What is the Optimum Time to Send an Email Newsletter

What is the optimum time to send an email newsletter? is a perennial discussion topic in email marketing. There isn’t just one ideal time, is the reply. You did read that correctly. It’s not as easy as choosing a specific day or time to enhance email engagement rates.

When it comes to email marketing, we “know a thing or two because we’ve seen a thing or two,” much like Farmers Insurance. We analyse more than 100 billion emails annually to provide a report on engagement and trends in email marketing. And what have we discovered, do you know? Depending on the sector, audience, and engagement objectives, there are several optimum times to send email newsletters. The best time to send an email newsletter varies depending on the recipient.

A newsletter created specifically for your product, brand, and target market is the basis of email marketing engagement. Your email campaigns must be tested, analysed, and optimised over time in order to achieve this. How does this seem in actual time? Let’s dig in.

Examine your emails

Testing what functions and doesn’t function for your audience in every area is the cornerstone to perfecting email engagement. This entails testing the email’s subject lines, text, images, and other crucial components at various times of the day.

Keep in mind that depending on your audience, product, and email kind (for example, feature announcement vs. welcome email), this may vary. Although though testing so many variables with several segments may seem intimidating, there is a methodical technique to conduct email tests that will make spotting trends easier: Testing A/B.

1. Organize your email list into segments.

Divide your email list into smaller lists based on important factors like demographics, business type, purchasing patterns, or region to segment your subscriber base. You may use segments to understand what affects each brand audience the most and to deliver future email marketing that is even more specifically focused.

The ideal segmentation tool for your email marketing platform will make this process simple. On the platform of Campaign Monitor, it functions as follows.

2. Develop a theory

After segmenting your lists, you should create a hypothesis, or “informed guess,” just like you would for a scientific experiment. Choose a section of your list to concentrate on before selecting a single aspect that is important to that group to test in order to establish your hypothesis.

You could, for instance, estimate with reasonable accuracy what would happen if you changed the time at which you send welcome emails. Your hypothesis should be S.M.A.R.T., much like a goal (Specific, Measurable, Achievable, Relevant, and Timebound). Sending welcome emails within 10 minutes of a user joining will, in this instance, enhance email open rates with the new user category over the next three months by 6%, according to your theory.

3. Separate each segment into two test groups, “A” and “B.”

With your hypothesis in place, divide the subscriber segment into two groups: “A” for the control group and “B” for the test group.

To prevent findings from being biassed, divide the section evenly at random. Using an email service provider (ESP) with integrated A/B testing is the simplest approach to accomplish random group selection.

To secure the most reliable data, determine if each group is big enough to produce statistically significant findings. The test will be more likely to simply reflect the outcomes of chance if the groups are too small or not sufficiently diverse.

4. Develop “A” and “B” test materials

Create two copies of the identical email with just that one part altered to test your theory, then compare the results.

Create two identical welcome emails, for instance, and send one at your usual time and the other at the time indicated by your hypothesis. The control email should be sent at the same time as the welcome email, using the previous example of the hypothesis as a guide. To compare the efficacy of your test group email to the baseline findings from your control group, the test group email might be issued 10 minutes after the new user joins.

The time you send each email should be the sole distinction between the two. Testing using several variables is referred to as multivariate testing. A multivariate test, for instance, might be used if you wanted to test alternative subject lines and the time the email is delivered. Only test combinations of various factors when you should utilise multivariate testing. Moreover, multivariate testing should only be used after each piece has been tested separately.

5. Choose a platform that can measure results when you run your test.

Finally, it’s time to start your test. To quickly analyse and evaluate the outcomes, make sure your email is sent through an ESP with a robust analytics dashboard. Just be sure to separate everything else from the variable you’re testing. Thus, avoid using varied subject lines and sending at various times of the day or week when testing send timings. Just modify the time sent, but keep the subject lines the same in both emails.

Study the information

When your test has been completed, it’s time to evaluate the results and decide whether or not your hypothesis was true. Examine the open rates for each email segment while verifying the aforementioned hypothesis, for instance, to gauge the effect of send time. The “winner” would be the group with the highest open rate.

If you’re utilising an ESP with integrated A/B testing, the platform should handle the majority of the labor-intensive tasks for you. For instance, you may simultaneously examine graphs of your findings and conversion figures in Campaign Monitor’s A/B test analytics dashboard.

Adapt optimization based on findings

The use of the data you collect and evaluate will determine how useful it is. Implementing the improvements suggested by the test results and iterating on them continually are crucial for long-term viability. Your email marketing strategies must alter as your audience’s requirements change and your brand most likely develops. A/B testing need to be a regular procedure for successful adaptation.

Choosing to optimise email will have a variety of effects. Hence, before making adjustments to your email marketing, it’s crucial to have a clear primary purpose. According to our study, the optimum time and day to send an email depends not just on your business but also on your objectives.