AI visibility research and measurement standards
Use this hub to distinguish RankEcho's published citation study, cross-audit measurement rules, and separate website-readiness screen. Each has a different population, denominator, and valid interpretation.
RankEcho's comparable citation analysis on this hub is the dated Q3 2026 study. Customer audit records are not pooled into a general industry rate because their prompt panels, configured providers, sample depth, observed denominators, and collection dates differ. The sections below explain the study and the evidence standards for any future category comparison.
A record-count threshold alone cannot show that domains are unique, prompts and surfaces are comparable, the sample represents a market, or the estimate is statistically sufficient.
Separate the instruments before comparing numbers
| Surface | Observation unit and frame | What it can answer | Status |
|---|---|---|---|
| Q3 citation study | One scheduled prompt × configured answer-surface × run cell; skipped cells remain disclosed. | Cited-domain-set overlap inside the completed portion of that fixed panel. | Published with CSV analysis export |
| Cross-audit records | One completed audit run; repeat domains and unlike run configurations are not removed. | Descriptive observations only; no pooled category rate is shown without a comparable cohort. | Not pooled |
| AI-readiness directory | One homepage-centred RankEcho-UA screen plus robots.txt and llms.txt checks, from a convenience and submission sample. | RankEcho's observed technical-screen signals, not citations or recommendations. | Separate directory |
| Rolling state report | The evolving audit-run stream plus proof-loop summaries. | Descriptive product observations; not the fixed Q3 panel or a census. | Separate rolling report |
The published fixed-panel study
The State of AI Citations, Q3 2026 is the citation analysis artifact distributed from this hub. Its design arithmetic is 48 predeclared prompts × 4 configured answer surfaces × 2 scheduled runs = 384 planned cells. The study schedule records the second run 24 hours later; the CSV does not carry timestamps that independently verify that interval.
The configured surfaces were Claude through Anthropic's Messages API with its web-search tool, Perplexity API, Gemini API with Google Search grounding, Google AI Overviews through SerpApi. They are API and search integrations, not interchangeable consumer products. The panel had no OpenAI/ChatGPT observation, covered US-English commercial prompts, and scheduled only two runs on 2026-07-09 and 2026-07-10. A stability value of 0.671 is a mean set-similarity statistic; its 0.329 distance is not a literal percentage of individual citations that changed. The mean includes 23 Google-AI-Overview empty/empty pairs scored as similarity 1; the report's separate 0.617 Google-AI-Overview figure uses 15 source-bearing pairs. This is a bounded first-party probe with a normalized-domain export, not a raw-answer archive, market census, or claim about every provider, geography, category, or date.
Read the full method and findings · Download the CSV · CC BY 4.0
What RankEcho audit records contain
The unit is a completed audit run, not a unique domain. Current cross-audit reporting does not define a deduplicated public cohort or common calendar window, so these records are not presented as a representative market rate.
- Runs can mix sitewide and page scopes, custom and generated prompts, free and paid provider sets, sampling depths, repeats, skipped observations, and answer-reuse rules.
- Industry is an inferred free-text label or “Other,” not a validated sampling stratum or stable public taxonomy.
- The observed prompt denominator can vary because an audit can contain unavailable or skipped engine cells. No common cross-audit prompt-by-engine panel is enforced.
Metric definitions used in audit reports
| Run | Positive prompts | Observed prompts | Run rate |
|---|---|---|---|
| Audit A | 1 | 2 | 1 ÷ 2 = 50.0% |
| Audit B | 6 | 10 | 6 ÷ 10 = 60.0% |
The current equal-run mean is (50.0% + 60.0%) ÷ 2 = 55.0%. Pooling prompt counts would produce 7 ÷ 12 = 58.3%. The current aggregate uses the first calculation. Neither result is a population rate without a defined cohort and sampling frame.
Evidence requirements for category reporting
These are minimum reporting standards, not a universal statistical law. Every category cut must satisfy all rows together, and meeting them still does not make a convenience sample representative.
| Criterion | Minimum | Required disclosure |
|---|---|---|
| Eligible population | ≥ 30 unique domains per category | Sampling frame, eligibility, deduplication, repeat-domain handling |
| Prompt panel | ≥ 5 predeclared prompts per category | Full prompt text, prompt class, additions and removals |
| Surface coverage | ≥ 3 named surfaces or models | Provider surface, model/version where known, failures and skips |
| Repeated measurement | ≥ 3 dated windows | Collection dates, interval, answer-reuse and cache policy |
| Usable observations | ≥ 100 domain × prompt × surface observations after exclusions | Planned, attempted, skipped, excluded, successful, and observed denominators |
| Supporting artifact | Required | Downloadable aggregate, uncertainty, limitations, review method, version and correction log |
Readiness is a separate technical screen
The AI-readiness directory uses a homepage-centred screen plus robots.txt and llms.txt requests made with RankEcho's user agent, not audit answers or a provider's crawler. Its current composite weights are 30% structured data, 25% crawl access, 25% machine readability, and 20% agent guidance. Bands begin at 85, 65, and 40.
The directory mixes harvested business-listing domains with submissions, counts successful captures, and does not expose a request-time freshness window on this hub. Category views require at least 30 sites, and category-by-metro views require at least 20; these are coverage thresholds, not evidence of statistical sufficiency. Metro describes a harvest-source location, not AI-answer request geography or personalization.
Six inferences this hub rejects
Choose the evidence surface that matches the job
Sources and provenance reviewed
These first-party sources support the public study, artifact, terminology, screening limits, and no-guarantee boundary. The audit-record definitions reflect RankEcho's documented aggregation method as reviewed on 2026-09-02.
Open the five-record evidence ledger
- The State of AI Citations, Q3 2026 The Q3 study scheduled 384 prompt-by-surface-by-run cells from 48 prompts, four configured answer surfaces, and two runs 24 hours apart; 368 cells are marked non-skipped and 16 skipped. Its prompt battery, method, limits, and normalized-domain CSV analysis export are public. Checked 2026-09-02 · Published 2026-07-10; fixed Q3 2026 study · First-party study review; the fixed panel is not merged with later customer audit runs or homepage screens.
- Q3 2026 normalized-domain analysis export (CSV) The Q3 normalized-domain analysis export is downloadable as CSV under CC BY 4.0. It contains prompt ID, category, intent, engine column, run index, skip flag, distinct normalized domain, heuristic source type, and zero-based export order among the cell's distinct domains. It omits prompt text, raw answers, original URLs and titles, model/version, timestamps, skip reasons, answer length, and session or geography configuration. Checked 2026-09-02 · Published Q3 2026 CSV analysis export · Artifact review; normalized citation-domain rows are not a raw-answer archive, and availability does not make the US-English probe a census or universal provider benchmark.
- RankEcho measurement methodology RankEcho's methodology treats citation, mention, recommendation, source mix, and re-test movement as distinct outcomes and rejects guaranteed placement or causal claims. Checked 2026-09-02 · Methodology updated August 4, 2026 · First-party methodology review; the hub keeps unlike measurement units separate and states where current audit definitions differ.
- Website AI Readiness study and limits The published website-readiness study reports a bounded homepage-screening sample and explicitly does not measure indexing, AI impressions, citations, traffic, or causal effects. Checked 2026-09-02 · July 2026 one-request homepage screening · First-party study review; the dated study and the evolving readiness directory are not represented as one population.
- RankEcho AI visibility disclosure RankEcho's disclosure states that observed visibility can vary by engine, model, retrieval state, time, location, account state, and prompt wording, and that no citation or business outcome is guaranteed. Checked 2026-09-02 · Current public measurement disclosure · First-party disclosure review; a threshold crossing or later observation is not converted into a causal or outcome guarantee.
Benchmark FAQ
Which RankEcho benchmark data can I download today?
The Q3 2026 citation study has a downloadable normalized-domain analysis export: 48 prompts across four configured answer surfaces in two runs scheduled 24 hours apart. Of 384 planned cells, 368 are marked non-skipped and 16 skipped. The export omits prompt text, raw answers, URLs, model versions, timestamps, and skip reasons, and the cross-audit records and readiness directory do not share this study's sample.
Why doesn't this page pool citation percentages across audits?
The current feed contains completed audit runs with varying scopes, prompt panels, engine configurations, sampling depths, skipped observations, and repeat domains. It does not give this page a deduplicated cohort, common calendar window, or common observed denominator.
When are cross-audit category percentages appropriate?
Only when the records use a disclosed eligible cohort, deduplicate domains, share a comparable prompt and surface design, cover dated repeated windows, and report missingness and uncertainty. A minimum record count alone does not establish representativeness or statistical validity.
Does an AI-readiness score predict citations or recommendations?
No. The readiness directory is a homepage-centred screen plus robots.txt and llms.txt requests made with RankEcho's user agent. It is not a real-provider access test, an indexing observation, a citation measurement, a recommendation outcome, or evidence that one signal caused an AI answer.
What does location mean in the readiness directory?
Location describes the business-listing harvest frame used for some screened domains. It is not the location of an AI-answer request, proof of local-market coverage, or evidence about personalization.
What evidence does a category benchmark need?
At minimum, a category benchmark needs 30 eligible unique domains per category, 5 disclosed prompts per category, 3 named surfaces or models, 3 repeated windows, and 100 usable domain-by-prompt-by-surface observations after exclusions. Dates, missingness, exclusions, uncertainty, review method, and a downloadable artifact must also be disclosed. Meeting these minimums is not a representativeness guarantee.
Review the measurement rules before using a number
The methodology defines RankEcho's measurement units, denominators, comparison rules, and interpretation limits.
Review the measurement methodology →Inspect the published Q3 study