A/B testing agency for winners that hold up.

What an A/B testing agency does

An A/B test shows two versions of a page to comparable groups of visitors and measures which one does better on one specific number. That sounds simple. Done properly, it is closer to a clinical trial than to a marketing experiment. An A/B testing agency agrees the number of visitors, the run time, the success measure and the visitor groups before launch, so the result is evidence, not a screenshot from a testing tool on day three.

Our A/B testing services run on a shared list of test ideas, scored on expected impact and effort. Each test gets a one-page plan your team signs off. It ends either with the winner built into your website or with the loser written up and stored with all past tests, so your team learns from every test, including the ones that lose.

Why companies choose LASEO for A/B testing

Honest numbers. On average, roughly 1 in 8 A/B tests wins, and our reported win rate sits at 12 to 18%, depending on how mature the testing program is. An agency that reports a 70 to 90% win rate is checking results too early, slicing the data until something looks significant, or both. We would rather show you one real winner than five false ones.

And because LASEO is a 100% SEO agency, every test is set up to keep your rankings safe. Browser-based testing tools can hide test content from Google or create test pages that compete with the original. Where possible we build test versions into your website itself and tell Google which page counts. A/B testing is one part of our conversion rate optimization, next to landing page optimization.

A/B testing for visitors from AI search

Visitors who come from ChatGPT, Perplexity or Google's AI Overviews often land on a deep page with one specific question in mind. They behave differently from someone who clicked a Google result or an ad. That is why we fix the visitor groups we will compare in the test plan before launch, with traffic source as one of them, so a test cannot win on average and lose with the visitors who matter most.

At the same time, AI SEO makes sure the winning version stays easy for AI tools to read and quote. A test that lifts conversions but hides your answer from AI search is not a real winner, and we check for that before anything gets built in.

A/B testing, explained without the dashboard theatre

When you commission an A/B test, you are buying evidence that one version does better than another on one specific number, for one specific group of visitors, with a known margin of error. Not a screenshot from a testing tool saying version B is winning by 12% on day three. Done properly, it is closer to a clinical trial than to a marketing experiment.

How many visitors vs. how long

Sample size is the number of visitors per version you need to detect a given improvement at 95% confidence and 80% power. Duration is how many days your traffic needs to deliver that number. You need both. A 2-week minimum exists to cover a full business cycle, but if you have not reached the number of visitors after 2 weeks, you keep running. Stopping at exactly 14 days, whatever the visitor count, is one of the most common mistakes.

Tests built into your site vs. added in the browser

Server-side tests build the test version before the page reaches the visitor's browser. No flicker, no SEO risk, and Google sees a stable page. Browser-based tools like VWO change the page after it has loaded, which makes the old version flash up first and hides the test content from search engines. For anything visible at the top of the page, or anything that matters for your rankings, building the test into your site wins.

Classic A/B tests vs. self-adjusting tests

A classic A/B test splits traffic evenly and gives you a clear win or loss, with a known chance of a false alarm. That is the default, and the right tool when you need a firm answer. A so-called multi-armed bandit test gradually sends more traffic to whichever version is doing better. That suits ongoing optimisation where showing a weaker version costs real money. We use it for rotating headlines and product recommendations, not for high-stakes redesign tests.

Results per visitor group

A flat overall result often hides a strong positive effect in one group (mobile, returning visitors, paid traffic) cancelled out by a negative effect in another. We fix the groups we will compare in the test plan before launch, so those results confirm something real instead of being dug up afterwards until something looks significant. Common groups: device, traffic source, new vs. returning, country, customer type.

Win rate as an honesty check

On average, roughly 1 in 8 A/B tests wins (around 13%). An agency that reports much more than that is a warning sign: checking results too early, slicing the data until something looks significant, counting partial wins as wins, or testing tiny changes that can never lose. Our reported win rate sits at 12-18%, depending on how mature the client's testing program is. We see that as a feature, not a bug.

Multivariate testing reality

Multivariate testing (testing several elements at the same time) needs roughly N times the visitors of an A/B test for N combinations. Most ecommerce sites simply do not have that traffic. We default to running A/B tests one after another, and only run a multivariate test on a homepage with millions of visitors a month, when the design team needs to know how elements affect each other.