In this second article, discover everything you need to know to australia whatsapp number data 5 million succeed in your first A/B tests . We will share with you the best practices and the mistakes to avoid. At the end of the article, we will zoom in on A/B testing in emailing, which is the best gateway to get started.
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Summary
Best practices for effective A/B testing
Mistakes to avoid in A/B testing
Focus on A/B testing in emailing
Best practices for effective A/B testing
1 - Carry out an initial audit
Doing an audit…this may seem off topic, but it is not. A/B testing is used to incrementally improve the performance of certain actions or marketing materials. What are the actions or materials that you would benefit from optimizing or that need to be optimized? The audit is used to answer this question.
We therefore recommend that you first assess the current state of your marketing performance. This step back will help you identify priority AB tests to conduct.
Ask yourself the right questions:
What are the engagement rates of your email campaigns (open rate, click rate, unsubscribe rate, etc.)?
How do users behave on your website? Analyze bounce rates, average session duration, conversion rates, and user journeys to identify strengths and pain points.
How are your landing pages performing in terms of conversions? Identify which pages are converting well and which are underperforming.
What content generates the most interactions on your social networks? Evaluate likes, comments, shares and engagement rate for each type of content.
Are there patterns in customer feedback that can indicate areas for improvement? User feedback is a goldmine of information for detecting what works well and what doesn’t.
How do your conversion rates vary across audience segments?
This overview will allow you to identify priority areas for A/B testing, those which present the greatest potential for optimization and impact.
2 - Set a goal
Each A/B test pursues a goal: improving the performance of an action or communication medium. To measure performance and analyze the results of the test, you must choose a specific and measurable objective , i.e. associated with one or more KPIs.
For example, if you want to improve customer engagement with your email campaigns, the KPIs might be: open rate, click rate, and response rate.
We recommend that you use “SMART” goals , i.e. specific, measurable, achievable, relevant and time-bound (SMART).
The choice of objectives comes from the initial audit . The insights obtained during this initial phase have allowed you to identify priority areas of intervention and the objectives likely to have the greatest impact on your performance.
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Let's say that the audit allowed you to identify an abnormally low completion rate for your form. Your goal could be to double the completion rate (= your KPI). You will conduct several ab or multivariate tests to identify changes that have a positive impact on the completion rate.
3 - Choose the elements to test
A classic A/B test consists of comparing variations of the same element . Which element to vary? That's the question. The selection of the element(s) is based on common sense, a certain amount of 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.
Let's imagine a version A that highlights customer reviews and a version B that highlights the benefits of your product. In the latter case, we will talk about “multivariate” tests in the sense that the test concerns a complex element (your home page) which is in reality a set of elements.
Choosing the elements to test is much simpler in the case of a classic A/B test . If you want to improve the opening rate of your email campaigns, you don't have to look far: it's the subject of the email that you'll have to test.
Determining the element to test is more or less obvious depending on the case. It can be a content element, a title, an image, a video, a call to action, a page structure, an offer… The art of A/B testing relies heavily on the choice of elements to test… but also on the content of the variations.
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4 - Create the variants
You’ve chosen the elements to test. The next step is to create the variations that will be subject to A/B testing . This is where creativity meets strategy. Each variation should be designed with the goal in mind. What changes to my text, my button, my email subject line could have a positive impact on the results?
Here again, you need marketing skills, intuition, common sense, but also creativity, skills in content creation, design, to imagine and edit relevant variants. It often takes several brains to produce variants that make sense. A/B testing is often a team effort .
In a traditional A/B test, it is very important that the difference between the two versions is limited to the element being tested to ensure that the test results can be accurately attributed to that specific variable. This is not the case for multivariate tests, which by definition are based on a diverse group of elements such as a web page or the body of an email.
We also recommend that you choose variations that really vary . To return to the example of the email subject, for example, choose a benefit-oriented subject and a subject formulated in the form of an intriguing question.
For example :
Boost your productivity with our AI features!
First name , are you ready to change the way you work?
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An example of two variations that are too close (so don't do them):
Discover our new product
Our new product is available
5 - Set up the test
Once you have imagined the test, the element to be tested, the variations, the next step is the test setup.
First, you need to choose a marketing tool that offers A/B testing features that are advanced enough that you are not limited in choosing what to test and what to include in the variations.
Most marketing software publishers are aware of the importance of A/B testing and have developed dedicated features. This is the case, of course, of Actito .
Once the tool is selected, you must configure the test : the target audience, the sampling, the duration of the test, the success criteria, the conditions for generalizing the winning version in the case of a semi-automated A/B test, the production in the tool of the two versions imagined on paper, etc.
The technical details associated with setting up an A/B test will of course depend on the nature of the test and the element being tested.
The test duration should be long enough to collect meaningful data, but not so long that external factors influence the results. There is a middle ground to be found. Typically, test periods last a few days.
6 - Analyze the results
Once the A/B test is complete, it's time to analyze the collected data to determine which version performed best and why.
Tools that integrate A/B testing features often offer analysis tools and reports that make the work much easier.
You should not stop at superficial analysis, that is, comparing the indicators for each version. You should also take the time to understand why one version worked better than another.
A/B testing is not only used to improve the performance of a campaign or a one-off action. An A/B test is also and above all used to improve knowledge of your customers, their expectations, the “drivers” of their engagement in order to structurally improve the performance of your current and future actions.
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Mistakes to avoid in A/B testing
Not having a goal
What are you looking to improve? This is the first question to ask yourself.
Lacking a clear goal in A/B testing is like sailing without a compass. Knowing what you want to improve is the first step before launching a test.
The objective, as we have seen, must be specific, measurable, achievable, relevant and time-defined (SMART).
Not having a clear hypothesis
A hypothesis is a more or less informed guess about what could be changed to achieve your goal. Lacking a well-defined hypothesis is tantamount to making changes at random.
A clear hypothesis must be able to be justified by a reason . You must be able to clearly articulate why you think changing a certain element or variation will have an impact on performance.
For example, if the goal is to increase the open rate of your emails, a hypothesis might be: “Including the recipient's first name in the email subject line will increase the open rate.”
Failure to clearly define success criteria
It is important to define your success criteria for A/B testing precisely. Success criteria are directly linked to the objective . They are defined as a degree of increase or decrease in the KPI you have chosen.
A criterion can be formulated in this way: “The A/B test is successful if I manage to obtain +10 points of opening rate on my email campaign”.
Have an insignificant volume of data
The reliability of the results of an A/B test depends on the amount of data collected .
Insufficient data can lead to erroneous conclusions: if the sample is too small, the variation in results between version A and version B may be due to chance and not to the effectiveness of the variant…
The sample size should be large enough to avoid producing insignificant results.
Starting with overly complex tests
It's a good idea to start with simple ab 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 modifying the header, design and content of a web page at the same time, start by modifying only the header. In other words, avoid starting to set up multivariate tests right away!
Conducting too many tests at once
Running multiple A/B tests in parallel on the same audience or channel can lead to what is called test interference: tests influence each other, which confuses the analysis of the results.
This type of situation makes it difficult to determine the exact cause of performance variations and can distort conclusions.
To avoid this, plan your tests to be mutually exclusive or segment your audience so that each group only gets one test at a time.
Change variants during testing
Once an A/B test is launched, it is important not to change the variations until the test is complete and data has been collected.
Changing variants during testing can introduce bias and invalidate the results . You will not be able to draw reliable conclusions.
If you discover a problem or think of a potential improvement during testing, note it for future testing.
Not allowing enough time for the test
One of the common pitfalls of A/B testing is to conclude the test too early, before having collected a sufficiently significant volume of data.
The risk is to jump to 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, etc. The duration of the test should be set in advance, based on the size of the sample required and the expected traffic patterns.
An A/B test is only of interest if the results are statistically significant and representative.
Neglecting post-test analysis
Once the A/B test is complete, it can be tempting to rush to implement the winning variation without conducting a thorough analysis of the results.
However, jumping straight to action without understanding the why behind the numbers is to miss the whole point of A/B testing.
Post-test analysis is not just about identifying which variant performed best, the goal is also to understand why the winning variant was more effective than the other.
Focus on A/B testing in emailing
We have repeatedly highlighted the example of A/B testing in emailing . And for good reason, it is by far the most commonly used test by marketing teams. The object test is THE great classic.
This is due to two main reasons:
Emailing remains, whatever people say, the main channel of communication between companies and their customers.
It is much easier to do 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 subject line of the email. It is possible to test other elements.
Our Actito software allows you to A/B test:
The content of your emails to identify which editorial approaches, which layouts, which CTAs, which products resonate best with your audience.
The sender name . Actito allows you to test different sender name combinations to help you find the one that inspires the most trust in your contacts and generates the best open rates.
The preheader . This is the short text that appears to the right of the subject in your recipients’ inbox. The preheader complements the subject. Its optimization can have a significant impact on the opening rate.
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 those during which your recipients are most receptive to your communications.
If you are just starting out, we recommend that you start by focusing on A/B testing your email campaigns. This will allow you to:
To improve the performance of what is certainly your main marketing channel: email.
To gain experience and skills in A/B testing, which will subsequently allow you to consider other types of more elaborate AB tests on web pages or others.
What you need to remember
Being able to conduct A/B tests is an essential skill to acquire as a marketing professional. It is one of the best tools to identify what performs best with your customers and continuously improve your marketing actions and materials.
Setting up A/B tests requires a lot of rigor and a solid methodology, as we have seen in this article. Identifying the ab tests to be conducted, defining the objective, choosing the element to be tested, creating the variants, deploying the test, analyzing the results: none of these steps should be neglected.
We recommend that you start by A/B testing your email campaigns. This is the best way to learn this technique. If you need advice, if you are looking for a software solution to carry out your A/B tests, do not hesitate to contact us to discuss it.
