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Boost your results with A/B Testing

Optimizing your visitor journey with A/B Testing can increase conversion by 400%.

Opt for the best performing variants by offering different visitor experiences with BEYABLE A/B Testing.

Request a demo

PARTNERS

PRICING

Sign In

En

Boost your results with A/B Testing

Optimizing your visitor journey with A/B Testing can increase conversion by 400%.

Opt for the best performing variants by offering different visitor experiences with BEYABLE A/B Testing.

Request a demo

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What is A/B testing?

A/B Testing, also known as split testing, comparison testing or ABC testing, is an experimental marketing method used to evaluate the effectiveness of two different variants (usually A and B) of an element, such as a web page, a user interface, an e-mail or an advertisement. The objective of A/B Testing is to determine which variant generates better results in terms of performance, such as conversion rate, time spent on a page, click-through rate, e-commerce cart size.

In an A/B Test, two groups of users or audiences are formed: one group is exposed to variant A (control group) while the other is exposed to variant B (test group). The two groups are randomly selected to minimize bias. Performance data is then collected and compared to determine which variant had a significant impact.

A/B testing allows marketers to make data-driven decisions and optimize their elements based on the results. It can be used to test multiple aspects, such as visual design, content, call-to-actions, pricing, promotional offers. The goal is to identify the variants that produce the best performance and thus improve the user experience and business results on the website.

How to do A/B Testing in 7 steps?

Step 1: Identify the objective

Clearly define the objective of your A/B testing, for example to increase the e-commerce conversion rate, to decrease cart abandonment, to generate qualified leads, to reduce the bounce rate, to do cross-selling and upselling.

Step 2: Define the variants of your A/B test

Decide on the variants you want to test, for example, change the color or font of a button, change the copy of your homepage, change the visuals according to the visitor's intention.

Step 3: Select the audience

In A/B testing, choosing the audience sample is essential, for example, you can launch your A/B test only on mobile visitors of your e-commerce site or on 100% of your traffic in order to obtain large scale results. Be careful not to stray too far from the customer journey you usually offer. An excellent A/B test is a test conducted on samples of visitors.

Step 4: Designing your A/B Test

Create the versions of the content you want to A/B Test and divide them into test and control groups in a random way to get better results and take advantage of A/B Testing.

Step 5: Run the A/B Test

To complete your A/B Testing project, run the test by measuring the results of both test and control groups over a set period of time.

Step 6: Analyze the results

Collect data from your A/B Testing campaigns and analyze the results to determine if the variant being tested has a significant impact on your original A/B Test objective.

Step 7: Apply the conclusions

If the A/B Test was successful, review the findings and implement the winning variant of your A/B Test.

Help: You are already familiar with the subject or you already have a service provider in charge of this subject but you are not fully satisfied with the results, the support or the prices of your solution? Discuss the subject with one of our A/B Testing Experts for a few minutes.

Consult an A/B Testing Expert

Why Opt for BEYABLE A/B Test ?

A solution in advance of its time

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AI personalisation - much more accurate testing

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Our dedicated team of experts will support you

Turnkey migration : From Google Optimize to BEYABLE
Onboarding, training and installation very easy and fast.
Customized support according to your needs and resources: 100% delegated to BEYABLE OR Mixed OR 100% autonomous.
An expert dedicated to your projects: A single contact available every day, at any time, for ultra-personalized exchanges.

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A/B test of a product recommendation in relation to the visitor's behavior

Leverage your traffic in depth

Experiment in a relevant and adaptive way. Be convinced of the results obtained thanks to the performance of the features deployed during your tests.
Our personalisation is based on the visitor's intention and behaviour, be confident during your tests!
Benefit from a customized support, all along your project. Ask our experts for advice at any time.

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What types of sites can do A/B testing?

Lead Generation sites

"Leads" or commercial contacts are information consciously left by visitors with the aim of being contacted again for marketing or commercial purposes. These leads enter a marketing or sales cycle in order to be converted into customers. A/B testing can be used to test different parameters in order to optimize the visitor's experience and encourage these visitors to leave their contact information.

Media sites

Editorial A/B testing is often used to test the success of different categories of content with their target audience and draw relevant conclusions with quantified evidence.

E-Commerce sites

A/B testing is commonly used in the E-Commerce field to improve the effectiveness of a website or an online sales application. The elements subject to A/B Testing can be for example: buttons, call to actions, fonts, colors, page variants.

Questions / Answers

What elements should be taken into account when interpreting the results of A/B testing?

To interpret the results of an A/B Test, you can rely on these 6 elements:

Statistical significance: The first thing to check is whether the results of your A/B Tests are statistically significant. This means that the differences observed between the two variants are not due to chance, but are the result of a real effect of the variable tested. To verify this, one can use statistical tests such as the Student's t-test or the chi-square test.
Sample size: The larger the sample size of your A/B Test, the higher the probability of obtaining significant results. It is important to verify that the sample size is sufficient to obtain reliable results.
Key Performance Indicators (KPIs): KPIs are the metrics that have been chosen to measure the impact of the variable being tested on performance. It is important to analyze the results according to these KPIs to determine which variant generated the best performance.
Consistency with assumptions: It is important to check whether the results are consistent with the original assumptions. If the results do not match the assumptions, it may be necessary to re-examine the data or revise the assumptions to understand why the results were different.
Duration of test: It is important to ensure that the test has been conducted for a sufficiently long period of time to obtain meaningful results. If the test was too short, the results may not be reliable. You must therefore continue your A/B test.
Market knowledge: Finally, it is important to take into account the knowledge of the market and the competition when interpreting the results. The results can be influenced by external factors, such as market trends or competitor actions, which must be taken into account when interpreting the results.

In summary, interpreting the results of an A/B Test requires careful analysis of the data collected, keeping in mind the specific objectives of the test and the external factors that may influence the results.

If you need in-depth advice on the subject of A/B testing, consult one of our experts for free.

What are the most common marketing elements for A/B testing?

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