A/B testing (also known as "AB Testing," "split testing," or "bucket testing") is a technique for comparing two versions of a web page or a mobile application.
A/B testing is a tool used during digital marketing. It measures the effect of a change in version of a content on the accomplishment of a goal (click, validation, filling out a form and more. In the field of digital marketing, A/B testing is used to test emails, web pages, landing pages, forms, advertising visuals, and other things.
Large websites have teams dedicated to A/B testing and multivariate testing implementation. The A/B test can compare two initial options equally or one challenger option against the previously used option.
In practice, A/B testing allows you to test the effectiveness of two landing pages and improve the conversion rates of your SEO or SEA campaigns, it shows you the effectiveness of your emails.
A/ B test is a testing tool that involves testing two variants of a web page or an email, for example, to determine which version performs the best in order to improve the results of your marketing devices.
Why Should I Do A/B Testing?
A/B testing is a quick and easy way to improve the effectiveness of your email campaigns.
Here is a partial list of the advantages of A/B testing:
It boosts an email campaign's open rates, click rates, and conversion rates,
It optimizes user experience,
It boosts a campaign's return on investment (ROI),
It improves a campaign's results,
Most importantly, it increases your sales!
What Is The Purpose of A/B Testing In Email Marketing?
A/B testing is essential for improving your email marketing strategy, whether you are addressing a B2B or B2C target audience. And this is true regardless of your industry: gaming, information technology, e-commerce, business services, health care, or design.
This is what allows you to give your results a meaning: why did this email work but not the other one? What factors caused your audience to respond so enthusiastically to one of your emails when you normally have very little engagement?
Relying on your intuition to guide you is not a winning strategy here. Even the most experienced marketers must test the effectiveness of their campaigns in order to progress and convert as many leads as possible!
As a result, we conduct email A/B testing for several reasons:
to determine the best practices for writing and sending e-mails to a specific target audience
to get a higher the return on investment of your email marketing campaigns
to discover what appeals to potential customers
to make objective decisions for the business's long-term viability
And there's no need to wait until you have 1000 subscribers to try an A/B test! You can use it whenever you want it, it’s not important how many subscribers you have.
Why Should I Use A/B Testing?
Confirm intuitions
If the statistical data support your predictions, A/B testing has the benefit of turning your intuitions into confirmed best practices. Like we have said before, we can not just make predictions here, so we need proof. And with A/B testing you get proof. This method constantly guides your decisions in a logic of constant improvement and continuous questioning, in addition to verifying some of your choices.
Continuous improvement
When discussing A/B testing, one goal that always comes up is "optimizing existing campaigns." This is not an objective, but rather a means to an end. In real sense, any A/B test is intended to optimize elements in an emailing campaign that are as diverse as they are.
Return on investment
Getting new subscribers is a costly web marketing challenge. A/B testing is useful for making this investment profitable because it allows you to optimize campaigns based on your marketing objectives and challenges. When we discuss issues, for example, it is to enhance subscriber retention by increasing campaign engagement. Because acquiring new subscribers takes time and money, we should keep the ones we have first.
Get everyone to agree
It happens that a colleague imposes his ideas or vision, "Hum! "I don't like the color of the button; please change it as soon as possible," in short, tastes and colors... These situations are all too common, and the A/B test is an excellent way to avoid them and ensure that everyone agrees.
Great Ways To Create An Efficient Email A/B Testing
Determine the variable to be tested.
You must first identify the variable you want to test before you can begin setting up your test. To accomplish this, examine the previous performance of your emails addressed to the base you intend to solicit in order to define and prioritize optimization opportunities.
If your open rate is low, for example, it may be worthwhile to test two subject line variations to see which generates more email opens.
For example, if your click rate is low, you should instead run a test on the email content (image or text) or the CTA (title or placement) to see which element can be improved.
Determine your objective.
Defining your goal is a critical step in the A/B testing process. This enables you to identify a key indicator on which to focus in order to assess the impact of the change on performance. On the other hand, by defining your goal prior to the email test, you ensure that you create a variant of your email that responds optimally to your initial hypothesis.
Make two versions of the email.
An A/B test, like we have said before, has two versions: the original version (A) and its variant (B). In terms of email, your original version could be an email that you have already sent to a contact database or an email template that you are used to using and for which you have past performance statistics.
Once you've decided on version A, you can create an alternate version in which you'll make a change that is likely to affect the targeted performance indicator.
For example, if you want to test the impact of a change on the click rate, you could change the body text of the email by writing a shorter text or using a different argument, or you could change the call to action (CTA) by positioning it above the fold line of the email or by creating a different hook.
Choose and segment your target audience
You have more control over audience targeting with email A/B testing (some say email testing) than you would with web page testing. To conduct a conclusive test, send your two versions to two recipient segments of equivalent size
If your contact list contains a large number of prospects or customers (at least several thousand), it is best to segment a small portion of it for your test. The best-performing version can then be distributed to the rest of the contact database.
However, in order for an email A/B test to be truly relevant, you must test your two variants on at least 1000 contacts. This segmentation is so important because if you fall below this threshold, you risk not collecting enough conclusive data at the end of the test!
Determine the best test duration.
“What is the optimal duration for my test to be conclusive?” is a common question when starting out in A/B testing.
In terms of mailing, the most basic method is to examine the performance of previous emails. Determine when your emails begin to perform well after they have been sent.
For example, if your emails generate almost no more opens or clicks after 24 hours, this could indicate that the optimal duration of your test is 24 hours. You would not receive any additional relevant information after this time period.
Once this last parameter is defined, you can configure and run your test in your software.
Examine the results of your tests
When your test is finished, you will be able to analyze the results using your analytics. Focus on your main indicator to measure the impact of your variant on performance in relation to the goal you set at the start.
You must, however, pay close attention to another statistic: the confidence indicator. This is a metric provided by all A/B testing tools that assists you in determining the viability of your test. This indicator is a percentage that indicates your chances of obtaining the same result for your future emailing campaigns (under strictly similar conditions in terms of observations).
For example, if your email sees a 10% increase in click-through rate with a 98% trust rating, it means your variant has a 98% chance of outperforming the original. This does not mean that you have a 98% chance that your gain will be 10% for the next sends; it could be as low as 5%.
This is not to say that you should not incorporate this change definitively into your emails because it has a high likelihood of generating outperformance in the future.
Now that you've finished your first test, you can start thinking about the next one. There are additional elements to test in order to achieve the best possible level of performance and return on investment for your email campaigns.
A/B Testing Best Practices
To effectively use A/B testing, you must adhere to a few golden rules; otherwise, your efforts will be futile.
Take a representative sample
Conduct your tests on a large enough sample of people. This holds true regardless of how many email addresses you have. Begin with a percentage: for example, run each test on 10% of your contact list. As a result, the conversion rate will be significant, allowing you to extrapolate with a relatively small margin of error.
Determine the various issues
Before taking action, always figure out what's wrong with sending a newsletter. To do so, examine your key performance indicators (KPIs) such as deliverability rate (the percentage of emails that do not end up in spam), open rate, and click rate on the CTA.
It's pointless to have a great template and personalized texts if no one reads your emails. Similarly, what is the point of caring for your newsletter if your audience reads it primarily on smartphones and your template lacks a mobile responsive design? Prioritize your testing based on the issues that need to be resolved.
Use a tried-and-true method.
Each element of your newsletter must be tested in six steps:
Make your primary content.
Change the chosen item for a different version.
Define the test parameters, such as audience targeting, percentage of your contact base, duration, and so on.
Gather the information.
Select the best option.
Begin again with a new item.
Only test one item at a time.
As you might expect, if you change six parameters between two versions of the same message, you will have no idea what each of them does. So, make sure to test the object first, then the text, and finally the photos. Not all of them at once!
Use a homogeneous audience for your tests.
If you've segmented your contact database, make sure to always test with the same group. For example, if you have both young dynamic employees and retirees in your CRM, it is best to separate them and run different tests on each group, because they will not respond to your emails in the same way.
An A/B test, like we have said before, has two versions: the original version (A) and its variant (B). In terms of email, your original version could be an email that you have already sent to a contact database or an email template that you are used to using and for which you have past performance statistics.
Each element of your newsletter must be tested in six steps: