A/B Testing in Email Marketing: How to do it correctly?
Posted: Tue Dec 03, 2024 10:15 am
A/B Testing in Email Marketing: How to do it correctly?
As we know, Email Marketing remains a primary and essential technique for any company, as it manages - in a relatively simple way - to increase the level of sales and connect with customers and potential customers in an economical way.
However, we cannot forget that many companies still today are slightly wary of this technique, given that despite their many efforts, they are unable to obtain the expected results and that their opening rates and click rates are really relevant.
However, today there are multiple tools or software that seek, with their strategies and techniques, to increase these rates and prove that, indeed, Email Marketing is an excellent technique for fluid and bidirectional communication with users.
One of the techniques used to show and verify the success of Email Marketing in companies is A/B testing , a test that - we could say - many people don't even know exists.
In today's article we are going to talk about what A/B tests are, what they are used for and how we should carry them out so that they have the expected success.
Post Content
How to use A/B testing in Email Marketing campaigns?
What are the benefits of A/B testing?
How to implement them in Email Marketing to achieve success?
How to use A/B testing in Email Marketing campaigns?
First of all, let's start from the beginning: what are A/B tests applied to Email Marketing?
Well, we can define them as tests of two or more variables when sending an email to our database of clients and/or subscribers, which seeks to compare their effectiveness.
In this way, these tests consist of sending two types of emails, with different elements and content, to group A and group B, and seeing which of them generates better responses, ultimately determining which campaign works best for our audience.
How to create an Email Marketing strategy from scratch
What are the benefits of A/B testing?
Among many other advantages that sending emails with A/B tests presents for the Email Marketing technique, we can highlight the following:
We will get to know our audience better , discovering what they like most or what works best with our target or objective public.
We will learn how to structure and design our email content more correctly.
We will get more conversions on the landing page , because we will offer the user what they really expect, and -thus- users will go directly "to the point."
How to implement them in Email Marketing to achieve success?
First, make sure you have enough (quality) data to run A/B tests, and determine your sample size:
To carry out an A/B test in Email Marketing, the first and most important thing is to have the capacity, in your company, to be able to do it. To do this, it is necessary that your database has a minimum of 1,000 subscribers ( once having passed the quality and hygiene filter in the database, the one we insist on so much ).
The reason is that A/B testing is obviously just an attempt to improve something, which is “trial and error” in a nutshell. Therefore, the larger your quality database, the fewer users you need to perform this type of testing compared to your total database, and therefore the less likely the disinterest of the user group at a general level.
Using your Email Marketing tool, calculate the categories of “ confidence level ”, “ confidence interval ” and “ population ”:
To obtain our sample size, it is necessary to calculate the data we mentioned above, but always in collaboration with your Email Marketing tool.
Why is it so important to personalize emails? We talk about its vp technical email lists psychological impact on Email Marketing
This is a basic calculation that mixes all three pieces of data together, and is undoubtedly necessary to calculate how big one of your variants should be , and (very briefly) you need to know that “population” refers to the group of people that represents your sample, “confidence interval” (also known as “margin of error”) is the range of results you can get from that A/B test when you apply it to the entire group, and finally, “confidence level” refers to the security and certainty of the test.
It sounds like Chinese, right? In reality, it is a statistical calculation that is simpler than you think, but it is worthwhile -of course- to let yourself be guided in the data as well as in obtaining it by an expert tool, such as Mittum .
Select the appropriate period to perform your A/B test
Before you run any A/B tests you define (in addition to the above), you need to know when is the best time to send your A/B tests and how long you plan to run them.
Obviously, neither of these two variables depends on statistical data that we can calculate with a magic formula, so you will need to use information from your brand's past campaigns to help you make the best decisions.
As we know, Email Marketing remains a primary and essential technique for any company, as it manages - in a relatively simple way - to increase the level of sales and connect with customers and potential customers in an economical way.
However, we cannot forget that many companies still today are slightly wary of this technique, given that despite their many efforts, they are unable to obtain the expected results and that their opening rates and click rates are really relevant.
However, today there are multiple tools or software that seek, with their strategies and techniques, to increase these rates and prove that, indeed, Email Marketing is an excellent technique for fluid and bidirectional communication with users.
One of the techniques used to show and verify the success of Email Marketing in companies is A/B testing , a test that - we could say - many people don't even know exists.
In today's article we are going to talk about what A/B tests are, what they are used for and how we should carry them out so that they have the expected success.
Post Content
How to use A/B testing in Email Marketing campaigns?
What are the benefits of A/B testing?
How to implement them in Email Marketing to achieve success?
How to use A/B testing in Email Marketing campaigns?
First of all, let's start from the beginning: what are A/B tests applied to Email Marketing?
Well, we can define them as tests of two or more variables when sending an email to our database of clients and/or subscribers, which seeks to compare their effectiveness.
In this way, these tests consist of sending two types of emails, with different elements and content, to group A and group B, and seeing which of them generates better responses, ultimately determining which campaign works best for our audience.
How to create an Email Marketing strategy from scratch
What are the benefits of A/B testing?
Among many other advantages that sending emails with A/B tests presents for the Email Marketing technique, we can highlight the following:
We will get to know our audience better , discovering what they like most or what works best with our target or objective public.
We will learn how to structure and design our email content more correctly.
We will get more conversions on the landing page , because we will offer the user what they really expect, and -thus- users will go directly "to the point."
How to implement them in Email Marketing to achieve success?
First, make sure you have enough (quality) data to run A/B tests, and determine your sample size:
To carry out an A/B test in Email Marketing, the first and most important thing is to have the capacity, in your company, to be able to do it. To do this, it is necessary that your database has a minimum of 1,000 subscribers ( once having passed the quality and hygiene filter in the database, the one we insist on so much ).
The reason is that A/B testing is obviously just an attempt to improve something, which is “trial and error” in a nutshell. Therefore, the larger your quality database, the fewer users you need to perform this type of testing compared to your total database, and therefore the less likely the disinterest of the user group at a general level.
Using your Email Marketing tool, calculate the categories of “ confidence level ”, “ confidence interval ” and “ population ”:
To obtain our sample size, it is necessary to calculate the data we mentioned above, but always in collaboration with your Email Marketing tool.
Why is it so important to personalize emails? We talk about its vp technical email lists psychological impact on Email Marketing
This is a basic calculation that mixes all three pieces of data together, and is undoubtedly necessary to calculate how big one of your variants should be , and (very briefly) you need to know that “population” refers to the group of people that represents your sample, “confidence interval” (also known as “margin of error”) is the range of results you can get from that A/B test when you apply it to the entire group, and finally, “confidence level” refers to the security and certainty of the test.
It sounds like Chinese, right? In reality, it is a statistical calculation that is simpler than you think, but it is worthwhile -of course- to let yourself be guided in the data as well as in obtaining it by an expert tool, such as Mittum .
Select the appropriate period to perform your A/B test
Before you run any A/B tests you define (in addition to the above), you need to know when is the best time to send your A/B tests and how long you plan to run them.
Obviously, neither of these two variables depends on statistical data that we can calculate with a magic formula, so you will need to use information from your brand's past campaigns to help you make the best decisions.