As email communications are major part of a marketing strategy, it’s important to ensure your emails are always in their best shape. A great way to constantly improve your campaigns is to optimise Pardot emails with A/B Testing functionality.
Pardot A/B Testing allows you to see what works best for your audience, without spamming them with too many emails.
You can enable this functionality under the Basic Information tab of your List emails:
When enabled, Pardot creates two versions of your email which will be sent to a portion of your list. Then based on the email’s engagement data it determines the winning version and sends it to the remaining audience.
Make sure you only change one element at a time between the A and B versions. Changing multiple elements in the email makes it difficult to decide which change made that version the winner, as there are too many variables in the mix.
Before setting up your A/B Testing for a list email, you need to confirm the following details:
- Testing object:
– Subject line, CTA button, etc
- Testing audience:
– Between 20% and 50%
- Testing time:
– Anything from 1 hour to 30 days
We recommend to enable A/B Testing once the content of the email has been approved, to make sure only the object of testing is different. If you would like to make any changes in the copy after this option has been turned on, you need to apply it on both versions.
As a first step, we recommend reviewing which emails would be the best fit for your first test. When choosing the email, take a look at the segmentation list that your email will target. For example, a list with more than 1,000 prospects included is likely to provide a conclusive result on your test. Testing with a larger audience could provide you a better insights for future campaigns.
Once you have selected your email and your audience, think about which statistic you would like to improve with your test: the open rate or the click through rate?
Based on your choice there are multiple areas you could consider to be your testing object, such as:
- Subject Line: The length, key phrases, personalisation (if your data is clean and populated correctly)
- Sender Name: A generic sender vs. specific sender (can look like it has been sent from a real person)
- CTA: Whether to use an image button, hyperlink text, text of the actual CTA, or the position of the CTA on the email.
- Images: Different images can make your audience behave in different ways. For example, some images may put people off from finding out more.
As standard, we usually recommend a 30% audience size for testing. However, you should adjust this based on the number of prospects on your email list. Remember, not everyone who receive the email will open it, therefore not everyone on your list will make an impact on the result of your test.
The selected testing time will determine how long the test will run in Pardot before it decides the winning version. If you set at least 4 hours of testing time, then Pardot will send the test emails in the morning, and the winning version in the afternoon. But if you are running your test in a larger audience, you might want to wait at least 1 day to collect further performance data.
Review the Results
Once you have scheduled or sent the emails, you are able to review the real-time results from your testing. You will see which version is more likely to become the winner, but it will only be certain once the testing time has passed.
In the example above, the results from the test are not conclusive – as the difference was only 3 opens. Therefore we can’t make assumptions that what we tested will work on other emails as well. If you are seeing results like this, you should repeat the test on a bigger audience and see if you have the same or different results.
Once your test has finished, you can take your learnings and apply them to any future campaigns. If you need help setting up A/B Testing with Pardot Emails, get in touch with our consultants.
Would you like to become an expert in Pardot testing features? Then take a look at what you can achieve with Multivariate Testing in Landing pages.