Why Industry Averages Mislead (and How to Use Them Anyway)
An industry average is defined as the mean value of a metric across a sample of stores, and industry averages mislead in four specific ways: mix, shape, definition and time. Knowing which distortion is operating tells you how much to discount a figure, and what survives is still useful for bounding a target, triaging whether to investigate and explaining a number to somebody outside the team. The widely quoted figure for cart abandonment, near 70% [Baymard Institute, 2025], is a real average that still says little about any one store. This guide shows each distortion and how to use industry averages anyway.
- Remember that a category average can fall while every store inside it improves, purely from a change in who is in the sample.
- Use the median, because conversion data is skewed and a mean describes a position most stores sit below.
- Assume most of the gap between two published benchmarks is definitional, not real.
- Avoid reading the distance between your rate and the average as a quantity of available improvement.
- Use benchmarks for bounding, triage and communication, not for deciding what to change.
Industry averages are the most widely used and least reliable number in eCommerce reporting. They get quoted in board decks, used to set targets, and treated as a standard a store is passing or failing, and almost none of that survives contact with how the figure was built. The useful move is not to discard them. It is to know precisely which distortion is operating, because each one has a different size and a different fix. Last updated: September 2026.
What an industry average actually is
Distortion 1: mix, or the average that moves when nothing moves
Mix effects are counterintuitive enough that they routinely survive review by careful people. The mechanism is simple: if a benchmark's sample grows to include many small, young, paid-traffic-heavy stores, the average falls. Every existing store may have improved. The published figure still went down.
Distortion 2: shape, or the mean that describes nobody
When a distribution is symmetric, the mean is a fair summary. Conversion rates are not symmetric. The floor is zero and the ceiling is soft, and a handful of stores with unusual traffic, subscription mechanics or extremely narrow catalogues sit far above the bulk of the field. Those stores drag the mean.
The fix is cheap when it is available: use the median. Where a source publishes only a mean, treat the figure as optimistic and do not build a target on it. Where a source publishes percentiles, use those instead of either, because a range tells you about the spread and a single number never can.
Distortion 3: definition, or two sources measuring different things
Definition drift is defined as two sources measuring different things under the same metric name, such as a session or a conversion. Even the CROBenchmark dataset of 7,000+ websites [CROBenchmark Report 2026, Omniconvert] is one source with one definition among many. For more, read benchmarks vs your own baseline and why two stores with the same CVR are not equal.
Distortion 4: time, or the average that already expired
| Distortion | What causes it | Typical size | What to do |
|---|---|---|---|
| Mix | The composition of the sample changes | Large, and invisible | Never compare a benchmark to itself year on year |
| Shape | Skewed distribution pulls the mean up | Moderate and consistent | Use the median, or percentiles if offered |
| Definition | Sources count sessions and conversions differently | Often the largest single gap | Reconcile definitions before comparing |
| Time | Unstated window, seasonality, publication lag | Large in seasonal categories | Check the window and the vintage, or discard |
| All four together | Compounding, in unknown directions | Unquantifiable | Treat the figure as a range, never a target |
The bottom row is the honest summary. Because the four distortions compound in directions you cannot determine from a published figure, the responsible reading of any single benchmark number is as a rough region rather than a point.
How to use industry averages anyway
Bounding. Knowing roughly where a category tops out stops a team committing to a target that nothing in the data supports. This works because it needs only the order of magnitude, which survives every distortion above. A target set at four times the category range is wrong regardless of how the benchmark was built.
Communication. An external reference is genuinely valuable when explaining a result to a board, an investor or a new stakeholder who has no context for what normal looks like. Used honestly, with the caveats attached, it makes a conversation possible that would otherwise be an argument about vibes.
What to do this week
- Delete the gap-to-average line from your reporting. It is the most misread number in a CRO deck, because it looks like available upside and is mostly composition.
- Write down your own definitions. Session, conversion, exclusions, device scope. One paragraph. Without it no external comparison you make is valid.
- Lead the weekly report with your own trend. Movement you cannot explain is the real alarm; distance from an average is not.
- Use the benchmark for bounding only. Set the plausible range at the start of planning, then put it away.
- Check the vintage of any figure you quote. If the window is not stated, say so when you quote it, or do not quote it.
Where the underlying difficulty is that order, traffic and cost data never sit together long enough to build a defensible baseline of your own, that is a data problem rather than a benchmarking one. Omniconvert Commander (formerly Nexus) is an AI for eCommerce growth engine that unifies commerce data, prioritises experiments by True Profit, and generates campaigns and creative you approve before they go live. Bain and Company's retention work with Fred Reichheld makes the adjacent case that the compounding advantages are the ones worth measuring properly, and those are precisely the ones a category average cannot see.
Once you know which distortion is biting, the next question is what to fix, and that is an audit rather than a comparison. Our neighbours at croaudit.marketing cover how to audit it step by step, and Omniconvert Explore is the CRO platform for validating the change afterwards, with A/B and multivariate testing, on-site surveys, heatmaps and session recordings.
Teams often quote a published industry average in a planning meeting without asking what the source counted as a session or a conversion. The target set from it is unreachable or too easy depending on a definition nobody checked.
AliveCor used Omniconvert to run a structured A/B testing programme and achieved +21% conversion rate, +5% revenue per visitor, and 94% statistical relevance across their experiments. [Omniconvert, AliveCor case study] Checking the definition before setting a target is the first step of any testing programme.
FAQ: industry averages and benchmark data
Why do industry averages vary so much between sources?
Because each source aggregates a different set of stores and defines the metric differently, and both differences are usually undisclosed. One dataset may be weighted toward large paid-traffic retailers and another toward small email-led brands, which produces genuinely different averages from equally honest measurement. Before comparing two published figures, check what counted as a session and what counted as a conversion in each, because that single definitional difference explains more of the gap than anything else.
What is a mix effect, and why does it matter for benchmarks?
A mix effect is when an aggregate number moves because the composition of the group changed, not because any member of the group changed. A category average can fall while every single store in it improves, simply because more low-converting stores joined the sample. This is why a benchmark moving year on year tells you almost nothing about whether stores are getting better, and why your own position relative to it can shift without you doing anything at all.
Should I use the median instead of the average?
Yes, wherever it is offered, because conversion data is skewed rather than symmetric. A small number of very high performers pull an arithmetic mean upward, so the average describes a position that most stores in the group are below. The median is the figure that answers the question people think they are asking, which is what a typical store in this category looks like. If a published benchmark does not say which it used, assume the mean and treat it as optimistic.
Are industry averages useful for anything at all?
Three things, and they are genuinely useful. Bounding tells you the plausible range so you do not set an impossible target. Triage tells you whether a number is unusual enough to justify investigation time. Communication gives a shared external reference when you need to explain to somebody outside the team why a figure is or is not alarming. What an average cannot do is tell you what to change, because it contains no information about your store.
How far from the average should I be before I worry?
Distance from an average is the wrong trigger, because the spread inside any category is wide enough that being well below it can be entirely normal for your traffic mix. A better trigger is movement in your own trend that you cannot explain. A store sitting far below a category average with stable, profitable unit economics has no problem. A store sitting comfortably on the average while its own rate declines month over month has a real one.
What is the most common mistake people make with benchmark data?
Treating the gap between their number and the average as a quantity of available improvement. It is not a gap that can be closed, because much of it is explained by traffic mix, price point and category composition rather than by anything on the site. Teams that plan against that gap set targets they cannot reach, and then read a normal result as a failure, which is a reliable way to abandon a programme that was working.
The bottom line
An industry average is a correct answer to a question almost nobody is asking. It describes a population accurately and an individual store badly, and the four distortions explain how badly: mix moves it when nothing moved, shape puts it above most of the field, definition makes two honest sources disagree, and time hides which world it was measured in. Knowing that is not a reason to throw benchmarks away. It is a reason to give them the three jobs they can actually do, bounding a target, triaging whether to investigate, and explaining a number to somebody who needs context, and to stop giving them the job they cannot do, which is telling you what to change. The single most valuable thing you can do this week is delete the gap-to-average line from your reporting. It is the number most likely to be read as available upside, and it is mostly a description of who else is in the sample.