What seasonal trends tells you
Seasonal trends are the repeating rises and falls in QR scans across weeks, months, and holidays. Spotting them helps you print enough, staff enough, and avoid judging a quiet week against a peak week.One year of data shows a pattern. Two or three years show whether the pattern repeats.
How to compute seasonal trends
Seasonal change = scans in one period minus scans in a comparable period, divided by scans in the comparable period, multiplied by 100.Illustrative example numbers, not real-world data: a December block of 600 scans versus a February block of 300 scans gives (600 - 300) / 300 x 100 = 100% higher in December. The gap is seasonal in this example, not proof that December creative was better.
How to collect the data
Compare equal-length periods, align them to the same weekdays, and note holidays, weather, and promotions that distort the pattern.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 seasonal trends as a single number. Break it down by week, month, holiday period, and placement type. 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 seasonal trends 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 seasonal trends 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.