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.
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