What is A/B Testing and why use It?

A/B Testing is a method used in marketing to evaluate two versions of the same element in order to determine which is more effective. The term "A/B Testing" could be translated into French as "comparative test."

A/B Testing therefore involves creating two versions:

• Version A, the baseline or reference version.

• Version B, which is a more or less modified version of A. This version can have one or several variations compared to version A.

In marketing, A/B Testing can be used to test numerous elements: web pages, email subjects, Google Ads advertisements, calls to action... The modifications between the two versions can pertain to the design, placement, or even the content of the elements.

The goal of A/B tests is to identify changes that generate increments in performance or customer engagement. Thus, conducting A/B Testing is part of an optimization process for marketing materials and actions.

To give more substance to our discussion, let's take a classic example of A/B Testing: testing email subjects.

Imagine you want to improve the open rate of your email campaigns. You decide to conduct a subject test for your new campaign. You create two versions:

• Subject A, with a direct formulation of your offer: "Get 20% off our new products!".

• Subject B adopts a more original, suggestive, intriguing approach: "Firstname, you're about to miss something important".

You send some emails with subject A and others with subject B. You analyze the open rate according to the subject used, allowing you to determine which is the most effective.

You have conducted what is known as A/B Testing.

Tip: we recommend that you analyze the click-through rate rather than the open rate, as today's rule changes at Apple and Google make it increasingly difficult to reliably measure email open rates.

We also recommend that you customise as much as possible. We can't stress this enough: customisation is the key to better performance! So test, customise and analyse!

Actito allows you to test different elements of an email, not just the email subjects. For example, you can test the sender, the pre-header, but also the content of the email.

As we mentioned earlier, A/B Testing is widely used in marketing and not only for emails. You can "A/B test":

• Landing pages (or other web pages), varying the layout, images, calls to action, etc., from one version to another.

• Advertising banners, varying the colors, messages, images...

• Call-to-action (CTA) buttons, conducting tests on the button text, color, its position on the page...

• Pricing strategies (discounts, free trials, etc.) to identify which generates the best performance.

• Distribution channels.

• Audience segmentations...

At Actito, we recommend A/B Testing to all our clients. Here are the main reasons or benefits of conducting A/B Tests in marketing.

1 - Measure the effectiveness of changes

One of the primary advantages of A/B Testing is its ability to accurately measure the effectiveness of changes made to a specific marketing element.

Whether it's a change in the design of a web page, the wording of a call to action, or even the color of a button, A/B Testing allows for evaluating the direct impact of these modifications on user behavior, performance, results.

Marketing is a creative field. Marketing professionals design strategies, actions, messages based on a number of assumptions about the needs, expectations, and receptivity level of customers.

When deploying a marketing action, whatever it may be, it's important to ensure the validity of these assumptions. A/B Testing acts as a truth test. It allows for challenging marketing ideas and studying how they translate in terms of actual performance.

Whether the hypothesis concerns an advertising message, an email subject, a promotional offer, or the structure of a landing page, A/B Testing has the advantage of providing clear and objective answers. It helps to avoid unnecessary spending on unprofitable ideas, but it also serves to make continuous adjustments that can have a significant impact on results.

By testing different versions of a web page, a form, a customer journey, or a user interface, you ultimately identify what works best with your audience, generating the highest engagement rate.

A/B Testing isn't limited to design elements; it can also pertain to the usability of a service, the speed of website navigation, and, more broadly, the effectiveness with which your users complete their actions.

For example, you can use A/B Testing to compare two different layouts of a contact form to determine which one is completed more efficiently by users. Based on the test results, you'll be able to identify changes that make the form more intuitive and increase the completion rate.

Improving conversion rates is the mantra of every self-respecting marketer. Whether it concerns sales, newsletter sign-ups, or downloads of a white paper, the conversion rate is often the key performance indicator.

A/B Testing, for all the reasons mentioned above, is a very powerful tool for detecting messages, layouts, designs that have the most impact on customers and on taking action.

A/B Testing allows for the optimization of costs and resources since the purpose of comparison tests is to identify the most effective marketing actions and materials in terms of user experience and conversion rates. Efficiency gains translate directly into resource savings. You become capable of doing better with the same amount of resources or doing more with unchanged resources.

1 - Classic A/B Testing (or Split Testing)

Classic A/B Testing is the most basic form and the most widespread type of A/B Testing. It's the form of A/B Testing that matches the simple definition we provided at the beginning of the article: it involves directly comparing two versions of a marketing element to see which one performs better.

A/B Testing is based on a random sampling of the audience: each group receives a different version (A or B), which then allows for clear analysis of the results.

Again, the elements tested can range from simple modifications of text or color to more substantial changes in design or structure.

An A/B Test focuses on a single element but can contain several variations of that element. For example, you can test both the color and placement of a button at the same time.

Tip: if you feel that you don't have a representative sample for your A/B test, we recommend that you carry it out on your entire target audience. So 50% will receive version A and the remaining 50% version B. You can then analyse the results between the 2 versions.

The A/B/C Test is an enriched variation of the classic A/B Test. As the name suggests, it involves testing not just one variant of A, but two: B and C.

This method can be interesting if you have several improvement ideas and you want to understand which one has the most impact on your objective without limiting your experimentation to a single alternative.

When deciding to deploy an A/B/C test, it's important to ensure that each version is presented to a sufficiently large population sample to guarantee the reliability of the results.

The example we are going to give will suffice to show the deep identity of A/B tests and A/B/C tests. The method is exactly the same, only the number of variants changes.

Here's the example: you want to optimize the call-to-action on your product page. You choose to test 3 variants of the same CTA. What changes from one variant to another is the message wording:

• Version A: "Learn more".

• Version B: "Buy now and save 10%".

• Version C: "Discover our product".

Which wording will achieve the best click-through and conversion rates? The A/B/C test will help you find the answer.

In the A/B/C test example we just discussed, only one element changes from one variant to another: the CTA.

Multivariate Testing (MVT) involves testing not just one element, but several elements simultaneously (or, equivalently, a complex element composed of multiple components).

You conduct a multivariate test, for example, when you decide to test several very different versions of a landing page. Each version offers a different layout, a different headline, different buttons, etc.

The logic of multivariate testing is the same as that of classic A/B testing. The difference lies only in the complexity of the tested element: a button or an email subject in the case of a classic test, a web page, a form, an email, or even a product sheet in the case of a multivariate test.

The A/A test is much less common than the A/B test. As its name suggests, it involves testing two absolutely identical versions. The A/A test is used to check the reliability of testing tools and processes in data collection, to ensure that the performances are roughly identical from one version to another. If there are substantial performance differences, this might reveal a technical problem or a methodology issue with the test.

A/A tests can also be used to determine the sample size needed to achieve significant results for future A/B Testing, A/B/C, or multivariate tests. The sample size is reliable when there is a very low variance between the two versions in the A/A test.

A/B testing is a valuable tool for continuously improving your marketing actions and practices. It should be part of every marketer's toolkit, and indeed, its use has spread significantly to become a common technique.

This method is not limited to testing superficial variables; it allows for a deep dive into your customers' preferences, validates marketing initiatives, and continuously adapts the user experience to enhance engagement and conversion.

Classic A/B Testing (A/B or A/B/C) is by far the most widespread. Multivariate tests can be useful in certain cases, such as when testing complex elements, web pages, forms, or other components of your websites. However, no matter the type of A/B tests used, the method remains the same.

The 4 keys to successful A/B Testing implementation are as follows:

• Use marketing software that offers A/B testing tools, as is the case with our Actito platform.

• Suggest relevant variations, sufficiently different from each other.

• Distribute the test to a sufficiently large sample of customers so that the results are significant.

• Analyze the results and identify over time what works best with your own audience.