How Spamfilters Work

AllSpammedUp has a post describing the primary techniques anti-spam filters use to identify mail as spam or not spam. While is this not sender or delivery focused knowledge, it is important for people sending mail to have a basic understanding of filtering mechanisms. Without that base knowledge, it’s difficult to troubleshoot problems and resolve issues.

Any anti-spam system that is worth using will contain a range of preventative measures and features that are used to determine whether an email is likely to be spam or not.  As a complete solution they can be very effective, but taken individually and their weaknesses become more apparent. […] when you combine a number of different techniques into a single system, with each technique applying a “likelihood” score to each email that is checked, the system can be quite effective.
For example, if an email is from an IP address that is not considered a likely spam source (no score increase), but contains spam-like content (score increased according to severity), and fails sender verification (increases score again) , the combined “likelihood” score may reach the configured threshold for the system and cause the email to be treated as spam.

This is the concept I try to convey by using my bucket metaphor.

Confirmed opt-in
Going out of business email strategies

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I spent the morning in multiple venues correcting mis-understandings of confirmed opt-in. The misunderstandings weren’t so much that people didn’t understand how COI works, but more they didn’t understand all the implications.
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The result is similar, when told their idea will hurt their delivery they fight tooth and nail. On good days they will argue and decide to listen. On bad days they go off and do what they were warned not to do.
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