Is it really important to discover the best time to send email marketing campaigns? Absolutely yes! If they are launched at a wrong time, then it can weaken the campaign ROI entirely.
The problem is that it’s quite difficult to determine it because it depends on numerous variables and specific situations, e.g. the content of your email, your business, your target.
We’d like to share some guidelines regarding the best & worst times to send your email campaigns.
MONDAY
Monday is considered the worst day to send a newsletter, especially if you send out your mails to businesses. People are burdened with all the e-mails received during the weekend and are busy settling into their week.
TUESDAY
Tuesday is considered the optimal day especially to reach a corporate audience. People have cleaned the weekend spam from their inboxes and have organized their week.
WEDNESDAY & THURSDAY
Statistics show that mail campaigns sent on Wednesdays score the highest open rates and click-through rates and that Thursday is the most popular day for sending email campaigns.
FRIDAY
Fridays are questionable. If you’re sending out emails to businesses on Friday, do it before lunch times. Friday afternoon…forget it! People will be more likely to ignore your messages and then delete them before they actually open them.
SATURDAY & SUNDAY
If you are B2B avoid weekend emails. Most people use their work computers for internet access and when they receive them on Monday morning at the bottom they will either not see them or they will be deleted. On the contrary if you are sending to customers try to schedule one your newsletter on Saturday. It could give you a good response rate.
Finally we do not suggest late night e-mails because often get kicked as spam (most ISPs increase their filtering levels during these times).
In short: There is no magical formula. All this may vary according to your products and distribution lists.
Our biggest advice is test, test and test. Select one day, send your newsletter then analyze results. Then, select another day and compare with past results to determine what performs better.