What benchmarks can and cannot do
Funnel analysis follows scanners through each step from exposure to conversion: seen, scanned, page loaded, action started, action completed. It shows exactly where people drop out, so fixes go to the step that leaks most.Benchmarks for funnel analysis 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 funnel analysis 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. A funnel with only two steps hides the problem. Break the journey into the real steps a scanner takes.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: Step conversion = people who reach a step divided by people who reached the previous step, multiplied by 100. Overall conversion = conversions divided by exposures, multiplied by 100. 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: 5,000 exposures lead to 400 scans, 120 action starts, and 60 completions. Scan-to-start is 120 / 400 x 100 = 30%, and start-to-complete is 60 / 120 x 100 = 50%, so the bigger leak in this example is between scan and start.
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.