In line with both baselines. Headline tests win 6% in SaaS / Software, against 8% for headline tests generally and 9% for SaaS / Software generally. Neither the category nor the vertical is moving this much.
The strongest change type we can measure in SaaS / Software is Styling at 13% on 100+ tests.
| SaaS / Software | all industries | |
|---|---|---|
| Tests analysed | 100+ | 500+ |
| Beat control | 6% | 8% |
| No measurable difference | 85% | — |
| Lost to control | 9% | — |
| Median lift when it won | +47.1% | — |
Median traffic per variant was 828 visitors. Most ran on homepages (166), landing pages (26), lead capture pages (16).
| change type | win rate in SaaS / Software | tests |
|---|---|---|
| Styling | 13% | 100+ |
| Split URL | 12% | 100+ |
| Body copy | 9% | 33 tests |
| Headline (this page) | 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 |
|---|---|
| Signup / registration | 54% |
| Purchase / order complete | 13% |
| Booking / appointment | 10% |
| On-page click / engagement | 9% |
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 headline benchmark.
The main heading is rewritten. Structure, styling and every other element stay put.
The hero headline and subhead copy were changed, and the hero image of a person with an app screenshot was replaced with a phone and laptop mockup collage.
The left-aligned headline and subhead were replaced with a centered mind-mapping headline, eyebrow text, and a new illustrated hero graphic with converging lines and avatars.
The plain text headline and static screenshot hero were replaced with a translated headline inside an illustrated mind-map graphic, and the CTA button was restyled as a pill shape.
The headline changed from a vague title to a short call-to-action inviting users to join the brand.
The centered generic headline was replaced with a left-aligned benefit-driven headline, a bullet list, category pills and focus-area buttons, and a product screenshot on the right.
The heading and subheading changed from a general introduction to a free medical evidence evaluation for veteran disability rating accuracy, with a prompt to answer the questions below.
The headline and subhead changed from saving on fleet costs to fewer parked vehicles and more mileage return, and the hero image and dashboard graphics also changed.
These are individual tests, not rules. Each ran on one site, with one audience, against one page we are not showing you. They are picked to be illustrative rather than sampled at random, and a change that won here can lose on your page for reasons none of this captures. Read them as prompts for what to test, not as findings to copy.
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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