5 Advanced Cold Email A/B Testing Techniques

If you’re already A/B testing your cold emails, you likely know how helpful it can be.

But once you’ve covered the basics and can test subject lines and CTAs, what’s next?

That’s where advanced A/B testing techniques come in. They can help you learn more, run more complex tests, and get answers faster. As a result, you find your best messaging quicker.

Basics of testing cold emails

But before we dive into advanced A/B testing techniques, here’s a quick refresher of the basics of cold email A/B testing.

Why do A/B tests?

A/B testing (also known as split testing) helps you find out what email copy is most effective at turning leads into customers.

Imagine someone recommends using AI videos in sales outreach to engage leads. But then someone else tells you to use infographics instead (which are faster and easier to make).

So how do you decide which to use in your next campaign – infographics or video?

Easy. 

Create two versions of your sales email – one with an infographic and the other with a video – and run an A/B test to see which is better. 

This allows you to drive your decision with data.

What email elements can you A/B test?

You can A/B test any part of your cold email, such as:

• Subject line: Do you use title or sentence case?
• Language style: How about a formal or laid-back style?
• Email body: Which email framework works best for your audience?
• Call-to-action (CTA): Should it be text or a button?
• Sender name: Do you use a full name and job title or just the first name?
• Send window: Which days and times should you send?

Even if you focus on just these elements, there are hundreds of tests you can run. 

Just make sure you test the most impactful elements first. For example, if you iron out a strong subject line, you’ll ensure people are at least reading your emails, allowing you to test the other elements more easily without skewing.

Principles of A/B testing emails

A reliable way to get trustworthy A/B test results is to follow these principles:

• Test one variable at a time – Your A and B versions must be exactly the same except for the element you’re testing. For instance, if you test the subject line, you shouldn’t change the email body and CTA.
• Use a large sample size – For reliable results, you’ll need at least 100-200 recipients per variation. The bigger the sample size you can get, the more reliable the test.
• Split your audience into groups – Your sample should be split equally and randomly into two groups. Otherwise, you can’t be certain that selection bias hasn’t affected the results.
• Run tests long enough – Allow your A/B tests to run for at least 1-2 weeks. Even if your audience leans strongly toward one of the versions, it’s better not to cut the test short, as these early results can be biased. 

Why go beyond basic A/B testing?

That said, while A/B testing is supremely useful for ironing out your email copy, typical A/B testing does have some drawbacks:

• You’re limited to testing a single variable at a time – You can’t cut corners by testing your subject line and email body side by side.
• For reliable results, you need a large sample – If you’re a small business with a niche market, you might struggle to find enough leads to ensure reliable results.
• It takes weeks to run tests that reach statistical significance – Two weeks is the earliest when you’ll be able to take action to optimize your outreach (and it might easily take up to a month if you factor in the time to analyze email performance).
• Test results can be overly broad – Since you can sample just one segment of your audience, you either have to apply the findings to everyone – risking inaccuracy – or run separate tests for each segment, which can be time-consuming.
• You need to control external factors to keep your results reliable – Ideally, all your A/B tests should run in exactly the same environment: same season, same time of day, same level of competitor activity, and same market dynamics. That isn’t going to work in real life though, as external factors are always at play, affecting your results.

Advanced A/B testing strategies can help you overcome some of these limitations, such as testing multiple variables in one go or getting results faster.

Here are a few scenarios where it might be a good idea to use advanced techniques to test email variations:

• You’ve already mastered the basics of A/B testing and want to make your testing process more efficient
• You see diminishing returns even though you run A/B tests regularly
• Your email campaigns consistently reach large numbers of recipients, and any change is risky and needs to be considered carefully 
• Your outreach is hyper-personalized
• You target multiple segments with different needs, e.g. established tech businesses and startups

Advanced cold email testing techniques can let you:

• Get more information for faster decision-making
• Obtain reliable test results with smaller samples
• Discover subtle patterns that often escape conventional A/B tests
• Find out what level of personalization works best for you

Advanced A/B testing techniques

There are five main types of advanced A/B testing techniques.

Multi-variant testing

Multi-variant testing allows you to test multiple elements of your cold email at the same time (yes, you can actually do that!). For example, test two subject lines and two CTAs to see which of the four combos converts most leads.

When to use?

You may want to use multi-variant A/B testing to:

• Get faster insights
• Optimize multiple elements in one go
• Reveal the interplay between email elements

Keep in mind, though, that multi-variant testing might not be the best fit for small samples (more on this in the next section).

How to set up?

The first step is to check that the A/B test platform you’re using supports multi-variant testing, or at least makes it easy to set up and track campaigns that follow different rules. 

If it does, the rest is relatively straightforward. You choose the elements you want to test, enter their variations, distribute your leads among them, and launch the test.

Mind that you’ll need a larger sample for multi-variant A/B testing than for conventional testing. 

Let’s say you’re testing two CTAs. You’ll need 100 respondents to receive emails with CTA 1 and 100 others to receive emails with CTA 2. But when you test two subjects and two CTAs, you’ll need 400 leads in total:

• 100 leads receive Subject 1 and CTA 1
• 100 leads receive Subject 1 and CTA 2
• 100 leads receive Subject 2 and CTA 1
• 100 leads receive Subject 2 and CTA 2

A rule of thumb is to double the sample size for each element you add to the test. Make sure you have enough leads before setting it up.

Example

You’re launching a cold outreach campaign for a teamwork software demo. You test two subject lines and two CTAs:

• Subject 1: Supercharge Your Team
• Subject 2: Upgrade [Company Name]’s workflow
• CTA 1: Book your demo
• CTA 2: Start a free trial

You create four versions of your email and send them to four equal groups of leads. Then, you do an email performance analysis to see which combo worked out better. 

In the end, you might discover that Subject 1 produced more conversions when paired up with CTA 1, but the ultimate winner was Subject 2 paired with CTA 2.

Sequential testing

Sequential testing means testing email variations in stages rather than rolling them out all at once. 

You can test the same element through all stages or switch between elements. For example, you can test Subject 1 and Subject 2, then pit the winner against Subject 3. Or you can add the winning subject line to both email versions and test different CTAs.

When to use?

You can benefit most from sequential testing when:

• You have a small or niche audience. Instead of burning through your list, you can use only a part of your sample at each stage, or reuse the leads.
• You want to test the first-touch cold email and follow-ups in one go.
• You want to optimize your email copy during testing, not afterward.

With sequential testing, you get something to work with at each stage. Instead of waiting for the test to run its course, you can make improvements on the go and immediately test them at the following stages.

How to set up?

For the first stage, you need to choose a high-level variation: an element that can make the biggest difference, such as the subject line or CTA. Create two distinct versions and run the test.

After the first test, refine your email using the winning variation. For example, add the winning subject line to both email versions and test the CTA placement.

Example

You’re testing an outreach campaign for sales software. During the first stage, you test two subject lines:

• Subject 1: Increase [Company Name]’s ROI
• Subject 2: Ready to get 30% more sales?

Let’s imagine Subject 2 wins. You add it to both email versions for the next test where you focus on CTA placement:

• CTA 1: In the middle
• CTA 2: At the end

Then say CTA 2 wins. You incorporate it into your email and move on to test another element that needs improvement. One element at a time, you gradually build a winning cold email formula.

Segment-specific testing

Segment-specific cold email testing means creating different variations for each lead segment. Instead of bundling everyone together, you approach each segment with an email tailored to their needs and pain points.

Alternatively, you may test the same variations across all segments but monitor their performance separately. That’s one way to find out which variations resonate best with each segment.

When to use?

You’ll want to consider segment-specific testing when:

• You target several distinct segments represented by different buyer personas
• You’re scaling into a new location or industry and want to optimize your outreach

To get meaningful results, you’ll need to have at least several hundred leads in each segment.

How to set up?

Identify your segments by product interest, industry, location, or firmographics. Next, set a goal for each segment. For example, you may want more opens from startups and more responses from established companies.

Then, choose the element to be tested and create email variations for each segment. Testing a small but impactful element – a subject line, CTA, or personalization tokens – usually works best.

Example

You’re targeting two segments – startup founders and tech company team leads – with workflow automation software. The goal is to determine which CTA will work better for each segment.

Use the same two variations for each segment:

• CTA 1: Start your free trial
• CTA 2: Grab your free 30-day pass

As a result, you might see that CTA 1 performs better with team leads, while startup founders prefer CTA 2. Now, you can approach each segment with the CTA that resonates with them.

Personalization-based testing

Personalization-based A/B testing focuses – yes, you’re absolutely right! – on personalized elements. You may start with one of our best cold email templates and see which personalized elements to add for the best result.

When to use?

Personalization-based testing can help you:

• Stand out in your respondents’ inbox by showing that you truly care and have done your research
• Understand which personalization elements trigger the desired response

Personalization-based testing works best when your outreach is already deeply personalized, or when you consider personalizing it to a greater extent. It can guide you in figuring out what email personalization techniques will benefit your campaigns the most.

How to set up?

You may want to test the following personalization elements:

• Greeting: Adding a greeting in English or the lead’s native language
• Name: Addressing the recipient by their first name (Chris) or their last name (Mr. Robin)
• Industry or role: Referencing the industry’s challenges or the lead’s specific role
• Pains or goals: Discussing their pain points or aspirations
• Case studies or broad picture: Including a mini case study of a company from the prospect’s industry or high-level stats as social proof

Create two versions of the element you choose. Use dynamic content to ensure that each lead receives a personalized email. For instance, in Version A, you can configure your campaign to use a greeting in French for leads based in France and in Spanish for those in Mexico, while Version B will greet everyone in English.

Example

Your goal is to test pain points vs. aspirations in an outreach campaign for a SaaS solution matching startups with investors. You write two email opening lines:

• Opening A: We can help [Lead’s Company Name] raise the money you need to fuel your growth and expand into [Lead’s Target Market].
• Opening B: We can help [Lead’s Company Name] raise the money faster and with less hassle. It’s hard to get funded in [Lead’s Industry] today, but we’ve found a workaround.

Say Opening A generates more responses. In this case, you can keep using it in your outreach, personalizing emails to address the leads’ aspirations rather than pain points.

Time-based testing

Time-based A/B testing means sending the same message at different times while adjusting for each recipient’s time zone. You’re not experimenting with email content here; you zero in on the best send window.

When to use?

Time-based testing makes the most sense when:

• You’re struggling to increase open or response rates. Send time can dramatically improve these. Your leads are likely to have fewer distractions or more bandwidth at certain times.
• You target a global audience. When one lead’s 9 a.m. is another lead’s 7 p.m., accommodating time zone differences ensures you reach everyone during their business hours.

Time-based testing can help you discover the optimal send window to make your email stand out. If everyone is blasting emails between 9 a.m. and 12 p.m. on Monday, your message could get buried. Simply timing it for a quieter moment can boost open rates.

How to set up?

You may want to test these time variables:

• Time of day: Morning vs. afternoon vs. evening
• Day of the week: Weekday vs. weekend
• Time zone: One local send time for everyone vs. different local times for different regions
• Holidays: Steering clear of major holidays vs. sending the day before (counterintuitively, the latter can sometimes drive higher engagement)

Next, group your leads by time zone. Set your send times to reach everyone at the intended hour, such as everyone’s local 4 p.m.. Then, deliver an identical message to all groups but at different times.

Example

The task is to test one local send time vs. different local times for different regions. You sample 100 leads from Europe and another 100 from South America, and then set Send time A and Send time B:

• Send time A: 12 p.m. for everyone’s time zone
• Send time B: 3 p.m. for Europe and 9 a.m. for South America

You configure 50 randomly assigned European leads to receive your email at 3 p.m. their local time and 50 randomly assigned South American leads to receive it at 9 a.m. their local time. The rest will receive your message at their local 12 p.m.

As a result, you can figure out the best send windows for different regions, and these windows might not be the same.

Metrics for advanced A/B tests

Beyond the usual email performance metrics (open rate, click-through rate, and conversion rate), there are other advanced metrics that can provide deeper insights.

Engagement over time

Engagement over time measures the time recipients keep your email open. It shows whether people actually pay attention or merely skim your message. 

Low engagement over time signals your content might be missing the mark. 

Bounce rate patterns

Tracking bounce rates across all your emails can show important patterns. A single spike can be a fluke, but consistently high or climbing bounce rates might be a signal of a deliverability issue. 

An increase in hard bounces could mean your lead list is deteriorating as more recipients abandon their emails. 

Soft bounces can hint that your emails might be triggering spam filters.

Funnel progression metrics

Funnel progression metrics measure the percentage of recipients who moved down your sales funnel by performing the target action:

• Downloading a lead magnet like a guide or ebook
• Requesting a price quote
• Booking a call
• Signing up for a free trial

You can use metrics that measure your sales funnel instead of the click-through rate to capture how many leads took a step closer to becoming customers rather than merely clicking the link.

What to watch out for when doing advanced A/B testing

As you can imagine, advanced A/B email testing can be tricky. 

Here are a few pitfalls to watch out for.

Overcomplicating test design

It might be tempting to test your subject line, email body, and sending window all in one go. But overcomplicating your test design can make it hard to understand what’s really working.

If you test too many things at once or use confusing setups, it’s easy to get unclear results. Not only does this make data and test management a pain, but you run the risk of lacking a large enough sample size to get valid test results. Besides, simpler tests often give clearer answers and are easier to manage.

Misinterpreting results

As you run more advanced or nuanced A/B tests, the data can get harder to understand. If you don’t read or understand your results correctly, you might end up with a version that doesn’t really work better. This can lead to wrong decisions and worse results over time.

Overlooking statistical significance

Advanced A/B testing strategies aren’t a way to circumvent statistical significance and sample size requirements. If you overlook statistical significance, you might think one version is better just because of random chance. Without enough data, the results aren’t reliable. This can lead to choosing the wrong option and making changes that don’t actually improve your cold emails.

Losing sight of the target audience

Data-driven email marketing revolves around numbers, but behind those numbers are real people. Make sure the changes you test are delivering meaningful value to your target audience.

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