Better than the SaaS / Software average, though not because the industry helps. Split URL tests win 12% there against 9% for all change types in SaaS / Software. Across all industries split URL tests run at 10%, so the category is carrying this, not the vertical.
The strongest change type we can measure in SaaS / Software is Styling at 13% on 100+ tests.
| SaaS / Software | all industries | |
|---|---|---|
| Tests analysed | 100+ | 1,000+ |
| Beat control | 12% | 10% |
| No measurable difference | 68% | — |
| Lost to control | 20% | — |
| Median lift when it won | +45% | — |
Median traffic per variant was 695 visitors. Most ran on homepages (54), lead capture pages (34), pricing pages (31).
| change type | win rate in SaaS / Software | tests |
|---|---|---|
| Styling | 13% | 100+ |
| Split URL (this page) | 12% | 100+ |
| Body copy | 9% | 33 tests |
| Headline | 6% | 100+ |
| CTA copy | 2% | 50+ |
A win rate only means as much as the thing being counted, so this is what these tests were actually optimising for.
| measured outcome | share of tests |
|---|---|
| Purchase / order complete | 25% |
| Reached a later funnel step | 19% |
| Signup / registration | 18% |
| Scroll or time on page | 12% |
Outcomes below 5% of the set are not listed, so these do not sum to 100. How the outcome was measured moves the win rate a lot — see the methodology.
These rates are not like-for-like. How often a test wins depends partly on what it chose to measure: a goal that records a soft signal — a click, a scroll — clears the bar more often than one that records a purchase. Across this data, tests measured against a proxy goal win 13%, against 9% for tests measured against a business outcome.
Categories differ a lot in that mix — proxy goals account for anywhere from 2% to 41% of a category's tests.
Hold the metric fixed and the spread narrows sharply. Comparing only tests measured against a business outcome, the gap between the highest and lowest category falls from 9 points to 6 — at which point the intervals overlap and the categories are not statistically distinguishable.
So read the table as a description of what happened, not as a ranking of what works. If you want the comparison to mean something, compare categories that were measured the same way. The methodology page has the full breakdown.
The industry overview is at SaaS / Software benchmarks; rates across all industries are on the split URL benchmark.
Traffic is split between two addresses. The variant is a different page at its own URL, not a modified version of the control.
A split URL variant is a whole separate page, so every description of what changed reduces to "traffic goes to a different page". The detail lives on the variant page itself, which belongs to the customer.
This is every Mida account that ran a readable test — in-house marketers, founders, product teams, and agencies working on client sites. Nothing here is filtered by who ran the experiment or how experienced they are.
Low win rates are normal in experimentation, including at the top end. Microsoft's experimentation team, reporting on its own platform, found that only about one third of ideas improve the metric they were designed to improve — and that roughly another third actively hurt it. That is a dedicated experimentation organisation with research, prioritisation and review behind every test.
A mixed population like this one runs below that. The gap is roughly what disciplined practice buys you: ideas grounded in research rather than opinion, one variable at a time, and tests built so the result can actually be read.
So treat these as a general base rate, not a target. If you would rather not close that gap the slow way, find a CRO agency in the United States, the United Kingdom, the Netherlands or Australia — or browse every region.
A win is a variant that beat its control on that test's primary goal with a statistically significant result. Tests that never got enough traffic to say anything either way are excluded. The methodology has the full detail, including what these numbers cannot tell you.
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