A/B Campaign testing consists of sending two emails and splitting your subscriber list randomly. Before sending the Campaign you will need to decide what you are testing. Be sure to change only one variable at a time. If you change more than one element in your email, you won’t know what element was responsible for the change in opens or clicks. Make sure you are comparing the same variable in each email. Then follow up by optimizing your emails, repeating another test to reduce variables and receive solid facts to help your marketing.
A/B tests usually consist of different content, a different subject line, or a difference “from” address. You can conduct your tests to just a small segment of your list to determine the best results and then send “the winning email” out to the remainder of your list. A/B tests help take the guessing game out of figuring out what’s working in your email marketing.
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A/B testing in WordFly is very simple - just follow these steps to get started!
1. Go to Emails > Create a campaign.
2. On the Settings tab, from the drop-down select “Standard”.
3. Check the box for A/B testing.
4. Select your A/B test.
There are 3 options.
- A/B subject line.
- A/B From name.
- A/B template.
5. Save the Settings tab to continue with assigning your template.
6. Assign your template as you would in any other standard campaign.
If you’re testing an A/B template campaign, you will select Version A and Version B at this point. In most cases you will duplicate Version B since your changes won’t be very different from the original template (remember, keep your test variable confined to one change).
7. Save your Template.
8. Import your subscriber list as you would in any other standard campaign.
WordFly will automatically split your list randomly so there is no other action item necessary needed under this section.
9. Test your email as you would any other standard campaign.
10. Send your email.
11. Review your Reporting.
*Tip: Label you’re A/B test campaigns with “AB test”. Once you have your results for a few A/B campaigns you can go to Reporting > Compare Campaigns to select this label and compare A and B results.
We’ve thought of 10 great A/B tests you can try in WordFly anytime:
|A/B TESTING IDEA||DETAILS|
|From Name||Use "A/B test from name" to see how didn't from names perform|
|Name Personalization (First Name etc)||Use either "A/B test subject line" or "A/B test template" for this one! You will use data fields to create personalization.|
|Subject line||Use "A/B test subject line". Put in separate subject lines.|
|Special characters in subject line||Use "A/B test subject line". Put in separate subject lines and insert special characters. Learn more about adding symbols to the subject line.|
|Animated gifs||Use "A/B test template" and insert different animated gifs in each template. Learn more about adding animated gifs to your email template.|
|Embedded video||Use "A/B test template" and insert an embedded video into one template and no video in the other template. Learn more about adding video to your email template.|
|Button text and color||Use "A/B test template" and adjust the styles in each email template to see how one works better. Learn more about global style updates in your email templates.|
|Button placement||Use "A/B test template" and adjust the placement of your buttons in each template. Does a button placed higher in the email template generate more engagement and conversions than placed lower in the email template?|
|Postcard vs Newsletter design||Use "A/B test template" and adjust the design of your email template. One design would be postcard and one would be newsletter style.|
|Time of day or week||This is the only test that is more about long term testing. There is not an option in WordFly to send at various times of day or week. The idea here is to send one A/B test at one time in a week and the next week you can try another A/B test at a different time or day. See how the results vary. Did one have better opens/clicks than the other? You can even do this same type of analysis without using A/B testing.|