8 minutes reading time
It’s the age-old debate in every email marketing conversation: “When is the best time to send an email newsletter?” The answer is — there is no best time. Yes, you read that right. If you want to increase email engagement rates, it’s not as simple as picking a specific day or time.
as Farmers Insurance, “We know a thing or two because we’ve seen a thing or two,” of email marketing.Every year, we study over 100 billion emails to curate a Annual report on email marketing trends and participation. Do you know what we found? The best times to send email newsletters vary by industry, audience, and engagement goals. There is no one-size-fits-all time for sending email newsletters.
At the heart of email marketing engagement is a newsletter tailored to your product, brand, and target audience. To do this, your email campaigns must be constantly tested, analyzed and optimized. What does this look like in real time? Let’s dig deeper.
Test your email
The foundation of perfecting email engagement is testing what works and what doesn’t work for your audience in every way. This includes testing the timing of your sending, subject lines, copy, graphics, and other key elements of your email.
Note that this may vary for each segment, product and type of email you send (ie feature announcements vs welcome emails). Testing so many things with multiple parts may sound overwhelming, but thankfully there is a systematic approach to email testing that will simplify spotting trends: A/B testing.
1. Segment your email subscriber list
arrive segmentation Your subscriber list, which divides your email list into smaller lists based on key characteristics such as demographics, business type, buying behavior, or location. Segmentation will allow you to understand the content that has the most impact on each brand’s audience and provide more targeted email marketing in the future.
Ideally, your email marketing platform should have a segmentation tool that can be done easily. Here’s how it works on the Campaign Monitor platform.
2. Form a hypothesis
Once you’ve segmented the list, it’s time to form a hypothesis, or “educated guess,” as you would in a scientific test. To develop your hypothesis, first choose a section of your list to focus on, then choose an element to test that is critical to that group.
For example, you can make an educated guess about the consequences of changing the time at which the welcome email is sent. Similar to setting goals, your assumptions should be SMART (specific, measurable, achievable, relevant, and time-bound). In this case, your hypothesis might be “Sending a welcome email within 10 minutes of a user joining will increase email open rates by 6% over the next three months with a new user base.”
3. Divide each section into “A” and “B” test groups
Now that you’ve formed your hypothesis, subdivide your subscribers into two groups: group “A” for the control group, and group “B” for the test group.
The segments are split equally at random to ensure the results are not biased towards either side.The easiest way to achieve random group selection is to use Email Service Provider (ESP) has built-in A/B testing.
Evaluate whether each group is large enough to provide has statistical significane results to ensure the most accurate data. If the groups are too small or not varied enough, the test will tend to reflect only random results. And larger groups will improve the accuracy of the results by reducing the probability of randomness.
A statistically significant group is determined by several factors and a lot of math.If you’re not a statistician or just don’t like doing math (because who would?), you can use A/B testing calculator. A good starting size is usually at least 1,000 subscribers, but again, this can be lower or higher depending on testing and subscriber lists.
4. Create “A” and “B” test assets
To test specific aspects of an email, create two variations of the same email, changing only a single element to reflect your hypothesis.
For example, create two identical welcome emails, but send one at the time you normally send welcome emails, and one at the time reflected in your assumptions. Following the hypothetical example above: if you normally send a welcome email two days after a user has joined, send your control email at this time. Your test group email may be sent 10 minutes after new users join to test effectiveness against your control group’s baseline results.
The only difference between the two emails should be when you send them. If you are testing more than one element, it is called multivariate testing. For example, if you’re testing when emails are sent and different subject lines at the same time, you’ll need a multivariate test. You should only use multivariate testing when you are testing combinations of different elements. It is best to implement multivariate testing only after testing each individual element.
E.g, back You test and find the most effective times to send emails, which you can then combine with winning subject lines to measure combined impact. If you try to test all aspects of an email at the same time, it can be difficult to determine which aspects have a positive or negative impact on the overall results.
5. Run the test on a platform where you can measure the results
Now it’s finally time to start testing. Make sure to send emails from ESP with a robust analytics dashboard to easily measure and evaluate results. Remember to isolate all variables except the one you are testing.So if you’re testing send times, don’t write a different subject line and Send on different days of the week or at different times of the day. Include the same subject line in both emails, just change the send time.
analyze data
After running the test, it’s time to evaluate the results and determine whether your assumptions are correct. For example, when testing the above hypothesis, look at the open rate of each email segment to measure the impact of delivery time. The group with the highest open rate is the “winner”.
If you’re using an ESP with built-in A/B testing, the platform should do most of the hard work for you.For example, in Campaign Monitor’s A/B Testing Analytics Dashboardyou can view both the results graph and the conversion value graph.
In addition to analyzing results related to individual tests, evaluate results against your overall email communication performance. This will give you further insight into the potential impact it could have on other email segments. For example, if personalizing the subject line increases open rates for new customers, consider running the same test on other listing segments.
Optimize based on results
The data you collect and analyze will only work if you implement it. The key to long-term viability is implementing the changes indicated by the test results and iterating on them continuously. Your audience’s needs will change, your brand may evolve, and as a result, your email marketing campaigns will need to adapt. To adapt effectively, A/B testing should be an ongoing practice.
Note that how you choose to optimize your emails will make a difference. Therefore, it is crucial to set a clear primary goal before making changes to your email marketing. our research Discovering the best days and best times to send emails isn’t just up to your subjectivity industry Also reach your goal.
For example, on average, Mondays have the highest open rates, but Tuesdays have the highest click-through rates (CTRs). So if your goal is to improve your open rate, Monday might be better. However, if a higher CTR is your goal, Tuesday is a better option. All of this is subjective to your industry and audience, so it’s important to test with your specific email list.
Tailoring changes for each segment is also important, because again, email optimization is largely dependent on the audience. Making sweeping, pervasive changes to your email marketing often doesn’t work well.they must be personalise And tailored to the needs of each audience for maximum impact.In fact, according to research Accenture, 91% of consumers are more likely to buy from brands that offer personalized experiences.
Discover data that tells you the right time to send your email newsletter to your audience
Campaign Monitor is an email marketing platform built for true marketing professionals. Our email marketing analysis reveals the trends that a successful email marketing strategy relies on.
Discover trends specific to your audience in your own Campaign Monitor dashboard. You won’t see any fancy email features, cute monkeys, or best guesses here. Instead, you’ll get real-time data that gives you clear direction on what your customers want and need. Not only will you find the best time to email them; you’ll discover what makes your audience convert.
