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When is the best time to send an email newsletter to your audience?

It’s a common discussion for every email marketing company: “when is the best time to send an email newsletter?” The honest answer is there isn’t one. If you want to increase engagement rates, it’s not simply picking a certain day or time.

The correct time to send an email newsletter will vary by industry, audience, and engagement goals. One size doesn’t fit all when it comes to sending an email newsletter.

The main factor for email marketing engagement is a newsletter catered to your product, brand, and target audience. To achieve this, it’s important to regularly test, analyse, and optimise your email campaigns.

Test your emails

The foundation to creating the perfect email engagement is testing what works and doesn’t work for your audience in all aspects. This should include testing the time of day you send, subject lines, copy, graphics, and other key elements of the email.

Be aware this may be different for each audience segment, product, and type of email you send. It may seem overwhelming to test so many things with multiple segments, but there’s a systematic way to approach email tests that will simplify uncovering trends: A/B testing.

1. Segment your email subscriber list

To organise your subscriber list, divide your email list into smaller lists according to key factors, such as demographic, business type, or location. This will allow you to see what has the most impact on each audience as well as provide more targeted email marketing.

Your email marketing platform should have a segmentation tool that will make it easy to do.

2. Form a hypothesis

Once you have segmented lists, it’s time to form a hypothesis. To develop your hypothesis, firstly pick a segment from your list to focus on, then pick a single element to test that’s key for that group.

You may make an educated guess about what the outcome would be changing the time you send emails. Similar to setting a goal, your hypothesis should be Specific, Measurable, Achievable, Relevant, and Time-bound (S.M.A.R.T).

3. Split each segment into an “A” and “B” test group

Now you’ve formed your hypothesis, split the subscriber segment into two: an “A” group for your control group and a “B” group for your test group.

Split the segment at random and equally to ensure the results aren’t one way or the other. An efficient way to get a random group selection is to use an email service provider (ESP) that has A/B testing.

Assess if each group is statistically significant to ensure the most accurate data using a large group is more effective. If the groups are not varied enough if too small whereas a larger group will increase the accuracy of results.

A significant group is determined by a few factors. You can find the right size by using an A/B test calculator. A good starting amount is usually at least 1,000 subscribers.

4. Create “A” and “B” test assets

To test a specific aspect of your email, create two variations of the same email with just that single element changed to reflect your hypothesis.

An example would be to create two identical welcome emails but send one at the time you usually send your welcome emails and one at the time reflected in your hypothesis.

Following the hypothesis above: if you typically send your welcome emails two days after a new user joins, send your control email at this time. Your test group email could be sent 10 minutes after the new user joins to test the effectiveness against your results from your control group.

The only thing different between these two emails should be the time you sent them. It is called multivariate testing is you test more than one element. An example, of a multivariate test would be if you were testing both the time the email is sent and different subject line. Only use multivariate testing when you are testing combinations of different elements. It’s best to implement multivariate testing only after testing each individual element.

For example, once you have done test to find the most effective time to send your email, you can then combine it with winning subject lines to measure the combined impact. If you attempt to test all aspects of an email at the same time, it can be difficult to work out which is having a positive or negative effect to the overall result.

5. Run your test on a platform that can measure results

Make sure you send emails from an ESP that has an analytics dashboard so you can measure and assess the results. Isolate all variables except the one you’re testing. If you’re testing send times, don’t change the times of day. Keep the same subject lines in both emails, and only change the time sent.

Analyse the data

After running your test, it’s time to assess the outcomes and decide if your hypothesis was correct or not. When testing the hypothesis above look at open rates for each email segment to measure the impact of send time. Whichever group had the highest open rate would be the one to stick to.

As well as analysing the results from the individual test, assess the results of your overall email newsletter performance. This will allow you to get further insights into the potential impact it could have on other email segments. If a personalised subject line increases open rates with new customers, consider running the same test with other list segments.

Optimise on the results

The data you gather and analyse will only go as far as you implement it. The key to long-term vitality is to implement the changes using the test results. Your audience’s needs change, your brand will likely evolve, and, your email marketing campaigns will need to adapt. To effectively adapt, A/B testing should be regularly.

It’s important to set clear goals before making changes to your email marketing.

Research shows Mondays, tend to, have the highest open rates, however, Tuesdays have the highest click-through rate (CTR). If you are wanting higher open rates, Monday may be a better option. However, if a higher CTR is your main goal, then Tuesday would be better. This is subjective to your industry and audience.

It’s also important to work your changes to each audience segment because email optimisation is dependent on the audience. Using universal changes to your email marketing tends to be less effective.

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