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How do I perform an A/B test on my trigger?
How do I perform an A/B test on my trigger?

In this article I will explain how to set up an A/B test for your trigger

Carola avatar
Written by Carola
Updated this week

It is possible to A/B test your trigger. For example, do you want to A/B test your open rate, click rate or number of unsubscribes? You can! In this article I will explain how an A/B test for a trigger works, how to set it up and how to analyze the results.

  • Why A/B test a trigger?

  • How does an A/B test for a trigger work?

  • Tips for A/B testing a trigger

  • Setting up an A/B test for a trigger

  • Analyzing A/B test results

Why A/B test a trigger?

With A/B testing a trigger, you test what works best in an email. Does a green button work best, or does a blue button? Does a straightforward subject generate the most opens, or is a teasing subject better? You'll find out thanks to an A/B test.

How does an A/B test for a trigger work?

In a nutshell, an A/B test involves creating two versions of your email. However, A/B testing a trigger works differently than A/B testing a newsletter. Whereas you send a newsletter all at once to a large target group, you send a trigger continuously only to matching profiles. This ensures that when you A/B test a trigger, you don't set a test duration. The trigger continues to A/B test continuously. That is, one matching profile receives version A of your email, another profile receives version B.

Tips for A/B testing a trigger

In an A/B test, you can test for three outcomes:

  • Open ratio

  • Click ratio

  • Unsubscribes

We'd like to give you some tips on what you test on:

  • Open ratio. To test your open ratio, let the sender's name, e-mail subject and pre-header differ from each other.

  • Click Ratio. We measure the number of clicks in your newsletter. Your click ratio is about the content of your email. Make the content of variant A different from variant B. Change only a button, hero image or text. Which variant gets the most clicks? Test it and make sure your content matches your target group.

  • Unsubscribes. We measure the number of unsubscribes per newsletter. Many people unsubscribe from the newsletter because the content is not interesting. Try to put yourself in your customer's shoes: why would you unsubscribe from a newsletter? Are there no interesting offers in your newsletter? Or is it the articles themselves? Test thoroughly why your customers unsubscribe from the newsletter.

โ—๏ธ Always test one element at a time. If you want to test all three indicators, weigh them all equally. So each indicator has a weight of 1/3. Are you testing two degree markers at the same time? Then we weigh them equally 50/50.

Setting up an A/B test for a trigger

Time to set up the A/B test for a trigger. Below I explain all the steps for you.

Step 1. Create a new trigger (or use an existing trigger)

You start by creating your trigger. In step 1 you select your target group. In step 2, the magic of A/B testing begins.

Step 2. Enable A/B testing

Now you are ready to create your A/B test. To enable A/B testing for your newsletter, click 'Enable' under A/B testing:

Now you see that the screen for A/B testing expands. It is now possible to fill in everything for your A/B test. You now have this screen in front of you:

Step 3. Explanation of all fields in your A/B test

The screen in front of you can now be divided into an A variant (top), the A/B test settings (middle) and B variant (bottom). I will cover all the fields you see in front of you now.

A variant:

The A variant is the first variant you will see on the screen. Here you fill in the following fields:

  • Name of sender

  • Sender e-mail

  • E-mail subject

  • Text for the pre-header

This is the same as creating a campaign.

Which indicators do you want to test for?

Fill in what you want to test for. You can choose from Open Ratio, Click Frequency and Unsubscribe. If you choose all three, we will test the newsletters on all three parameters and you can decide which newsletter will be the winner.

B variant:

The B variant is the second variant you see on the screen. Here, you fill in the following fields:

  • Name of sender

  • Sender e-mail

  • E-mail subject

  • Text for the pre-header

Have you filled out everything? Then go to the next step.

Step 4. Design the e-mail

In the next step you create the e-mail that belongs to your trigger. At the top you will see a drop-down menu where you can choose between variant A or variant B:

Always create variant A first. Variant A is as it was duplicated live to variant B. This way you don't have to create your newsletter twice. Now save variant A.

โ—When you save version A and continue with version B, the variants will be disconnected from each other! This means that any changes you make to variant A will no longer be applied.

Now continue with variant B. In variant B you make some changes that you want to test. You will see that variant A stays as it is.

Did you also create the layout of your mail as usual? In the summary you can see again that you are running an A/B test and what the differences are:

Analyzing A/B test results

You can see the results of your A/B test live. This is your dashboard for your A/B test results:

On the left-hand side, you can see the probability in percentages for the best performing variant.

The grade shows how well the variants are doing compared to each other. It shows how much of the total score was achieved by the individual variants. A 50/50 distribution of the score means that both variants are equal. For example, a 1/3 (33.33%) versus 2/3 (66.67%) distribution of the pie chart indicates that one of the variants scores twice as much as the alternative. The score itself is calculated on the basis of the performance indicators (clicks, opens, etc.).

Next to that you have both variants with the performances:

Are you curious about the "regular" statistics that you always have with a trigger? Click on one of the lines of variant A or B. This will take you to the unique performance of the newsletter in question.

On the bottom right you will then see the performance of exactly what you are testing. In this example, you can see that the test is being conducted on open ratio, click ratio and a number of unsubscribes:

Have you changed something in your e-mail in the meantime? You will also find this in the plot:

An adjustment to a variant may cause the variant to perform better or worse. This change in performance is immediately visualised by the plot. If an upward trend can be seen after an adjustment, then the adjustment has had a positive effect on the performance indicator and vice versa. Please note that an increase in the number of unsubscribes is never a good sign.

Good luck with testing! ๐Ÿ™Œ

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