A/B Testing Agency
Boost your performance with A/B testing
We help you improve your conversion rate by A/B testing visual elements, copy and user journeys.
They trust us
Data working for your conversions
We leverage your data and user behaviour to turn every experiment into concrete decisions and measurable gains.
Competitors
Eskimoz Method
Experimentation at the heart of CRO
CRO is moving towards a continuous, data-driven approach powered by intelligent engines. A/B tests no longer serve solely to improve conversion rates. They build a detailed understanding of users, their behaviour and the levers that drive engagement. Combined with analytics tools and AI, experimentation gives your digital growth a lasting foundation.
Eskimoz App
A platform to bring together data from your channels and measure the impact of tests on the user journey.
Performance
Analyse the real impact of your A/B tests on conversions, revenue and KPIs.
Monitoring
Track your rankings, competitors and opportunities with daily monitoring enriched by SERP data.
Omnichannel
Centralise your marketing data to analyse the overall impact of your tests across every channel.
LLM Ranking
Bring together SEO, SEA and marketing to identify high-performing levers and optimise your cross-channel investment.
Our A/B testing methodology
A structured approach to continuously optimise your conversion performance.
An AI-powered approach to CRO
We use artificial intelligence to accelerate your tests, improve the reliability of your analyses and deploy optimisations at scale.
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Our AI agents analyse your analytics data, user behaviour and performance to automatically generate relevant test hypotheses. They speed up your iteration cycles by identifying high-impact opportunities. You test more, with a greater ability to scale your CRO strategy.
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Artificial intelligence continuously analyses your user journeys to detect friction, blockages and conversion losses. It cross-references behavioural data with performance metrics to surface concrete recommendations. You get a dynamic CRO audit that is constantly updated, feeding your testing pipeline.
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We use AI to produce variants of content, messaging and visual assets for testing. Headlines, CTAs and visuals: every element becomes testable at scale. This accelerates your experimentation, enriches your A/B tests and maximises your chances of identifying the highest-performing combinations.
Our A/B testing expertise
Conversion audit
DATAAnalyse user behaviour and friction points across your conversion funnels
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We leverage data from GA4, heatmaps, session recordings and funnels to pinpoint breakdown zones. The analysis combines bounce rates, scroll depth, interactions and drop-off rates by step. We segment by device, source and user type to identify precise patterns. Friction points are then qualified by their business impact. This approach enables us to build reliable hypotheses that feed directly into your tests.
A/B testing
DATARun reliable A/B tests with rigorous technical setup and full tracking control
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Every test is built on a structured experimentation plan: primary KPI definition, sample size estimation and minimum duration. We configure testing tools with controlled traffic splits and server-side tracking where required. Variations are deployed free from rendering or loading bias. We monitor results continuously to ensure stability before any decision is made.
Multivariate testing (MVT)
DATATest multiple variables simultaneously to identify the best-performing combinations
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We use multivariate testing to measure the combined impact of several elements: headlines, visuals, CTAs and page structures. This approach requires sufficient traffic volume and rigorous modelling. Our teams structure variants to avoid biased interactions and interpret results using performance matrices. The goal is to identify precisely which combinations perform and to industrialise optimisation on high-traffic pages.
CRO
DATABuild a data-driven CRO strategy based on prioritisation and continuous experimentation
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We build a CRO roadmap based on ICE scoring (Impact, Confidence, Effort). Each action is prioritised according to its potential gain and complexity. Tests are then integrated into a continuous cycle, fed by data and user feedback. We avoid siloed optimisations by building a holistic approach aligned with your objectives and growth ambitions.
User journey optimisation
DATAOptimise every step of your user journeys to reduce friction and improve the overall experience
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We map user journeys using analytics data and real behavioural patterns. Funnels are analysed step by step to identify where conversions are lost. We also work on information hierarchy, micro-interactions and page readability. Every optimisation is tested to validate its real impact.
Statistical analysis of tests
DATAValidate your A/B tests using robust statistical methods and reliable analysis
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We apply advanced statistical methods to validate results: significance tests, p-values, confidence intervals and statistical power calculations. We control for sampling bias, seasonality effects and traffic anomalies. Results are analysed over complete periods to ensure their stability. This helps our teams avoid false positives and safeguard every optimisation decision.
Personalisation
DATAActivate personalised experiences based on your user data and real behaviour
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We leverage your first-party data (CRM, analytics, browsing history) to create actionable segments. Experiences are adapted according to source, device, history or user intent. We test different personalisation approaches to measure their real impact. Scenarios are deployed dynamically via your marketing and testing tools to improve conversion performance.
Performance analysis
DATAManage your performance with granular data analysis and KPI tracking
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We connect your analytics, testing and CRM tools to centralise data in unified dashboards. Performance is analysed by segment, channel, device and user type. We track all key indicators including conversion rate, average order value, revenue per visitor and engagement. This detailed view helps us quickly identify optimisation levers and continuously refine your strategy.
Our A/B testing experts, by your side
Our CRO consultants, data analysts and experimentation specialists structure your A/B tests, analyse user behaviour and leverage your data to turn every iteration into an actionable learning and a lasting growth lever.
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Benoit Perrotin
SEO Director
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Candela Sánchez
Performance Consultant
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Guillaume Houdouin
AI Product Builder
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Florian Painset
International SEO Director
They trust our expertise
“Eskimoz enabled us to develop an effective SEO strategy despite limited resources. The results were quick to materialise and had a direct impact on our growth.”
Pierre Monnier
Founder
+ 60 %
Increase in SQLs
+ 149 %
Increase in organic impressions
Explore, understand and anticipate the evolution of A/B testing
Access expert resources to structure your tests and refine your experimentation.
Become essential
FAQ
What is A/B testing?
A/B testing is a method that involves showing a sample of visitors a web page with different elements. Whether it is a button colour, a page headline or an image, it makes it possible to determine, from solid data, which elements are most effective at maximising conversions on your website.
A/B testing helps improve the user experience for visitors, boost the conversion rate on your site and reduce the bounce rate.
How do you run A/B tests?
To set up an A/B testing strategy, you first need to identify which elements on your site need improvement. This means analysing your conversion funnel carefully.
Hypotheses are developed using CRO tools. From there, you move into the test design phase. Before launching your A/B test, you need to segment your traffic. The test can then be run and the results interpreted to drive optimisations.
As an A/B testing agency, Eskimoz supports you throughout the process of optimising your site following the necessary A/B tests.
How much traffic do you need to run reliable A/B tests?
An A/B test requires sufficient traffic to reach reliable statistical significance. In practice, you typically need several thousand sessions per variation. We calculate the required sample size upfront based on your current conversion rate, the expected effect and the desired confidence level. Without this volume, results can be biased and unreliable.
How long should an A/B test run?
A test should run for at least one full activity cycle, typically between two and four weeks. This allows for variations in behaviour across different days and avoids seasonality bias. We set a minimum duration based on traffic and sample size, then extend if necessary until statistical results have stabilised.
How do you know if an A/B test is truly conclusive?
A test is conclusive when results reach a sufficient level of statistical significance (often 95%) and remain stable over time. We analyse p-values, confidence intervals and the consistency of performance by segment. Without these validations, a winning variation may be the result of chance, leading to poor decisions.
Can you run A/B tests without a dedicated tool?
Technically yes, but it is strongly inadvisable. Specialist tools manage traffic splitting, tracking, data collection and statistical analysis. Without them, tests are often biased due to display issues, incomplete tracking, poor traffic distribution and similar problems. A reliable tool is essential to ensure results you can act on.