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Campaign Estimator

QR Footfall Scan Estimator

Estimate how many scans a location should produce from daily foot traffic, how many people notice the code, and how many of those decide to scan. The range is computed from your assumptions — it is a planning estimate, not a measured forecast.

1. Enter your assumptions

Formula: central estimate = footfall × notice rate × scan rate × placement factor. Range = 0.6× to 1.6× the central estimate, reflecting uncertainty in the assumed rates.

Result
Expected scans per day
30 – 80
central estimate: 50
People who notice it1,000
Weekly scans (central)350

If the range is too low for your goal, the levers are visibility (bigger code, eye level, better lighting) and motivation (a clearer offer next to the code).

Footfall Is Not Scans

A busy street does not guarantee scans. Most passers-by never look at the code, and most who do look decide not to scan. Breaking the journey into footfall, notice, and scan decision makes the bottleneck visible: a code nobody sees needs placement work, while a code people see but ignore needs a better reason to scan.

Using the estimate

Start with conservative rates, compare two or three candidate placements, and treat the winning placement as a hypothesis to test with a short pilot. Once real scans are flowing, replace the assumed rates with measured ones and the estimate becomes a reliable forecast.

Frequently Asked Questions

Is this a prediction of actual scans?

No — it is an estimate computed from the assumptions you enter (footfall, notice rate, scan rate, placement). Use it to compare placements and set expectations, then replace the assumed rates with measured rates from a pilot once you have real scan data.

What is the difference between notice rate and scan rate?

Notice rate is the share of passers-by who actually see the code. Scan rate is the share of those who notice it and decide to scan. Splitting them shows whether a weak result is a visibility problem or an offer problem — the fixes are different.

Why is there a range instead of one number?

Because the inputs are estimates. The range (±40% around the central estimate, wider upward) reflects that uncertainty honestly instead of pretending a single precise figure is known.

How do I improve the estimate?

Run a short pilot: count footfall, count scans from your analytics, and derive the real notice and scan rates. Feed those back in and the estimate becomes a forecast grounded in your own location.