work as a standalone article, and I use the logs to decide whether anyone is loading it. I will not let a year-old survey override last week’s crawl of our own pages. A national poll is not a substitute for the URL-level export I pull from Search Console. Sample size stays in the notes cell.</p>

Google reports I now file as GEO statistics

<p>Search Console is the first Google surface I treat as a primary source for this file. Google Search Console introduced dedicated performance reports for generative AI features in Search and Discover. I file those reports the week they appear in a property I maintain, with the export date in the same row as the metric. I do not wait for a third-party dashboard to interpret them. I now file those exports as figures I can re-open later.</p>

What the generative-AI performance reports include

<p>I copy five fields from the generative-AI performance report into the weekly sheet: impressions, pages, countries, devices, and dates. Google listed those dimensions in its Search Central post. I do not add clicks I did not see in the export. I do not invent a citation rate by dividing impressions by a prompt count I ran myself. If a field is empty for a property, I log the empty field and the date I looked.</p>

<p>I keep the property hostname next to the row so two sites never share a total. Country and device cuts stay as filters, not as new headline stats. When I publish a number from this report, I name the date range I exported, because impressions without a window is not a figure I will defend later. I also note whether the row is Search or Discover, since the same announcement covers both surfaces. I do not roll Discover into Search.</p>

Features Google says the visibility data covers

<p>I only tag a row as Overviews or AI Mode when the source names those features. Google stated that generative-AI visibility data is available for features including AI Overviews and AI Mode. I copy those two names into the feature column. I do not add Gemini, Copilot, or ChatGPT to that column, because this report is a Google Search Console export, not a multi-engine crawl.</p>

<p>I also refuse to treat an Overviews impression as an AI Mode impression. If a later export splits the two features, I will split the rows. Until then I keep the feature names exactly as the post wrote them, and I put any blended total in a notes cell labeled “not split in this export.” Scale figures from other Google posts stay in a different tab. This tab is visibility inside Google’s generative features, dated to the export. I do not rename those features.</p>

How I pull these numbers into my weekly sheet

<p>Every Monday I export the generative-AI performance report for each property I still maintain. I save the CSV with the property hostname and the ISO date in the filename. Then I paste impressions, page list, country cut, device cut, and the date range into the weekly sheet. I do not overwrite last week’s row. If Google’s UI has renamed a column since the last export, I add a new column rather than mapping old values onto a new label.</p>

<p>I record the account I used and whether the property was verified. I do not round impressions. I do not publish a screenshot as the statistic; the sheet row is the statistic, and the CSV is the backup. When an export fails, I log the failure, not a guessed number. I keep the announcement URL in the source cell. That routine is how later generative engine optimization statistics stay comparable.</p>

Citation counts I add to my GEO statistics

<p>Public reports do not tell me whether a brand I work on was cited. I keep a first-party citation log beside the public GEO statistics. I run the same prompt set, score the same way, and date every pass. Those counts are not Search Console impressions or Pew shares. They record whether a URL appeared as a source in a generated answer. I file them next to the public series; they do not replace it.</p>

Prompt sets I reuse so counts stay comparable

<p>I reuse a frozen prompt set so week-over-week counts mean something. Each prompt is a full question I would actually type, saved in a text file with a version number. Version numbers are mandatory. I do not add a new prompt mid-week because a page I like failed. I do not drop a prompt because it stopped citing us. Changes go into version n+1, and I keep version n readable.</p>

<p>I run the set on the same engines, in the same order, on the same weekday when I can. I paste the raw answer into an archive folder named with the ISO date. The sheet only stores the score and a short note. If I change a prompt’s wording, I log the old wording next to the new one so later me can see why a count jumped. Comparable work is mostly this refusal to improvise.</p>

How I record citations without inflating them

<p>I count a citation when the generated answer points to our URL as a source, a link, a named source chip, or an explicit “according to [our domain]” that I can screenshot. I do not count a brand name that appears in a list of examples with no URL. I do not count our words being paraphrased without attribution. I do not count a competitor page that happens to mention us.</p>

<p>Passing mention goes in a separate column labeled mention, not citation. That column never feeds the weekly total. If I am unsure, I mark “unsure” and I do not increment the citation count. Two citations of the same URL in one answer still count as one URL-answer pair. I would rather under-count than publish a total I cannot re-run. The screenshot and the prompt version are what make the row defensible. I date the screenshot in the filename. No double-counting across those columns.</p>

Engine-by-engine notes I keep beside the totals

<p>I do not publish a blended “AI citation” total as the headline. I keep a row per engine: ChatGPT, Perplexity, Overviews, Gemini, Copilot, Grok, and Claude. Each row gets the prompt-set version, the pass date, citation count, mention count, and a one-line note (login wall, empty answer, source list present, answer refused). The total is a sum I can undo. I never blend those rows.</p>

<p>If one engine is down, I do not impute last week’s number. I write “no run” and I leave the cell blank. A blank is more honest than a carried-forward count. When Overviews and AI Mode both appear in a Google answer, I follow the Search Console feature names where I can, and I still keep the chat engines in their own rows. The notes column is where I record that an engine changed its citation UI, so a drop is not automatically a content failure.</p>

How I convert GEO statistics into editorial decisions

<p>I do not keep a stats file so I can paste charts into decks. I keep it so a page either stays live, goes back to draft, or gets a source and schema pass. After each weekly pull I sit with the sheet and decide three things: which URLs moved enough to rewrite, which numbers are still too thin to act on, and what one-page brief I send the editor. The rest of this section is that conversion, not a recap of the numbers themselves.</p>

Thresholds that trigger a rewrite

<p>I send a URL back to draft when two conditions hold in the same week. First, my citation log for that URL drops on at least two engines I already track with a fixed prompt set, ChatGPT, Perplexity, AI Overviews, Gemini, Copilot, Grok, or Claude, against the prior four-week median. Second, Search Console generative-AI impressions for the same URL fall in that window. One engine wobble is noise. Two engines plus a matching impression dip is a rewrite trigger.</p>

<p>I do not wait for a round number. I wait for direction that repeats. If the page still ranks in classic blue links but the generative citation count is what moved, I treat that as an editorial problem, not a ranking report. The rewrite brief names the missing claim, the source I will add, and whether schema needs a pass. I ship the rewrite, then re-run the same prompts the following week so the next row in the sheet is comparable.</p>

When a statistic is too thin to act on

<p>A movement that fails my sample-size cut never reaches the rewrite queue. I ignore a citation swing if I logged fewer than ten comparable prompt runs for that URL in the trailing four weeks. I ignore a Search Console impression change if the date range is under seven days or if the country/device split is still incomplete for that URL. Recency is the other cut: I will not change a live page on a figure I pulled more than fourteen days ago without a refresh.</p>

<p>Survey figures sit in a different column and never trigger a rewrite on their own. A Pew readership number can change how I weight an audience, but it does not tell me this URL is broken. If I cannot name the engine, the prompt, the date, and the sample, I leave the page alone. Thin stats stay in the file as notes only. They do not become production tickets.</p>

The one-page brief I send after a stats review

<p>After the cuts, I write one page, not a slide. The brief has five blocks: the URL, the dated rows I am acting on, the engines that moved, the editorial change (rewrite, add a source, or schema), and the re-measure date. I paste the exact prompt I will re-run. I do not paste the whole spreadsheet. I keep the language operational so an editor can ship without opening my file.</p>

<p>I send that brief to whoever owns the page the same day I close the weekly pull. If nothing cleared the thresholds, the brief is one sentence: no production change this week, and I stamp the next pull date. That sentence still goes out. Silence is how pages rot. The brief is the only artifact I treat as a decision; it is how those figures leave the sheet. When the rewrite ships, I attach the new URL state to the same brief so the following week's comparison has a paper trail.</p>

Gaps I refuse to fill in my GEO statistics

<p>I leave holes in the file on purpose. If I cannot point to a primary URL, a date, and a definition, the cell stays empty. Filling it with a round number from a webinar would make the sheet look complete and make later comparisons false. This section is the list of categories I still omit, the labels I use when I do keep an estimate, and why most of the charts I draft never leave my draft folder. Readers can cite what is here because I refused to invent the rest.</p>

Numbers I still cannot verify from primary sources

<p>I still have no official URL for engine-wide citation share by brand, for the share of answers that include a link versus a paraphrase, or for a public, comparable citation rate across ChatGPT, Perplexity, Gemini, Copilot, Grok, and Claude. Vendor decks sometimes show those figures. They are not in the file. I do not paste them in from memory either. I also omit any global AI search market share number that is not on an engine's own blog or a methods-documented survey.</p>

<p>I omit conversion rates from a generative citation to a site visit unless the visit is in my own logs for my own properties. Cross-property click-through from an AI answer is not something I can verify from a primary source as of this writing. I omit win rate against named competitors unless I ran the same prompt set on the same date. Those categories stay blank. Blank is a fact about the evidence, not a claim about the engines.</p>

How I label estimates versus sourced facts

<p>Every row in the sheet has a source-type cell. I use three labels only: sourced, first-party log, and estimate. Sourced means I can open a URL I stored next to the number, Google's Search I/O 2026 product post, the June 2026 Search Console generative-AI reports note, or Pew's 2026 study of Americans and AI, and read the same figure. First-party log means I ran the prompt and recorded the citation myself. Estimate means I derived a number from sourced inputs and I write the formula in the notes cell.</p>

<p>I never mix those labels in a published sentence. If a paragraph needs an estimate, the word estimate appears in that sentence. If I cannot label it, I delete the row. That is how I keep generative engine optimization statistics honest when I reuse the file months later. Color in the sheet follows the label, not my confidence. An unlabeled cell is a draft cell, and draft cells do not get quoted.</p>

Why I publish fewer charts than I draft

<p>I draft more charts than I publish because a chart implies a complete series. If one year in the series is an estimate and the rest are sourced, I do not draw the line. If a vendor renamed a metric mid-year and I cannot map the old definition to the new one from an official page, I drop the chart rather than splice the two series. Readers treat a line as a fact. If the underlying figures cannot be sourced, the chart stays in draft.</p>

<p>The unpublished drafts stay in a folder dated by the week I built them. I sometimes reopen one after a new Google or Pew release to see whether the hole closed. Most weeks it has not closed. I leave those drafts unpublished. Publishing fewer charts is the same discipline as leaving cells blank: the public file remains something I can cite, not something I have to walk back.</p>

How I maintain living GEO statistics

<p>The file only stays usable if I refresh it on a calendar and archive it when a definition moves. I do not rebuild the sheet from memory after a product rename. I keep the old rows, stamp the change, and start a new series. This last section is that cadence and that archive habit, the two things that keep last year's numbers readable next year. Without both, I would be comparing unlike things and calling it a trend.</p>

Cadence I use to refresh each source

<p>Weekly: I export Search Console generative-AI performance numbers, impressions, pages, countries, devices, dates, and I re-run the fixed prompt set across the engines I log. I date-stamp both pulls the day I take them, not the day I write the brief. I do not batch-date a week of exports.</p>

<p>Monthly: I re-open the Pew report and Google's AI Mode scale posts to see whether the published figures or the surrounding definitions changed. If they have not, I write unchanged as of the check date in the notes cell so a later reader knows I checked.</p>

<p>Post-announcement: any official blog post that names AI Overviews, AI Mode, or a Search Console report gets a same-day pass. I do not wait for the monthly slot. Platform-scale figures and survey figures move on the vendor's calendar, not mine. First-party citation logs move on mine. Mixing those cadences in one column is how I used to create false week-over-week swings. Each source of generative engine optimization statistics gets its own refresh row.</p>

What I archive when a vendor changes a definition

<p>When Google or another vendor renames a report, I do not overwrite the old rows. I copy the last dated export and store the old metric name, the new name, the announcement URL, and the date I noticed. I freeze the old series at that date. New numbers go in a new tab or column pair. I keep both series readable on purpose. A year later, impressions before and after the generative-AI performance reports may not be the same object.</p>

<p>I archive the prompt text too. If an engine's UI label for a citation changes, the old prompt still has to mean what it meant. I keep a dated paste of the answer layout from the week of the change. I do not need the paste for vanity. I need it so a later comparison does not treat two different UI objects as one series. The archive is why last year's generative engine optimization statistics remain readable as evidence rather than as a puzzle.</p>

Frequently asked

About the author

Furkan Yaman — AEO & GEO practitioner

Furkan Yaman is an AEO and GEO practitioner. He has grown AI citations 200% quarter over quarter, taken a client from 0% to over 70% AI visibility in three months, and built AI Rank Checker to measure brand visibility across ChatGPT, Perplexity, Google AI, Grok and Claude. Rankus AI is where he documents what works.

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