Marketers should adapt their send time to each user individually, and send campaigns closer to the times when they are more likely to engage in downstream activity.
As you might have read in our previous blog post “Re-Thinking Send Time Optimization in the age of the Always On Customer“, Blueshift focuses on “Engage Time Optimization” rather than what marketers traditionally call as “Send Time Optimization”. Since we’ve posted this article, we’ve elaborated a bit on the details of the development of that feature on Quora (When is the best time (day) to send out e-mails?). Through this post however, we would like share more of those insights, and advocate for focusing on optimizing downstream user engagement metrics rather than initial open rates.
The idea of “Send Time Optimization” is not new, and has been around for quite some time. One of the more recent reports on this was posted by MailChimp in 2014, but articles and discussions on this topic go back as far as 2009 and older. The data science team at Blueshift followed the hypothesis that if there is a specific hour of the day, or day of the week that an audience is more likely to engage, that should reflect in increased open (or even click) rates when messaged at different times.
Open Rates vs Click Rates
In order to observe this effect (or the absence of it), we analyzed over 2 billion messages that were sent through Blueshift. Some of the results are presented in the graphs below for one of our biggest clients.
Through the Lens of Open Rates
“irrespective of the segment that was targeted, the audience size and the send time, the open rate is the highest in the first two hours after the send”
We looked at the open rate (%, shown on the Y-axis) in the first 24 hours after the send was executed (in hours, shown on the X-axis).
What you see are 18 email campaigns from one client over the period of one month (totaling over 20 million emails). On the top left, we see campaigns sent out on Monday, next, Tuesday, and so on – through Saturdays on the bottom right. There were no campaigns on Sunday for this client during this month. These campaigns were sent to audiences ranging from tens of thousands of users in specialized segments (e.g. highly engaged customers) to large segments of 2–3M users. The send times varied from 5AM – 12PM (in parenthesis in the legend).
What you can see from this graph, is that even though the campaigns were sent out on different days of the week and at different hours, the initial response in term of open rates is very predictable for the first hours. The conclusion from these plots is that irrespective of the segment that was targeted, the audience size and the send time, the open rate is the highest in the first two hours after the send. Depending on the actual time of the send you can achieve a slightly higher open rate in the first hour, but you might loose more ‘area’ in the following hours, accumulating to more or less the same open rates after some hours.
Through the Lens of Click Rates
Naturally, the question comes to mind if there is any measurable effect when we look at clicks, which can be considered as a deeper form of engagement by the users that received the message:
But as you can see from these second set of graphs where the Y-axis represents the click rate (%), we observed a very similar behavior: the actual response rate in terms of clicks does not significantly change when a campaign is sent at a different time.
We came to the same conclusion when repeating this experiment for opens and clicks for other clients in our dataset as well. After doing more in-depth analysis on our datasets, we observed that users that were targeted in email campaigns at certain times, showed engagement (e.g. visits to the website or app) at other times. Users prefer to engage deeply at certain hours of the day while casually browsing through out. Marketers should adapt their send time to each user individually, and send campaigns closer to the times when they are more likely to engage in downstream activity. You can find more info about this “Engage Time Optimization” in this post.