What attribution tells you
Attribution is the task of deciding which touchpoint gets credit when a scanner converts. QR codes often sit in a chain of exposures, so the model you choose changes the story your numbers tell.Attribution is a model, not a measurement. It allocates credit by rule; it does not prove what caused the sale.
How to compute attribution
Under last-scan attribution, 100% of credit goes to the most recent QR scan before conversion. Under first-scan attribution, 100% goes to the earliest scan in the window. Position-based models split credit across touchpoints.Illustrative example numbers, not real-world data: a customer scans a poster code, then a receipt code, then buys. Last-scan attribution credits the receipt code with 1 conversion. First-scan attribution credits the poster code with 1 conversion. The sale is the same; only the credit changes.
How to collect the data
Choose one model, write it down, and use it consistently. Tag every code so scans can be joined to conversions, and set an attribution window in advance.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 attribution as a single number. Break it down by placement, model, and window length. 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 attribution 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 attribution 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.