- Establish Your Hypotheses
Take yourself back to high school science class, and channel your favorite lab partner. It’s important to determine what specific results you expect to see from these tests so you can identify success. For example, you might hypothesize that increasing your email send frequency from once a week to three times a week will increase your click-through rate by 35%, or perhaps it will increase the number of “wheat bread” leads that move to the prospecting stage as a result of your nurturing by 15%. Or perhaps you have an unnervingly high opt-out rate, and you think decreasing your email send rate from daily to every other day will also decrease your number of unsubscribes. You can (and should!) create more than one hypothesis to make the most out of these tests, and be extremely specific with the terms of your hypothesis.
- Choose a List Segment
Think of this as your sample size. Since your email list is already segmented (right?), select one segment that you will test, and ensure it is sizable enough to provide meaningful data. Make sure the list segment you select also aligns with the hypotheses you are testing. For example, if you are testing for an increased offer click-through rate targeted toward prospects, it isn’t wise to test on a customer list segment. Instead, you might decide to choose a sample (a sample, not the entire list) from your blog subscriber list that is not only sizable enough to provide meaningful data, but is also used to receiving emails with offers from you.
- Establish Baseline Metrics
Now that you know what you want to test and on whom, you can establish your current performance metrics for that sample. This step is crucial, because you need something against which to measure the results of your test. Note the email marketing metrics you’ll need in order to determine success in your test such as your open rate, deliverability rate, unsubscribe rate, and click-through rate for that particular sample. And don’t be afraid to expand your scope beyond traditional email marketing metrics to website performance metrics. For example, if you were to use the hypothesis of increasing an offer’s click-through rate, you would also be interested to know how many of the email recipients not only clicked through the email offer, but also completed the form required to obtain their offer.
- Create and Schedule Your Test Emails
Create a handful of test emails to rotate through the list sample, following your regular email marketing best practices. Now is not the time to experiment with creative new subject lines, test a new sender in the “from” field, or create a new email template. These types of content changes can skew your results, and should be reserved for a separate set of tests. Once you’ve created the emails, schedule them for the sending frequency you outlined in your hypothesis. For tests that exceed a week in duration, be sure to select the same days and times so as not to add another variable to the equation, as time of day and day of week has been known to skew results. Again, this is an important test to perform, but reserve it for another time.
- Measure and Analyze Results
Measure your results against the hypotheses you established in the beginning and the baseline results you recorded. You should monitor results frequently throughout the experiment, too, so you can respond to any dramatic swings that may crop up because of your change in emailing frequency. Are the results you’re seeing positive? Do they confirm the hypotheses you’ve outlined? Do they allow you to increase your email send even more to see positive gains to your bottom line without sacrificing things like the size or quality of your list? Or is a decrease in sending what’s in order? Now that you have a new baseline for success, iterate off of it by beginning a new email test, whether for frequency, template design, subject line, message copy, offer content, or any other host of items you can test to make your email marketing more effective.