Email is one of the most successful marketing tactics to engage with your audience. If you’re unable to get desired results, it may be because something is off in your campaign. You’re not only failing to generate the desired results, but also missing out on potential hot leads. That’s where A/B testing comes in.
To find out where your email campaign falls short, you should consider A/B testing, as it analyzes the performance of your emails based on predefined parameters.
Still trying to figure it out? Let’s explore what A/B testing is and why it’s so essential in email marketing.
What Is A/B Testing?
A/B testing in email marketing compares two or more versions of an email campaign to determine which performs better in achieving the desired outcome, such as increasing open rates, click-through rates, or conversions.
A randomized sample of subscribers is split into two groups, each receiving a different version of your subject line, sender name, email design, etc.. The results are then analyzed to determine which email version performs better based on predefined success metrics.
In other words: The purpose of A/B testing in email marketing is to improve email campaigns’ performance by identifying which email version is more effective in achieving the desired outcome.
How to Perform an A/B Test: 4 Steps
Below are the general steps you can follow to try A/B testing for the first time. And if you’re not sure which tests to run, we’ve listed 14 email marketing tests you really should try.
1. Choose Your Variable
First, determine which variable you want to test. This could be anything from:
- Subject lines
- Email content
- Headlines
- Calls to action
- Visual elements
- Sender name
- Sender email
- Send time
Which variable you choose will, of course, depend on which kinds of test your email marketing software offers.
2. Create Two (or More) Versions
To determine which version of your email works best, create a control group (version A) and a test group (version B) with the variation you want to test. Note: some email marketing apps do allow you to create more than two variations. Send each version to a different group of individuals within your target audience. For example, you could send version A to 20% of your subscribers, and version B to another 20% of subscribers. The winning version will be sent to the remaining 80% of your subscribers.
Usually, the control group will be sent your “usual” email, while the test group(s) will be sent the other variation(s).
3. Run the Test
Send each email version to their respective groups and monitor performance metrics like open, click-through, unsubscribe, spam, and conversion rates, putting close emphasis on the metric you’re trying to improve. Be sure to give the test enough time to gather significant results, which may vary depending on your list size.
4. Analyze the Results
Compare the performance metrics of the two groups and determine which version performed best. Remember that statistical significance is vital, so use a statistical significance calculator to determine if your results are significant.
Best Practices for A/B Testing
Here are some best practices for conducting effective A/B testing:
- Only test one variable at a time: Test only one variable at a time to accurately determine what drives performance changes.
- Keep everything else consistent: Keep the design and messaging consistent (for example) between the two versions to ensure that any performance differences can be attributed to the tested variable.
- Allow enough time for results to accumulate: Give your test enough time to collect a statistically significant sample size. This will help ensure that your results are accurate and meaningful.
- Continuously iterate and improve: Use the results of your A/B test to continually optimize your email marketing strategy.
Final Thoughts
In short, A/B testing is essential for any email marketing campaign as it allows marketers to identify what works best for their audience. Companies can optimize their emails and improve key metrics like open and click-through rates by testing different variables and measuring the results.
