Everyone in local marketing repeats the same claim: a radius wastes budget, fitting the real zone saves it. We wanted the number, so we measured it. 450 realistic delivery zones built from official Australian suburb boundaries, four targeting methods each, measured with the same engine that plans production campaigns.
On the median zone, a circle drawn to cover the whole delivery area puts 66% of itself outside it. A default 3 km radius puts 64% outside. A set of circles fitted to the zone puts 12% outside on Meta and 11% on Google. Roughly five times less of the targeted area lands where the store cannot sell.
The radius that covers everything is the radius that buys the most ground the store does not serve. Median precision 34%, and half the zones sit between 28% and 42%. Full coverage, mostly waste.
The common default does two things wrong at once. Its median precision is 36%, and on a quarter of the zones it also failed to cover at least 13% of the area, because real zones are not centred on the store.
A median of 5 include circles, with cut-outs where they earn their keep, covering 93% of the zone with 88% of the targeted area inside it. Half the zones fit between 83% and 93% precision.
Google cannot cut circles back out, so its plans use a median of 6 include circles, cover 91% of the zone and keep 89% of the targeted area inside it. Less coverage, less spill, a different trade.
| Method | Covers the zone (median) | Outside the zone (median) | Circles used (median) |
|---|---|---|---|
| One covering circle | 100% by construction | 66% of the targeted area | 1 |
| Fixed 3 km radius | 100% on small zones, gaps on large ones | 64% of the targeted area | 1 |
| Fitted, Meta | 93% | 12% of the targeted area | 5 include, exclusions where useful |
| Fitted, Google | 91% | 11% of the targeted area | 6 include |
Swipe the table sideways to see the rest.
The two fitted rows are not at 100% coverage, and that is the honest part of the result: coverage and precision pull against each other, and covering the last awkward corner means buying ground outside the zone. The engine exposes that trade as a setting, so a network can lean towards protecting reach or protecting budget. What no setting can fix is the first two rows.
Each zone is a union of 1 to 4 adjacent real suburbs from official Australian boundary data (NSW, Victoria, Queensland), sized 3 to 30 km², which is how real networks draw delivery and service areas. No client files are used anywhere.
The fits come from the same solver that plans live campaigns in Amplaro: greedy max-coverage circle fitting, separate plans for Meta (include plus exclude) and Google (include only), platform minimum radius of 1 km respected.
Seeded zone construction, sorted inputs, 150 zones per state. Every per-zone result is kept, so any row can be checked on request. The fits are consistent with what the engine reports on real territory files in production use.
One honest caveat: precision here is a share of targeted area, and money follows area only approximately, because people are not spread evenly. The exact spend share differs per zone. The direction does not: for a delivery business, the area outside the zone converts at zero, whatever it cost to reach. The reasoning is on radius vs delivery zone, and the translation method on turning a zone into an ad audience.
Two thirds of the targeted area, measured. Spend follows area approximately, not exactly. For a delivery business the direction is not in doubt: the outside share cannot produce an order at any conversion rate.
Because the last corner of an awkward zone costs more outside-spend than it is worth. The balance is a setting per network, not a fixed answer.
Different rules. Meta can cut circles back out, Google cannot, so the plans are solved separately. Google kept slightly more spend inside the zone while covering slightly less of it.
Yes. Seeded construction, public boundary data, method above. The per-zone results are kept; ask us and we will share the table and look at any difference together.
The reasoning behind these numbers: what a circle cannot know about a real trade area.
The product page: the same engine, fitting every store's campaigns to its own zone automatically.
Where targeting shows up in the return, and how to measure the rest of it honestly.
The benchmark says what happens on typical zones. Your network's number depends on your shapes, and measuring them takes one demo.