How to implement an effective A/B Testing strategy?

1 - Conduct a preliminary audit

Conducting an audit may seem off-topic, but it's not. A/B testing is used to incrementally improve the performance of certain actions or marketing materials. Which actions or materials would benefit from optimization, or need it? The audit is designed to answer this question.

Therefore, we recommend initially evaluating the current state of your marketing performance. This overview will help you identify the priority A/B tests to conduct.

Ask yourself the right questions:

• What are the engagement rates of your email campaigns (open rates, click-through rates, unsubscribe rates...)?

• How do users behave on your website? Analyze bounce rates, average session durations, conversion rates, and user journeys to identify strengths and irritants.

• What is the performance of your landing pages in terms of conversions? Identify the pages that convert well and those that underperform.

• Which content generates the most interactions on your social networks? Evaluate likes, comments, shares, and engagement rates for each type of content.

• Are there patterns in customer feedback that can indicate areas for improvement? User comments are a goldmine of information for detecting what works well and what does not.

• How do your conversion rates vary from one audience segment to another?

This review will allow you to identify the priority areas for A/B testing, those that offer the greatest potential for optimization and impact.

Each A/B test aims at a goal: improving the performance of an action or a communication medium. To measure performance and analyze test results, you need to choose a specific and measurable goal, meaning one associated with one or several KPIs.

For instance, if you wish to enhance customer engagement with your email campaigns, the KPIs might include the open rate, click-through rate, and responsiveness rate.

We recommend using "SMART" objectives, meaning objectives that are specific, measurable, achievable, relevant, and time-bound (SMART).

The choice of objectives stems from the preliminary audit. The insights gained during this initial phase have enabled you to identify priority areas for action and the objectives likely to have the greatest impact on your performance.

Let's say the audit helped you identify an abnormally low completion rate for your form. Your goal could be to double the completion rate (= your KPI). You would conduct several A/B or multivariate tests to identify changes that positively impact the completion rate.

A classic A/B test involves comparing variations of the same element. What element to vary? That's the question. The selection of the element(s) relies on common sense, some marketing expertise, and sometimes a dose of intuition.

For example, if your preliminary analysis reveals that your homepage has a high bounce rate, you might consider testing different versions of your header... or two completely different versions of the homepage.

Imagine a version A that highlights customer reviews and a version B that highlights the benefits of your product. In this latter case, we're talking about "multivariate tests" in the sense that the test focuses on a complex element (your homepage), which is actually a set of elements.

Choosing the elements to test is much simpler in the case of a classic A/B test. If you aim to improve the open rate of your email campaigns, there's no need to look far: it's the subject line of the email that needs to be tested.

Determining the element to test is more or less obvious depending on the case. It could be a piece of content, a title, an image, a video, a call to action, a page structure, an offer... The art of A/B testing relies heavily on choosing the elements to test... but also on the content of the variations.

Now that you've chosen the elements to test, the next step is to create the variants that will undergo A/B testing. Here, creativity meets strategy. Each variant should be designed with the goal in mind. What changes to my text, button, or email subject could positively impact the results?

Again, this requires marketing skills, intuition, common sense, but also creativity, content creation skills, design skills to imagine and edit relevant variants. It often takes several brains to produce meaningful variants. A/B testing is often a team effort.

In the context of a classic A/B test, it's very important that the difference between the two versions is limited to the tested element to ensure that the test results can be accurately attributed to that specific variable. This note does not apply to multivariate tests which, by definition, involve a diverse group of elements like a webpage or the body of an email.

Moreover, we recommend you choose variants that are truly different. Taking the email subject example, choose, for example, a benefit-oriented subject and a subject formulated as an intriguing question.

For example:

• Boost your productivity with our AI features!

• First Name, are you ready to change the way you work?

An example of two variants too similar (thus, to avoid):

• Discover our new product

• Our new product is available

After designing the test, identifying the element to test, and creating the variations, the next step is the implementation of the test.

Firstly, you need to select a marketing tool that offers sufficiently advanced A/B testing features so that you're not limited in your choice of elements to test and in the content of the variations.

Most marketing software providers are aware of the importance of A/B testing and have developed dedicated features. This is the case, of course, with Actito.

Once you've selected the tool, you need to set up the test: the target audience, sampling, duration of the test, success criteria, conditions for rolling out the winning version in the case of a semi-automated A/B test, creating the two versions envisioned on paper in the tool, etc.

The technical details associated with setting up an A/B test, of course, depend on the nature of the test and the element being tested.

The duration of the test should be long enough to collect meaningful data, without being so long as to allow external factors to influence the results. There's a balance to be found. Generally, testing periods last a few days.

Once the A/B test is completed, it's time to analyze the collected data to determine which version performed better and why.

Tools that integrate A/B testing features often offer analysis tools and reports that greatly simplify this work.

It's important not to limit yourself to a superficial analysis, that is, merely comparing indicators for each version. You also need to take the time to understand why one version was more successful than another.

A/B testing is not just about improving the performance of a campaign or a one-time action. An A/B test is also and especially about enhancing your understanding of your customers, their expectations, the drivers of their engagement in order to structurally improve the performance of your current and future actions.

Not having a goal

What are you looking to improve? This is the first question to ask yourself.

The goal, as we have seen, must be Specific, Measurable, Achievable, Relevant, and Time-bound (SMART).

Not having a clear hypothesis

A hypothesis is a more or less informed assumption about what could be changed to achieve your goal. Lacking a well-defined hypothesis is like making changes at random.

A clear hypothesis must be justifiable by a reason. You should be able to clearly articulate why you think changing a certain element or variation will impact performance.

For example, if the goal is to increase the open rate of your emails, a hypothesis might be: "Incorporating the recipient's first name in the email subject will increase the open rate."

Not defining success criteria clearly

It's crucial to precisely define your success criteria for the A/B test. Success criteria are directly linked to the goal. They are defined as a degree of increase or decrease in the KPI you have chosen.

A criterion can be stated in this way: "The A/B test is successful if I achieve a +10 point increase in the open rate of my email campaign."

Having an insignificant volume of data

The reliability of the results from an A/B test depends on the amount of data collected.

Insufficient data volume can lead to incorrect conclusions: if the sample size is too small, the variation in results between version A and version B may be due to chance and not the effectiveness of the variant…

The sample size must be large enough to avoid inconclusive results.

Starting with too complex tests

It's wise to start with simple A/B tests, changing only one element at a time. This will help you isolate and understand the specific impact of each change on performance.

For example, rather than changing the header, design, and content of a web page all at the same time, start by changing just the header. In other words, avoid diving into multivariate tests right away!

Conducting too many tests at the same time

Running several A/B tests in parallel on the same audience or through the same channel can lead to what is called test interference: the tests influence each other, which muddles the analysis of the results.

This situation makes it difficult to pinpoint the exact cause of performance variations and can skew conclusions.

To avoid this, plan your tests so that they are mutually exclusive or segment your audience so that each group is only subjected to one test at a time.

Changing the variants during the test

Once an A/B test is launched, it's important not to alter the variants until the test is complete and data has been collected.

Changing variants mid-test can introduce bias and invalidate the results. You won't be able to draw reliable conclusions.

If you identify a problem or think of a potential improvement during the test, make a note of it for future tests.

Not allowing enough time for the test

A common pitfall in A/B testing is concluding the test too early, before collecting a sufficiently significant volume of data.

The risk is drawing hasty conclusions based on temporary variations or anomalies rather than on stable and reliable trends.

The duration of the test varies depending on the nature of the test, the content or medium tested, the day of the week, the time of year... The test duration should be set in advance, based on the necessary sample size and expected traffic patterns.

An A/B test is only useful if the results are statistically significant and representative.

Neglecting post-test analysis

Once the A/B test is completed, it might be tempting to rush to implement the winning variant without conducting a thorough analysis of the results.

However, moving straight to action without understanding the why behind the numbers means missing out on the philosophy of A/B testing.

Post-test analysis is not just about identifying which variant performed better; the goal is also to understand why the winning variant was more effective than the other.

We've frequently highlighted A/B testing in emailing as an example. And for good reason, it's by far the most commonly used test by marketing teams. Testing the subject line is THE classic.

This is due to two main reasons:

• Emailing remains, despite what some may say, the primary communication channel between companies and their clients.

• It's much simpler to perform an A/B test on an email subject than on a landing page... Email tests are technically easy to manage.

A/B testing an email campaign is not limited to testing the email's subject. It's possible to test other elements.

Our software Actito enables you to A/B test:

• The content of your emails to identify which editorial approaches, layouts, CTAs, and products resonate best with your audience.

• The sender's name. Actito allows you to test different combinations of sender's names to help you find the one that instills the most trust in your contacts and generates the best open rates.

• The pre-header. This is the short text that appears next to the subject line in your recipients' inbox. The pre-header complements the subject. Optimizing it can have a significant impact on the open rate.

• The time and day of sending. The timing of an email campaign can have a significant impact on performance. Actito gives you the ability to test different sending times to identify when your recipients are most receptive to your communications.

If you're just starting, we advise you to focus on A/B testing your email campaigns first. This will allow you to:

• Improve the performance of what is likely your main marketing channel: email.

• Gain experience and skills in A/B testing, which will enable you to consider other types of more elaborate A/B tests on web pages or others later on.

Being able to conduct A/B tests is an essential skill for any marketing professional. It's one of the best tools for identifying what performs best with your customers and continuously improving your marketing actions and materials.

Implementing A/B tests requires a lot of rigor and a solid methodology, as we've seen in this article. Identifying which A/B tests to conduct, defining the goal, choosing the element to test, creating variants, deploying the test, analyzing the results: none of these steps should be overlooked.

We advise you to start by A/B testing your email campaigns. It's the best entry point for learning this technique. If you need advice or are looking for a software solution to carry out your A/B tests, don't hesitate to contact us to discuss it.