5 Step Test to Determine the Best Email Frequency

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There is a fine line between sending out infrequent emails and flooding your email recipients with messages to the point where they opt out. How can you determine what the best email sending frequency is for your subscriber list?

You guessed correctly if you said “test”! While conducting tests and publishing data on email sending frequency, each brand’s email marketing campaign objectives and user lists are different, requiring quite well testing to establish optimum sending frequency.

So, how do you begin an email send frequency test? Many people have been hesitant to conduct this test for fear of jeopardizing their lead generation efforts, but it is actually fairly straightforward. Let’s go over the steps you can take to run this test so you can get a sense of how frequently you should contact your email subscribers.

Step 1: Form Your Hypotheses

Reminisce about your favorite lab companion from high school science class. It is critical to assess success by determining what particular findings you anticipate observing from these tests. You may anticipate that raising your email send frequency from once a week to three times a week would boost your click-through rate by 45 percent, or that it will raise the number of “multigrain brain” leads who go to the prospecting stage as a consequence of your nurturing by 25 percent. 

Perhaps you have an alarmingly high opt-out rate and believe that reducing your email send frequency from every day to every other day would reduce your number of unsubscribes. You should generate several hypotheses to get the most out of these tests, and your hypotheses should be quite detailed.

Step 2: Select a List Segment

Consider this your sample size. Since your email list has previously been split, choose one section to test and make sure it is large enough to offer significant data. Make sure the list segment you choose corresponds to the hypotheses you’re testing. For example, if you’re looking to enhance your offer click-through rate among prospects, it’s not a good idea to test on a customer list section. Instead, you may pick a sample from your blog subscriber list that is not only large enough to provide useful data but is also used to receive messages from you with discounts.

Step 3: Determine Baseline Metrics

So now you understand whatever you want to test and whomever you want to test it on, you can define your current performance metrics for that sample. This step is critical because you will need something to compare the findings of your test against. Take note of the email marketing data you’ll need to assess test success, such as your open rate, deliverability rate, unsubscribe rate, and click-through rate for that specific sample.

And don’t be hesitant to broaden your focus beyond typical email marketing KPIs to include website performance indicators. For example, if you were to test the hypothesis of improving an offer’s click-through rate, you’d want to know how many email recipients not only clicked through the email offer but also filled out the form necessary to receive their offer.

Step 4: Design and Schedule Your Test Emails

Create a few test emails to spin through the list sample, adhering to your standard email marketing best practices. Now is not the time to try out fresh subject lines, put a new sender in the “from” column, or design a new email template. Such content modifications might distort your findings and should be reserved for a different round of tests.

After you’ve produced the emails, schedule them to be sent at the frequency specified in your hypothesis. For experiments that last more than a week, make careful to use the same days and hours to avoid adding another variable to the equation, since the time of day and day of the week have been known to distort findings. Again, this is a vital test to run, but save it for later.

Step 5: Measure and Analyze the Outcomes

Compare your findings to the hypotheses you developed at the outset and the baseline results you recorded. You should also review the data often throughout the trial so that you can respond to any significant fluctuations that may occur as a consequence of your change in emailing frequency.


Are the results you’re seeing encouraging? Do they support the assumptions you’ve proposed? Will these enable you to raise your email sends even more in order to see a positive impact on your bottom line without compromising factors like list size or quality? Is a reduction in sending necessary? Now that you can have a new baseline for success, iterate on it by starting a new email test, whether for frequency, template design, subject line, message copy, offer content, or any other number of factors you may test to improve the effectiveness of your email marketing.

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