Are you still batch sending your emails? You’re annoying MANY of your subscribers. Are you still wondering when’s the RIGHT time to send emails to your users? It’s time to think differently about when to send your emails. Individualized Delivery Time Optimization is the key to greater engagement and higher satisfaction from your subscribers.
What’s the best time to send out a marketing email?
With a 5 minute search I found countless articles reference the best days to send your emails. (there are too many to list) And still other sources that go deeper and talk about the best time of day to send your emails:
- “Your emails may have a greater chance of being seen from 8 am – 10 am or 4 pm – 6 pm. ”
- “Open rates are highest in the early morning.
- “Consumer promotion emails are best sent between 7pm and 10pm.”
- “So if you want an email to get read, send it Sunday afternoon or early in the morning.”
- “What Are the Best Times to Send Email? 10:00 – 10:30 a.m. and 1:00 – 1:30 p.m.”
- “Subscribers’ top engagement times are 8 a.m. – 10 a.m. and 3 p.m.- 4 p.m.”
- “Subscribers are most likely to open email after 12pm, and the most active hours are 2-5pm.”
- “Response rates were highest at night and early in the morning.”
There are plenty of opinions, backed by data, discussing best day and time to send emails. Unfortunately, much of the data is built upon batch sending to manually segmented lists that treat the results in aggregate. While this seems efficient to send at the same time, in the long run, it creates more manual work and makes you work harder to acquire new users to replace the ones you have annoyed/alienated with your general send windows.
Here’s the catch: If you send enough emails, chances are something will stick…eventually.
The studies aren’t totally wrong on the surface. However, you need to look at the affect this method would have on your user base. You are annoying your users and risking massive churn.
So many different responses.
What advice should we take?
When is the best time to send out a marketing email?
Fundamentally, we’re asking the wrong question.
We shouldn’t ask what’s the ONE best time to send to EVERYONE.
We could ask: “When is the BEST time to send to each INDIVIDUAL.”
The better question to ask:
“When is each person most likely to engage with your brand?”
(after all, that’s really what we want – not just someone opening an email and reading it.)
Each subscriber on every list will be different. When you treat everyone the same, even if you segment your list by industry, demographics, preferences, or a mountain of other criteria, you’re ignoring the individual.
There is no clear, single best time or day to send marketing emails when you ignore the individual. There are only aggregate better and worse times.
Each subscriber’s individual behavior tells you when they are most likely to engage with your content. Over time, you will begin to build time windows when they are most proven to open emails and engage onsite. Easy, send at those times. In theory, it just sounds so simple, right? But how do you actually do that?
Enter Delivery Time Optimization (DTO)
So, what is Delivery Time Optimization?
Delivery Time Optimization (DTO) solves the important problem of when to deliver your emails to consumers, to maximize their chances of being noticed.
Done well, marketers remove the batch send methodology and, instead, send each individual email to each person at the optimal time. To actually do it well, you need machine learning to drive a deep understanding of each user, subscriber, or customer that builds behavioral profiles. The time at which you email is delivered has a direct impact on whether the person will actually read the email. After all, we’re all flooded with emails throughout the day. If your email is in their inbox when they are most likely to read email, your email is higher in their queue, and they are most receptive to receiving messaging from you. That individual is VERY likely to engage with that email and your brand.
The Curse of Position Bias
Marketers who can reach their email subscribers when they’re actively in their inboxes will gain a competitive advantage. Quite simply, having your message at the top of the inbox makes it much more likely to be seen and acted on – also known as Position Bias.
To illustrate this point, imagine if two marketers from competing companies sent out similar emails to the same person on the same day sometime in the morning. Their emails are similar, the only difference is their position within the reader’s inbox. One is close to the top, the other is close to the bottom. Which one do you think is most likely to be read and engaged with? If you said the email that‘s closer to the top, you would be correct. There is a great study on this Position Bias phenomena produced by Microsoft, “An Experimental Comparison of Click Position-Bias Models”.
Marketers who can find the magic moment to make sure that their message is higher up in their subscriber’s queue have a definite advantage. Delivery Time Optimization uses past user behavior to optimize when an email is delivered to each recipient to not only place higher in their message queue, but also to simply reach them at the time they are most likely to open your email and engage with it.
However, finding that “magic moment” has been a major challenge for email marketers. It isn’t magic at all; it’s sophisticated algorithms that actually learn from past user behavior. What does it take to leverage Delivery Time Optimization? Simply, powerful technology that leverages Machine Intelligence to understand each individual user receiving your emails and an automated system to take action on the data gathered.
Machine Intelligence Makes Scalable DTO Possible
The obstacle to overcome in Individual Delivery Time Optimization is to find the best performing time slot for each person. Individual User Behavioral Data is half of the effort needed to solve this problem — the other half involves Machine Intelligence.
In Part 2 of this post, we will look at the technology behind Individualized Delivery Time Optimization, how it works, and what results publishers see when they move from batched sending to DTO.