As email marketers, we constantly juggle with design aesthetics, captivating copy, and that ever-elusive perfect subject line. But, how do we truly discern which element elicits the best response from our audience?
That's where A/B testing comes into play.
A/B testing is sending out two versions of an email and seeing which one gets a better response. It's a straightforward, yet powerful way to fine-tune our emails based on real feedback.
In this blog, we'll look at what A/B testing is, and how you can use it to improve the results you get from your emails. Whether you're just starting out or looking for ways to improve, this guide will give you the tools to make your email campaigns more effective.
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What is A/B Testing?
Imagine you have an online shoe store and you want to send out an email about a weekend sale.
Version A (Email A):
Subject Line: "Huge Weekend Sale on All Shoes!"
Version B (Email B):
Subject Line: "Get 50% Off All Shoes This Weekend!"
You send Email A to 100 people and Email B to another 100 people. After a day, you see that 70 people opened Email A and 50 people clicked on a link inside. For Email B, 80 people opened it and 60 people clicked on a link.
Even though Email B had more people opening it, Email A had a higher number of clicks. So, you decide to send Email A to the rest of your subscribers because it encourages more people to check out the sale on your website.
That's A/B testing!
A/B testing in email marketing is the process of comparing two versions of an email to determine which one performs better in terms of specific metrics, such as open rates or click-through rates, by sending each version to a subset of subscribers and then analyzing the results.
It helps you figure out what your email subscribers prefer, so you can send them content they'll engage with.
Deciding What to Test for A/B Testing in Email Marketing
A/B testing is a critical tool for email marketers aiming to optimize their campaigns.
But with so many elements in an email, how do you decide what to test? Here's a systematic approach:
1. Understand Your Metrics:
Before you decide what to test, understand what metrics matter to you.
Are you trying to boost your open rates, click-through rates, or conversion rates? Knowing this will help narrow down the aspects of your email that might be influencing these metrics.
2. Review Past Campaigns:
Your past emails are a goldmine of information. Identify areas that didn't perform as expected or any consistent trends. This can give you an idea of what might need tweaking.
3. Focus on High-Impact Elements:
Prioritize elements in your email that are most visible and influential. Typically, these might include:
- Subject lines
- Call-to-action (CTA) buttons or text
- Images and graphics
- Email body content
Here's my inbox showing Morning Brew subject lines (I've signed up using two different emails) that they test with every email to see what kind of subject lines people prefer —
4. Listen to Your Audience:
Feedback from your subscribers can offer direct insights into what might be lacking or confusing in your emails.
5. Forming a Hypothesis:
Once you've chosen an element to test, you'll need to form a hypothesis. This is a predictive statement about what you expect to happen based on the change you're making.
Formula for forming a hypothesis:
"If [specific change you're testing], then [expected outcome], because [rationale]."
"If I include the recipient's first name in the subject line, then the open rate will increase, because personalized emails feel more relevant to the reader."
6. Ensure Variations Are Distinct:
When creating your 'A' and 'B' versions, ensure that the differences are clear and distinct to accurately measure the impact of that change.
7. Limit Variables:
It's essential to test one change at a time. If you alter multiple elements in an email, you won't know which change influenced the outcome.
8. Remember Context:
Sometimes, external factors, like current events or holidays, can influence email performance. Always consider these when deciding what to test.
6 Best Practices for Running A/B Tests in Email Marketing
1. Segment Your Audience Carefully:
Every subscriber on your email list may not have the same preferences, behavior, or demographics. By segmenting your list, you're tailoring your A/B tests to more specific and homogenous groups, ensuring more accurate results.
If you’re a global brand, some regions may prefer a more direct marketing approach, while others might appreciate a softer touch. Testing an email's content or design in North America might yield different results than in Asia due to cultural nuances. By segmenting, you can pinpoint what works best for each specific audience.
2. Matched Groups:
For the results of an A/B test to be reliable, the groups you're comparing should be as identical as possible in every aspect, except the one you're testing.
Random sampling can be a beneficial method here. If you know that 30% of your audience are in the age group 18-25 and 70% are 25 and above, maintain this ratio in both your test groups. This ensures that any observed difference is likely due to the variable you're testing, not underlying demographic differences.
3. Advanced Scheduling:
The time of day can significantly impact email engagement. It's not just about A/B testing different content but also ensuring it reaches your audience when they're most likely to engage.
Analyze past email campaigns to determine peak engagement times. If Group A tends to open emails in the morning and Group B in the afternoon, schedule your tests accordingly. This maximizes the chances of your email being seen and acted upon.
4. Multivariate Testing for the Experienced:
While A/B testing focuses on one variable (e.g., subject line A vs. subject line B), multivariate testing examines how multiple variables interact and influence user behavior.
Suppose you want to test a new subject line and a new call-to-action button simultaneously. Multivariate testing would allow you to do this, showing combinations of both changes to different segments. But remember, you'll need a considerably larger audience to draw reliable conclusions since you're testing multiple combinations.
5. Minimize External Influences:
External factors, like holidays or significant news events, can skew your A/B testing results by influencing subscribers' behavior.
If there’s a major holiday coming up, be aware that email engagement might be atypical during this period. People might be on vacation or busy with festivities, affecting open and click rates. Postpone your test or factor in these anomalies when analyzing results.
6. Duration & Consistency:
Running a test for too short a time might not give you enough data to make reliable conclusions, while too long might introduce new variables, complicating results.
For example, if you start an A/B test on a Monday and end it on a Wednesday, you might miss out on how subscribers engage with emails over the weekend. Aim for a full business cycle to ensure you're capturing a representative sample of user behavior. However, avoid stretching it too long, like over a month, as other external factors might come into play.
Future Trends in A/B Testing for Email Marketing
A/B testing, a cornerstone of digital marketing optimization, has evolved over the years and is set to experience even more advancements in the near future. Here's a glimpse into what lies ahead:
- Real-time Optimization: Instead of waiting for the conclusion of A/B tests to decide on a winner, technologies are emerging that shift traffic in real-time to the better-performing variant, ensuring optimal user experience and conversion throughout the test.
- Personalization at Scale: As businesses collect more data on their users, the future will focus on hyper-personalized experiences. Rather than just A/B testing, brands will create multiple variants tailored to different audience segments.
- Integrated Testing Platforms: Tools will offer comprehensive solutions that merge A/B testing with other functionalities like heat mapping, session recordings, and user surveys, providing a holistic view of user behavior.
- Predictive Analytics: Instead of solely relying on past data, businesses will use predictive analytics to forecast how certain changes can impact future user behavior, guiding the A/B testing process.
- Predictive Personalization: With AI's capability to analyze vast amounts of data, it can predict the preferences of individual users, allowing for real-time customization of emails even before the user interacts with them.
- Adaptive Learning: As opposed to traditional A/B testing where one variant is declared a winner at the end, machine learning algorithms can constantly adapt and tweak email parameters based on ongoing user interactions, ensuring continual optimization.
Effective email marketing requires more than intuition; it needs data-driven decisions. A/B testing provides that essential data, guiding you to better understand your audience's preferences. As we discussed, implementing A/B testing can enhance your campaign's results. If you're looking for a tool to help streamline this process, consider using SendX.
SendX is an email marketing platform for companies that send large numbers of emails and want a high deliverability. It's not just another platform; it's your email marketing Swiss Army knife. Whether you're A/B testing, segmenting your audience, or crafting the perfect automated sequence, SendX has got your back. Take a free trial here & test for yourself.