How we ask: the method behind every audit

A visibility score is only as good as the questions behind it. Here are ours in full — including the rules that cost us points we could have quietly claimed.

Shaik Arif Hussain

Local SEO and Google Business Profile consultant since 2019. He has helped more than 650 business profiles through suspensions, reinstatement, verification and ownership disputes.

3 September 2026 · 8 min read · updated 29 October 2026

Everything downstream of the question set is arithmetic. Ask the wrong things and you will produce a confident number about nothing — a business named in questions nobody types is not visible in any way that pays, and a business absent from questions everybody types has a problem no average will show you.

So the questions come first, and they are generated under three rules that are load-bearing rather than stylistic. Each one costs something. That is usually how you tell a rule from a preference.

Not once, anywhere in the run.

The obvious way to test whether an engine knows about you is to ask it about you. It is also the fastest way to manufacture the answer you were hoping for. Name a business in the prompt and you have handed the engine the retrieval term, the subject and the sentiment; it will find something to say, and what it says tells you almost nothing about whether it would have reached for you unprompted.

Every question is therefore phrased the way a stranger who has never heard of you would ask it. Someone who needs the job done, knows the trade and the town, and knows nothing else. If you are not in that answer, you are not in the answer a real customer gets either — which is the only finding worth paying for.

Questions are written from three things: your category, your city, and the services your own website lists. Never your name, and never anything only you would know. They are written in a fixed, plain shape, so two businesses offering the same service in the same town are asked the identical question, word for word. Whatever your services do not fill comes from a library built per category and city, which is the same for everyone in that market.

The shape is what pays for the product. An engine’s answer to a question about the best in town is not about you specifically, so it can be asked once and read for everyone who shares the question. A question that varied per business for no reason would silently cost a full-price probe every time. And once your set is written it is saved, so every later audit asks the same questions.

The category is whatever the owner confirms, from a list of several hundred category names in Google’s own wording, which means the library has to cover every one of them — including the ones we have never seen.

That guarantee used to be false and shipping. Seven recognised categories got the full depth; everything else got the general questions only, which ran out well short of the depth the pricing page was advertising. A florist was sold one number of questions and received another. Two smaller defects sat underneath it: a business Google had no category for was quietly probed as though it were a plumber, and Google’s own wording never matched our internal keys, so the categories most likely to be misnamed got the thinnest treatment.

The library now has a general set large enough to fill a full audit on its own, for any category on earth, plus intent questions for several dozen recognised trades layered on top. The general set is the floor; the trade-specific questions are an upgrade, never the coverage. A category we do not recognise gets a valid categorical measurement rather than somebody else’s questions, and a test fails if any category cannot reach full depth.

It used to come from Google’s own primary type, and Google is confidently wrong often enough to end the conversation. A photocopy shop that Google had filed as a manufacturer was audited as one — every engine asked to name the best manufacturer in the city, and a result that looked broken to the one person who could see why it was not.

So before anything is asked, we suggest up to three categories from what the business’s own site says, and the owner keeps, removes or adds to them. A confirmed category is recorded as having come from a human, and the next scan cannot quietly restore Google’s answer. The correction is folded into the shared market key so a retyped category cannot fork a market on capitalisation alone, and it is pinned to the audit at planning time so a change mid-run cannot rewrite a measurement already counted. Scores are only ever compared within the same category, for the same reason they are only compared within the same scoring version.

Three kinds of question, all probed identically, tagged so the report can tell them apart.

A comparing question is somebody choosing between businesses: who is the best in town, who would you recommend. A booking question is somebody ready to act: who is open right now, who is taking new customers this week. Being named there is the sale, and being absent is a lost one — and the two are separate because they fail for different reasons. A business can be the best-reviewed in town and still lose every booking question because its hours are wrong.

A research question is the same person twenty minutes earlier, working out what the job involves and what it should cost. Nobody is choosing yet — but this is where an engine reaches for a page that explains something, and the business with a real pricing or guidance page is the one it reaches for. It is also the only lane an owner can win by writing something, which makes it the lane the fix list can actually act on.

A fixed share of every full audit is reserved for each kind, taken from inside the total rather than added on top. The three compete for the same budget, which is honest about the trade-off rather than pretending there isn’t one.

Each question is banded by roughly how often it gets asked — high, medium or low — and that band becomes its weight in the presence pillar. A mention in a high-band question is worth about three times a mention in a low-band one.

The high band is kept deliberately small. Most real demand concentrates into a handful of phrasings, and spreading the weight evenly across every variation would flatten the one thing that makes the presence number mean anything.

The report shows how often you were named in each kind, and that is often the shape of your problem — plenty of businesses are visible when people compare and vanish the moment the question mentions money.

We probe each provider’s own API with its native grounded search switched on, and with the location set to the market being measured. Location is the whole ballgame for local intent; without it the model answers for nowhere in particular and the result is noise.

A full audit puts twenty-four questions to every engine we run, three times each, and records every answer verbatim. The free scan is a much smaller version of the same thing: five questions, once each, across fewer engines. The report names the engines that actually ran; one that did not run is not counted.

Here is the disclosure that most of this industry leaves out. A grounded API response is a proxy for what a person sees in the consumer app. It is the most reproducible measurement available and it is close to what a customer gets, but it is not identical — the apps personalise, carry memory, and ship features unevenly. Anyone claiming to measure the consumer app itself is measuring something they cannot observe. An engine we did not probe is never drawn as a miss; it is simply not drawn.

Every question is asked of every engine more than once, and the score records how many of those runs named you rather than whether any of them did.

That is not padding. Ask an engine the same question twice and you will often get two different answers, for reasons that have nothing to do with your business — which is worth understanding before you read your own report. One answer is an anecdote. A rate across repeated runs is a measurement.

Answers are stored per question, per location, per engine, per run, per day, and never reused across days. Engines change their minds; a corpus that carried yesterday’s answers forward would quietly freeze your score while the world moved.

Deciding whether you were named sounds trivial and is not. Business names are short, generic and frequently shared with a street, a suburb or a rival two towns over.

So each answer is read twice. A deterministic string match — case folded, accents stripped, punctuation normalised, link URLs discarded so a bare directory link does not count as a mention — finds the name literally in the prose and, where the answer is a ranked list, the position it appears at. Separately, a model reads the same answer and gives its own verdict, the businesses named, the sentiment and the specific claims made about you.

You count as named only when both agree. Where they disagree, the disagreement is recorded rather than resolved by whichever reader is more convenient. A model asked whether a business was mentioned will happily say yes about a paraphrase, a near-miss name, or nothing at all. The string match cannot, which is exactly what makes it useful as a check on the part of the pipeline that cannot be proven.

Claims an engine makes about you — hours, contact details, location — are checked against what Google actually holds, and a claim is only ever called wrong when it asserts a value we hold and that value confidently disagrees.

Everything vague, partial or ungrounded is skipped. Skipped is not the same as correct: it is outside what we checked, the same discipline we apply to engines we did not ask.

It cannot see what any individual customer was shown, because that answer was personalised and is gone. It cannot see the consumer apps directly. It cannot tell you your share of a market, because nobody outside the providers can. And it cannot promise that fixing what it finds will put you in the answer, only that the things it found are the things standing between you and it.

What it can do is show you the questions, the answers they produced, and exactly how those became a number.