How to Send Emails that Get Opened

Written by Jeremy Hoover

Recently, there has been a lot of discussion aboutrepparttar (impending) death of email. From what you read on forum boards and newsletters from well-known internet marketers, email marketing is dead. Too many ISPs are taking it upon themselves to limitrepparttar 109488 number of emails you can send at one time, or are blocking your emails from your subscribers inboxes.

As a way around this, many marketers are telling you to get a blog and an RSS feed. This makes some sense, but before you invest in an ebook or multimedia course from a marketer, ask yourself if that marketer has a vested interest in moving you over to an RSS system (i.e., they conveniently sell an RSS starter kit, or affiliate for someone who does).

Onrepparttar 109489 contrary, though, email marketing is not dead. Recently, on a membership-only

Bayesian Filters

Written by Debbie Hamstead

A common problem with filters isrepparttar fact that they are a one-size-fits all solution to SPAM. The rules are concrete and only change based on input from updates fromrepparttar 109487 Anti-spam service.

SPAM changes too quickly to make that method effective. Additionally, what is SPAM to you may not be to someone else. That is where Bayesian filters come in.

They are very effective at eliminating SPAM and have very low false-positive rates for their users.

Bayesian filters are based on Bayesian logic, a branch of logic named for Thomas Bayes, an eighteenth century Mathematician.

This type of logic applies to decision making by determiningrepparttar 109488 probability of a certain event based onrepparttar 109489 history of past events.

Using this as a model seemed a logical step for SPAM filtering. If you can predict what SPAM will look like now based on what is has looked like inrepparttar 109490 past, you are halfway to repparttar 109491 solution.

To finish solvingrepparttar 109492 problem, Bayesian filters were developed to be dynamic and continue to be effective asrepparttar 109493 SPAM changes.

Bayesian filters are content based. They look for characteristics in each email that you receive and calculaterepparttar 109494 probability of it actually being SPAM.

These characteristics are generally words inrepparttar 109495 content andrepparttar 109496 header file information that each email contains. They can also include common SPAM HTML code, word pairs, phrases, and repparttar 109497 location of a phrase inrepparttar 109498 body ofrepparttar 109499 email.

Typical words in SPAM would be "Free" and "Win", while "humility" would probably not appear. The filter begins with a 50% neutral score forrepparttar 109500 email, and then adds points for SPAM characteristics.

Likewise, deductions are made for non-SPAM characteristics present. The total score is calculated and then action is taken based on its likelihood of being SPAM.

The filter does not assume that all arriving email is bad, rather that all email is neutral and should be considered equally.

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