They test send times, then choose the time that delivers the most opens and clicks. This only works if your customers are all in the same time zone. If not, there is a better way. We have already said that there is no better day to send e-mails. That's true when you generalize the data, but in your own business you definitely see lifts at peak times of the day or week. Best day to email finding the best time to send an email seems pretty straightforward. Just test a control group against a few variables and track the clicks, right? This neglects the extremely important
Variable mentioned above: time zones . You might find, for example, that sending a campaign at 10 a.M. Est gets the most clicks. But that could mean that some customers receive the company mailing list same email at 4 p.M. Or 3 a.M. It's the "Best" time compared to the other times you've tested, but it's not really optimal. It is a local maximum and not a global one. In general, emailing a group of people at exactly the same time, regardless of their location, will never yield the best results. Kill time there are several ways to tackle the tricky challenge of optimizing time zones. The rudimentary approach is to prioritize the most common time zone and send it to everyone at once. If you want to go further, you can segment users
By time zone and schedule newsletters separately. It works, but it's tedious. You need to find a way to track customers' time zone and persist that data. Send a message to your users without synchronizing data connect csvs, google sheets, airtable, databases, etc., define an audience and send a newsletter, all without having to sync your data again . This challenge becomes more difficult if you also trigger behavioral and transactional campaigns, as you have to manage two different sets of data. You cannot, for example, create segments based on a user's click or open behavior in a newsletter unless you export the data to excel and download new lists and vice versa. There's quite a bit of manual work, and you usually end up with two sets of data and one group of customers. It's far from ideal. “one team, one tool” one of the product mantras here at vero is “one team, one tool”.