8 Email A/B Testing Best Practices to Achieve the Best Possible Results
A/B, split, or bucket testing is a strategy to understand which elements in your email campaigns work better, especially if you’re trying out new formats or techniques.
Imagine you create two email versions and want to determine which one has higher conversions. What do you do?
You A/B test your email campaigns to understand what messages resonate with your audience and get more conversions.
If you’re wondering where to start, we’ll show you the way — we’ve compiled email A/B testing best practices to boost your conversions and, ultimately, your revenue.
What Does A/B Testing Mean in Email Marketing?
Email split testing is a marketing practice that compares different versions of email campaigns to determine the most effective one.
To understand your campaign performance, you’ll typically compare email campaign engagement metrics such as open rates, click-through rates, and conversions.
This experiment is carried out by sending campaign variations to different audience segments. Usually, these tests have one variant element to decipher its importance. You can test two or more elements, but tracking them will be challenging.
Typically, you’ll follow these steps to configure A/B testing:
- Identify variables: Identify the elements that you want to test — it could be subject lines, sender names, email copy, CTAs or any other element that may impact your email campaign performance.
- Create variations: Next, create different versions of elements. For example, you can create a straightforward subject line and another with a creative approach.
- Distribute randomly: Divide your audiences into groups and send the email variations to different subscriber segments.
- Measure performance: Analyze the performance, including critical email metrics such as open and click rates.
- Choose a winner: Based on the performance metrics, determine which version of your email campaign achieved a better outcome.
- Iterate and improve: Regularly test and optimize your email campaigns for improved results.
When done right, A/B testing helps you maximize the effectiveness of subscriber communication.
The Importance of Email A/B Testing
Split testing your email campaigns will help you present solutions in a way that interests your audiences. Plus, it gives you valuable data for future marketing campaigns.
A/B testing is important because of different reasons, namely:
- Optimizing performance: It allows you to identify which elements of your email campaigns are engaging enough for a response.
- Data-driven decisions: Instead of making assumptions or best guesses, you’ll have concrete evidence to support your strategies.
- Personalization techniques: A/B testing helps you tailor email campaigns to different audiences based on their behavior and preferences.
- Maximizing ROI: Bucket testing ensures you get the maximum returns on your email budget.
Start by identifying common conversion issues — what could be improved or added to email campaigns?
These common email metrics will help you get started.
Common Metrics to Consider During Email Split Testing
The simplest method of choosing a primary metric is to observe what you want to improve. Compare your marketing campaign results with your quarterly or annual goals to figure out the differences in data.
These key metrics are the most used in A/B testing:
| Email Campaign Metric | Formula | What It Measures |
|---|---|---|
| Conversion rate | Number of conversions / Number of visitors x 100 | Directly measures subscriber interaction with your website, social media, or newsletters. |
| Click-through rate | Number of people who clicked / Number of impressions x 100 | Represents how many people clicked on a link such as a digital asset. |
| Bounce rate | Number of website visitors that took no action / Total number of website visitors x 100 | Measures how engaging or useful your website content is depending on subscriber actions. |
You can test any email campaign variable, such as mobile signups, free trial conversion rates, or sales. Let’s show you the common email variables you can test.
What Email Components Can You A/B test?
Re-visit your brand goals to decide what needs A/B testing. For example, if you’re running a promotion, you might want to test how well the CTAs perform. Or, if you want to build your email list, test the length, design, and readability of your emails.
Here are 5 common email components to test:
- From name: You can test how well subscribers respond to different sender names. For example, “Campaignrefinery”, “Alex@Campaignrefinery”, etc.
- Subject line: Experiment with different subject line lengths, tones, and offers. This is vital to increasing your open rates.
- Preview text: Another critical component that decides the success of your open rates — test variations of preview/preheader text.
- Email length and copy: Do subscribers want more content? Is my email copy performing well? You’ll know when you test them.
- Automation timing: Analyzing different send times allows you to send emails when your audiences are the most active, increasing the likelihood of conversions.
A/B testing your emails regularly helps you stay on trend and retain customer satisfaction. To help you get started, here are email A/B testing best practices.
8 Email A/B Testing Best Practices
Email A/B testing can consume a lot of time and must follow proper methods to get accurate insights.
If you’re just getting started, here are best practices to make testing easier.
1. Test One Variable at a Time
For successful split testing, start with one element in your emails.
Clubbing too many variables can lead to inaccurate results — for example, if you choose the subject line, image, and CTA as the variables in your split testing.
Although you may notice a significant improvement in open and click rates, you won’t know which individual element performed well.
Selecting one variable is crucial; here’s why:
- Determines the impact: Testing one element allows you to know the impact on the audience.
- Straightforward analysis: Analyzing helps you understand the effect of each element in your emails.
- Prevention of metric masking: Experimenting with multiple variables can introduce the risk of contradictory factors, where the effect of one metric masks the other.
- Resource efficiency: Testing one element requires less time, effort, and audience size.
2. Test Randomly
Segment your email list based on relevant criteria for more targeted insights. It’s best to choose a smaller batch and test randomly, ensuring you select the same sample size for all tests.
Random testing can increase the reliability and accuracy of results and also:
- Minimize bias: Randomly assigning recipients to different variations ensures your test groups represent the overall audience. This decreases the risk of bias and shows how your audiences would react to each variation.
- Fairness: Random assigning will give your recipients a fair chance to participate in the tests, regardless of factors such as demographics or preferences.
- Ethical considerations: It ensures no audience group is intentionally favored or disadvantaged in the testing process.
3. Choose a Large Sample Size
Select a larger test group to increase the accuracy of the results — the size of your sample can affect the statistical validity. Statistical significance is the likelihood that any differences in the results are not due to random chances.
Let’s imagine you have an email list with 10,000 subscribers, and you’re running an A/B test for promotional campaigns for a new product launch. Your goal is to measure the click-through rate to determine which variation is more engaging.
You have an audience size of:
- Sample A: 2000;
- Sample B: 1000.
In this scenario, the smaller group may not be representative of your overall audience, resulting in variations in performance. Additionally, it becomes challenging to make a fair comparison since variations on smaller groups may appear to perform better or worse than they would have if tested on a larger audience group.
4. Set a Time Frame
Assuming most of your audiences respond in a day or two, set a time frame for your A/B tests. This will ensure you don’t wait around for a long time to analyze results.
Setting a timeframe is crucial for these reasons:
- Paves the way for controlled experiments: A defined time frame ensures consistency and makes it easier to compare variations without the interference of external factors.
- Helps with efficient resource management: It allows you to make timely decisions and optimize your future email campaigns.
- Aids in continuous learning: A time-bound approach leads to regular analysis, helping you improve your email campaigns efficiently.
5. Test on Different Email Clients
It’s no surprise that your A/B testing can become challenging as the emails render differently on different email clients.
By using an email preview tool, you can preview, test functionality, and optimize your email campaigns from a single place. Or you can opt for test sending and viewing the email on each client separately, although this may take longer.
Here are more reasons to test your variations on each email client:
- Avoid rendering issues: Testing across different email platforms helps you identify rendering issues before sending emails to your entire subscriber list.
- Maximize reach: Your subscribers may use different email clients to access their emails. Testing ensures your messages are optimized for maximum reach.
- Improve engagement: A well-rendered email increases the likelihood of recipient engagement and overall campaign performance.
6. Identify Your Goals and Adjust Your Hypothesis
Don’t test email elements just for the sake of testing — define the purpose of email A/B testing. Identifying your goals measures the outcome and increases relevancy to your marketing objectives. This means you must understand what you want to measure and why.
Create a hypothesis — what do you believe will make your email campaigns better? You can find this out by comparing your previous campaign metrics.
The hypothesis can look something like this:
- Including a question mark in the subject line may perform better than a straightforward line.
- Changing the color of the CTA button to red will increase click-through rates compared to the current yellow button.
- Simplifying the number of images in emails will increase readability and engagement.
- Changing the sender name to a recognizable individual will increase open rates compared to a generic brand name.
Regularly refine your hypothesis based on your goals and test results to improve your email marketing strategy over time.
7. Test Continuously, Improve, and Repeat
Not all your A/B tests will result in a positive outcome. You may notice lower click-through rates or no conversions at all. The key is to have patience and keep testing vital email elements.
Rigorously testing your emails can have one or all of these beneficial effects:
- Adaptation to changing trends: As the digital landscape grows and audience preferences evolve, testing is the only way to stay ahead of the game.
- Optimization of key metrics: Regularly monitoring and testing campaigns helps you optimize important metrics such as open rates and conversions.
- Identification of best practices: Consistently testing variants helps you understand the strategies that work for your email campaigns.
- Risk removal: You no longer have to rely on assumptions and common practices; you’ll be able to avoid potential pitfalls in the long run.
8. Use the Best Testing Tools
A/B testing can be a meticulous process and you don’t want to lose all the precious data. That’s why it’s important to back them up using a reliable platform. You can choose from various code-free A/B testing tools to conduct error-free experiments.
Here are a few A/B testing software.
| Software | Pricing Model |
|---|---|
| Hubspot A/B Testing Kit | Free |
| Google Optimize | Free |
| Coschedule Subject Line Analyzer | Free |
| VWO | Pricing upon request |
If you want more perks, such as integrated list cleaning, powerful automation and segmentation, and analytics, Campaign Refinery can help. We tell you more about it below.
If you’re wondering where to start, here are 4 examples to get your A/B tests running.
4 A/B Test Examples to Improve Your Email Campaign Performance
We deep dive into the 4 logical A/B test examples to improve your chances of higher ROI.
| Test | #1 | #2 | #3 | #4 |
|---|---|---|---|---|
| Variable Tested | Subject line | CTA button color | Email layout | Send time optimization |
| Hypothesis | Curiosity and urgency increase open rates | Contrasting color attracts more clicks | Simple layout improves readability | Timing affects open and click-through rates |
| Variation A | “Last chance, don’t miss out on our sale” | Blue CTA button | Single-column layout with simple design | Emails sent on Tuesdays and Wednesdays at 10.30 am |
| Variation B | “Unlock your VIP access to limited-time offers” | Red CTA button | Mutil-column layout with additional sections | Emails sent on Sunday at 6.30 pm |
| Key metrics | Open rate | Click-through rate | Click-through rate | Open rate, click-through rate |
With A/B testing, the sky is the limit — you can experiment with email copy, links, email length, email content, personalization, emojis, preheader text, and so on.
Sometimes, it can get overwhelming, and you might have additional questions about split testing. We delve into the key aspects in detail in the section below.
Usual Questions About A/B Testing
Here are additional resources to simplify A/B testing for you.
What are the Disadvantages of A/B Testing?
While A/B testing is a valuable tool, it can have certain drawbacks — it can require additional resources, have intensive sample size requirements, provide limited insights, and increase the risk of false positives.
Additionally, it can become challenging without the right tools and practices. If you have a small email list, it could be difficult to attain statistical significance.
How Do I Analyze A/B Test Results?
Analyze your results by comparing key metrics to the original and variation groups. Look for statistical significance differences to understand which variation performed better.
How Long Should I Run Email A/B Testing?
The duration depends on your audience size, brand goals, and traffic volume. Generally, it’s recommended to run tests for a full business cycle to draw upon daily or weekly fluctuations.
Is A/B Testing Accurate?
A/B testing can be accurate when done properly. However, it also depends on your sample size, send times, and consistency. It’s important to be patient until the testing process is completed to optimize your email campaigns.
Which A/B Testing Tool is Best?
Choosing a tool depends on your budget, technical expertise, and specific needs. Google Optimize, Adobe Target, Split.io are great tools to get you started. If you’re looking for an all-in-one platform for emails and analytics, Campaign Refinery is the best choice.
Get More Leads to Improve Your A/B Testing
We discussed how a smaller sample size can affect your split testing outcome. If your email list is in the growth phase, you’ll need more subscribers before you can start experimenting with email elements.
While you can temporarily purchase email lists, it’s not a good email marketing practice and can affect your sender reputation in the long run.
If you feel stranded, this resource will help you grow audiences the right way.
Download the Lead Magnet Multiplier Short Course to learn the inside secrets to getting more subscribers and increasing your lead magnet consumption. You’ll have 7 days to discover our winning strategies and keep your access to the course forever.
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