With regard to email marketing, we know a thing or two because we've seen a thing or two, similar to Farmers Insurance. Our annual report analyzes over 100 billion emails to determine trends and engagement in email marketing. And guess what? It depends on the industry, the audience, and the engagement goals when to send an email newsletter. There is no single-size-fits-all time to send an email newsletter.
For email marketing to be effective, newsletters must be tailored to your brand, product, and audience. To achieve this, you need to continually test, analyze, and optimize your emails.
Test your emails
Testing your email engagement in all aspects is the foundation for perfecting it, including the time of day you send, the subject line, the copy, the graphics, and other elements.
For each audience segment, product, and email type (for example, a feature announcement vs. a welcome email), this may vary. You may think it’s overwhelming to test so many different things with multiple segments, but A/B testing simplifies the process of discovering trends.
1. Segment your email subscriber list
Using key characteristics, such as demographics, business type, purchase behavior, or location, segment your email list into smaller lists. You will be able to see what is most effective for each brand audience and provide more targeted email marketing as a result of segments.
You should have a tool in your email marketing platform that makes segmentation easy. Here's how Campaign Monitor's platform does it.
2. Form a hypothesis
It's time to form a hypothesis, or "educated guess," just as you would in a scientific test. First, choose a segment of your list to focus on, then identify a single element that's key to that group.
Changing the time of your welcome emails, for example, may cause you to make an educated guess about the outcome. Just like setting a goal, your hypothesis needs to be S.M.A.R.T. (Specific, Measurable, Achievable, Relevant, and Timebound). In this case, your hypothesis could be “Sending welcome emails within 10 minutes of a user joining will result in an increase in open rates of 6% over the next three months with the new user segment.”
3. Split each segment into an “A” and “B” test group
Divide the subscriber segment into two groups: a control group and a test group. Once you've formed your hypothesis, divide the subscriber segment into two groups: a control group and a test group.
By using an email marketing services provider (ESP) that offers built-in A/B testing, you can ensure the segment is split equally at random to avoid skewed results.
If the groups are too small or not varied enough, the test will just reflect the results of randomness. To ensure the most accurate results, make sure the groups are large enough to provide statistically significant results. As a result of reducing the probability of randomness, a larger group will produce more accurate results.
An A/B test calculator can help you find the right size if you aren't a statistician or just don't like doing math. If you don't like math, you can use an A/B test calculator instead. The ideal starting size is usually 1,000 subscribers, but again, this can vary depending on the test and the subscriber list.
4. Create “A” and “B” test assets
Test a specific aspect of your email by creating two variations with just that one element changed.
Create two identical welcome emails, but send one at the time that your welcome emails are typically sent, and one at the time reflected in your hypothesis. Following the hypothesis example above: If you typically send your welcome emails two days after the user joins, send your control email at this time. Test the effectiveness of your test group email 10 minutes after your new user joins your control group to see if it compares to your baseline results.
You should only change the time when you sent the two emails. Multivariate testing is when you test more than one element at the same time. For instance, a multivariate test would be if you tested both the email's time of delivery and a different subject line. Multivariate testing should only be used when testing a combination of different elements. It is best to conduct multivariate testing only after testing each individual element individually.
To measure the impact of combining winning subject lines with the most effective time to send your email, you can, for example, test and find the most effective time to send your email. Testing all aspects of an email at the same time can make it difficult to determine which contributes to the overall result positively or negatively.
5. Run your test on a platform that can measure results
Send your test from an ESP with an analytics dashboard so you can easily measure and assess the results. Don't forget to isolate all variables except the one you are testing. You can test send times by using the same subject lines in both emails, and just changing the time they are sent. So if you are testing send times, don't write different subject lines and send them on different days.
Analyze the data
Once you have run your test, it is time to assess the outcomes and determine if your hypothesis was correct or not. To determine if the hypothesis above is true, for example, compare open rates for each email segment to see how the send time impacts open rates. The group with the highest open rate would win.
With an ESP that offers built-in A/B testing, the platform should do most of the work for you. Campaign Monitor's A/B test analytics dashboard, for example, allows you to see graphs of results along with conversion values.
In addition to analyzing the results as they pertain to the individual test, assess the results in light of your overall email newsletter performance. In this way, you will be able to gain additional insights into its possible impact on other email segments. Consider running the same test with other list segments if a personalized subject line increases open rates with new customers.
Optimize based on the results
In order to achieve long-term vitality, you need to implement the changes identified by the test results as well as continuously iterate on them. The data you collect and analyze can only go so far. A/B testing should be an ongoing practice in order to effectively adapt to your audience's changing needs, your brand's evolution, and, as a result, your email marketing campaigns need to evolve as well.
Before making changes to your email marketing, it is essential to establish a clear primary goal. The way you optimize your email will have a varying impact. As a result of our research, it has been discovered that the best time and day to send an email is not just subjective but also influenced by your goals.
On average, Mondays have the best open rates, but Tuesdays have the highest click-through rates. So, if you want higher open rates, Monday might be the best day. If you want a higher CTR, Tuesday would be a better choice. All of this is subjective to your industry and audience, so it's important to test this with your specific email list first.
Your changes should also be tailored to each segment of your audience since, once again, email optimization is largely dependent on the segment. Personalized, universal changes to your email marketing are generally less effective. In order for them to have the greatest impact, they must be tailored to the needs of each audience segment. According to Accenture research, 91% of consumers prefer brands that offer personalized experiences.
Uncover the data that will tell you the right time to send an email newsletter for your audience
A winning email marketing strategy is based on identifying the trends that a winning email marketing campaign follows.
Using Campaign Monitor, you'll be able to discover the trends specific to your audience. There are no gimmicky email functions, cute monkeys, or best guesses here, only real-time data that gives you a clear picture of what your customers want. In addition to discovering the best time to send emails to your audience, you'll also discover what converts them.
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