What we measured
We ran 240 fresh 13×13 grid scans across 6 verticals (HVAC, dental, legal, restaurants, plumbing, salons) in 11 metros between February and April 2026. Each scan covered a 6 km radius, so every node represented one Serper /places query at a unique UULE coordinate. We then logged each business's rank at every node and regressed rank against straight-line distance to the business pin.
The shape of the curve
Plotting rank against distance for the median business reveals three distinct zones. Inside ~600 m almost nothing happens — proximity is essentially saturated. Between 1.5 and 4 km the curve falls off a cliff. Past 4 km, rank flatlines at "unranked" (position 21+).
The cliff doesn't sit at the same distance across verticals. "Emergency plumber" hits the cliff around 1.6 km — past that you might as well not exist for that query. "Wedding venue" stays viable to 8 km because the implicit centroid is closer to the metro centre than to the user. Average it all together and you get the curve above, but the inflection point is what matters operationally.
Why decay is non-linear: the moving centroid
The cleanest mental model: 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.
User-anchored queries
- →Intent words: "near me", "open now", "24/7", "emergency", "closest".
- →Centroid sits at user coordinates → steep decay. The ~1.5 km cliff is brutal.
- →Dominant in: plumbing, locksmith, urgent care, towing, walk-in dental, taxi.
- →Implication: hyper-local presence wins; large polygons of coverage matter.
Centroid-anchored queries
- →Intent words: "best", "top", "highest-rated", or no intent words at all.
- →Centroid drifts toward city / district centroid → softer decay, longer reach.
- →Dominant in: wedding venues, fine dining, bars, hotels, attorneys, med spas.
- →Implication: review depth + category alignment do the heavy lifting; proximity becomes one of three near-equal signals.
Decay coefficient by vertical
Same dataset, broken out by industry. The "50% rank loss" column is the distance at which the median business drops from a top-5 result to outside the top-10 — i.e., the operational cliff edge.
Weight column = % of rank variance attributable to proximity in that vertical. The remainder is split across category specificity, review profile, and behavioural signals.
How rank trackers get this wrong
Single-point rank trackers report "rank #4". One number, one location. The proximity curve we showed 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 entire decay zone.
What proximity can — and cannot — solve
"Proximity is the gatekeeper, not the prize. It decides whether you're eligible to rank at a given centroid. Reviews, categories, and behavioral signals decide where in the eligible bucket you land."
— Internal calibration notes, April 2026
Once a business is inside the steep zone, proximity contributes very little additional lift — being 200 m closer than the next competitor barely moves the needle. The hierarchy flips: review velocity, category specificity, and click-through-rate dominate. Outside the steep zone, no amount of optimization closes the gap; you simply don't qualify for that centroid.
This is why grid trackers matter more than single-point rankers. The grid tells you exactly where the cliff sits and how steep it is. From there you decide whether to (a) optimise within the zone you already serve or (b) open a satellite location to extend coverage.
The operational playbook
Use grid tracking to map your decay curve, then make decisions with it.
- 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.
- 02Identify your operational cliff distance — the radius at which median rank drops below #10. This is your *real* service area, not the polygon you typed into GBP.
- 03Cross-reference cliff distance with your top 3 competitors' cliffs. Where their cliffs extend past yours, that's a coverage gap — you'll never close it without a new location or rented address.
- 04Inside the cliff, run a category audit: are you matching the exact primary category for the query? Misalignment costs you 2–4 ranks at every node inside the steep zone.
- 05Outside the cliff, stop spending money on "local SEO". Spend it on review velocity and category specificity for the centroid where you're already eligible.
- 06Re-scan monthly to detect cliff drift. We've seen cliffs migrate 200–400 m after a strong review velocity month — the decay curve actually softens with prominence.
Common myths, cleaned up
A few proximity claims that survived too long.
Map your proximity cliff in one scan.
Geogrid runs a real 13×13 (or 21×21) grid against Google Maps with native UULE coordinates — exactly the way your customers see the SERP. Plot the cliff, find the gap, prioritise the work. 200 free credits to map your first location.