How the Proximity Factor Actually Works on Google Maps

What is the proximity factor?

The proximity factor is the distance between a business and the location Google attributes to a search. Google lists it as "distance", one of three local ranking factors next to relevance and prominence. Closer businesses get a boost, but the boost fades with distance at a rate that depends on the query, so the same listing ranks differently across a city.

Two consequences follow. First, a business does not have one Google Maps rank for a keyword: it has a different rank at every point where someone searches. Second, proximity only decides who is eligible near a given point. Once a listing is close enough to compete, relevance and prominence (categories, reviews, links) decide the order.

You cannot read the proximity factor from a single rank check, because one check is one distance. You see it by checking the same keyword from many points around the business and watching where the rank drops. That is what a grid scan does, and it is why city rank tracking reports a map instead of a number. To see your own drop-off without an account, run the free Google Maps rank checker on a 3×3 grid around the listing.

What we measured

Every completed Google Maps grid scan run on Geogrid between 16 September and 18 October 2026: 261 scans, 7,743 land points, from 107 accounts in 26 countries. 16 September is the day per-point location handling was rebuilt; earlier scans are left out because some of their points were answered for the wrong area.

For each point we know the business's position on the first page of Google Maps results (top 10) or that it was not there. Distance is measured from the grid centre, which is the location the user chose to scan around, normally the business address. Points over water are excluded. The figures are aggregates only: no business, keyword or account is identified.

Limits worth stating up front: most scans come from free accounts and use 5×5 grids, so distance bands between 0.5 and 1.5 km hold few scans; ranks are only known down to 10; and the sample is observational, so industries differ in keywords, countries and competition as well as in how proximity works.

The shape of the curve

Among the 117 scans where the business reached the top 10 at least once, here is the share of points where it was in the top 10, by distance from the grid centre.

The decline is steady and modest: about 16 points between the centre and 4 km, then flat. There is no distance where visible businesses fall off a cliff on average. Across all 261 scans, including businesses that never reached the top 10, the top-10 share goes from 48% within 1.5 km to 36% between 1.5 and 4 km and 30% beyond 4 km.

The more surprising number is what did not move. Where these businesses appeared, they held a top-3 spot at roughly 40% of points in every band, from 42.5% at the centre to 38% beyond 6 km. Distance mostly decides whether a listing shows up at a point, not how high it sits once it does.

Why decay is non-linear: the moving centroid

A useful model, and one our sample is too small to test query by query: every query has a centroid that Google computes implicitly. For "plumber near me" the centroid sits exactly at the user's coordinates. For "best wedding venue" the centroid drifts toward the city centre. For "24/7 emergency dentist" it stays glued to the user. Distance decay is then measured from this moving centroid, not from the raw user GPS.

Drop-off by industry

Same sample, split by the industry Geogrid tags each scan with. Only industries with at least 20 scans are shown. The bar is the share of points in the top 10 within 1.5 km of the grid centre, across every scan in that industry, visible or not.

Bar = share of grid points in the top 10 within 1.5 km of the grid centre. Industries are tagged automatically from the keyword and business. Groups are small and differ in keywords and countries, so read the direction, not the decimals.

How rank trackers get this wrong

Single-point rank trackers report "rank #4". One number, one location. The change across the area shown above is invisible to them. ZIP-code trackers are worse — they sample the centroid of a 7 km wide polygon and call it a day, hiding the whole drop-off.

What proximity can — and cannot — solve

Once a business is visible in an area, being a few hundred metres closer than the next competitor rarely changes much: in our data the share of top-3 spots barely moved with distance. Review profile, category match and behaviour decide the order. Where a listing does not appear at all, distance is usually the reason, and optimisation inside the profile has a hard time closing that gap.

This is why grid trackers matter more than single-point rankers. The grid shows where your visibility thins out and how fast. From there you decide whether to (a) optimise within the zone you already serve or (b) open a satellite location to extend coverage. To see whether you lose visibility at all before committing to a full grid, a nine-point pass with our local rank checker is free.

The operational playbook

Use grid tracking to map your decay curve, then make decisions with it.

1. 01Run a 13×13 grid scan at 5–6 km radius for your most important keyword. This gives you 169 sample points spaced ~750 m apart — enough to resolve the cliff.
2. 02Find your drop-off distance: the radius where your share of top-10 points falls below half. That is your real visibility area, not the service area typed into your profile.
3. 03Compare it with where your top 3 competitors still appear. Where they reach further than you, that is a coverage gap a new location may close faster than profile work.
4. 04Where you do appear, run a category audit: does your primary category match the query exactly? That is where profile work moves positions.
5. 05Where you never appear, do not expect profile tweaks alone to fix it. Put effort into reviews and category fit for the area where you are already eligible.
6. 06Re-scan monthly with identical settings and check whether the drop-off distance moves after review or category changes.

Common myths, cleaned up

A few proximity claims that survived too long.