Search visibility score
A search visibility score summarises a site's rankings across a keyword set, through a model the tool supplies. What that model weights, and what unit it hands back, differ from one tool to the next. What they share is the model itself: the rankings are observed, and what each ranking is worth is assumed.
How it is calculated
There is no single calculation, and the phrase does not name one metric. Two of the better documented ones differ on nearly every choice that matters.
Semrush reports Visibility as a percentage based on click-through rate, showing a site's progress through Google's top hundred for the keywords in a tracking campaign you set up yourself. Search volume is not part of it, so a first place on a term almost nobody searches counts as much as a first place on one everybody does. Semrush reports that weighting separately, as Share of Voice, which takes the combined volume of the tracked keywords and gives the ratio of traffic each site receives.
The SISTRIX Visibility Index is not a percentage at all. It runs on about a million queries chosen to represent a country's search volume rather than on a list of yours, weights each ranking by that keyword's search volume and by the expected click-through rate at the position measured, and distributes a hundred thousand points across every ranking it finds. A domain's index is the sum of its weighted values, in points.
Three things therefore vary before any number appears: whose keyword set it runs on, whether search volume is weighted in or deliberately left out, and whether the answer comes back as a percentage or an index. Two tools can report different figures for the same site on the same morning, and neither of them has made an error.
What is a good search visibility score
The question has no answer that survives leaving the tool it was computed in, because the number is a property of the keyword list as much as of the site. Forty percent across twenty terms that include your own brand name, and forty percent across two thousand competitive terms, are not the same result, and no threshold separates them.
Changing the keyword set can change the score while no ranking moves at all. For a weighted average the direction depends on the addition measured against what is already there: a term raises the result only if its contribution sits above the current average. A site holding first place right across its set gains nothing by adding another first place, and loses ground by adding a second place, which is a ranking most sites would want. Neither the difficulty of the term nor how well the site ranks for it settles the matter on its own. So the conditions have to travel with the number rather than the number alone.
Stated, those conditions make real comparisons possible. Two domains can be compared when the metric, the keyword set, the location, the device and the observation date are the same for both, which is what a tracker's own competitor view holds constant. A comparison of a score with itself over time needs the set unchanged and the vendor's model unchanged as well, and the second of those is not yours to hold still. Used inside its conditions a score is a reasonable instrument. Quoted as a grade, it is a number without a population.
What it estimates, and what it cannot see
- The click curve is modelled, not yours
- Both forms above weight a ranking by an assumed click-through rate for its position, drawn from search results at large rather than from your property. On one property we measured, pages averaging the same position band differed nearly tenfold in combined click-through, between the ten shown most and the other 170. Position is not held constant inside that band, so how much of the gap it accounts for is not established.
- The volume is estimated, where it is used at all
- A score that weights by search volume multiplies by figures nobody outside Google measures, and the error in those estimates carries through to the result. A score that leaves volume out avoids that and buys a different problem: a first place on a term nobody searches counts the same as a first place on one everybody does.
- The keyword set has an edge
- A score counts only the queries inside the set it runs on, whether you assembled that set or the vendor did. Queries outside it do not contribute to the figure. What share of any given property's impressions those queries carry is not established here.
- It says nothing about value
- A score counts estimated clicks. It does not know which of them could ever have become a reader, a subscriber or a sale, and a page can hold a high score on queries that were never going to convert.
The first of those is not a guess. Two findings here measure click rates inside a single position band on a property we operate: the spread within one band and what the long tail does inside those bands. Both carry their window and their sample size, and both describe one property rather than yours.
What Search Console measures instead
Impressions and clicks that were recorded rather than modelled, on queries nobody had to think of in advance, for your property alone. That is a different kind of number, and it carries its own limits, which are worth stating as plainly as the score's: the interface exports at most a thousand query rows, rare queries are left out of the table entirely while their clicks stay in the total, the history stops at sixteen months, and the most recent days can still change.
Those limits are knowable, which is the difference that matters. You can measure how much of your own report the query rows actually hold, and you can check whether two periods you are comparing line up on the calendar, both from the export itself and in your own browser: the tools are here.
Why there is no score on this site
Not because a composite is bound to be opaque. It is not: a formula, its inputs, its window and its limits can all be published beside the result, and a combined rate here is reported that way.
The reason is narrower. Every figure published on this site is counted rather than modelled. A visibility score is built from an assumed click curve, and in most forms from estimated search volumes as well, both of which are modelled rather than counted, and the method rules modelled data out as a finding. The method page sets out what that means and the claims it rules out.