When 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 compile 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 different 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 appear in actual time? Let’s get going.
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, copy, 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. Even though testing so many variables with numerous 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 can 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.
improved insights to create successful campaigns
improved insights to create successful campaigns
Our analytics package provides you with insightful, useful information to enhance your efforts.
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 following 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 segment equally at random. Utilizing an email service provider (ESP) with integrated A/B testing is the simplest approach to achieve random group selection.
To secure the most reliable data, determine whether each group is sizable enough to produce statistically significant results. The test will be more likely to simply reflect the outcomes of chance if the groups are too small or not sufficiently diverse. A larger group, however, will improve results’ accuracy by lowering the likelihood of randomness.
A few variables and a lot of math determine whether a group is statistically significant. Use an A/B test calculator to quickly determine the correct size if you’re not a statistician or simply dislike math (because who does?). Typically, a suitable beginning size is at least 1,000 subscribers, though this number can vary depending on the test and subscriber list.
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 effectiveness 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 only distinction between the two. Testing with multiple 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 sent. Only test combinations of various factors when you should use multivariate testing. Additionally, multivariate testing should only be used after each piece has been tested separately.
For instance, once you’ve tested and determined the best time to send your email, you may combine it with effective subject lines to assess the overall impact. It can be challenging to ascertain which elements of an email are contributing favourably or unfavourably to the overall result if you try to test them all at once.
5. Use 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 results, make sure your email is sent through an ESP with a robust analytics dashboard. Just be sure to isolate everything else from the variable you’re testing. Therefore, 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
After 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 can simultaneously view graphs of your findings and conversion values in Campaign Monitor’s A/B test analytics dashboard.
Examine the results in light of your overall email newsletter success in addition to how they apply to the specific test. You’ll be able to learn more about how it might affect different email segment types as a result. Consider conducting the same test with different list segments, for instance, if a customised subject line boosted open rates with new clients.
Adapt optimization based on results
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 change as your audience’s needs change and your brand most likely develops. A/B testing ought to be a regular procedure for effective adaptation.
Please take note that the effects of your email optimization choices will differ. Therefore, before making adjustments to your email marketing, it’s crucial to have a clear primary purpose. According to our research, the optimum time and day to send an email depends not just on your business but also on your objectives.
For instance, Tuesdays typically have the highest click-through rates whereas Mondays typically have the highest open rates (CTR). Therefore, Monday might be a better day if your goal is to increase open rates. But if a higher CTR is what you’re after, Tuesday would be a wiser choice. Testing all of this with your specific email list is crucial because it’s dependent on your business and target market.
Since the audience is once again a key factor in email optimization, it’s crucial to customise your adjustments to each audience segment. Broad, all-encompassing modifications to your email marketing strategy often have lower success rates. For the maximum impact, they must be customised and geared toward the requirements of each target segment. In fact, 91% of consumers are more likely to shop with a brand that provides a personalised experience, according to Accenture study.
Find the information that will allow you to know when to send your audience an email newsletter.
The email marketing software created for actual marketing experts is called Campaign Monitor. A successful email marketing plan is predicated on the trends that our email marketing statistics reveal.
In your own Campaign Monitor dashboard, find the patterns that are particular to your audience. Here, you won’t find any gimmicky email features, adorable monkeys, or educated guesses. Instead, you will receive up-to-the-minute information that will help you understand what your clients want and need. You’ll learn what motivates your audience to take action in addition to when to send them emails at the optimal moment.