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Do split URL A/B tests work?

They win 11% of the time — and they carry the worst measurement trap in testing. A split URL test routes traffic to a genuinely separate page rather than altering the original. Win rate is 11%, but the median winner moves +23.9%, well above average, because you are testing a whole page rather than one element.

Big swings, modest hit rate, and a setup mistake that manufactures fake wins. Read the warning below before you trust a split URL result.

The numbers

Tests analysed1,000+
Beat control11%
No measurable difference71%
Lost to control18%
Median lift when it won +23.9%

Typical winning range: +13.3% to +44.9% for the middle half of winners. Median traffic per variant was 1001 visitors. Most ran on landing pages (318), product pages (244), homepages (242).

What counts as a split URL test

Traffic is split between two addresses. The variant is a different page at its own URL, not a modified version of the control.

Control and variant wireframe for a split URL A/B test
Control on the left, variant on the right. Only the changed element is highlighted.

Adjacent categories, and how often they win: layout (19%), hero image (17%), styling (15%).

Split URL tests by page type

page typewin ratetests
landing pages 8%250+
product pages 9%100+
homepages 14%100+
lead capture pages 12%100+
content pages 10%50+
pricing pages 14%36 tests

Only page types with at least 30 split URL tests appear here.

Why there are no examples on this page

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.

Why the rate looks like this

**The trap.** If your conversion goal is a pageview of the variant's own URL, every visitor sent to the variant converts the instant they arrive, while the control converts almost never. That produces a variant at ~100% against a control near 0%, and a lift in the thousands of percent. It is not a win; it is the goal measuring the redirect. We found and excluded these when building this benchmark — the worst example recorded a lift of over 75,000%. If a split URL test is showing you an implausibly large win, check whether the goal fires on the variant page itself before you ship anything.

How this compares

change typewin ratetests
Layout 19%351
Hero image 17%160
Form 16%81
CTA colour 16%32
Styling 15%1414
Price framing 13%117
CTA copy 12%315
Body copy 11%337
Split URL (this page) 11%1164
Headline 10%918
Social proof 9%196

FAQ

When should I use a split URL test instead of an on-page test?

When the variant is too different to build by modifying the original — a new template, a different flow. For a single element, an on-page test is easier to read.

Why is the win rate lower than the size of the wins?

Whole-page variants are high-variance. More of them miss, and the ones that land move more, because more changed.

How do I know my split URL test is measured correctly?

The conversion goal must sit past the variant, not on it. If control and variant cannot both reach the goal page, the test is measuring the redirect.

Who ran these tests

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.

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.