7 Steps to Effective A/B Split Testing

A/B split testing is one of the best ways to optimize your email marketing campaign. It is easy to implement and the results will help you improve open rates, click through and conversion rates. Basically A/B testing involves testing two versions of your email among a group of your subscribers. Results based on factors that you decide are vital for your email campaign will help you choose which version is best. And that winning version will be used for rest of your subscribers. You may use this method to test elements of website landing pages.

Here we provide seven steps essential for implementing an effective A/B split test to help improve your email-marketing return on investment:

Choose what you want to test. There are countless variables within an email campaign that, with a seemingly small change, can make a big impact on your results. The following give you an idea of some email campaign variables you may want to test:
  •         Email subject lines
  •         From lines
  •         Calls to action (e.g., location, size, color, copy)
  •         Headlines
  •         Message layouts
  •         Design
  •         Images
  •         Offers and incentives
  •         Adding a video
  •         Fonts (type, colors, and sizes)
  •         Personalization
  •         Best weekdays to send
  •         Time tests (e.g., morning versus late afternoon)

Choose what measure of success you’ll use to determine the winner. Success measurements for email campaigns include:
  •         Open rates
  •         Click-through rates
  •         Conversion rates
  •         Revenue.

When choosing this measure, be sure to consider what results you’re really interested in or what results matter you the most. Let’s say you’re testing subject lines, for example, and lead generation is the primary goal of your email campaign. One subject line may result in higher open rates, yet the other may actually produce more qualified leads.

Develop a research question to test. This is a good way to help you stay focused on the outcome of your A/B split test. Some examples of research questions include:
  •         Which call to action produces the highest conversion rates?
  •         Which version provides a higher click-through rate — with a video or without a video?
  •         Which day will generate a higher open rate — Monday or Tuesday?
  •         Which headline results in the highest click-through rate?

Be sure to test only one variable at a time. For accurate results, be sure you’re testing only one variable at a time. In other words, if you’re testing subject lines and you send version A to one group in the morning and version B to a group in the afternoon, you won’t know whether it’s the time of day or the subject line that had the biggest effect on the open rates.

Randomly divide your list into two equal segments. Although it would be ideal if your two test groups had similar demographics, behavioral segmentation, income, age, gender, etc., you can still get valid results by simply dividing your list randomly.

Be sure your sample size is large enough to make a statistically valid decision. How big should your sample sizes be? It all depends on how large your email list is. If you have a very large list, you may be able to use only 10% of your list for testing. But if you have a small list, you may have to use the entire list to get statistically valid results on an A/B split test. If statistics isn’t your thing, don’t spend too much time trying to figure it out. Leverage help from online tools, such as the Split Test A/B Test Marketing Calculator.

Make A/B split testing faster and easier by using readily available testing tools. Many email service providers (ESPs) offer useful tools for running A/B split tests on emails and websites today. Some examples of what these tools can do include:
  •         Randomly select your two samples.
  •         Track results and generate a report with a side-by-side comparison of the results.
  •         Select the winning version and automatically send it to the rest of the sample.

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