1. Freeze the question set
Version the exact questions, locale, audience, and intent labels. Changing the set changes the denominator.
Gixo Quill · AEO field guide
LLM visibility scoreboard: how to measure AI citations honestly
An LLM visibility scoreboard should separate four layers: provider-reported citations, controlled synthetic probes, AI-referral visits, and business conversions. Combining them into one opaque score hides provenance. Report the query set, time window, cited domains, and limitations so changes can be reproduced.
First-party measurement: Gixo recorded approximately
6,700 Copilot citations
during a measured quarter. The count comes from a
Bing AI Performance citation export; it is a citation measurement, not a claim that every cited answer was accurate.
What is LLM visibility?
LLM visibility describes how often and how prominently a brand, domain, or source appears in AI-generated answers for a defined question set. It can mean a mention, a linked citation, a cited domain, or a referral visit, so every report must define which event it counts.
Provider exports are the strongest observation when they contain real citation events. Synthetic probes are useful experiments but reflect the chosen prompts, model, location, account state, and run time. Referral analytics capture only visits that carry detectable referrer information.
Gixo's public number on this cluster is deliberately narrow: approximately 6,700 Copilot citations in a measured quarter from a Bing AI Performance export. It is not presented as total cross-model visibility.
Which LLM visibility signals should stay separate?
| Dimension | What it observes | Main limitation |
|---|---|---|
| Provider citation export | Observed citations recorded by that provider for the export scope | Coverage and definitions depend on the provider |
| Synthetic prompt probe | Whether a controlled model run mentioned or cited a domain | Outputs vary and the prompt set is researcher-selected |
| AI referral analytics | Visits arriving with a detectable AI-product referrer | Many answer views never become visits; attribution can be lost |
| Conversion analytics | Business outcomes attributed to an AI referral or assisted journey | Low volume and multi-touch journeys complicate attribution |
| Traditional search data | Indexation, impressions, clicks, and queries | It does not directly report selection inside generated answers |
How do you build a reproducible citation scoreboard?
2. Record provenance
Store provider, model or surface, date, account context, export source, and whether the event is observed or synthetic.
3. Count at multiple levels
Report citations, unique cited queries, cited pages, cited domains, and share of authority instead of one unexplained score.
4. Join referrals carefully
Track AI referrers and conversions as separate downstream layers. Do not infer a visit for every citation.
5. Publish limitations
State missing providers, sampling choices, query bias, and product changes. A reproducible caveat is more useful than false precision.
What does Gixo's citation number prove—and not prove?
The measurement proves that Gixo pages were selected as sources in the cited Copilot/partner query export during the measured period. It does not prove universal visibility, answer accuracy, causation, or future performance. Citation totals vary with query demand, index freshness, product behavior, and the measurement window.
Frequently asked questions
What is an LLM visibility score?
It is a summary of how a brand or domain appears across a defined set of AI answers. A useful score discloses its query set, event definition, provider, and time window.
Are AI citations the same as AI referrals?
No. A citation is source inclusion in an answer; a referral is a visit. Many users read a cited answer without clicking.
What is share of authority?
For a controlled query set, share of authority is the proportion of observed citation presence attributed to a domain or brand under a disclosed counting method.
Can prompt tests measure real visibility?
They can measure reproducible samples, not universal exposure. Label them synthetic probes and repeat them consistently.
Why not combine every signal into one score?
Citation exports, probes, referrals, and conversions measure different stages with different denominators. An opaque blend can rise even when the signal you care about falls.
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