How Far Can a Google Business Profile Rank? We Studied 570 Locations

Local SEO advice often treats reviews, photos, categories, and profile completeness as levers that can keep widening a business's map coverage. Our data says something more constrained: better profiles showed a mild relationship with wider reach, but none of those signals carried a single-location business across a market boundary.

The reach distribution

We grouped each business by the farthest sampled point where it appeared in a local pack. The four groups sum to all 570 single-location businesses in the clean dataset. The profile-signal columns reproduce the bucket summaries from the research run.

Most of the sample stopped inside four kilometers

The counts make the shape easier to see. Of the 570 businesses, 186—or 32.6%—had a maximum observed reach of 0–2 kilometers. Another 260, or 45.6%, landed in the 2–4 kilometer band. Combined, 78.2% of the clean sample stopped inside four kilometers. Only 22 businesses, 3.9% of the sample, reached the 6–8 kilometer band.

Those percentages describe this dataset, not the odds that any randomly selected profile will reach a given distance. Category, market density, query language, device, and the exact probe layout can all change what a grid records. The useful pattern is the concentration near the listing and the absence of a clean single-location result at the distant sampled points—not a promise that every market follows the same curve.

“Maximum reach” is a narrow measurement

A business entered a distance band when its farthest observed appearance landed there. That does not mean it ranked at every point inside the radius, held the same position in every direction, or appeared for every keyword. Local results can be irregular: a listing may surface along one corridor and disappear sooner in another. This study measures the outermost observed point, not a perfectly circular service territory.

Reviews plateaued. Distance did not care.

The review count moved from 35 in the 0–2 kilometer group to 50 in the 2–4 kilometer group. Then it stopped moving: 51 in the 4–6 kilometer group and 50 in the 6–8 kilometer group. Whatever reviews contributed to relevance, prominence, trust, or conversion, more of them did not correspond with a wider measured radius after two kilometers in this dataset.

Photos, ratings, and claimed status showed a mild gradient. Every business in the 6–8 kilometer group was claimed, and that group had the highest reported rating and photo count. That does not prove those signals caused the extra reach. Stronger operators may simply maintain stronger profiles. The honest conclusion is narrower: profile quality varied with reach, but no profile signal overcame geography.

This is why a rank check from your office is not enough. You need a geographic grid to see where visibility fades. Our guide to Google map pack rankings explains the moving result set, and the local visibility audit gives you a way to inspect your own footprint.

The exclusions changed the answer

The first version of this analysis joined listings by business name. That sounds reasonable until the same brand has several branches, two unrelated businesses share a similar name, or Google returns a generalized pin for a hidden-address listing. Those cases created spectacular but false reach outliers.

The corrected identity join removed two classes of records:

• 263 pack appearances associated with 69 multi-location networks. Those were real appearances, but they measured a network's doors—not one location's reach.
• 31 appearances from hidden-address listings. Their displayed pins could not support a defensible distance calculation, so we excluded them rather than manufacture extreme results.

Once we joined on Google's stable place_id and removed those cases, every apparent extreme-distance outlier disappeared. This is the methodological lesson worth keeping: in local search research, entity resolution is not cleanup after the analysis. It determines the result.

How the study worked

1. Observe local packs from fixed geographic probes. Each grid recorded the ordered businesses Google returned at known coordinates.
2. Join identity with place_id. Display names were not used as the entity key.
3. Keep single-location businesses only. Multi-location networks were separated so one brand's branches could not masquerade as one far-reaching pin.
4. Remove indefensible origins. Hidden-address listings were excluded because the published pin was not a reliable operating-location coordinate.
5. Measure maximum observed reach. For each remaining business, we calculated the distance from its published pin to the farthest sampled probe where it appeared.
6. Group and compare profile signals. We summarized reviews, photos, ratings, and claimed status across the four reach bands.

Why the identity join matters

A name is presentation; a place ID is entity identity. Names can be abbreviated, punctuated differently, shared by unrelated operators, or reused across every branch in a network. A name-based join can therefore attach a ranking observed near one door to the coordinates of another door. The resulting distance may look like exceptional performance even though it is only an identity error.

Joining on place_id changed the research question from “How far does this brand appear?” to “How far from this specific published pin was this specific listing observed?” That distinction removed the dramatic outliers and made the remaining reach measurement defensible. It also explains why network coverage should be studied separately: multiple locations may create broad brand visibility, but that is a location strategy, not evidence that one profile ignored proximity.

The caveat that matters: the grid has a blind band

The grid samples densely from the center through roughly 8 kilometers. It then jumps to the next market, around 20 kilometers away. That means the study does not continuously observe the 8–14 kilometer interval. We can say the farthest observed business reached 7.5 kilometers and none appeared at the more distant sampled points. We cannot say the universal cutoff is exactly 7.5 kilometers.

Locating the cliff requires a design change: add a second ring around 10 kilometers—about five more probes per keyword, per market. Until that follow-up exists, “roughly 5–7 kilometers in this sample” is the defensible finding. “Google caps every profile at 7.5 kilometers” is not.

What local service businesses should do with this

1. Win the radius you can actually serve. Complete the profile, improve the offer, earn reviews, publish useful local proof, and make the call or booking path obvious. Those actions still matter even when they do not move the pin.
2. Stop promising that more reviews will erase distance. Reviews can influence trust and prominence. In this dataset, accumulating beyond roughly 50 did not correspond with additional geographic reach.
3. Measure coverage before opening another location. A real grid shows where visibility falls away and whether the uncovered area contains enough demand to justify operational expansion.
4. Expand with real operations, not fabricated pins. Metro-wide presence in this research came from multiple locations. If expansion makes business sense, open and staff a legitimate location. Our multi-location GBP playbook covers the compliant planning questions.
5. Build a site that supports each real market. Pair every legitimate location with a useful, distinct page and proof from that market. The local SEO guide explains how the profile, site, citations, and conversion path work together.

Use profile work and expansion work for different jobs

Profile improvements still deserve attention. Accurate categories, useful photos, current information, and a steady review program help a customer evaluate the business and may support local relevance and prominence. The mistake is assigning those tasks a job they did not perform in this sample: turning one address into metro-wide coverage.

Treat geographic expansion as an operating decision. First map the current footprint, validate demand in the uncovered area, and decide whether the economics support a real additional location. Then build the operational and web presence around that location. This keeps local SEO from becoming a substitute for market planning—and keeps market planning from being based on a misleading rank check.

What this study does not prove

What it does provide is a cleaner baseline for the question every local operator asks: “How far can one location realistically reach?” In this sample, the answer was local in the literal sense. Strong profiles stretched farther within the market. None crossed it.