In line with both baselines. Layout tests win 11% in E-commerce, against 15% for layout tests generally and 10% for E-commerce generally. Neither the category nor the vertical is moving this much.
The strongest change type we can measure in E-commerce is CTA copy at 20% on 50+ tests.
| E-commerce | all industries | |
|---|---|---|
| Tests analysed | 100+ | 250+ |
| Beat control | 11% | 15% |
| No measurable difference | 72% | — |
| Lost to control | 17% | — |
| Median lift when it won | +24.8% | — |
Median traffic per variant was 4668 visitors. Most ran on homepages (58), product pages (55), category pages (27).
| change type | win rate in E-commerce | tests |
|---|---|---|
| CTA copy | 20% | 50+ |
| Layout (this page) | 11% | 100+ |
| Styling | 11% | 500+ |
| Split URL | 10% | 250+ |
| Social proof | 9% | 50+ |
| Price framing | 8% | 50+ |
| Hero image | 7% | 50+ |
| Headline | 7% | 100+ |
| Body copy | 6% | 100+ |
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 | 69% |
| On-page click / engagement | 13% |
| Add to cart | 10% |
| Product or content page reached | 5% |
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 E-commerce benchmarks; rates across all industries are on the layout benchmark.
Blocks move, reorder or change position. No copy is rewritten and no new content is added. If you moved a section <em>and</em> rewrote it, that is two variables and the result will not tell you which one worked.
The first two navigation menu items were swapped in order in both the mobile and desktop nav bars.
The page moved the reviews widget section and the footer to fixed later positions in the layout.
The simple product image and gallery page was replaced with a redesigned layout that added an announcement bar, headline copy, star ratings and reviews, bullet benefits, a guarantee badge, and a sticky bottom CTA bar.
The mobile navigation menu items had text only before, and then thumbnail images were added to them.
The page replaced the Products title with a trust banner and simpler category headline, removed the benefits list and bundle call to action, and added star ratings under each product.
A new sticky add-to-cart bar was added at the bottom of the product page on mobile, and the product price was shown inside the button.
The price element was moved above the title on product cards, and the spacing and position between the price and button were adjusted.
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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