What benchmarks can and cannot do
Unique versus repeat scans split your scan total into first-time scanners and people who have scanned before. The split tells you whether a code is still reaching new people or mainly serving returning visitors.Benchmarks for unique versus repeat scans are tempting because they promise a shortcut: one number that tells you whether you are doing well. In practice, a benchmark is only as useful as its definition, and definitions vary widely. One source may count every scan, another may count only unique scanners, and a third may measure a different placement type entirely. Two benchmarks that disagree are not a puzzle to solve; they are a signal that the underlying measurements differ. Treat any benchmark as a rough reference, never as a verdict.
Why benchmarks for unique versus repeat scans vary
Benchmarks vary for reasons that have nothing to do with quality. Placement changes everything: a code at a checkout behaves differently from a code on a poster. Audience matters too: existing customers scan for different reasons than strangers do. Offer strength, season, device mix, and time of day all shift the number. Even the definition shifts it: changing the denominator or the time window changes the result without changing the campaign at all. Neither a high unique share nor a high repeat share is good on its own. The right mix depends on whether the code exists to acquire, serve, or retain.This variation is why copying a published benchmark as a target is risky. You may be comparing your menu code against someone else's checkout code, or your first month against their twelfth. The gap you see may be a measurement gap, not a performance gap, and chasing it can lead you to fix things that were never broken.
How to build your own baseline
Your own baseline is the benchmark that matters most, because it shares your definition, your placements, and your audience. Build it in four steps. First, write down the definition: Unique share = unique scanners divided by total scans, multiplied by 100. Repeat share = 100 minus unique share. Second, measure it for a fixed period on a stable placement and record the raw counts, the date range, and the method. Third, repeat the measurement for two or three more comparable periods. Fourth, use the range you observed as your baseline, not a single point, because a range respects normal variation.Illustrative example numbers, not real-world data: 300 unique scanners out of 400 total scans gives 300 / 400 x 100 = 75% unique and 25% repeat. A loyalty code would reasonably show the opposite pattern, with repeat scans in the majority.
Once the baseline exists, judge new results against it. A result inside the baseline range is normal variation. A result outside the range for two consecutive periods is a signal worth investigating. This approach is slower than copying a published number, but it is honest, and it improves as your own history grows.