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A/B
Professional A/B Testing

STOP GUESSING.
START
PROVING.

You change elements on your pages hoping it will work better? That is gambling, not optimization. A/B testing pits two versions of a page against real traffic and gives you a clear answer: version A or version B. No opinions, no internal debates, no intuition — statistically reliable data that proves what works. Each winning test improves your conversions permanently and cumulatively. It is compound interest applied to your business.

Page TestsCTA TestsForm TestsStatistical Analysis
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A/B TESTING star SPLIT TESTING star MULTIVARIATE star STATISTICS star HYPOTHESES star OPTIMIZATION star DATA star CONVERSION star
The problem

WHY YOUR OPTIMIZATIONS FAIL

Without A/B testing, you are navigating blindly. Here are the mistakes you are probably making.

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You make changes based on gut feeling

You change the color of a button because your designer thinks it looks nicer. You rewrite a headline because your director prefers the new version. You redo an entire page without knowing if the old one was really the problem. Every untested change is a gamble — and in most cases, you lose without even knowing it.

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Your conversions are stagnating

You have done a redesign, launched new campaigns, changed your offer, but your conversion rate remains desperately stable. You do not know which elements hold your visitors back or which ones push them to act. Without testing, every change is a shot in the dark. Your conversions plateau because you optimize without method.

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Internal debates paralyze your decisions

Marketing wants a short headline, sales wants more details, the CEO prefers a different image. Meetings drag on endlessly, opinions clash, and ultimately it is the opinion of the highest-ranking person that wins — not what is best for your conversions. A/B testing puts an end to these debates: it is your visitors who vote.

Our expertise

RIGOROUS A/B TESTING,
NOT GUESSWORK.

Our approach to A/B testing is scientific. Each test is designed with a clear hypothesis, a sufficient sample size and rigorous statistical analysis.

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Data-driven hypotheses

Before testing, we analyze your heatmaps, session recordings and conversion funnels. Each test answers a precise hypothesis: if we change X, then Y will improve because Z. No random tests — every experiment is strategically planned to maximize impact.

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95% statistical rigor

We never declare a winner too early. Each test reaches a 95% confidence threshold before any conclusion. We calculate the required sample size upfront and wait until the data is sufficient. Your decisions rest on solid proof, not random fluctuations.

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Prioritized testing roadmap

We use the ICE framework (Impact, Confidence, Ease) to prioritize your tests. High-impact, low-effort tests go first. You get a clear roadmap with upcoming tests planned, hypotheses to validate and expected gains for each experiment.

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Multi-element tests

Beyond simple A/B, we run multivariate tests that test multiple combinations of elements simultaneously. Headline A + Image B + CTA C against other combinations. This advanced approach identifies the optimal combination more quickly than a series of sequential tests.

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Tests by segment and device

A change can improve conversions on desktop but degrade them on mobile. We segment results by device, traffic source and audience type. You get a granular view that allows you to optimize the experience for each visitor segment.

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Reporting and documented learnings

Each test is documented with the hypothesis, results, analysis and takeaways. You build a knowledge base of what works for your audience. These learnings accelerate future tests and prevent repeating the same mistakes.

Our process

FROM DIAGNOSIS TO RESULTS IN 4 STEPS

A scientific method applied to your conversions. Each step is structured to maximize the impact of every test.

01
Analysis & Hypotheses

We analyze your data: heatmaps, user journeys, conversion funnels. We identify friction points and formulate testable hypotheses ranked by potential impact.

Week 1
02
Variant Design

We create the alternative versions to test: new headline, new CTA, new layout, new form. Each variant is designed to answer the formulated hypothesis.

Week 2
03
Launch & Measure

We launch the test with a 50/50 traffic split. We monitor results in real time and wait to reach statistical significance before any conclusion.

Week 2-4
04
Deploy & Iterate

The winning version is deployed to 100% of traffic. We document the learnings and launch the next test. It is a continuous improvement cycle that advances your conversions month after month.

Ongoing
Our results

THE DATA DOES NOT LIE

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Tests completed

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Winning test rate

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Average gain per test

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Statistical confidence

E-commerce

+34%

Nebulae Motors

Test of the main headline and CTA on the flagship product page. The variant with a benefit-oriented headline outperformed the original by 34% in conversions.

SaaS B2B

+58%

Zenith Crypto

Registration form test: the version with 3 fields instead of 7 increased sign-ups by 58% without degrading lead quality.

Services

x2.1

Arcane Paris

Series of 8 tests on the booking funnel. Conversions multiplied by 2.1 thanks to the cumulative optimization of each step in the journey.

Frequently Asked Questions

YOUR QUESTIONS ABOUT A/B TESTING

How much traffic do you need to run an A/B test? expand_more
To obtain statistically reliable results, you generally need between 1,000 and 5,000 visitors per variant, depending on your baseline conversion rate and the size of the expected improvement. With 500 visitors per month, you can still test by extending the test duration. We calculate the required sample size before each test.
What is the average duration of an A/B test? expand_more
A test lasts on average 2 to 4 weeks, enough time to collect sufficient data for a reliable conclusion. The duration depends on your traffic volume and the performance difference between variants. We never cut a test short to avoid false positives.
What can you test with A/B testing? expand_more
Practically everything: headlines, subheadings, images, button colors, call-to-action text, form length, element layout, listed prices, testimonials, social proof, entire pages. The key is to prioritize tests by potential impact and test only one variable at a time.
How do you know if a test is conclusive? expand_more
We use a statistical confidence threshold of 95%, which means there is less than a 5% chance the result is due to randomness. We also wait until each variant has received a minimum number of conversions. No hasty decisions — only solid results count.
What tools do you use for A/B testing? expand_more
We use professional platforms like Google Optimize, VWO and AB Tasty depending on your needs and budget. These tools allow you to create variants without touching code, distribute traffic randomly and measure results with complete statistical rigor.
Will A/B testing slow down my site? expand_more
No. Modern A/B testing tools load asynchronously and do not perceptibly impact your website speed. We configure tests to minimize any impact on performance. The lightweight script added is far outweighed by the conversion rate gains.

READY TO TEST?

Request your free CRO audit. We identify the first tests to launch to boost your conversions quickly.

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