How to know if it's working: test it! - Mailify Blog

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How to know if it’s working: test it!

How to know if it’s working: test it!

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Every time you do a marketing campaign, it’s a great opportunity to see what works…and what might work better!  

“The classic way to know if it’s working is to test.  Never trust your hunches.”

Paul de Fombelle, General Manager of Mailify Latin America & Spain.

An A/B split test is when you take two randomly selected sample groups of your subscribers, the first receives the standard newsletter and the second receives a newsletter with a slight variation, ideally all other elements of the email’s copy and layout remain constant (identical).  That way you can see if the slight change gives better or worse results or has no effect.

Testing brings results.  According to Monetate, in their 2014 personalisation e-book, A/B testing is the second most valuable method for improving conversion rates:

2014 05 19 seg both bits

For example you might test 2 different subject lines:

2014 05 13 split test subject

And then watch the click rate, open rate and even purchase rates (depending on what is important to you) of the 2 groups to see which group reacts best.  For example, group A got a 9% open rate  and the email for group B has a 4% opening rate: so group A’s subject line was the winner.

Following this the general campaign for the rest of the recipients would be sent, using the subject line for group A.

NB: it’s recommended that the minimum sample size is 100 people.  The bigger the sample groups the more reliable the test.  The sample should be representative of the population that you wish to send the final email to, ie chosen randomly. 2014 05 13 soft ab take out no recip fakes nos What can you test

There are lots of things a marketer can test including:


  • From line
  • Subject line: 50% or half price?
  • Personalization
  • Call-to-action buttons: color, text; position in the email
  • Images and layout
  • Message
  • Offers


  • Who you send the mail to: slice and dice your database by for example demographic data: age ranges, gender, location…


  • The time
  • The day: for example send the same mail to 100 different people each day, over 7 days to see the best day.
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