An AI visibility score is a single number, normally 0 to 100, that combines how often AI answer engines name your brand, how prominently, whether they cite your pages as the source, and how that compares to competitors on the same questions. A low score means buyers asking about your category are being shown someone else. The number itself is a smoke alarm. Everything useful lives in the prompt-level detail underneath it.
Buyer asks: “best AI visibility tools for a small marketing team”
For smaller teams, the tools most often recommended are Koalr and two of its competitors, which cover multiple answer engines at accessible price points.
Sources: a rival's roundup post, a Reddit thread
Illustrative example. The brand is named, but every cited source belongs to someone else.
Your brand is named, so mention rate goes up. But not one of the cited sources is yours, so citation rate does not move, and the buyer's next click lands on a competitor's page. A score that counted only mentions would record this as a win. That gap between the two is the whole reason the composite exists.
What does an AI visibility score actually measure?
It measures four things that behave independently, then weights them into one number. Understanding which component is dragging is what tells you what to fix, because the fixes are unrelated to each other.
| Component | What it asks | What a low reading means |
|---|---|---|
| Mention rate | On what share of tracked prompts does an engine name you at all? | Buyers in your category are never shown you. A content problem. |
| Prominence | Where in the answer, and how high in any list? | You are an also-ran. A positioning and third-party-proof problem. |
| Citation rate | Does the engine link your page as its source? | Someone else's page is describing you. An off-page problem. |
| Share of voice | How does your mention rate compare to named rivals on identical prompts? | Absolute numbers look fine while you lose ground. A competitive problem. |
The four come apart in practice. A brand can hold a 40% mention rate with a 2% citation rate, which means engines know the name but learn about it entirely from other people's pages. A brand can hold a high citation rate with poor prominence, which means its pages are trusted as reference material while a competitor gets the recommendation.
Treating the composite as one dial hides that. This is why a score is a starting point for a diagnosis, not the diagnosis.
Which engines should the score cover?
Seven surfaces matter, and they disagree with each other enough that a score from any one of them tells you very little about the rest. Koalr tracks all seven: ChatGPT, Claude, Gemini, Perplexity, Microsoft Copilot, Google AI Overviews and Google AI Mode.
The disagreement is measurable. Across 295,746 citations we looked at which page type each engine prefers to cite:
| Page type | ChatGPT | Copilot | Perplexity | Gemini | AI Mode | AI Overviews |
|---|---|---|---|---|---|---|
| Homepage | 34.1% | 30.0% | 10.9% | 5.6% | 8.2% | 9.3% |
| Product or feature page | 11.0% | 4.0% | 21.2% | 10.5% | 23.7% | 24.4% |
| Pricing page | 14.8% | 12.3% | 24.1% | 4.0% | 21.2% | 22.6% |
| Blog post | 5.2% | 12.1% | 17.3% | 12.8% | 24.0% | 26.1% |
| Question-style page | 7.8% | 3.9% | 19.8% | 7.2% | 27.2% | 29.8% |
Read down the columns and the strategy separates cleanly. ChatGPT takes 34.1% of its citations from homepages and 5.2% from blog posts, so a blog-only programme is close to invisible there. Google's two surfaces take 51% of their citations from question-style pages and 50% from blog paths, so they reward exactly the content ChatGPT ignores.
A tool that only checks ChatGPT and reports a low score is telling you your homepage is weak. It is not telling you anything about your blog.
How is the score actually calculated?
Most vendors, us included, build the composite the same way: a base appearance rate, adjusted by where in the answer the mention lands, then modified by whether a citation was attached and what sentiment the surrounding language carries.
- 1
Run a fixed prompt set
The same real buyer questions every cycle, across every engine. Koalr runs 25 per scan. A changing prompt set makes the trend line meaningless, because you cannot tell a real move from a different question.
- 2
Record presence, position and source
Per prompt, per engine: were you named, how early, and which URL was cited. This per-answer record is the data. The score is just its summary.
- 3
Weight and combine
Appearance rate multiplied by a position weight, plus a citation bonus and a sentiment adjustment, normalised to 0 to 100 so it is comparable across categories.
Three methodology choices decide whether the resulting number is worth acting on.
Where the prompts came from. Prompts derived from real search and buyer behaviour produce scores that track commercial reality. Prompts invented by the vendor produce a score for questions nobody asks. This is the single largest source of variance between tools.
How many there are. Small prompt sets move with model sampling noise. Any single answer can differ between two runs an hour apart with no change to your site at all, so a handful of prompts gives you a number that jitters. Enough prompts, held constant, averages that out.
When it was measured. Engines update. A score from three months ago describes retrieval behaviour that may no longer exist. A score with no date attached is not a measurement.
Why do two tools give the same brand different scores?
Because they are not measuring the same thing, and none of the differences are bugs. Expect a spread of 15 to 60 percentage points on the same brand in the same week.
| Cause of divergence | Effect on the number |
|---|---|
| Different prompt sets | The largest single cause. Different questions, different answers. |
| Different engine coverage | A tool tracking three engines cannot see the four where you might be strong. |
| Different position weighting | Being named fifth counts almost as much as first in some rubrics, and far less in others. |
| Different citation handling | Some count a mention with no source as a full hit. Others discount it heavily. |
| Different sampling cadence | A daily average and a weekly spot check produce different numbers from identical behaviour. |
| Geography and language | UK-phrased prompts return different sources than US-phrased ones. |
The practical consequence: never compare your score across tools, and never celebrate a jump that coincided with a change to the prompt set. A score is only meaningful against its own history, measured the same way. Comparing your Koalr score to a competitor's Peec score is comparing two different measurements that happen to share a scale.
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How do you establish a baseline worth trending?
The order matters. Most teams start by running prompts, then discover the prompts were wrong and lose their history when they fix them.
| Step | What to do | Why this order |
|---|---|---|
| 1 | Write down the two to four buyer types who actually decide | Prompt phrasing follows from who is asking |
| 2 | Map the questions each asks by purchase stage | Awareness, comparison and evaluation questions behave differently |
| 3 | Fix the prompt set and version it | Changing prompts later resets your trend to zero |
| 4 | Scan all seven engines together | Cross-engine gaps are the finding, and they are invisible one engine at a time |
| 5 | Record per prompt, not just the total | The aggregate cannot tell you which page to write |
| 6 | Re-run on a fixed cadence | Monthly for trend. A single scan is a snapshot, not a signal |
Localise from the start if you sell in the UK. British phrasing pulls different sources than American phrasing for the same question, and a prompt set written in US English will quietly under-report the UK content you have already published.
What actually moves the score?
Two workstreams, on very different clocks. The mistake is sequencing them, because the slow one takes months and only starts when you start it.
On-page, measurable in weeks. Put a direct answer to the buyer's question in the first sentence under each heading, in server-rendered HTML. Many AI crawlers do not execute JavaScript, so a comparison table rendered client-side does not exist from the engine's point of view. Keep URLs short: in our data, pages with 1 to 3 word slugs earn 9.6 citations each at a 48.3% mention rate, against 5.6 citations and 19.5% for slugs of 10 words or more.
Off-page, measurable in months. This is where most of the score actually lives, and it is the part teams underinvest in. Across the 267,963 citations in our earlier off-page study, 84% pointed at pages the mentioned brand does not own.
That directory figure is the one that changes plans. Sorted by how reliably a citation turns into an engine actually naming you:
| Source type | Citations | Mention rate |
|---|---|---|
| Reference and wiki-like | 7,047 | 65.1% |
| Directory | 19,068 | 64.6% |
| Review site | 4,211 | 62.8% |
| Marketplace | 8,780 | 57.7% |
| Your own website | 229,651 | 36.6% |
| 2,794 | 37.7% | |
| Blog | 1,619 | 5.2% |
Directories, review sites and marketplaces convert citations into mentions at roughly 1.8 times the rate of your own website and 12 times the rate of a blog post. A G2 or Capterra listing does more for a visibility score than several months of blog output, and it is a one-week job.
This is not an argument against publishing. It is an argument against expecting the blog alone to move a composite whose largest component is off-page.
How long should each change take to show up?
Setting the expectation correctly is what stops a programme being cancelled in month two.
| Change | Realistic lag | What you should see first |
|---|---|---|
| Fixing a blocked crawler | Days to 2 weeks | Citations appear where there were none |
| Adding direct answers under headings | 2 to 4 weeks | Prominence improves before mention rate does |
| Shortening URLs on new pages | Next crawl cycle | Applies to new pages, not retrospectively |
| A new directory or review listing | 4 to 8 weeks | Mention rate moves on comparison prompts |
| Earned editorial coverage | 3 to 6 months | Share of voice, the slowest and most durable component |
Measure each against the specific prompts the change was meant to affect, not the headline score. Cluster-level movement is attributable. Headline movement is too noisy to tell you anything about a single page.
Key takeaways
| Point | Details |
|---|---|
| The score is a smoke alarm | It tells you whether you have a problem. The per-prompt detail underneath tells you which page to write. |
| Four components, four different fixes | Mention rate, prominence, citation rate and share of voice come apart. Diagnose which is low before acting. |
| Engines disagree structurally | ChatGPT takes 34.1% of citations from homepages and 5.2% from blogs. Google's surfaces are close to the reverse. |
| Never compare across tools | A 15 to 60 point spread between vendors on the same brand is normal. Compare a score only to its own history. |
| Off-page carries most of the weight | 84% of citations point at pages you do not own. Directories convert at 64.6%, blogs at 5.2%. |
| Run both clocks at once | On-page shows in weeks, earned coverage in months. Sequencing them wastes the slow one. |
A practitioner's view on what the number is worth
I spent a decade signing off marketing budgets before I worked on this, so my first question about any new metric is what decision it changes. For an AI visibility score the honest answer is that it changes a lot in month one and very little by month four. In month one it is genuinely diagnostic: you find out whether engines know you exist, and that reframes the plan. By month four you are watching a line move two points and building a story about why, which is not analysis, it is decoration.
So my rule is that the composite is a reporting artefact and the prompt cluster is the working unit. I almost never open the headline number first. I open the list of prompts where a competitor is named and we are not, because that list is a content brief that writes itself and the score is just a lagging summary of how well we worked through it.
The mistake I made early was treating a low citation rate as a content quality problem, which is the expensive way to read it. It is usually a distribution problem wearing a content costume. We funded page improvements that were not the constraint, when the real one was that almost nobody else on the internet was describing us, so engines had nothing to cite except our own claims about ourselves. The 84% figure is what changed my mind, and it should probably change yours: most of what an engine knows about your brand, it learned somewhere you do not control.
The uncomfortable part, if you are the person defending the budget, is that the work which moves the number is the work that reports worst. Publishing is visible and attributable. Getting listed, reviewed and written about by other people is slow, lumpy and hard to put in a monthly deck. It is still where the number moves, and pretending otherwise just means funding the wrong half for longer.
Where Koalr fits
Koalr runs 25 real buyer prompts from your category across all seven engines and returns an AI Visibility Score from 0 to 100, with the per-answer record behind it rather than just the number.
The parts that matter for the workflow above: Prompt Explorer shows exactly which questions name you and which name a rival, Sources ranks the domains and URLs the engines actually cited in your category, Competitors lines your score up against the brands appearing beside you, and GEO Audit scores how readable your pages are to a machine that does not run JavaScript. Actions turns each gap into a task rather than a chart.
Plans start at £79 a month with a 7-day free trial, and the free scan returns a baseline across all seven engines before you pay anything. If the scan shows a category where rivals own every answer, that is the finding, and it changes the plan more than any feature list will. Questions about your score go to hello@koalr.ai.
Frequently asked questions
There is no universal threshold, because the score is relative to your category and prompt set. A 30 in a crowded category where the leader holds 45 is a different situation from a 30 where the leader holds 90. Judge it against named competitors on identical prompts and against your own previous scans, never against an absolute benchmark.
Sources
- Which pages do AI engines cite when they mention your brand? (Koalr). Covers the per-page-type citation breakdown behind the engine table above, from the same dataset. Useful if you need to decide which page to fix first.
- What is generative engine optimisation (GEO)? (Koalr). Covers the underlying discipline: entity definitions, extractable answers and schema. Read this if the score diagnosis points at on-page work.
- How to get cited by AI engines through content you do not own (Koalr). Covers the off-page workstream in detail, which is where 84% of citations originate. The slow lever, and the one that matters most.
- How to find out which AI engines are citing your competitors instead of you (Koalr). Covers turning a share-of-voice gap into a specific content brief, which is the working unit described above.
