How often should you a/b test your email newsletters and how long should you run each test?

With A/B split testing you want to set one element at a time. Subject line - for open rates Preview text - for open rates Opening - read time Body copy - read time Call to action - engagement Once you find the winners you can start combining some the elements. Subject line and Preview text Opening line and Body copy Subject line and Call to action If you want to maximize your results, always be testing.

In the newsletter business, tiny nuances can flip your success upside down. The only way to find the impact is to test what works and what works much better. In the email marketing is known as A/B or Split Testing. It involves sending two versions of an email to different segments of your audience. By comparing performance metrics, you can identify which version resonates better, helping refine your email strategy.

A/B testing for email marketing is like running a mini-experiment. I create two versions of the same email, but change one element—like the subject line or call-to-action. For example, I might test a straightforward subject like "Get 20% Off Today" against something playful like "Ready for a Deal You Can’t Resist?" I split my email list in half, sending one version to each group. Then, I track the open or click-through rates to see which one performs better. After a day or two, I’ll know which version worked best, giving me valuable data to improve future emails and boost engagement.

It is important to set out with clear testing goals in mind. What are you trying to achieve and in what timeframe? After you have your goals in mind you can go about testing the various elements of the newsletter.

Do not forget when A/B testing to only test one variable at a time, otherwise it will be tough if not impossible to understand the outcome. Using the SMART goal framework can assist in focusing yourself.

A/B testing is essential for optimizing engagement. Suppose you're torn between two CTAs - "Shop Now" and "Explore Deals." Conducting a test reveals which resonates more with your audience, guiding you to craft emails that drive higher click-through rates and conversions.

Always be testing! There's always something you can learn about making your content more valueable to your subscriber base. At the very least test Subject Lines (SL) for each newsletter with a subset of your list, then send the winning SL to the remainder of your list to maximize readers.

Strive for at least one A/B test per month to stay agile. For instance, if you're uncertain about the ideal sending time, test sending at 10 am versus 3 pm. Adjust the frequency based on your email volume and objectives, ensuring a steady stream of insights to enhance your strategy.

Any chance you get until something sticks. Check the progress and feedback and keep trying new things. The world we live in is very fast paced so be sure your audience preferences will keep changing with it.

You should always be testing as long as you're prepared to make changes to your email program based on what you learn in the test. An A/B test without an action plan (whether version A wins or version B wins) is not valuable. If you're not testing in your email newsletters, you're not learning about your subscribers / customers. If you're not learning about your subscribers / customers, you're not able to deliver content that aligns optimally with their preferences. Always plan on testing assuming you're prepared to do something different with your email marketing program.

You should A/B test your email newsletters regularly, but not every single send. I try to run a test at least once a month or whenever I’m trying something new, like a different subject line style or layout. It’s important to give each test enough time and a big enough audience to get reliable results. If you're always testing, you’ll get a better understanding of what your audience likes, but you don't want to overwhelm yourself. Consistency is key, so make sure you're testing frequently enough to improve, but not so often that it becomes unmanageable.

How long you run tests will be highly dependent on your sample size and in many cases for email marketers, you'll never hit a high enough volume for your test to be statistically significant. In which case, I'd recommend testing until you see a definite trend or pattern in the results you're seeing. And if you're not seeing a trend then consider the test to be a null test and move on to try testing something else.

Consider a 7-14 day duration to gather sufficient data. Imagine you're testing different email designs; a two-week span captures diverse engagement patterns. This timeframe balances the need for statistical significance without delaying important campaigns, allowing for the timely implementation of successful variations.

It definitely depends a lot on the email sequence, and the audience sample size. If it's for one-time email, I recommend A/B testing for 1 day before deciding on a winner.

Another vote for what @Jaina Mistry says here! If your list is too small to make A/B testing a useful exercise, concentrate most of your efforts on growing your list instead. There are many other stats in your reports that you can use to improve the performance of your campaigns, too.

Focus on conversions or click-through rates to understand test success. Open rates are notoriously unreliable due to corporate filters, bots, image caching, and Apple's Mail Privacy Protection.

Define a clear goal; for instance, if you're uncertain about the effectiveness of emojis, test emails with and without them. Create two versions with distinct variations and randomly split your audience. Use an ESP platform, like Beehiiv or ConvertKit, to track metrics. This way, you can analyze which version contributes to higher engagement.

Did your test make a statistical impact on your goals? Certain variables will have an impact and others will not. By testing, you can find the variables that have the biggest impact on your business goals.

Testing a variable multiple times or repeat a successful A/B test will enable you to validate and confirm your findings. In reality there are more factors than we can imagine that affect customer behaviour.