Subject lines are one of the first things your subscribers see when they go into their inbox and have a proven and measurable impact on whether they go on to open, click and convert. Which is why so many email marketers spend so much time AB testing their SLs.
As you can see from the chart below, subject line B “Incentivize your email program without devaluing your brand” went into the lead almost immediately, and after 15 days had generated 32% more unique opens and 20% more unique clicks than subject line A “Incentive Programs: Your guide to giving”. The classic approach to AB testing subject lines would be to create 2 (or more) test cells of about 1% of the list, send each cell a different SL; monitor open and/or clicks for a number of hours or until the numbers are deemed to be representative and then send the winning SL to the rest of the list. Case closed!
But wait, life is never that simple; although the test results tell us which SL performed better they do not tell us why. Building your understanding of what works and why should be the key objective of your SL tests. If you rely on your test results alone - and many people do, it is much more difficult to achieve long term and consistent improvements in performance.
Here are 3 simple steps to follow if you want to ensure you make your subject line tests more meaningful:
Are you interested in improving opens, clicks or sales. It is not uncommon for the SL with the least opens to generate more click and/or sales.
Make sure that whoever conducts the tests goes on the record and documents which SL they predict will win (or lose), and more importantly why.
Do not reinvent the wheel every time you send an email out, which is what happens if you focus on creating the “perfect” subject line; you are much more likely to deliver consistent results if you have a proven and well-tested methodology.
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Where Alchemy Worx is concerned, we focus on clicks, so our subject line tests are primarily designed to optimize clicks. In this case our goal was to identify whether shorter and therefore more ambiguous SLs make more subscribers click through to the article than longer more specific SLsWe predicted the longer more specific SL would win, but were surprised by the margin! We have subsequently attributed this (and tested the theory) to readers looking to avoid the implied and personalized threat of “damaging your brand”.
Our long term take on this, and where it fits in to our SL methodology, is that when it comes to optimizing for clicks, the key is to convey as much information about the article(s) contained in Alchemy Worx as possible, even if it means a very long SL.There are a couple more things to bear in mind when conducting subject line tests.
Optimizing the click to open rate may lead you to a different conclusion. Many companies prefer to measure clicks as a percentage of opens rather than as a percentage of delivered and as you can see from the chart above that would have lead us to a completely different conclusion. If you are a company that does this, make sure you sense check your results by looking at actual numbers and not only percentages. More opens mean more clicks?
Don’t fall into the trap of thinking that subject lines will only affect your opens, or that more opens automatically means more clicks.
The example below shows the difference between opens and clicks generated by another subject line test. As you can see, the two subject lines generated the same number of opens, but a significantly higher proportion of subscribers who opened the message with the more descriptive subject line and then went on to click on a link.
Achieving a high open rate will increase the visibility of your message and your brand reach, but unless your key objectives are based around branding, getting clicks and conversions or sales should always be your objective.When should you cut off a subject line test?
The key to any testing in email is patience. Wait as long as you can to analyze results, and you're more likely to get a true picture of the impact, particularly when revenue/purchases are taken into account. In the example above, subject line that generated the most clicks was not in the lead until after 6 hours – longer than some automated systems are set to work with, and longer than most test-and-send testing is run for.
Similar analysis of sales based messages consistently shows a peak in purchases toward the end of the time scale, making it even more important for patience. A small difference in your purchase rate can have a significant affect on ROI.
Got a test result you’d like to share? Get in touch.