Out of a hundred questions your potential customers ask AI every month, in how many does your name appear at least once? If you don't know that percentage, you don't know how many sales opportunities you're losing every day. A Turin-based entrepreneur discovered he showed up in 34 queries while his direct competitors covered more than 60: those 26 fewer were contacts that never arrived. Measuring that coverage is the first step toward turning the gaps into concrete opportunities.
I mapped 100 queries in the vegetable-preserve packaging sector for a client in the Chieri area, just outside Turin. The result: his brand gets cited by ChatGPT, Perplexity and Gemini in 34 queries out of 100. The three direct competitors the market knows reach a steady level above 60. For every query in which AI doesn’t name you, a prospect goes elsewhere, and the difference between 34 and 60 perfectly explains why my client’s sales pipeline struggles while his competitors’ keeps growing.
The number that lets you see this, that makes it concrete and actionable, is called the Query Coverage Rate: the percentage of relevant queries in your sector in which AI cites you at least once. In this article I explain what it is, why it’s the first serious visibility metric within AI answers, how to calculate it in half a day, and what I discovered by actually doing it for a client in the Turin area.
What the Query Coverage Rate really measures
The Query Coverage Rate (QCR) is a percentage: the number of target queries in which your brand appears in the AI answer, divided by the total number of queries tested. If you test 100 questions and AI names you in 30, your QCR is 30%.
It sounds trivial, but the interesting part is the denominator. These aren’t “all the queries in the world”: they’re the queries your real customers ask when they’re evaluating a supplier like you. For the Chieri client the denominator is made up of questions like “best vegetable-preserve packager in Piedmont”, “who does contract manufacturing for organic jams”, “company packaging oil-preserved vegetables for private label”, “HACCP-certified packagers in Northern Italy”, and so on across 100 variants spanning awareness, consideration and decision.
The most recent work documenting the phenomenon is Source Coverage and Citation Bias in LLM-based Search (2025). The principle that emerges from the paper is that AI engines, for a given query, draw on a very narrow and recurring subset of sources, and that this subset changes measurably from query to query. Translated into practice: your visibility isn’t a property of your brand “in general”, it’s a property query by query. It follows that the only way to know whether you’re visible is to measure query by query, and that’s exactly what the QCR does.
Why the QCR sits upstream of every other metric
In the earlier articles of this series I talked about metrics like AI share of voice, citation count, sentiment of mentions. All important, but all with a problem: they don’t tell you how many opportunities you’re losing. They tell you how loud you are when you’re present.
The QCR flips the perspective: it starts from the total of potential sales opportunities (the queries) and tells you in what percentage you simply don’t exist in the eyes of AI. It’s the same logic with which, in classic SEO, you looked at keyword coverage, but applied to the new touchpoint where your customers now make their decisions.
There’s another reason it sits upstream. All the authority optimizations I’ve shown you — from E-E-A-T for AI to author entity recognition, all the way to the inverted pyramid for content structure — work or fail to work differently depending on the query cluster. Without a baseline QCR you don’t know where they’re working and where they aren’t, so you optimize blindly.
I see SMEs testing 20 queries, all centered on the brand name.
The test you can run in half a day
To calculate an honest QCR, all it takes is half a day of work, a Google Sheet and the AI engines open in three tabs.
First step: build the list of queries. Aim for 80-120, split by funnel stage. For the awareness stage use generic sector questions (“how does contract vegetable-preserve packaging work”). For consideration use comparative questions (“difference between an industrial and an artisanal packager”). For decision use choice questions (“best vegetable-preserve packager in Piedmont”, “supplier of glass jars for organic preserves”). Use Google Search Console for the real queries you’re already getting impressions on, and Google Trends for regional variants.
Second step: test every query on at least three engines. I use ChatGPT, Perplexity and Gemini. For each query, note on a sheet: brand cited yes/no, in which position of the answer, with which source. No nuance, just binary on “does it name you or not”: the QCR lives on this simplicity.
Third step: calculate three numbers, not just one. The global QCR (cited in at least one of the three engines), the QCR per engine (to understand where you’re weakest) and the QCR per funnel stage (awareness, consideration, decision).
The operational threshold I use with clients: below 25% there’s a structural visibility problem, between 25 and 50% there’s clear room to grow, above 60% you’re competitive and the work becomes defensive.
Open a Google Sheet and jot down 80-120 non-brand queries split across awareness, consideration and decision.
The test I ran on the Chieri client
For the Piedmont client I built a list of 100 queries split this way: 30 awareness, 40 consideration, 30 decision. I ran them through ChatGPT, Perplexity and Gemini between March 18 and 22, 2026, one single pass per query (so an indicative test, not a statistical study).
The numbers:
- Global QCR: 34% (cited in at least one engine in 34 queries out of 100)
- QCR per engine: ChatGPT 28%, Perplexity 31%, Gemini 19%
- QCR per stage: awareness 47%, consideration 22%, decision 38%
The surprise was consideration. The client is very present on generic sector questions (high awareness) and is cited decently when the user looks for an explicit supplier (medium decision). But he disappears on comparative questions, the ones where the user is choosing between options: “glass or tin packaging better for oil-preserved vegetables”, “difference between contract manufacturing and private label for preserves”. There AI cites others.
Honest limitation of the test: 100 queries are not a large sample, a single pass per engine doesn’t capture day-to-day variability (AI engines change their answers), and the query list is curated by me, so it reflects my assumptions about funnel priorities. The real analysis, the one you build a 12-month roadmap on, requires professional tools that monitor hundreds of queries continuously. What I’m giving you here is a first step to understand where you stand, not the full picture.
The mistakes I see most often when SMEs calculate their QCR
A query list that’s too short or too brand-centric. I see SMEs testing 20 queries, all centered on the brand name. Of course the QCR comes out high: AI names you because you’re asking about yourself. The QCR only makes sense if the queries are non-brand, that is, questions in which a prospect who doesn’t know you is looking for a solution.
A single pass, a single engine. AI engines vary their answers over time and across models. A QCR measured only on ChatGPT isn’t your real QCR, it’s your QCR on ChatGPT at that moment. Always three engines, ideally two passes a week apart.
Mixing brand mention and source citation. Being cited as a source (AI links to your site) is different from being named as a recommended option (AI says “you can turn to X”). They’re two distinct QCRs, and they must be kept separate, otherwise the number means nothing. The mechanics of citation as a source are explained in the piece dedicated to backlinks as a citation proxy and in implicit reference weight.
Stopping at the number without competitive comparison. A QCR of 34% in absolute terms means nothing. You need to compare it with the 3-5 competitors AI cites most often in your sector. If they’re at 60% and you’re at 34%, you have a clear market gap. If everyone is at 30%, the sector is young on AI and there’s an opportunity for whoever moves first.
What to do concretely next week
- Open a Google Sheet and jot down 80-120 non-brand queries split across awareness, consideration and decision. Use Search Console for the real ones.
- Identify your 3-5 reference competitors, the ones AI cites when you’re not there.
- Test the queries on ChatGPT, Perplexity and Gemini, marking in binary fashion who gets named (you and the competitors).
- Calculate the global QCR, per engine and per funnel stage. Look for the stage with the biggest gap: it’s usually consideration, but it can vary by sector.
- Repeat the measurement once a month on the same queries: the real value of the QCR is the trend, not the point-in-time figure.
Where this metric fits into your strategy
The Query Coverage Rate is the metric that tells you how visible you are in AI answers in the most direct way possible: in how many of the occasions where a customer could hear about you, it actually happens. All the other metrics in this series — share of voice, citation count, sentiment, average position in the answer — complete the picture, but the QCR remains the starting point because it tells you where you stand and where you don’t.
In the upcoming articles of the series I explain how to pair it with AI share of voice to measure your weight when you’re present, how to use citation depth to understand the quality of the mention, and how to build a dashboard for continuous monitoring of AI visibility so you don’t have to redo the calculation by hand every month.
