Generative Engine Optimization Statistics: 40 Key Numbers

The most useful generative engine optimization statistics are the 40 numbers a brand can measure for itself: citation share, answer inclusion rate, entity accuracy, prompt coverage, AI referral conversion and the reliability metrics that show whether those readings can be trusted. Search is moving from rankings and clicks to citations and AI answers. A buyer who asks ChatGPT, Perplexity, Gemini or Google AI Overviews for "the best cloud security platform for a 2,000-person company" gets a short list. Most never see the ten blue links underneath it.

Much of the industry's GEO data rests on a weak base. Market figures circulate without methodology, sample size or date range, and the AI answers they describe change week to week. This guide, updated in October 2026, skips the untraceable percentages. Instead, it numbers the 40 metrics that tell a CMO or SEO lead three things: whether the brand is in the answer, how the answer describes it, and what that is worth in pipeline. For the strategy behind the metrics, see our enterprise guide to generative engine optimization.

How These 40 GEO Numbers Were Chosen

These 40 numbers made the list because a marketing team can measure each one on its own category queries, compare it week over week, and act on it. Every metric passes three tests:

• It can be measured repeatedly with a stated method: tracked queries, number of runs, engine, location and date range.
• It connects to a decision: what to publish, which thread to answer, which page to update, or where to spend.
• It holds up against the five usual reliability problems: stale date ranges, weak sources, over-attribution, prompt variance and inconsistent citations.

Industry-wide percentages failed the first test most often. When two published figures disagree, measure your own category instead of picking the more dramatic number.

GEO, SEO, AEO and the terms around them

The disciplines overlap, but each has a different target. Our breakdown of where GEO and SEO split and where they don't goes deeper. In short:

Visibility Statistics: Citations, Mentions and Share of Answer

Visibility statistics measure whether AI engines cite, mention and correctly describe your brand when buyers ask category questions. They replace "where do we rank?" with "are we in the answer, and what does it say?"

1. Citation share. Citations pointing to your domain divided by all citations across your tracked queries. This is the closest GEO equivalent of organic market share.
2. Answer inclusion rate. The percentage of tracked prompts where your brand appears at all, linked or not. It is the headline number for a board slide.
3. Unlinked brand mention rate. ChatGPT can name a brand without linking to its site, so track mentions and citations separately.
4. Mention-to-citation ratio. Mentions divided by citations. A high ratio means models know the brand but source their claims about it elsewhere, often from review sites or Reddit.
5. Competitor citation share. The same formula for each named competitor. Your share only means something next to theirs.
6. Third-party citation share. Citations going to Reddit, review sites, social platforms and reference pages. When these dominate a query, owned pages alone will not win it.
7. Reddit citation presence. The share of queries where the AI Overview, the answer or Google's discussions block cites a Reddit thread. Perplexity often cites Reddit and Wikipedia when they are relevant.
8. Answer position. Whether the brand appears first, mid-list or in a caveat. Leading a five-vendor shortlist is a different outcome from being named last.
9. Mention sentiment. Positive, neutral or negative framing of each mention. An answer can include you only to warn buyers off.
10. Entity accuracy rate. The share of answers that describe your product, category, customers and positioning correctly. Outdated pricing models and retired product names show up here first.
11. Citation durability. How many consecutive weeks a citation holds. A page cited for twelve straight weeks is an asset; a page cited once is noise.
12. Week-over-week citation movement. Net citations gained or lost per week, by domain type. This trend line shows whether the program is working.

How citation visibility differs by engine

Each engine shows its sources differently, so the same metric is easier to measure on some than on others:

Coverage Statistics: Queries, Prompts and Content Freshness

Coverage statistics measure how much of the category's buying conversation your tracking and content reach. A 60% inclusion rate on 20 easy prompts tells you less than 30% on the 300 prompts buyers actually type.

13. Prompt coverage. Tracked prompts divided by the full set of buying questions in your category. Build the set from sales calls, support tickets, Reddit threads and Search Console queries.
14. Comparison query inclusion. Inclusion on "X vs Y" and "X or Y for [use case]" prompts. These sit late in the funnel and often go to whoever publishes the clearest comparison page.
15. Recommendation query inclusion. Inclusion on "best [category] for [segment]" prompts. This is where shortlists are made.
16. Software evaluation coverage. Inclusion on "alternatives to", "pricing", "integrations" and "reviews" prompts. Gaps here send buyers to competitor-authored pages.
17. Definition query citation rate. Citations on "what is [concept]" prompts. These build entity authority, though they rarely convert directly.
18. Product discovery coverage. Inclusion on problem-first prompts ("how do I stop [problem]") where the buyer has not yet named a category.
19. Answer-shaped page count. Pages whose first sentence answers one buyer question on its own. Models lift self-contained statements and rarely lift a buried paragraph.
20. Freshness interval. Days since the last substantive update on each cited page. ChatGPT, Gemini and AI Overviews all lean toward recently updated content.
21. Citation decay rate. The share of previously cited pages that lose citations over a four- or eight-week window. When decay rises, update the pages you have before adding new ones.
22. Factual density. Verifiable specifics per 100 words, such as named products, thresholds, versions and steps. For example, a page with 8 to 10 specifics per 100 words gives a model far more to quote than one with 2.

Which content formats earn citations

Priority metrics by vertical

• Healthcare and finance: regulated claims and rates go stale fast, and wrong answers carry the highest risk, so entity accuracy (number 10) comes first.
• B2B software and cloud security: comparison and alternatives prompts dominate, so track numbers 14 to 16 weekly.
• Ecommerce: product discovery and "best for" prompts drive shortlists, so prioritize product pages and review-based content.
• Travel and local: results shift by location, so number 35 (location variance) matters more than in national categories.
• Media and education: definition prompts dominate, so watch citation durability and attribution of original reporting.

Business Statistics: AI Referral Quality and Zero-Click Value

Business statistics measure what AI visibility is worth when many answers send no click at all. Fewer visits does not have to mean less influence, so the job is to measure influence directly.

23. AI referral sessions. Sessions from chatgpt.com, perplexity.ai, gemini.google.com and copilot.microsoft.com, grouped in a custom GA4 channel. Treat this as a floor, because some visits arrive without a referrer.
24. AI referral conversion rate. Conversions divided by AI referral sessions, compared with organic search on the same landing pages.
25. AI referral engagement. Engaged-session rate and pages per session against organic. A buyer who arrives after reading an AI summary often comes with a narrower question.
26. AI-assisted conversions. Conversions where an AI referral appears anywhere in the path, not only as the last touch.
27. Self-reported AI attribution. The share of demo requests that name ChatGPT, Perplexity or "AI" in an open "how did you hear about us" field. It is cheap to collect and often the clearest signal.
28. Branded search lift. Change in branded query impressions in Search Console after inclusion gains. Buyers who see you in an answer often search for you by name next.
29. Influence-to-click ratio. Answer inclusion rate against clicks from the same queries. If the ratio rises while pipeline holds steady, count it as a zero-click win rather than a traffic loss.
30. Pipeline from cited pages. Opportunities whose journey touched a page AI engines cite.
31. Cost per covered query. Program cost divided by queries where you hold inclusion. For example, a $40,000 quarter that secures inclusion on 200 buying prompts works out to $200 per prompt, a figure to set against paid search CPCs for the same intent.

Program and Reliability Statistics: What to Trust and How to Measure It

Program and reliability statistics measure whether your GEO data is stable enough to act on and whether the team ships fast enough to change it. AI answers are black-box outputs that vary by prompt wording, run, location and device, so a number taken without these controls describes one screenshot and nothing more.

32. Prompt variance. The share of runs in which the same prompt returns the same brands. Low stability means you need more runs before reporting a change.
33. Runs per prompt. The number of samples behind each reading. For example, inclusion in 4 of 10 runs is a 40% reading, while a single run can only read 0% or 100%.
34. Engine coverage count. How many surfaces you measure, out of ChatGPT, Perplexity, Gemini, Claude, Copilot, Google AI Overviews and AI Mode.
35. Location and language variance. The gap between US results and other markets for the same query. Do not apply US readings to global categories.
36. Device variance. Differences between desktop, mobile and voice-style conversational prompts, which tend to be longer and more specific.
37. Refresh cadence. How often each metric updates. Weekly is enough for strategy; daily readings mostly capture variance.
38. Reddit reply survival rate. The share of placed replies still live after moderation. A removed reply cannot be cited.
39. Publish velocity against gaps. Pages shipped per month divided by the uncovered buying prompts identified. Tracking without shipping does not move number 1.
40. Time to first citation. Days from publishing to the first observed citation. It tells the team how quickly its work shows up in answers.

A measurement rollout

1. Build the prompt set of 100 to 300 buying questions, tagged by query type.
2. Run a baseline on each engine with repeated runs, logging date, location and device.
3. Label every cited domain as owned, competitor, Reddit, social, review site or reference.
4. Close the biggest gaps first, starting with comparison and evaluation prompts where competitors or third parties dominate.
5. Report weekly and review monthly, tying visibility numbers to numbers 23 to 31.

Where these numbers come from: tracking and execution platforms

Several platforms collect parts of this data, and our roundup of generative engine optimization tools covers more options. Ratings below are from G2 as of October 2026.

G2 reviewers praise Profound's prompt tracking, citation analysis and competitor benchmarking, and criticize its pricing and learning curve. Semrush is valued as an all-in-one SEO hub, but reviewers describe its AI visibility features as monitoring rather than a closed-loop workflow. Conductor users like seeing AI and traditional SEO in one place and want more flexible reporting. For a small team that only needs monitoring, Semrush's AI Visibility Toolkit is the more proportionate buy.

Tellr: Running the Program Behind the Numbers

Tellr reports these numbers every week and then does the work that moves them. Each week, the team sees citation share for the category's queries, labelled by domain type with week-over-week movement (numbers 1, 5, 6 and 12), the Reddit threads found and the replies still live (number 38), and where shipped articles rank and get cited (numbers 39 and 40). Tellr is a managed program priced for enterprise budgets, so a small team that only needs a monitoring dashboard is better served by a self-serve tracker. Its senior team acts on the gaps the reports show:

• Comparison pages, reviews and answer-shaped articles built from your knowledge base and real user reviews, published to WordPress.
• Guideline-checked Reddit replies behind an approval gate, with an audit trail.
• Paid-media creative informed by category ad intelligence.
• "Tellr for Claude", an MCP server for asking about the program in plain language, plus API access.
• Reporting that runs without GA4 or Search Console access.

Is SEO Still Worth It in 2026?

SEO is still worth it in 2026, because AI engines cite authoritative, structured, recently updated pages, and SEO is the discipline that produces those pages. What has changed is the scorecard. A first-page ranking that never appears in the AI Overview or the ChatGPT shortlist is losing value, even if the rank tracker looks fine.

Where SEO still does the work

• Authority and structure: the backlinks, site structure and technical signals behind organic rankings are the same signals that make a page citable.
• Freshness: an update cycle driven by decay rate (number 21) keeps pages cited longer, so updates deserve budget alongside new pages.
• Entities: consistent naming and structured data feed entity accuracy, which affects what every buyer who asks is told.
• Measurement: Search Console branded impressions remain the cleanest proxy for zero-click influence.

Takeaways by role

• CMOs: track numbers 2, 5, 28 and 30 at board level.
• SEO leads: extend rank tracking with numbers 1, 13 and 21, measured per engine.
• Content teams: build for numbers 19, 20 and 22, and write first sentences that can be quoted on their own.
• Ecommerce managers: focus on numbers 15, 18 and 24.
• Agency leaders: report reliability numbers 32 to 37 alongside every result, so clients can trust the trend.

Market-wide figures will keep circulating, but the generative engine optimization statistics that should drive budget are the ones you measure on your own category's queries, every week, on every engine your buyers use.

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