What A/B testing tells you
A/B testing for QR codes means showing two versions of a code, a call to action, or a landing page and comparing results. It replaces opinions about what works with evidence from real scanners.A test with too little traffic proves nothing. A small difference on a small sample is noise, not a finding.
How to compute A/B testing
Lift = conversion rate of variant B minus conversion rate of variant A. Relative lift = lift divided by the conversion rate of variant A, multiplied by 100.Illustrative example numbers, not real-world data: variant A converts 40 of 400 scans, which is 10%, and variant B converts 60 of 400 scans, which is 15%. The lift is 5 percentage points, and the relative lift is 5 / 10 x 100 = 50%.
How to collect the data
Split traffic evenly between variants, keep everything else the same, and decide the winning metric before the test starts. Record the test dates and the exact change you made.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 A/B testing as a single number. Break it down by call to action, code size, placement, and landing page headline. 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 A/B testing 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 A/B testing 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.