A/B split testing emails: Everything you need to know

Split testing might sound technical, but it’s one of the simplest – and most powerful – tactics to improve your email marketing performance.

You’re probably already doing some version of it. Maybe you’ve tested two subject lines or compared click-through rates from different send times.

But email split testing (also known as A/B testing for email marketing) goes deeper than a hunch. When done properly, it’s a data-led way to refine your strategy, eliminate guesswork, and send emails your audience actually wants to open, read, and click.

Whether you’re running tests in-house or working with a B2B email marketing agency, knowing what to test and how to interpret results is what separates average emails from high-performing ones.

This guide unpacks everything you need to know – from what A/B email testing is, to which elements to test, how to run effective tests, and how AI is transforming the process.

Whether you’re fine-tuning a lead nurture sequence or launching your next big email campaign, it’s time to level up your email marketing strategy.

What is an email split test?

Let’s start with the basics.

An email split test (or A/B test) involves sending two different versions of the same email to a small portion of your audience. The goal? See which version performs better across key metrics – typically open rates, click-through rates, or conversions – and then send the winner to the rest of your list.

You’re testing one variable at a time, like:

• Subject lines
• Preview text
• Call-to-action buttons
• Email layout or imagery
• Sender name
• Personalisation tactics

Think of it as scientific experimentation for your email campaigns, but without the lab coat.

Why is A/B testing for email marketing so powerful?

Because email marketing is saturated. Your message has milliseconds to grab attention and earn a click.

Split testing helps you understand what resonates with your audience, allowing you to improve performance over time – rather than throwing content into the void and hoping for the best.

With consistent A/B testing, you can:

• Boost open rates with subject line tests
• Improve click-through rates by optimising your CTAs
• Increase conversions by tailoring layout, language, and timing
• Reduce unsubscribes and spam complaints with more relevant content
• Fine-tune your email marketing campaigns based on behavioural data, not assumptions

And if you’re using email automation or marketing funnels, the compound impact of tiny improvements at each stage can be massive.

What’s the difference between A/B and multivariate testing?

Good question.

• A/B testing = testing one element at a time (e.g. subject line A vs. subject line B)
• Multivariate testing = testing multiple elements at once (e.g. subject line AND CTA AND image)

Multivariate testing can be powerful, but it requires a large audience to reach statistical significance. If you’re working with a smaller list, stick to classic A/B tests to get reliable results faster.

What is statistical significance?

A statistically significant result is, basically, just another way of saying, ‘Are these results real or a fluke?’.

When it comes to A/B testing, statistical significance helps you decide whether one version of your email actually performed better than the other. And not just by chance, but because of changes you made.

Reaching statistical significance means you’ve collected enough data to trust the outcome. 

Without it, you could be choosing a winner based on randomness – that’s not strategy; it’s a gamble.

Why email A/B testing matters

If you’re still relying on your marketing team’s “best guess” when creating email campaigns, you’re leaving performance on the table.

Email split testing isn’t just a nice-to-have – it’s a must-have. Why? Because it delivers concrete, measurable improvements to your email marketing strategy, backed by real audience behaviour rather than assumptions.

Here’s what effective A/B testing can help you achieve:

1. Maximise engagement – across every audience segment

Your audience isn’t one-size-fits-all. By split testing everything from subject lines to content formats, you can find out how different segments respond. Then, you can tailor future campaigns to speak their language. Higher open rates? Check. More clicks? You bet.

2. Improve email performance over time

Each test gives you valuable insight into what works (and what doesn’t). Over time, you build a playbook of winning elements: subject line lengths, button placements, CTA phrasing, send times, layout design, personalisation variables, you name it.

The more you test, the more you know. And the better your email performance metrics become, campaign after campaign.

3. Boost conversion rates – not just opens and clicks

Testing subject lines might get you noticed, but testing CTAs, body copy, and layout helps you nudge your audience through the funnel. The end goal? More conversions, better ROI, and a sales team that actually wants to hug you.

4. De-risk your big creative ideas

Got a bold new campaign idea but worried it might flop? A/B testing provides a way to pilot-test messaging, visuals, or formats before committing to them. It’s like putting a helmet on your creativity – you still take risks, but they’re smart ones.

5. Make the case for bigger campaigns (with data)

If you’re trying to secure more budget, resourcing or exec buy-in for email marketing, a track record of statistically significant test results gives you powerful proof. It’s hard to argue with clear evidence that version B outperforms version A by 27%.

What to test in your email marketing campaigns

A well-run email split test is all about precision. The secret? You don’t change everything – you isolate one variable at a time, so you can see exactly what’s driving results.

But with so many moving parts in a typical email, where do you even start?

Here are the most important email elements to A/B test – along with what you can learn from each one.

The subject line

The first (and sometimes only) thing your audience sees. Test for:

• Length: Short and snappy vs longer and descriptive.
• Tone: Formal vs conversational.
• Personalisation: “You” vs the recipient’s actual name or company.
• Curiosity vs clarity: Trial Teasers or straight-up offers.
• Emojis: 🚨or 🤢? Only your audience knows.

Preview text

This is the bit that shows up after the subject line in the inbox. Try:

• Supporting vs standalone copy.
• Urgency vs value-led messaging.
• Personalisation and brand tone.

Why it matters: Preview text can tip the balance between an open and a scroll-past.

The sender’s name and address

People open emails from people they trust. So test:

• A named sender, e.g. Kit from Sopro, vs your company name.
• Different departments (sales@ vs marketing@).
• Human vs brand tone.

Why it matters: Sender name impacts trust, familiarity, and open rates.

Email copy

This is where the magic does or doesn’t happen, so test things like:

• Body length: Short and punchy vs longer-form.
• Paragraph structure: Dense text vs plenty of white space.
• Tone of voice: Friendly vs authoritative.
• First vs second person (“we do this” vs “you’ll get this”).

Why it matters: Your copy sells the click. This is where you create demand, tackle pain points, and build momentum.

Thinking of changing up your prospecting emails? Read our guide to injecting a bit of humour into them.

The call to action (CTA)

CTAs can be make-or-break. Try testing:

• Copy: “Book a demo” vs “See it in action”.
• Position: Above the fold vs the bottom of the email.
• Design: Button vs hyperlink.
• Quantity: One CTA vs multiple touchpoints.

Why it matters: CTAs drive click-through rates and ultimately lead to conversions.

Landing pages

Don’t forget, the prospect’s journey doesn’t stop when they’ve read your email. If the page you send people to doesn’t match the message, people will lose interest fast.

What to test:

• Headline alignment with email CTA.
• Visual consistency.
• Lead capture forms (length, fields, CTAs).
• Personalised content on landing pages.

Wondering how to make your landing pages stick? Discover four ways to make kick-ass landing pages.

Images and design

Visuals influence attention and engagement. Test:

• Static images vs GIFs.
• Stock vs branded visuals.
• Plain-text vs HTML layouts.
• Mobile optimisations (check rendering across devices).

Why it matters: Design affects skimmability, tone, and how your key messages land.

Send times and days

Timing matters – even with the best content in the world. To figure out what the best time of day to send prospecting emails is, try:

• Morning vs afternoon.
• Weekdays vs weekends.
• Optimised send time per segment (via AI or automation tools).

Why it matters: A perfectly crafted email means nothing if it hits the inbox at 2am.

Testing these variables over time gives you a full playbook of what works best for your audience – which means stronger performance across every future campaign.

How to set up email A/B testing the right way

Running an email split test might sound simple – send two versions and see which wins, right? Well, kind of. But if you want reliable, statistically significant results you can actually learn from, it’s worth doing things properly. Here’s how.

Choose one variable to test

This is the golden rule: test one thing at a time.

Want to compare subject lines? Great – make sure everything else stays exactly the same. 

Testing two completely different emails won’t tell you what actually made the difference.

Common single-variable tests include:

• Subject line
• CTA copy
• Email layout
• Image type
• Preview text
• Sender name

If you want to test multiple elements, you’re not doing an A/B test – you’re doing multivariate testing (which is a different beast entirely).

Set a clear goal

Before you hit send, you need to know exactly what success looks like. This will give your test purpose and help focus your analysis.

Some common goals:

• Open rate → test subject lines, preview text, or sender name.
• Click-through rate → test body copy, CTA wording, design, or layout.
• Conversion rate → test landing pages, offers, or CTA strength.
• Engagement time → test copy structure, tone, or content length.

Pick one key metric that lines up with the variable you’re testing and stick to it.

One metric we don’t recommend tracking is open rates. Seriously, it’s a vanity stat. Don’t agree? Check out why it’s time to stop tracking open rates in our guide. 

Split your audience properly

A good A/B test starts with a clean, randomised sample.

Here’s how:

• Divide your list into equal-sized segments (50/50 split is standard).
• Ensure each segment is demographically and behaviourally similar.
• If your audience is small, consider testing a subset first before rolling out the winning version to the full list.

Avoid segmenting based on geography, job title, or industry unless you’re specifically testing targeted messaging.

Divide your market properly with our step-by-step guide to B2B market segmentation.

Send both versions at the same time

To ensure a fair fight, you need to minimise external variables, like time of day, day of week, or news cycles.

So send your A and B versions:

• At the same time
• On the same day
• Using the same infrastructure

That way, if there’s a difference in results, it’s more likely due to the variable you’re testing, not the 2pm inbox slump.

Give it time

Too many marketers panic-send a test and call it after 30 minutes. But results take time to settle.

A good rule of thumb:

• Wait at least 24-48 hours for open and click-through rates.
• For conversion-based tests, wait a little longer (depending on your sales cycle).

Always aim to reach statistical significance. This could be 95% confidence that the observed difference isn’t due to random chance.

Analyse and take action

Once your test concludes:

• Identify the winning variation based on your chosen metric.
• Look at secondary metrics too (e.g. did one version have more unsubscribes?).
• Think about why the winner worked – not just that it won.
• Apply your insights to future campaigns.

Running structured A/B tests like this not only boosts performance, but it also builds a deep understanding of your audience’s behaviour, preferences, and motivations.

How to turn A/B testing into an ongoing strategy

A/B testing works best when it’s not just a one-off experiment, but part of a repeatable, strategic feedback loop that constantly improves your email marketing.

Build testing into your campaign planning

Don’t tack on an A/B test at the last minute. Factor it into your:

• Campaign briefs (e.g. “We’ll test two subject lines targeting X pain point”)
• Timeline (build in test send windows and analysis time)
• Resourcing (ensure someone owns setup, monitoring, and reporting)

When testing becomes a standard step, you improve more quickly and efficiently.

Test strategically – not just frequently

Testing for testing’s sake wastes time. Be selective and prioritise high-impact variables.

Focus on areas like:

• Underperforming metrics (low opens? test subject lines)
• New audience segments (how do they engage differently?)
• Key campaigns (e.g. product launches or seasonal promos)

Use past data to inform future tests – the goal is always to optimise, not just experiment.

Document your results and learning

Too many teams run tests and then… forget about them.

Avoid that fate by creating a simple testing log:

• What you tested
• Which version won
• The performance uplift (or not)
• What you’ll do differently next time

Over time, this becomes a goldmine of behavioural data you can use to sharpen your email strategy.

Use automation and AI to streamline testing

Manually building and comparing test versions is fine for simple campaigns, but it doesn’t scale.

That’s where AI-powered testing tools come in. More on that in the next section

How AI is changing the A/B testing game

AI is no longer just a buzzword floating around your marketing meetings – it’s now a powerful tool reshaping how marketers design, test, and optimise email campaigns. And when it comes to A/B testing, it’s a total game-changer.

Let’s explore how artificial intelligence is taking the guesswork – and grunt work – out of your email split testing.

Faster test generation

One of the most time-consuming parts of A/B testing is simply creating the variations. Writing two subject lines or CTAs might not sound like much, but scale that across multiple campaigns and audiences, and it adds up

AI writing tools like ChatGPT, Jasper, and Claude can generate:

• Variations of subject lines in seconds
• Alternative CTA copy that aligns with your brand tone
• Different versions of body content, including language tailored to different audience segments

Top tip: Use AI to ideate, not automate blindly. Think of it as your creative assistant, not your replacement.

Predictive performance insights before you hit send

Tools like Seventh Sense or Phrasee use AI to analyse historical performance data and predict which email variation is more likely to perform best, before you even launch your test.

These tools evaluate:

• Word choice and tone
• Subject line structure
• Personalisation impact
• Sentiment and emotion

That means less time spent on trial and error, and more confident decisions about what goes out the door.

Smarter segmentation

AI doesn’t just help with content; it helps ensure you’re testing on the right segments, too.

It does this by analysing:

• Behavioural data
• Engagement patterns
• Purchase history
• Website activity

AI-powered tools can automatically segment your audience into meaningful groups, then help you tailor and test content that resonates best with each one.

That’s not just better targeting, it’s more effective testing and more relevant messaging across the board.

Adaptive A/B testing in real-time

Traditional A/B testing waits until the test ends before rolling out a winner. AI-powered platforms can do better.

Some tools (like Mailchimp’s multivariate testing) will:

• Monitor test results in real-time
• Automatically declare a winner
• Instantly shift traffic to the best-performing variation

This is called adaptive testing, which means you get better results faster, with zero manual intervention.

Multivariate testing at scale

AI makes it easier to move beyond basic A/B tests and into multivariate testing, where you’re testing several variables at once.

This was traditionally complex, but modern AI tools can:

• Suggest which elements to test based on your goals
• Automatically generate combinations
• Analyse which specific element combinations drive the best performance

Perfect for fine-tuning subject lines, preview text, and CTA all at once.

Smarter reporting and insights

Once the test is complete, AI tools can dig into the data and highlight patterns a human might miss. Instead of manually checking click-through rates and guessing what worked, AI can surface:

• Which phrases drive more action
• What emotional tone boosts opens
• Which CTA lengths or button colours convert better

These insights feed directly into future email campaigns, creating a flywheel of continuous performance improvement.

It works with you

Let’s be clear: AI is powerful, but it’s not perfect. The most effective A/B tests come from a blend of AI’s speed and your team’s strategic thinking.

AI helps you:

• Generate and refine test ideas
• Predict and analyse performance
• Save time and scale effort

You bring:

• Brand voice
• Strategic intent
• Campaign goals

Together? That’s a winning combination.

Common A/B testing mistakes to avoid

Running email A/B tests is one of the smartest ways to optimise your email marketing strategy. But when it’s done badly? It’s like throwing darts in the dark and hoping for a bullseye.

If your results feel off, your tests flop, or your “winning” version underperforms in future campaigns, chances are one of these mistakes is to blame.

Testing too much at once

It’s tempting to change the subject line, header image, call to action, and sender name all at once. But if both versions perform differently, how do you know what actually caused it?

Instead, only test one variable at a time. That way, you isolate the impact of that specific change and gain a useful, repeatable insight.

Ending tests too early

Email performance doesn’t always stabilise in the first few hours. If you call a winner before the majority of recipients have opened the email, you risk making decisions based on incomplete data.

Good things come to those who wait, so let the test run long enough to reach statistical significance. Many platforms can help you calculate this, or wait until at least 80% of your audience has had the chance to interact.

Ignoring statistical significance

This one’s big. A tiny difference in open rate doesn’t mean much if your sample size is too small to be reliable.

Avoid this by using a significance calculator (many email platforms have them built in) to confirm that your results are statistically meaningful. A 2% uplift means nothing if your test only had 50 recipients.

Testing without a clear hypothesis

If you throw enough of something at a wall, eventually it’ll stick. But it’s not really a test, is it? It’s a shot in the dark.

Why not try setting a clear hypothesis?

This could be something like: “A shorter subject line will increase open rates among mobile users.” Now that you have direction and something to measure against, you’re on the right track.

Not segmenting your audience

Sending the same A/B test to your entire list ignores the fact that different audience segments behave differently.

To fix it, segment your test groups based on buyer personas, past engagement, or demographic data to gain more accurate and relevant insights.

Explore why you need to create buyer personas and how to do it in our guide.

Drawing conclusions from just one test

Even a well-run test can be a fluke. Maybe it coincided with a holiday, a news event, an anomaly of a week, or maybe the planets AND your chakras aligned at the same time (if only you’d played the lottery as well).

It’s better to be safe than sorry, so look for consistent patterns across multiple tests. The more data you collect over time, the more informed and effective your decisions become.

Forgetting to apply your learnings

You’ve run a great test, picked a winner, and then your next campaign uses something completely different. What’s the point?

Make the testing process matter by using your results to shape future campaigns. Apply the winning elements, build on what works, and keep learning with each iteration.

A/B testing is powerful but only when you do it right. Avoid these common mistakes and you’ll be rewarded with more reliable data, stronger email performance, and a clearer understanding of what your audience responds to.

What are the key metrics for A/B testing emails?

Your A/B test is only as good as the metrics you track. Before you start tweaking subject lines or testing CTAs, ensure you understand what success actually looks like.

Here are the key metrics that matter and when to use them:

Open rate

What it tells you: How many people opened your email.

Use it for: Subject line tests, sender name tests, or send time optimisation.

One thing to be aware of – open rates have become less reliable since Apple introduced its Mail Privacy Protection changes. They’re still useful, but treat them as directional, not gospel. For more insights on why open rates aren’t the be-all and end-all anymore, check out our guide on the end of email open rates.

Click-through rate (CTR)

What it tells you: The percentage of recipients who clicked a link in your email.

Use it for: Testing CTAs, images, layout, and body copy. If your subject line gets the open, CTR shows how well the rest of the email performs.

Conversion rate

What it tells you: How many recipients completed a desired action, like signing up, booking a demo, or making a purchase.

Use it for: Testing landing page content, offer framing, or messaging that connects clicks to action.

Bounce rate

What it tells you: How many emails didn’t get delivered.

Use it for: Monitoring deliverability. This isn’t a direct result of your A/B test, but critical for spotting issues with certain test variations (like spammy subject lines).

Unsubscribe rate

What it tells you: How many people opted out after receiving your email.

Use it for: Testing tone, frequency, or relevance. A spike in unsubscribes after one test version? Probably worth a rethink.

Spam complaint rate

What it tells you: The percentage of recipients who marked your email as spam.

Use it for: Avoiding inbox disasters. Even small changes (like a misleading subject line) can cause major trust issues.

Time spent reading

What it tells you: How long recipients stayed engaged with your email content.

Use it for: Gauging engagement with longer-form emails or newsletters. It’s not always about the click; sometimes it’s about attention.

Revenue per email (if tracked)

What it tells you: How much revenue you generated per email sent.

Use it for: Testing campaigns directly tied to purchases or high-intent CTAs. This metric brings everything back to what matters: bottom-line impact.

Don’t track everything, every time. Choose the one metric most closely tied to what you’re testing, and stick to it.

That way, you get clean data, clear results, and insights you can actually use.

Ready to turn A/B tests into actual results?

Running A/B tests is smart. Running tests that actually improve performance? That’s where the magic happens.

At Sopro, every single campaign we run is fuelled by data, backed by smart testing, and fine-tuned to hit your goals. This means:

• Smarter subject lines
• More clicks from the right people
• Emails that actually deliver ROI

Whether you’re starting from scratch or scaling a well-oiled engine, we’ll help you build campaigns that don’t just land, they convert.

Let’s optimise your outreach. Book a demo today.