What a Good QR Scan Rate Looks Like, by Channel

A marketer's boss asks one question after almost every QR campaign: is that scan rate good? The honest answer is that "good" doesn't exist as a single number, because a scan rate is a fraction, and the bottom half of that fraction, how many people actually saw the code, is rarely known with any precision at all. What follows is what published data says, channel by channel, how confident to be in each figure, and how to judge your own number when no external benchmark quite fits your placement.

Why a scan rate is meaningless without its denominator

A scan count is exact: the platform knows precisely how many times a code was scanned. Exposure is the part nobody counts directly. A code scanned 80 times off 1,000 opened product packages has a very different reliability behind its 8% rate than a code scanned 80 times off a print run whose actual pickup and readership nobody tracked. The scan side of a QR scan rate is a fact. The exposure side is almost always an estimate, and the quality of that estimate is what makes one 8% figure trustworthy and another one closer to a guess with a percentage sign attached.

This is the single most important caveat in this entire topic, and it's worth stating as plainly as the vendor data itself: published QR benchmarks are almost all self-reported figures from QR platforms and marketing content sites, built from whatever exposure estimates their own customers supplied, not from independently audited studies. That doesn't make them useless. It makes them a starting point for a conversation, not a target to hit.

It's also worth separating scan rate from the metrics a QR platform actually reports on your behalf. A platform can tell you, precisely, how many scans a code received, when, and roughly where from. No QR platform, MyQRx included, can tell you how many people walked past a shelf-edge display without scanning, because that half of the fraction never touched the code at all. Scan rate is a number you calculate yourself, by supplying your own best estimate of exposure; it isn't a report any analytics dashboard hands you already finished.

Published ranges, by channel

Every range above should be read as "what vendors have reported," not "what your placement will do." Two independently published sources covering the same channels put packaging as low as roughly 5% and, once bundled together with event codes in a broader "packaging and events" category, as high as 40%: the same physical channel, described with barely overlapping numbers, because one source separated it from events and the other didn't.

Estimating exposure when nobody hands you a number

Print and outdoor placements are the hardest cases, because nobody counts the audience the way a mailing list or a ticketing system does. A few workable proxies, in order of how defensible they are: for a flyer or leaflet, the print run size, since it's a hard ceiling even though not everyone printed will be seen; for a shop window or in-store fixture, a rough estimate of daily foot traffic past that exact spot, multiplied by the campaign's duration; for outdoor media, the impression estimate the media owner's rate card already provides, treated as a modelled figure rather than a headcount; for an event, registration or ticket numbers, which are usually close to exact.

None of these produce a number worth publishing as an industry benchmark. They produce a number worth comparing against your own placement's past performance, which is a more honest use of a scan rate than comparing it to somebody else's vendor blog post.

Where two proxies are both available for the same placement, use the more conservative one rather than averaging them, since exposure estimates tend to run optimistic by default: a print run size assumes every copy left the printer's warehouse and reached a hand, and a rate-card impression figure assumes every modelled exposure was a real pair of eyes on the creative for long enough to notice a code in the corner. Treating the smaller of two estimates as the working denominator keeps the resulting scan rate from looking artificially weak against a channel benchmark that was itself built on an equally generous assumption.

Is 0.4% good? A worked example

A leaflet drop of 12,000 pieces through letterboxes producing 48 scans works out to exactly 0.4% by scan count over print run. Read against the print-advertising range above, 0.4% sits below the reported 1-5% band, which looks like underperformance at first glance. But the denominator matters here more than the headline number: a letterbox drop has no guarantee of being opened, let alone read, the way a magazine ad guarantees a reader is at least looking at the page it's printed on. Against print-run size as the denominator, 0.4% for an unaddressed leaflet drop is closer to what a colder, less-targeted print channel should produce than a sign of a failing campaign.

The number that actually diagnoses whether 0.4% is a problem isn't a channel benchmark at all: it's the same leaflet's own scan rate the previous time this list was used, or a second leaflet in the same drop with a different call-to-action, tested against each other. A/B testing two versions against a shared placement answers "is this good" far more reliably than any published range can, because it holds the denominator constant and only varies the thing you're actually trying to improve.

Why the disagreement itself is the most useful data point

Notice which channels show published ranges that roughly agree across sources, and which don't. Direct mail and events, both channels with a genuinely countable audience, show ranges that overlap closely between independently published sources. Packaging and outdoor advertising, both channels where "how many people were exposed" is inherently fuzzy, show ranges that diverge by a factor of two or more depending on which vendor's blog you read.

That pattern isn't a coincidence, and it's more informative than either range on its own: a benchmark is only as reliable as its denominator, and the channels with the most benchmark disagreement are, by no coincidence, the channels where the denominator was hardest to pin down in the first place. If you're deciding how much weight to give any single published figure, that's the test to apply, not which source looks more authoritative.

The same test works in reverse when you're setting your own expectations for a new placement type you haven't run before. If the channel has a natural, countable audience, a guest list, a mailing list, a ticketed door, treat the published range as a reasonably useful starting point. If the channel's audience is inherently a guess, a shelf that gets browsed by an unknown number of people, a billboard seen by an unknown fraction of passing traffic, treat any published range as illustrative at best, and put more weight on your own first campaign's result as the baseline for the second one, rather than on whatever number a vendor blog quotes for "retail" in general.

What this means for your next campaign

Use the ranges above as a rough sanity check, not a target, and weight your confidence in each one by how countable that channel's audience actually is. Build your own exposure estimate as honestly as the channel allows, using print run size, foot traffic, or registration numbers rather than a number borrowed from somebody else's campaign in a different market with a different creative. Then track your own placements against their own history over time, because reading how a scan curve moves across a campaign's run tells you far more about whether something is working than a single scan-rate percentage compared to an industry range ever will. Where the code was placed has more influence on the number you'll see than almost any other variable, which is one more reason a single universal "good" scan rate doesn't exist.

Track your own scan rate over time