Shopify A/B Tests

Every detail in your store counts, and small changes can translate into a big increase in results. A/B testing will help you find out which variant of the site actually attracts your customers and increases sales.

Together, we will test multiple solutions to find that perfect fit for your store. Our experience on the Shopify Plus platform gives you the confidence that any change you make will have a real impact on your business.

Our A/B testing service is much more than a simple analysis - it's a true partnership in optimizing your store. We go to great lengths to look at every detail, even the smallest, from the color scheme and shape of buttons, to engaging headlines, to precisely placed content and graphic elements. We try to discover which solutions appeal most to your customers and what small changes can translate into a significant increase in interaction and audience loyalty.  

We base our activities on the use of state-of-the-art tools and proven methodologies, which allows us to achieve accurate and reliable results. Thanks to systematic testing of various options, we are able to identify those elements that most affect purchase behavior. By partnering with us, you gain full insight into what works and what is worth changing, which is key to increasing conversions. Partnering with us means you'll not only deliver a better experience for your customers, but also implement accurate, data-driven decisions that will drive long-term growth for your business.

What kind of
A/B testing
do we do?

We offer a wide range of A/B tests tailored to your needs.

Test A/B

We compare the original version of the site with one modified variant to see which element brings better conversion results.

A/B/n test

We simultaneously test more than two variants of the site to determine the most effective solution.

Multivariate Testing

We simultaneously test several variables on a page to find the optimal combination of elements to increase user engagement.

Segmentation Testing

We match different versions of the site to specific audiences, such as new users or regular customers, to increase the conversion rate in each group.

Multi-Armed Bandit Testing

We dynamically allocate traffic to the best-performing variants to get the optimal site configuration faster.

Split Page Path Testing

We compare the different paths users take through the site to identify the path that best increases engagement and conversions.

Take a look our process

Get in touch
01
Data review and hypotheses

We analyse GA4, heatmaps, session recordings and your funnel to find where customers drop off, then turn each finding into a testable hypothesis.

02
Prioritisation

Every idea is scored by expected impact, confidence and effort, so the first tests target the pages and elements that can move revenue the most.

03
Test design and build

We design the variants and build them in Liquid, Hydrogen or your headless front-end, with tracking set up for the metrics that matter: conversion rate, AOV and revenue per visitor.

04
Launch and monitoring

Tests run until they reach statistical significance. We monitor them for bugs, traffic splits and outside factors such as promotions that could skew the result.

05
Analysis and rollout

You get a clear readout of each test. Winning variants are rolled out to the whole store and the learnings feed the next round of hypotheses.

Frequently asked questions
01

How long does it take to run an A/B test?

The duration of the test depends on your store's traffic - it usually takes 2 to 4 weeks to collect sufficient data.

02

Do A/B tests guarantee an increase in sales?

While it is impossible to give a 100% guarantee, our thorough analysis and recommendations usually translate into a significant increase in conversions.

03

What tools do you use?

We use modern and proven a/b testing tools (Intelligems, postHog etc.), which enables us to accurately measure the effectiveness of changes.

04

How to define the objectives of A/B testing?

We start from your business goals and pick one primary metric per test, such as conversion rate, average order value or revenue per visitor. Each test gets a clear hypothesis, a target audience and the minimum effect worth detecting, so the result leads to a decision.