Share of Local Voice (SoLV)

What is share of local voice (SoLV)?

Share of local voice (SoLV) is a metric that measures what percentage of the time a business appears in the local pack — the top 3 map results — for a defined set of target keywords across a defined geographic area, relative to every possible opportunity to appear. It's the local SEO equivalent of "share of voice" in advertising: a measure of presence across a market, not just a single position.

The mechanism: why grid data produces a percentage, not a rank

SoLV is computed from geo-grid data. A geo-grid → plots a service area as a matrix of points, and at each point, for each tracked keyword, a business either appears in the top 3 (a win) or it doesn't. SoLV takes every one of those keyword-point checks across the whole grid and expresses the wins as a percentage of the total:

SoLV = (Number of top-3 appearances / Total keyword × grid-point combinations) × 100

Example: a clinic tracking 5 keywords across a 7×7 geo-grid — 49 points — has 245 total keyword-point combinations. If it appears in the top 3 for 98 of them, its SoLV is 40%.

How to compute SoLV in practice, step by step

Computing a real SoLV number takes four decisions made in order, before a single rank check happens. First, define the geographic boundary — the actual area customers come from, not an arbitrary radius, drawn from where existing customers or patients live if that data exists, or from a realistic driving-time boundary if it doesn't. Second, lay a grid over that boundary; a 5×5 grid works for a tight urban service area, a 9×9 or larger for anything spanning multiple neighbourhoods or a wider suburban spread. Third, choose the keyword set — five to ten terms that reflect actual customer search language for the category, pulled from real search data rather than guessed, since a keyword nobody searches contributes nothing but noise to the final percentage. Fourth, run the rank check at every grid point for every keyword on the same day, log a binary top-3-or-not result for each combination, and divide the wins by the total combinations.

The output is only as trustworthy as those four inputs. A business that skips the boundary step and uses a generic radius, or picks keywords from intuition rather than search data, gets a number that looks precise to two decimal places and means very little.

Why SoLV beats average rank

Average rank is a single-point metric, and it obscures geographic variation in a way that can be genuinely misleading. A business with an average rank of 2.1 but an SoLV of only 25% is actually being outranked across 75% of its service area, even though "2.1" sounds strong on a report. The average is dragged up by a handful of points where the business happens to dominate.

The reverse case matters too. A business with an average rank of 3.4 but an SoLV of 68% has broad coverage across its whole market, even if it's rarely sitting in position 1 at any single point. For a multi-location or wide-service-area business, that broad coverage is usually worth more commercially than a narrow rank-1 spike in one neighbourhood.

What's evidenced vs. widely repeated

SoLV is a calculated metric built entirely from geo-grid rank data that a business or its platform collects directly — it isn't a figure Google publishes or confirms in any form, and no vendor's SoLV number is comparable to another vendor's unless both used the same grid size and keyword set. That's worth stating plainly because SoLV sometimes gets presented as if it were an objective, standardized industry benchmark. It isn't. It's a real, useful, and fully calculable number, but it's only ever as meaningful as the grid and keyword set behind it, and it varies by design depending on how those are configured.

What a defensible competitor set looks like

SoLV comparisons fall apart quickly when the competitor set is chosen carelessly. A defensible set is built from who actually shows up in the same top-3 results across the same grid and keywords being used for the business's own SoLV — not a list of who the business owner considers its rivals, and not the three biggest, best-funded names in the category regardless of whether they compete for the same searches. A neighbourhood clinic's real competitive set is the handful of other clinics that appear in the same local pack for the same searches in the same area, which may include a well-known chain and may just as easily not, depending on whether that chain actually shows up locally for those specific terms.

The practical test: pull the businesses that occupy the top 3 positions across the grid being tracked, tally how often each one appears, and treat the two or three most frequent names as the real competitive set — that list is derived from the same data producing the SoLV number itself, rather than assumed in advance, which is what keeps the comparison honest.

SoLV as a competitive benchmark

SoLV becomes most useful once it's calculated for competitors too. Knowing that a clinic holds 40% SoLV while its top competitor holds 58% gives a specific, geographic target, and geo-grid data shows exactly which parts of the service area the competitor is winning and the client isn't. That's a very different, more actionable finding than "we're ranked lower than a competitor on Google Maps →," because it points at specific streets and neighbourhoods rather than an abstract gap.

Worked example: two dermatology clinics in Bengaluru

Take two dermatology clinics in Bengaluru's HSR Layout, both tracking the same five keywords across the same 7×7 grid. Clinic A holds an average rank of 2.8 and an SoLV of 52%. Clinic B holds an average rank of 3.6 but an SoLV of only 31%. On average rank alone, Clinic B doesn't look far behind. The SoLV gap tells a sharper story: Clinic A is winning the top 3 across roughly half its grid, while Clinic B is winning less than a third of it — meaning Clinic B is losing the top spot outright across large stretches of the neighbourhood, not just slipping to fourth or fifth occasionally. Geo-grid data for the same period would typically show exactly which streets those losses concentrate in, often the ones furthest from the clinic's own address, which is where SoLV starts pointing at a specific fix rather than a vague "improve rankings" goal.

SoLV trends over time

Month-on-month SoLV movement is one of the clearest available measures of managed local SEO progress. A rising SoLV means more of the geographic market is actually being captured in the local pack, not just that one keyword nudged up a spot. Because it's grid-based rather than point-based, it's also less noisy than tracking a single rank number, which can jump around from Google's algorithm variance alone.

How to measure it and what a bad reading looks like

Measuring SoLV requires three fixed inputs applied consistently: a keyword set that reflects what customers actually search for the business's category, a grid sized to the real service area (a 7×7 or 9×9 grid for most single-location businesses, larger for wide service areas), and a re-check cadence — weekly or monthly — run at the same points every time. A bad reading looks like a grid that's too small to say anything meaningful: a 3×3 grid with a single keyword can swing from 20% to 80% SoLV from one Google algorithm update alone, purely from noise, not from anything the business changed. A trustworthy reading holds the grid and keyword set constant for at least three consecutive measurement periods before drawing a conclusion from the trend line.

Why a bad grid or keyword set makes the whole metric misleading

The failure mode here is specific, not vague: SoLV doesn't fail loudly when the inputs are wrong, it produces a confident-looking number that's simply measuring the wrong thing. A grid drawn too tight around the business's own address will overstate SoLV, because it never checks the outer parts of the service area where competitors are actually stronger — the number looks great and the business is genuinely losing customers three kilometres out where nobody's checking. A grid drawn too wide does the opposite, diluting a strong core position with irrelevant points from areas the business barely serves, understating real performance. Keyword sets have the same problem in reverse: a keyword set stuffed with branded or ultra-specific terms nobody actually searches inflates SoLV artificially, since a business ranks easily for searches with almost no volume, while a keyword set missing the two or three terms that actually drive volume misses the metric's whole point. None of these failures show up as an error message; they show up as a number that simply doesn't match what a phone-call volume or footfall trend is saying, which is usually the first sign the inputs need re-checking before the score itself is trusted.

SoLV in the AI era

As Google's AI Overviews reduce the prominence of the traditional local pack for some query types, pure SoLV measurement risks becoming a partial picture. A business could hold strong SoLV in the local pack while losing ground in AI Overview citations for the same queries — a gap SoLV alone won't show. Measurement is starting to evolve toward tracking AI Overview citation share alongside local pack presence, which is closer to what share of AI voice → measures. Businesses serious about local visibility increasingly need both numbers, not one.

Common mistakes

Tracking too few grid points or too few keywords produces an SoLV number that looks precise but isn't statistically meaningful — a 3×3 grid with one keyword tells you almost nothing about a real service area. Another mistake is comparing SoLV across businesses that used different grid sizes or keyword sets, since the metric isn't standardized the way, say, a Google Analytics session count is. Any competitive comparison needs the same grid and keyword set applied to every business being compared. A third mistake is treating a single bad month as a trend before checking whether the grid or keyword list itself changed between measurements — a widened grid alone will lower SoLV even if actual rankings held steady, simply because more points are now being checked.

Related terms: Geo-grid → · Local pack → · Local ranking factors → · Share of AI voice → · Rank OS → · Multi-location SEO → · Prominence → · Local search intent → · Near-me searches →

How Angryturtle tracks SoLV: Rank OS geo-grid data produces SoLV calculations for every managed client, reported monthly and compared against top-3 competitors, so a client sees not just their own trend line but where they stand against whoever they're actually competing with.

More on this: Geo-Grid & rank tracking (learning centre) → · Local SEO measurement (learning centre) → · Product: Geo-Grid → · Product: Rank OS → · Competitor ranking higher on Google Maps → · Managed Local SEO →