How to Run an A/B Test

A/B testing, also known as split testing, is a conversion rate optimization strategy where you compare different versions or elements of an email, SMS message, or web page to see which performs better. Think of it as a scientific experiment for your marketing: you create two versions (A and B), show them to different segments of your audience, and measure which one drives better results.

For example, if you create two landing pages with differing color schemes and layouts, you can collect information about how users interact with your website across both mobile and desktop devices. A/B testing measures the differences between the two versions until you achieve statistical significance – the point at which you can be confident that any improvement in results isn't just due to chance.

A/B tests provide concrete data about various metrics that matter to your business:

• Email open rates and click-through rates (CTR)
• Spam complaints and unsubscribe rates
• SMS response rates and link clicks
• Purchase completion rates
• Form submission rates

A/B testing is versatile. You can test any element of your marketing campaigns, from email subject lines to SMS message timing, to discover exactly what drives your audience to take action.

While A/B testing compares two variations, some marketers opt for multivariate testing when they need to test multiple elements simultaneously. A multivariate test might examine how different combinations of headlines, images, and CTAs work together. For instance, testing how Headline A performs with Image B and CTA C versus other combinations. However, multivariate testing requires significantly larger traffic volumes to achieve statistically significant results, making A/B testing the more practical choice for most campaigns.

Additionally, before A/B testing, some marketers might start with an A/A test, which is testing the same version against itself to validate their testing setup. Running an A/A test helps confirm your testing tool is working correctly and establishes a baseline for future tests.

Common split testing variables in marketing

Successful marketing campaigns don't happen by chance. Every element in your email and SMS messages can significantly impact your conversion rates. By systematically testing these variables, you can create campaigns that drive better results. Here's a detailed look at the elements you should be testing:

• CTA text and button color: Your call-to-action influences whether viewers take the desired next step. Test different action words (like "Get Started" vs. "Learn More") and button colors that stand out from your design. For SMS campaigns, experiment with link placement and CTA phrasing that creates urgency.
• Headlines and subject lines: These are your first impression and determine whether your message gets opened. Test personalization, length variations, and the use of emojis in both email subject lines and SMS opening text. Questions often perform differently than statements, while including numbers can boost open rates.
• Images and visual elements: The right visuals can dramatically increase engagement and comprehension. Test different types of images (product photos vs. lifestyle shots), video thumbnails, and the ratio of images to text. For SMS, compare MMS messages with product images against text-only versions.
• Landing page layout and design: The web pages you send people should deliver on your message's promise and guide visitors toward conversion. Test elements like form placement, hero image variations, and mobile-responsive designs. Single-column layouts often perform differently than multi-column designs.
• Email content length: Finding the perfect content length can significantly impact engagement. Test short, scannable formats against longer, detailed versions. Break up text with white space, subheadings, and bullet points to see what resonates with your audience.
• Pricing display and offer descriptions: How you present your offer can dramatically affect conversion rates. Test different discount formats (percentage off vs. dollar amount), price anchoring techniques, and promotional language. For SMS, experiment with leading with the offer versus building up to it.
• Social proof placement: Customer testimonials, reviews, and trust indicators can powerfully influence decisions. Test placing social proof near your CTA versus higher in your message. For SMS, experiment with condensed social proof statements that pack impact into limited characters.

What is the goal of A/B tests?

Marketers use A/B testing to optimize every aspect of their marketing campaign through data-driven decisions. By systematically testing different elements – from web page layouts to email subject lines – you can discover exactly what resonates with your target audience and drives better test results.

A/B testing helps identify which messaging approaches, design directions, and content formats generate the strongest response from your audience.

Every A/B test is a scientific experience that begins with a test hypothesis about which elements should outperform others.

The process helps you:

• Understand which visual styles drive more engagement
• Determine the most effective messaging tone and format
• Identify the optimal timing for communications
• Discover what motivates your audience to take action
• Fine-tune your content strategy based on actual user behavior

When implemented properly, A/B testing helps reduce marketing costs by eliminating guesswork and focusing resources on proven approaches. Instead of launching broad campaigns based on assumptions, you can make targeted improvements based on real data. This methodical approach helps you build stronger connections with your audience while ensuring every marketing dollar is well spent.

Why A/B testing matters

Marketing decisions shouldn't be based on guesswork. A/B testing gives you real data about what works and what doesn't, helping you make smarter choices about your campaigns. Here are a few reasons why A/B testing is essential for your marketing success:

Data-driven decision making

When you test variations of the same SMS, web page, or email campaign, you gather concrete evidence about what works. Instead of relying on opinions or industry "best practices," you can make decisions based on how your specific audience behaves. This data becomes invaluable for future tests, creating a cycle of continuous improvement based on real user interactions rather than assumptions.

Improved conversion rates

A/B testing directly impacts your bottom line by showing you exactly what drives conversions. Systematically testing different elements can help you identify what persuades visitors to take action.

Even small changes, when validated through testing, can lead to significant improvements in conversion rates. Each successful test builds upon previous wins, creating a compound effect that can dramatically improve your marketing performance.

Enhanced user experience

Unlike usability testing, which focuses on how people navigate your content, A/B testing helps you understand which content versions perform better across different devices and platforms. Tracking behavior patterns and engagement metrics allows you to identify pain points.

This insight allows you to create smoother, more intuitive experiences that keep users engaged and moving toward conversion. Whether it's simplifying a signup form or reorganizing navigation, every improvement contributes to a better overall user experience.

Reduced risk and cost

Running A/B tests before fully implementing changes helps you avoid costly mistakes. Instead of rolling out major changes to your entire audience, you can collect data from a smaller sample to validate your ideas. This approach can help you save money while protecting your brand by ensuring new designs actually improve performance. When you do invest in changes, you can do so with confidence, knowing they've been proven effective through testing.

Step-by-step guide to running an A/B test

Running an A/B test is simpler than you might think. We've broken up the testing process into simple steps anyone can follow:

Define your objective

Every successful test starts with a clear goal. Figure out exactly what you want to improve – whether that's email open rates, SMS click-throughs, or landing page conversions.

Set specific, measurable metrics for success, such as "increase newsletter signup rate by 25%" or "improve email click-through rate by 15%." Having clear objectives helps you focus your testing efforts and measure success accurately.

Choose a variable to test

Select just one element to change in your test. This could be your CTA button color, headline copy, offer amount, or any other single variable.

While it's tempting to test multiple elements at once, focusing on one change ensures you know exactly what caused any improvement in results. For example, if you're testing an email campaign, choose whether to test the subject line, CTA text, or hero image – but not all three at once. This focused approach leads to clear, actionable insights.

Create a hypothesis

Before running your test, form a clear prediction about what you expect to happen. Your hypothesis should follow a simple format: "If we change [element], then [metric] will improve because [reason]."

For instance, "If we change the CTA button color to orange, click-through rates will increase because it creates better visual contrast with the page background." The test hypothesis helps you think through why you're making each change and what you expect to learn.

Split your audience and assign variants

Divide your audience randomly into two equal groups to ensure reliable results. Group A receives your control version (current design), while Group B sees the variation.

Most email marketing platforms and testing tools can handle this segmentation automatically. For accurate results, ensure each group has enough participants – typically at least 1,000 to 5,000 subscribers for email campaigns.

Run the test

Let your test run long enough to gather meaningful data. This can typically take anywhere from two to twelve hours. The exact duration depends on your sample size and email list size. Larger lists generally need less time to reach statistical significance. Watch your results closely during the testing period to identify clear performance patterns and ensure you're gathering reliable data before making decisions.

Analyze the results

Once your test has run its course, evaluate the results to see if there's a statistically significant difference between versions. Pay attention to both primary metrics (like conversion rate) and secondary metrics (like opens or clicks) to understand the full impact of your changes. Most testing tools will calculate statistical significance automatically.

Implement change based on feedback

If your variation performs better with statistical significance, implement the winning version. But don't stop there. Use these insights to inform future tests. For example, if a shorter subject line won in your email test, try testing even more concise versions in follow-up tests.

Sometimes, tests reveal that your original version performed better. That's valuable information, too. Each test, whether it produces a winner or not, provides insights that help optimize your marketing strategy.

How long does it take to run an A/B test?

Timing is crucial for accurate A/B testing results. You want to run your test long enough to get reliable data but not so long that you're wasting time and potential conversions. Our analysis of testing duration shows us some clear patterns about how long you should run different types of tests.

We looked at almost 500,000 of our users’ A/B tests that had our recommended 5,000 subscribers per test to determine the best wait time for each winning metric (clicks, opens, and revenue). For each test, we took snapshots at different times and compared the winner at the time of the snapshot with the test’s all-time winner.

For each snapshot, we calculated the percentage of tests that correctly predicted the all-time winner. Here’s how the results shook out.

For opens, we found that wait times of 2 hours correctly predicted the all-time winner more than 80% of the time, and wait times of 12+ hours were correct over 90% of the time.

Clicks with wait times of just 1 hour correctly chose the all-time winner 80% of the time, and wait times of 3+ hours were correct over 90% of the time. Even though clicks happen after opens, using clicks as the winning metric can more quickly home in on the winner.

Revenue takes the longest to determine a winner, which might not be surprising. Opens, of course, happen first. Some of those opens will convert to clicks—and some of the people who click will end up buying. But, it pays to be patient. You’ll need to wait 12 hours to correctly choose the winning campaign 80% of the time. For 90% accuracy, it’s best to let the test run for an entire day.

Split testing best practices

Want to get the most reliable results from your A/B tests? These three best practices will help you avoid common mistakes and ensure your tests give you trustworthy data you can act on.

Run only one test at a time

Running multiple tests simultaneously might seem efficient, but it can muddy your results. When you test one email subject line while also testing button colors in the same campaign, you won't know which change caused the improvement. Keep it simple and test one element at a time, measure its impact, then move on to your next test.

Ensure a large enough sample size

Size matters when it comes to testing. You need enough people in each test group to get reliable results. The larger your test sample size, the more confident you can be that your results aren't just due to chance.

Avoid testing during unusual periods

Holiday seasons, major sales events, or unusual news events can skew your results. For example, testing email subject lines during Black Friday week won't give you reliable data about what works during regular business periods.

Wait for typical business conditions to run your tests. This ensures your results will be useful year-round. If you do need to test during special periods, compare those results only to similar timeframes.

A/B testing tools

While there are various testing platforms available for marketers, Mailchimp offers a robust and user-friendly A/B testing solution designed specifically for email and SMS campaigns.

Mailchimp's A/B testing features make it easy to optimize your campaigns:

• Test up to three variations of your emails against each other
• Track real-time results through an intuitive dashboard
• Test subject lines, content, send times, and more
• Set your own test criteria and winner selection rules
• Get detailed reports showing exactly how each version performed

Just choose what you want to test, create your variations, and Mailchimp takes care of the rest.

Boost digital marketing success with split testing

A/B testing eliminates the guesswork from marketing by showing you exactly what works for your audience. Testing different elements of your campaigns helps you make improvements based on real data rather than assumptions. Mailchimp's testing tools handle the technical parts, making it easy to run tests and understand the results.

Mailchimp's features help you put these insights into action. You can automatically send the winning version to your audience, track results in real time, and use what you learn to improve future campaigns. Our platform's testing tools work for both email and SMS campaigns, giving you everything you need to create more effective marketing messages. Sign up for Mailchimp today.

---

Key Takeaways

• A/B testing helps optimize email and SMS campaigns by comparing different versions to identify what resonates best with your audience.
• Testing one variable at a time provides clear, actionable insights for improving campaign performance.
• Proper test duration and sample size are crucial for reliable results.
• Data-driven decisions from A/B tests can significantly improve conversion rates and ROI.

---