If you’ve spent any time in email marketing (or marketing in general), you are likely familiar with A/B testing. The concept is fairly simple. Test two versions of your marketing email (different subject lines, CTAs, send times, etc.) and send them to comparable audiences (often literally A/B, one email gets one version, the next gets the other, and back and forth it goes through the entire list). Then watch the performance, compare KPIs, and identify the winner. Then repeat. The winner becomes your new control group, and you test against it in new A/B tests. It’s simple, effective, and a staple of email marketing, going back decades.
While that simplicity is a benefit, it’s also a hindrance. Chances are, you have a wide variety of variables you would like to test. If you can only test one variable or combination of variables at a time, your campaign optimization process will be relatively slow and tedious.
This is where multivariate testing comes in. While most marketers are comfortable with the concept of A/B testing, multivariate testing is a whole different ballgame. If you asked 100 marketers to describe multivariate testing in one word, ‘complicated’ would likely come up a lot.
So What Exactly Is Multivariate Testing?
At its core, multivariate testing is the practice of testing multiple variables in your email simultaneously to find the best-performing combination. Rather than isolating one element at a time like A/B testing, multivariate testing lets you evaluate how different elements work together to drive performance, and analyse the results all at the same time.
Think of it this way: A/B testing tells you whether Version A or Version B wins. Multivariate testing tells you which specific combination of elements is responsible for the result.
For example, instead of just testing two subject lines against each other, a multivariate test might evaluate:
- Subject line A paired with Image 1 and CTA: “Shop Now”
- Subject line A paired with Image 2 and CTA: “See the Deals”
- Subject line B paired with Image 1 and CTA: “Shop Now”
- Subject line B paired with Image 2 and CTA: “See the Deals”
Each combination gets sent to a comparable sample segment of your list, and the performance data shows which version drives the highest performance from your audience, not just which single element performed best in isolation. For all you know, it may not work when combined with another element you hadn’t considered.
What Elements Can You Multivariate Test?
Most elements of an email are fair game for multivariate testing. Some commonly tested variables include:
- Subject lines — different phrasing, offers, length, or even the use of emojis
- From name — your personal name vs. your brand name vs. a combination
- Preheader text — the preview line that appears next to the subject line in the inbox
- Email body copy — tone, length, structure, or key messaging
- Images — product-focused vs. lifestyle, static vs. animated, or placement in the email
- Call-to-action (CTA) — button color, placement, wording (“Buy Now” vs. “Learn More” vs. “Claim Your Offer”, etc.)
- Send time — morning vs. afternoon, weekday vs. weekend
- Offers — different promotions or incentives tested against the same audience segment
The key is identifying which variables are most likely to optimize performance toward your specific goal, whether that’s open rate, click-through rate, conversions, or overall revenue.
Multivariate Testing vs. A/B Testing: What’s the Difference?
This is the question most email marketers have when they first encounter the term, so it’s worth being clear:
| A/B Testing | Multivariate Testing | |
| Variables tested | One at a time | Multiple simultaneously |
| List size needed | Smaller lists work fine | Works best with larger lists |
| Best for | Quick answers on a single element | Understanding how elements interact |
| Output | Which element wins | Which combination wins |
A/B testing is a great starting point and still has plenty of value, especially for marketers with smaller lists or when you need a quick answer on a single element. Multivariate testing is the next level, giving you a more complete, data-driven picture of what’s actually driving performance in your campaigns. It’s worth noting that while A/B testing works across list sizes both large and small, multivariate testing requires a large enough list to produce statistically significant results.
One doesn’t replace the other. Many email marketers use A/B testing to narrow down strong individual elements, and then use multivariate testing to find the optimal combination of those elements at scale.
Why Does Multivariate Testing Matter for Email Deployment?
Here’s where multivariate testing connects directly to your deployment strategy, and why it’s worth building into your process, not just running occasionally.
It removes guesswork from campaign decisions: Instead of going with your gut on what subject line to write or which offer to lead with, multivariate testing gives you real data from real subscriber behavior. Over time, those data-driven decisions compound into significantly better campaign performance.
It helps you understand your audience at a deeper level: You might discover that one audience segment responds best to urgency-driven subject lines with a bold CTA, while another segment prefers a softer, benefit-focused approach. Multivariate testing reveals those nuances more quickly than A/B testing can achieve.
It maximizes ROI on every send: Every email you deploy is an opportunity to learn. Marketers who build continuous testing into their deployment process gradually close the gap between what they’re sending and what their audience actually wants to receive, which translates directly into better open rates, higher click-throughs, and more conversions.
It protects your sender reputation: Campaigns that consistently generate engagement, opens, clicks, replies — signal to inbox providers that you’re sending content people want. Multivariate testing helps you get there faster, keeping your sender reputation healthy in the process.
A Few Things to Keep in Mind
Multivariate testing is powerful, but there are a few practical considerations before you dive in:
- List size matters. Because you’re splitting your audience across multiple combinations, you need a large enough list to ensure each variation gets statistically meaningful results. If your list is smaller, A/B testing may be the better starting point.
- Don’t test everything at once. Start with the variables most likely to move the needle, typically subject line, offer, and CTA, before expanding to more elements.
- Give tests enough time. Results can shift depending on when contacts engage. Rushing to a conclusion before your data has stabilized can lead to misleading outcomes.
- Let the data lead. It’s tempting to favor a version you personally prefer. The goal of any testing program is to let your audience tell you what works, not to confirm your existing instincts.
Building Testing Into Your Deployment Strategy
Multivariate testing isn’t just a one-time experiment; it’s most valuable when it becomes a consistent part of how you deploy campaigns. The best email programs aren’t built on a single winning send; they’re built on continuous learning and optimization over time.
That means making testing a standard step in your deployment process, not something you do when you have extra time. Whether you’re running a single campaign or managing a high-volume email program, the marketers who test consistently are the ones who improve consistently.
Thinking about how to build smarter testing and optimization into your email deployment strategy? OPTIZMO’s Deploy is designed to orchestrate continuous offer and creative testing automatically, so your campaigns are always working toward their best performance. Learn more about Deploy and be the first to know when it’s available.
To learn more about email deployment, email compliance, and other industry insights, check out our full blog here.
