How to Forecast a QR Campaign Without Inventing Numbers
Forecasting a QR campaign is simple arithmetic wrapped around hard assumptions. The arithmetic is the easy part: exposures times scan rate gives scans, scans times conversion rate gives conversions, conversions times order value gives revenue. The hard part is the two rates, because they are the numbers that decide everything and the numbers nobody can know in advance. This simulator keeps those two things separate on purpose. It does the arithmetic for you, shows every formula, and refuses to supply the rates, because any rate it supplied would be invented. Your job is to bring rates from your own past campaigns, a pilot run, or an explicit scenario you are willing to defend.
The most honest way to get a scan rate is a small pilot. Print a few hundred pieces, place them exactly as the full run would place them, and count real scans for a week. That pilot rate, even from a small sample, beats any published benchmark, because it reflects your audience, your placement, and your offer rather than someone else's. If a pilot is impossible, run three scenarios instead of one: a pessimistic rate, a realistic rate, and an optimistic rate. If the campaign only works in the optimistic scenario, it does not work, and you have learned that before spending the print budget rather than after.
Exposures per piece is the assumption people forget. A flyer handed to one person gets one exposure. A poster in a busy corridor gets many. A code on a receipt gets one exposure from the buyer and possibly none from anyone else. Estimating exposures honestly is what stops the forecast from drifting: if you assume a poster is seen fifty times a day for thirty days, write down why you believe that, and check it against foot traffic you can actually observe. An exposure is a person close enough to the code to read the call to action, not a person who walked past the building.
Cost per conversion is the number that should drive the decision, not total scans. A campaign that produces many cheap scans and few conversions can be worse than a campaign with fewer scans that convert well, because every scan that does not convert still cost print, placement, and attention. Compare the cost per conversion against what you would pay for the same conversion through another channel you already use. If the QR route is more expensive per conversion, the QR route needs a better offer, a better placement, or a smaller print run, not a bigger one.
Finally, treat the forecast as a plan for measurement, not a prediction. Decide before launch what scan rate would make the campaign a success, what rate would trigger a change of placement or offer, and what rate would end it. Write those thresholds down, measure the real numbers weekly, and adjust the campaign against them. A forecast that is never compared against reality is decoration. A forecast with decision thresholds becomes a management tool, and it is the difference between a campaign that drifts and a campaign that improves.