A/B Testing Email Subject Lines
A/B testing email subject lines is the practice of sending two or more subject line variants to separate portions of your list to determine which drives higher open rates before sending the winner to the remainder.
What Is A/B Testing Email Subject Lines?
The mechanics are simple enough: split your list into test groups, give each group a different subject line for the same email, wait for a statistically meaningful window (usually two to four hours), then send the winning variant to everyone else. What makes it tricky is that most senders get the execution wrong. They test on sample sizes too small to mean anything, declare a winner after 20 minutes, or test two variants that are so similar the result is noise dressed up as data. Good subject line testing isolates one variable at a time. You're either testing length, or tone, or a question versus a statement, or personalisation versus no personalisation. Testing 'Weekly update' against 'Your 5-minute briefing for Tuesday' looks like a fair fight, but you've actually changed length, structure, specificity, and personalisation all at once. When one wins, you've learned nothing you can apply next time. The best testers keep a log of every test, the hypothesis behind it, and the result, so they build genuine knowledge about their audience rather than just getting lucky with a single send. It's also worth remembering that open rates, while the obvious metric for subject line tests, aren't the whole story. A subject line that's deliberately provocative or misleading can lift opens while tanking click-through rates and damaging trust over time. The goal isn't to trick someone into opening. It's to find the framing that genuinely reflects the email's value and resonates with the people you're trying to reach.
Why It Matters for Newsletters
For newsletter creators, the subject line is the single highest-leverage piece of copy you write for every issue. It determines whether months of editorial work gets read or ignored. Even a modest improvement, say moving from a 32% open rate to a 38% open rate, compounds dramatically across hundreds of sends and thousands of subscribers. Over a year, that's a meaningfully larger engaged audience, which matters whether you're selling sponsorships, driving traffic, or running a paid subscription. Subject line testing also tells you something deeper about how your audience thinks and what they value. Patterns emerge over time. Maybe your readers respond better to specificity than cleverness. Maybe they open more when you use their first name, or maybe that feels gimmicky to them and they don't. You won't know until you test, and you won't remember what you've learned unless you track it systematically. Treat every send as a small experiment and you'll understand your list far better after 12 months than any analytics dashboard alone will tell you.
Best Practices
- Test on a minimum of 1,000 subscribers per variant before treating results as meaningful. Anything smaller and you're flipping a coin.
- Change only one element per test. If you want to test tone, keep length and structure identical across both variants.
- Set your testing window before you send, not after. Two to four hours is standard for most newsletters. Peeking early and calling a winner on gut feel defeats the purpose.
- Track every test in a simple spreadsheet: the date, the hypothesis, both variants, the sample size, and the result. The log is where the learning lives.
- Don't optimise purely for opens. Check whether the winning subject line also held up on click-through rate and didn't spike unsubscribes. A misleading subject line that wins on opens can quietly erode list trust.
How Aldus Handles This
Aldus makes subject line testing straightforward without burying it in a maze of settings. You can set up variants, define your test group size, and let Aldus automatically send the winning subject line once your testing window closes, so you're not manually checking results and scrambling to hit send. Results feed back into your performance history, so you can start to see patterns across your sends rather than treating each test as a one-off event.