The objective was to optimize the results generated by email marketing by determining which times of day were ideal for carrying out a campaign.
There are different ways to approach the question we are raising here. One option is to establish a testing system whereby the same version of the email is sent at different times of the day over several days . The main chemical manufacturers email lists problem with this option lies in the need to repeat the tests over a sufficient period of time for the conclusions to be significant. From an operational point of view, this is a tedious exercise that takes a lot of time and the conclusions can take a few weeks (depending on the frequency of the sendings). A second option, which we already analysed in a previous post, consists of analysing when the inflection points in openings occur
48 hours after the sending. Here we will address a different approach. It involves analysing the hours of the day when there were a greater number of sessions on the site.
In order to obtain data that was as “uncontaminated” as possible, it was decided to analyse the sessions during a period in which there were no email marketing sendings. This is important because email marketing campaigns have a significant impact on traffic, conditioning the moment when the user visits the site. By eliminating this variable, the data obtained more accurately reflected the “natural” behavior of the user regarding the moment of access to the site.
This is the comparison of traffic in the two markets.
analytics_markets
We have highlighted, using a red frame, the moments when a turning point begins prior to a rebound in sessions. We take into account a window of at least 3 hours after the rebound, so that the largest number of sessions is concentrated.
In market A, the trend changes occurred between 9 a.m. and 12 noon, between 2 p.m. and 4 p.m., and between 8 p.m. and 10 p.m. In market B, they occurred between 7 p.m. and 9 p.m. and 9 a.m. and 11 a.m. So, if we accept that the best time to send an email is right when the “trend change” in traffic occurs, we can conclude that the best times to send are 9 a.m., 2 p.m. and 8 p.m. in market A, and 9 a.m. and 7 p.m. in market B.
This analysis helped us detect the times of day when email marketing campaigns could have the greatest impact and enabled us to improve the metrics in general. It is a simple analysis that allows us to detect the time slots in which the greatest number of sessions are concentrated. Therefore, we will try to launch the campaigns when the user is most likely to check their email and spend time browsing. This analysis can be complemented with the one that determines the best time to send an email by analyzing the openings 48 hours after sending it.
