Original research
18 local SEO statistics for Australia and New Zealand
Almost every local SEO statistic in circulation is American, and most of the rest are a citation of a citation. These are measurements I took myself, in Australian and New Zealand cities, and I have published the method so they can be argued with.
The sample is 480 grids, 231,907 observations, 20 industries, 24 AU/NZ cities. Every figure below links back to the full study, including the parts that did not survive a larger sample and the three predictions I got wrong.
Use these figures
You are welcome to quote, chart or republish anything on this page, commercially or otherwise, with a link back. No permission needed and nothing gated. If you want the underlying per-industry or per-city numbers rather than the summary, get in touch and I will send them over - I would rather this got checked than admired.
Tom McSherry, "Map Pack Visibility Study 2026" (480 grids, 231,907 observations, 20 industries, 24 AU/NZ cities). https://tommcsherry.com/learn/map-pack-visibility-study-2026Local SEO statistics on competition: visibility travelled less far where more businesses competed
This is the clearest pattern in the data, and it is the one that changes what you should plan for. Sort every grid by how many businesses Google returned inside the same 8 by 8 km square, and how far a business held a top-three position fell away cleanly as that count rose. It is the only cut in the study where the confidence ranges do not overlap. It is an association, not a law: the study measured what went with a wide footprint, not what caused one.
4.63 km vs 0.73 km
In the least crowded quarter of markets a business holds a top-three Maps position for 4.63 km in every direction. In the most crowded quarter it manages 0.73 km. Same country, same searches, often the same industry.
Median top-3 reach, quartiles by competitor count (5 - 46 against 103 - 374 businesses per 8 x 8 km square). 95% ranges 3.81-5.59 and 0.69-0.78, which do not overlap.
6.3x
The spread between the thinnest and densest markets. Every step down is monotonic and no range overlaps its neighbour, which is not true of any other cut in the study.
4.63 km / 2.78 km / 1.47 km / 0.73 km across the four quartiles.
32.6% to 27.8%
The share of businesses that reach the top three somewhere barely moved as markets got more crowded. In the busier grids it was not that fewer businesses were visible - it was that each visible business covered less ground. The pie was cut into about the same number of slices; the slices got smaller.
Contestedness by competitor quartile, against a 6.3x move in reach over the same quartiles.
Every adjacent pair
Cut the same grids by industry instead and every neighbouring pair of confidence ranges overlaps. The industry table supports "a cafe reaches roughly an eighth as far as an arborist" and does not support "a physio beats a psychologist".
20 industries, 24 grids each. Density separates; industry does not.
How far local visibility actually travels
Reach here means the distance from a business’s own address at which it still holds a top-three Maps position. Not where it appears at all - where it appears in the three results people click.
1.87 km
Median top-three reach across the whole sample. Across the original 12-city core panel, which is weighted more towards metros, it is 1.35 km.
480 grids, 231,907 observations, 20 industries, 24 AU/NZ cities. The frame is deliberately weighted to secondary centres rather than population, so absolute distance runs a little wide; both figures are reported for that reason.
6.17 km to 0.46 km
The range across industries, from pest control at the top to cafe at the bottom - a factor of about 13. The top figure is a floor: in thin markets the grid edge cut the footprint off, so the widest industries reach at least this far.
Medians per industry, 24 grids each. Read the ends, not the order: adjacent pairs overlap.
67%
Of every appearance a business makes within 1 km of its own front door, this share is NOT in the top three. Standing on your own doorstep, two appearances in three are outside the positions anyone clicks. Proximity is necessary and nowhere near sufficient.
95% range 66 to 69. Worst: mobile mechanic at 79%. Best: locksmith at 26%.
About 3 in 10
Of the businesses Google returned somewhere in a typical measured grid, roughly three in ten reached the top three at any sampled point. The share clusters near 30% and barely shifts with how crowded the market is, which is why the visible competitor list from any one search is incomplete.
From locksmith at 46.8% down to mobile mechanic at 18.6%.
How fast you disappear as people move away
Top-three share by distance from the business’s own pin, in exclusive bands. The cliff is in the first kilometre and everything after it is a slow decay.
32.0% to 18.5%
Top-three share nearly halves between the first and second kilometre, then decays slowly. Past about 3 km a business that still appears is mostly appearing below the positions a customer ever sees.
All-industry falloff across the bands: 32.0% / 18.5% / 15.8% / 13.5% / 12.2% / 9.4% / 8.1%.
63.7%
The best doorstep performance in the study, from locksmiths - a thin market, so they win their own area outright, and still a physical call-out business, so they fade with distance.
Falls to 11.0% beyond 8 km.
21.4% to 14.2%
Mobile mechanics have no cliff at all. The only industry measured where distance from the registered address is nearly irrelevant, which is what a genuinely address-less business looks like in this data.
The single exception to the density pattern, and the only industry with a flat falloff curve.
Reviews and ratings: the local SEO statistics most people get backwards
Spearman’s correlation computed among the businesses competing at one single grid point, then averaged, so that a competitive point is not confused with how businesses rank within it. It is a raw within-point association: the searcher-to-business distance has not yet been removed from it, so part of what it measures is proximity.
0.194
The whole-sample correlation between review count and Map Pack position. More reviews went with a better position in 17 of 20 industries, and in most grids within them - plumbers in 24 of 24, dentists and car servicing in 23 of 24. The effect of any one review is small, but the direction is consistent, so review count is a genuine ranking signal as well as the thing that wins the click.
95% range 0.174 to 0.213, across 480 grids.
0.428 to -0.099
The spread by industry, from plumber at the top to cafe at the bottom. Cafe is the exception and is reliably negative: in the densest premises markets the businesses with the most reviews sat slightly further down the pack. The likely reason is that those packs are being decided by proximity and the heavily-reviewed places are not the closest ones, but that explanation was not tested.
cafe interval -0.152 to -0.046, which excludes zero, negative in 20 of 24 grids.
-0.005
Star rating against Map Pack position. The correlation is flat because nearly every business that appears in a pack already sits between four and five stars, so there is no spread for the number to find. Read it as "you need a strong rating to be in the game", not "rating does not matter": rating and review count both matter a lot, and one bad review can cost the call even when the ranking does not move.
95% range -0.02 to 0.009, positive in 230 of 480 grids - the sample has almost no low-rated businesses in the pack to compare against.
0.229
Within an industry, grids where visibility travels further are grids where reviews correlate more strongly with position. Reviews matter more where proximity decides less. The industry-level version of that sentence is NOT established and an earlier write-up was wrong to state it.
95% range 0.093 to 0.363, 15 of 20 verticals. Across industries it is 0.287 [-0.199, 0.713], which includes zero.
The sample behind these local SEO statistics, and what it cannot tell you
Published so the figures above can be argued with. A statistic without a method behind it is a slogan. The grids were collected in August 2026; repeat captures to measure run-to-run noise have so far been done on one industry, and extending them across the rest is outstanding.
231,907
Google Maps results captured across 480 usable grids - a 5 by 5 pattern of search points in each of 24 Australian and New Zealand cities, 20 industries, 41,716 business-grid pairs.
Total API cost US$23.95. No grid exceeded the 15% dead-point threshold.
Correlation
None of this is causation. Businesses with more reviews tend to be older, better established and better linked, and those travel together. The study measures what accompanies a good position, not what causes one.
Stated as a limitation in the published method rather than buried.
3 of 12
Pre-registered predictions that failed. Every prediction was recorded in the frame file before a single grid of the new industries was collected, because recording it afterwards would have been worthless. Three were wrong, and all three are published.
Vet, chiropractor and gym were all predicted under 1 km and came in at 2.85, 1.86 and 1.15 km - all premises industries in thin markets, which is the cleanest evidence for the density finding.
Read the study these come from
The full write-up has the method in full, the confidence ranges, the limitations, and the figures that did not survive when the sample doubled.