Email Marketing
December 4, 2024
How can you use A/B Test to find the best email solution?
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A/B Test, in simple terms, is a comparative experiment. We compare two or more versions to find a better-performing email solution. Different elements (such as titles, images, promotional activities, etc.) can be tested to determine which element or combination of elements can lead to a higher conversion rate. During the A/B Test process, the data results from a small portion of the group are used to identify the best practice, and then the more effective solution is sent to the remaining group.
Have you encountered the following problems? Why do email marketing results always fall short of expectations? Low open rates? Few order conversions?Usually, at this time, we all wonder whether the title is not eye-catching enough or the content lacks persuasiveness. Is the push time inappropriate or the sender's information not suitable? 🤔But how exactly can we find the reasons? The best way to find the problem is through practical verification!
Through A/B testing, you can unearth the topics and content that customers truly like from a data perspective. Moreover, it doesn't require additional resources, effectively reducing the cost of trial and error and increasing the return on investment (ROI) of EDM. Data shows that 95% of American companies conduct A/B tests on email marketing activities. The ROI of enterprises that conduct A/B tests on each email is 37% higher than that of enterprises that never conduct A/B tests. (From Litmus)
Firstly, we need to define the test objective clearly. It could be the open rate, click - through rate, number of orders, etc. it's the indicator that you want to improve.
The test content refers to the comparison of different email elements, which may include the email subject, preview text, sender's name, sender's address, email body, etc. You can test a single element alone or conduct a combination test of multiple elements.
In the AB Test process, there are test groups and control groups, with a total proportion of 100%. The test groups are the experimental versions, and each group should account for at least 10%. When one group wins after the experiment ends, the winning content will be sent to the control group.
It is not advisable to set the test time too short, otherwise the data may not be accurate enough. It is recommended to set it at least 24 hours or more, and the maximum can be configured as 10 days or 240 hours.
Let's give a simple example to help everyone better understand the practical process of A/B testing:
Test Purpose: Are users more attracted to short or long copy in the email channel?
Experiment:
-Email A, the only variable: Email opening copy: "30% off 👜👛🛍️" - Sent to 50% of users
-Email B, the only variable: Email opening copy: "Your very personal selection from our sale 👌 up to 30% off" - Sent to 50% of users
Test Duration: 48 hours
Results (Hypothetical): After the test, we found that the open rate of the short copy was higher.
Conclusion: In the future, short copy can be used more often as the title in the email channel.
•Diverse test variables：The email subject, preview text, sender's name, sender's address, and email content are all supported for testing.
•Testing of multi - type marketing plans：Marketing emails and automated tasks can all be tested.
•Great customization space：You can test a single element alone or in various combinations.
•Multivariate testing can be set up：supports up to 5 versions. According to the suggestion of Invesp, setting the number of versions between 2 and 5 is a relatively appropriate choice.
•Data indicators in more dimensions：It provides test data references for open rates, click-through rates, and the number of orders to meet different testing needs. After the test, the group of email content with better data will be sent to the control group of people for drawing conclusions and improving marketing effectiveness.
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