Email Deliverability – Part 1


Email Deliverability – Part 1


By: Matt Bacak

For marketers, emailing is a huge part of what you do. There are some reasons why your email might not make it to who you wanted it to.

  • The email server could be down
  • The email address could be invalid
  • And worse of all, it might be filtered

When an email is not received because the server is down or the address is invalid you are notified of its lack of delivery.

When an email is filtered, you have no idea if your email actually made it through or not.

There are a few huge factors that affect Filtering:

  • Company Reputation 77%
  • Content 17%
  • Links 6%

Those are the top 3 factors a spam filter searches for when filtering emails and deciding if your email is spam or not.

Another option is an auto responder server. Make sure you’re not sharing the server. You don’t want to be associated with spammers. A great way to find out what exactly filters search for when filtering emails, you can take a look at your own junk folder and look for common elements. Be sure to keep those elements away from your marketing emails.

When sending out emails to your marketing lists you definitely don’t want to look like spam. There are some easy ways to keep yourself from appearing like a spammer.

1.       Don’t Use the Recipient’s First Name in the Subject

2.       Always include a from line

3.       Do not use excessive punctuation

4.       Don’t send attachments

5.       Don’t sentences in ALL CAPS

These are the top tactics that most spammers use to get attention and make people read their emails.

Auto-Filters aren’t the only way that your emails might be blocked from being put in the inbox and admitted straight to the spam folder.

Users can also make changes to their filters to block

  • Specific Email Addresses
  • Specific Words or Phrases
  • Links or Images
  • Addresses not in their Address Book
  • Encoding Settings
  • Attachments

There are also different types of filters called Trained Content Filters. These filters learn from user selections and create their own schemes that figure out what is unwanted according to their users.

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