What unique versus repeat scans tells you
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.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.
How to compute unique versus repeat scans
Unique share = unique scanners divided by total scans, multiplied by 100. Repeat share = 100 minus unique share.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.
How to collect the data
Use a redirect or analytics tool that distinguishes devices or sessions, and decide in advance what counts as a repeat, such as a second scan within 30 days.Keep the raw counts, not just the final percentage. Raw counts let you recompute the metric if you later fix the definition, and they make it possible to spot a denominator problem, such as a week where exposures were estimated instead of counted. Store the counts in one place with the date range, the placements included, and the name of the person who pulled them. That small discipline turns a spreadsheet into an audit trail.
Reading the result
Never judge unique versus repeat scans as a single number. Break it down by placement, offer type, and time since launch. A campaign that looks average overall may be strong in one segment and weak in another, and the fix is completely different in each case. Compare the same placement against its own past, and compare different placements only when their audiences and contexts are genuinely similar. A short trend over four or more comparable periods is worth far more than a single snapshot, because it separates a real shift from weekly noise.Also check the relationship between unique versus repeat scans and the metrics around it. If scans rise while conversions fall, the new scanners may be less interested visitors, or the landing page may have changed. If cost rises while unique versus repeat scans stays flat, you are paying more for the same outcome. Metrics make sense in groups, not in isolation, and the habit of reading two or three together prevents most misreadings.