The core of email marketing engagement is a newsletter tailor to your product, brand, and target audience. To accomplish this, it’s essential to continually test, analyze, and optimize your email campaigns. What does this look like in real-time? Let’s dig in.
Test your emails
The foundation to perfecting email engagement is testing what works and doesn’t work for your audience in every aspect. This includes testing the time of day you send, subject lines, copy, graphics, and other key elements of the email. Note that this Cambodia Phone Number Data may be different for each audience segment, product, and type of email. You send, It may sound overwhelming to test so many things with multiple segments. But thankfully there’s a systematic way to approach email tests that will simplify uncovering trends: A/B testing.
Segment your email subscriber list
To segment your subscriber list, divide your email list into smaller lists according to key characteristics. Such as demographic, business type, purchase behavior, or location. Segments will allow you to see what has the most impact on each brand audience as well as provide more targeted email marketing in the future. Ideally, your email marketing platform should have a segmentation tool that will make it easy to do. Here’s how it works on Campaign Monitor’s platform.
Form a hypothesis
Once you have segmented lists, it’s time to form a hypothesis, or “educated guess,” just like you would in a scientific test. To develop your hypothesis, first pick a segment Betting Email List of your list to focus on, then pick a single element to test that’s key for that group.
Split each segment into an “A” and “B” test group
Now that you’ve formed your hypothesis, split the subscriber segment in two: an “A” group for your control group and a “B” group for your test group. Split the segment equally at random to ensure the results aren’t skewed one way or the other. The easiest way to achieve random group selection is to use an email service provider (ESP) that has built-in A/B testing.
Create “A” and “B” test assets
For example, create two identical welcome emails, but send one at the time you typically send your welcome emails and one at the time reflected in your hypothesis. Following the hypothesis example above: if you typically send your welcome emails two days after the user joins, send your control email at this time. Your test group email could be sent 10 minutes after the new user joins to test the effectiveness against your baseline results from your control group.
