Blog Insights
A/B Testing Your Email Marketing
We often hear, and we inherently know, that when it comes to email marketing, A/B testing is incredibly important. So much so that most mainstream email marketing software platforms have A/B testing functionalities embedded directly so that it’s that much easier to do.
A/B testing, also known as split testing, is a method of comparing two versions of an email element against each other to determine which one performs better. A/B testing is an effective way to identify potential areas for improvement in your emails, especially if an element isn’t performing as expected. It allows you to test new designs, layouts, and content. While research from 3rd parties can tell you what colors may tend to perform better than others, no audience is the same. The main point of A/B testing is to find out what works for your audience. While there multiple benefits to using it, A/B testing remains at the bottom of most priority lists, as emails get pushed out the door with little to no review process, resulting in chronically-low open and click-through rates. If this rings true for you, the good news is that it’s never too late to course-correct. Before we begin our experiment, we need to determine the necessary factors for effective A/B testing. As you embark on or rethink your existing approach to A/B testing, here are some of the most important things to stick to.Define the goal
Why are you considering A/B testing? What do you want to achieve by doing this? More opens? More click-throughs? Make sure this funnels up into your overarching digital goals and organizational goals.Decide what to test
What element do you want to experiment with? Is there something that isn’t getting results as expected? The element you choose should reflect the goal you have set in advance, e.g., if you’re looking to improve open rates, try A/B testing the subject line or friendly forms. It’s best practice to only test one thing at a time, as this allows you to track exactly what caused the outcome of your experiment. Common elements to experiment with are:- Subject line
- Call-to-action (CTA) text
- Button color
- Text, image or button size
- Copy (length, voice, tone, etc.)
- Images
Create sample sizing to ensure the most accurate results
Do you want to test your whole list or just a portion of it? Of course, the larger the sample size, the more accurate your results will be. However, how much of your list do you really need to use for the testing portion? To determine this, you’ll need to consider the statistical significance. A great way to manage this is by using a sample-size calculator. The main elements that make up statistical significance include:- Confidence level: the probability that your sample accurately reflects the attitudes of your population.
- Population size: the number of total subscribers on your email list.
- Margin of error: the amount of possible variance in an email’s test results. The smaller the margin of error, the more exact your results will be at the given confidence level. For most purposes, you can leave the margin of error at 5%.
