Scores and Metrics — Visibility, Recommendation Share, Recommendation Levels, Ranges, Citations and Sentiment
Exactly how each number is computed: the workspace visibility score, mentions, citations and share of voice, the mention-to-recommendation rates with 95% ranges, weighted recommendation share, the recommendation level of each answer, coverage and position, sentiment, context health, and the AEO readiness score on generated articles.
Visibility score (Workspace gauge, 0 to 100)
The share of tracked prompts mentioned by at least one AI engine within the current filter (market, platform, date range): prompts mentioned ÷ prompts checked × 100. It follows the filter bar so you can look at one market or engine on its own.
Mentions, citations, share of voice
- Mention: one per prompt × engine × scan where the brand appears in the answer.
- Citation: one per answer that links to a URL on your domain. "Cited pages" counts distinct URLs.
- Share of voice: your mentions ÷ (your mentions + competitor mentions in the same answers) × 100.
- Change compares the average of the second half of the period with the first half, which is steadier on small samples than first-versus-last.
Recommendation level (per answer)
Being mentioned is not being recommended. Each answer gives your brand a recommendation level, shown with its reason in the evidence drawer:
| Level | Rule |
|---|---|
| Top pick | Ranked first, or the answer explicitly calls it the top recommendation |
| Recommended | In the top 3, or the answer uses recommending language |
| Listed option | In a list of options but not explicitly recommended |
| Mentioned only | Named in passing, not listed and not recommended |
If a level is wrong, open Earlier answers in the evidence drawer, click "Judged wrong?", choose the level it should be and submit. The correction is stored next to the answer; the answer itself is never changed.
From mention to recommendation
The Workspace card From mention to recommendation counts answers as a funnel: AI answers → mention you → cite your site → recommend you → first pick, expandable per AI engine. It also shows:
- Of answers citing you, share that recommend you: whether being cited turns into a recommendation.
- Of answers mentioning you, share that recommend you.
- Weighted recommendation share: each recommendation is weighted by the tracked competitors in the same answer: recommended alone counts 1, alongside 3 competitors counts 1/4; divided by all answers. The Competitors page uses the same method for every brand.
- Sole recommendations: answers that recommend you and name no tracked competitor.
See From mention to recommendation.
Ranges and data source
Rates show a 95% range: measured again under the same conditions, the value would very likely fall inside it. Under 10 answers the card shows x/n instead. A narrow range means many answers; when it is wide, wait for more scans or turn on repeated sampling.
Every metric also states its data source: Real product UI (the same as what users see in the AI app) or Official API, and the models used. Coverage confidence on Prompt Library → Coverage carries a 95% range as well; see Prompt coverage and Measurement confidence.
Coverage and position per prompt (Prompt Tracking)
- Coverage: engines that mention you ÷ engines that answered × 100, counting only the engines the market scans. Engines that errored or hit a quota are excluded so outages don't drag the number down.
- Position: the index of the first numbered or bulleted line containing your brand, shown as #n on the engine icon. Mentioned outside a list counts as "mentioned"; position 1 is "top result", 1 to 3 is "listed".
- Repeated sampling: with "Ask 3× per scan" on, each engine answers three times and the result shows as mentioned x/3, so you can see how often the brand appears across answers.
- Cited: the host of a link in the answer is your domain or a subdomain.
- Competitors in the answer: the first column of a comparison table if there is one, otherwise the list items, filtered to real names and capped at eight.
- Best rank, average rank, change are stored per scan and feed trends and achievements.
Sentiment
Every answer gets a sentiment score. Above +10 is positive, below −10 negative, in between neutral. The Workspace "positive sentiment" figure is positive ÷ (positive + negative); neutral answers are left out of the denominator.
Context health
Each answer is judged against three failure modes: the brand described as the wrong kind of company, an off-topic answer, or an answer in the wrong language. The verdict is match, mismatch or unrelated; mismatch and unrelated show a context badge and the "context issue" filter on Prompt Tracking. The judgement uses the Business info in Content Rules, so keep it accurate. Wrong phone numbers, addresses and prices in answers to brand searches are checked separately on Prompt Tracking → Brand searches.
AEO readiness of generated articles (GEO score)
A deterministic score out of 100 across five dimensions:
| Dimension | Max | Earned by |
|---|---|---|
| Structure | 30 | H1, enough H2s, an FAQ section with Q&A pairs, BlogPosting and FAQPage schema |
| Entity clarity | 25 | Brand and topic repeated and present in the first paragraph |
| Data support | 20 | Numbers with units, plain numbers, comparison-table rows |
| Length | 15 | Sweet spot around 1,200 to 2,200 words (2,500 to 4,000 characters for Chinese / Japanese) |
| Direct answer | 10 | First paragraph names the entity, answers directly, is long enough and contains a number |
The article page shows how many dimensions pass (at least 60% of their max): 5 = fully optimized, 4 = good, 3 = ready to publish, 2 or fewer = needs work.
Frequently asked questions
Why does the Workspace number differ from Prompt Tracking? The Workspace follows the filter bar and counts a prompt as covered if any engine mentions you; Prompt Tracking shows each prompt's own coverage across engines.
Everything dropped on the same day? Check the Workspace for Platform-wide changes. When an engine changes for every project at once (fewer cited links per answer, a new model), the cause is usually the platform.
Does the score predict traffic? No. It measures presence in AI answers. Visits from AI are on AI Traffic, and the leads and revenue they bring are on Revenue Attribution.
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