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Do styling A/B tests work on homepages?

About the same as anywhere else. Styling tests win 10% of the time on homepages, against 11% across all page types. This page neither helps nor hurts the change type.

Layout tests carry a higher published rate on homepages — 20% on 100+ tests — though see the note below before reading that as a recommendation.

The numbers

Won 10% No measurable change 77% Lost 13%
homepagesall page types
Tests analysed500+1,000+
Beat control10%11%
No measurable difference77%
Lost to control13%
Median lift when it won +20.9%

Median traffic per variant was 3850 visitors.

What else is worth testing on homepages

change typewin rate on homepagestests
Layout 20%100+
Price framing 16%49 tests
Hero image 15%50+
Split URL 13%100+
CTA copy 11%100+
Social proof 10%50+
Styling (this page) 10%500+
Headline 8%250+
Body copy 8%100+

Only change types with at least 30 tests on homepages appear here.

What tests on homepages measured

A win rate only means as much as the thing being counted, so this is what these tests were actually optimising for.

measured outcomeshare of tests
Purchase / order complete43%
Signup / registration16%
On-page click / engagement14%
Lead / form submission8%

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.

Why this table is not a ranking

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.

What counts as a styling test

Visual treatment shifts — spacing, weight, borders, colour, shadows — with the same content in the same order. Move the content and it is a layout test. Rewrite it and it is a copy test.

Control and variant wireframe for a styling A/B test
Control on the left, variant on the right. For how this differs from adjacent categories, see the styling benchmark.

Styling tests that won on homepages

  • The slideshow banner slides and related overlay links and images were hidden, and on mobile the hero image, heading, and link were resized and repositioned without changing any text.

    E-commerce+20.9%

  • The medium buttons changed from their previous background color to orange-red, while the wording stayed the same.

    SaaS / Software+22%

  • The cart drawer product recommendations section was hidden.

    E-commerce+17.9%

  • The page hid the badges on the product media.

    +17.9%

Styling tests that did not

  • The old element was hidden and the new element was forced to show, while the wording stayed the same.

    SaaS / SoftwareNo significant difference

  • The request info button changed color while its text stayed the same.

    EducationNo significant difference

  • The old cart footer and free-shipping bar were hidden, and the optimized cart footer was shown instead without wording changes.

    E-commerceNo significant difference

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.

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.