A/B Testing - Zephyra Studio

What A/B testing is and who it's for

A/B testing is a controlled method for comparing two versions of a page, element or checkout step. Visitors are automatically and randomly split into group A and group B, and software records the difference in a key metric: conversion rate, items added to cart, forms started, or order value. Once enough data has accumulated, the better-performing version becomes the new standard.

It's for sites with regular traffic already (typically several dozen visits a day or more), since without enough of a sample even the best idea can't be confirmed statistically. Typical clients are online stores wanting a higher completed-purchase rate, service businesses wanting more submitted enquiries, and SaaS products wanting more signups. If traffic isn't there yet, the earlier stage is measuring and improving UX, not testing.

Advantages compared to alternatives

The classic alternative is deciding based on opinion, intuition or generic best-practice rules. The problem is that best practice knows nothing about your specific visitors: a button that works great in one industry can lower conversions in another. A/B testing replaces assumption with proof from your own site and your own audience.

Another alternative is a big redesign bundling ten changes at once. If conversion goes up, you don't know what caused it; if it drops, you don't know what broke it. A/B testing changes one variable per test, so the result is legible and applicable to other pages. A third alternative, multivariate testing, tests several elements at once, but needs significantly more traffic, so in practice it's introduced only once basic A/B testing is already working.

How we work on A/B testing

The process starts with defining the target metric and a hypothesis - for example, whether adding a phone number above a form increases submitted enquiries. Priority is then set by potential impact, not by what's easiest to change. Implementation runs through a testing tool or directly in the site's code where custom logic is needed. Tests run until they reach statistical significance, and the result is documented along with a conclusion applied to other pages.

A reliable result takes time, typically 2-3 weeks per test depending on traffic and the size of the difference the test needs to detect. Stefan runs the whole process directly with the client, and every measured result comes back to you in a short, understandable report.

Approximate price

The price depends on the number of tests, the tool used and how complex the site changes are, so we don't publish it in advance - you get it through the calculator on our pricing page or a conversation once we've seen your site and traffic.