In the heart of email lies communication. It was designed as a fast and efficient way for communication between two points. Email made data sharing easy and it also helped us to archive shared information. But every shine has its dark side. For email, spamming is one of those dark sides. Top ESP’s these days are fighting with spammers. Many efficient and intelligent codes have been written worldwide just to filter spams from hams.
We can classify spam codes on the basis of source as follows,
To fight better with spammers ESP’s do not revel their matrix and concepts of filtering emails. One can surely assume but its really very hard to pin point ESP’s filtering algorithms. ESP’s are using learning based codes to filter the behavior of spammer which makes it even harder to guess those key points.
Other than ESP’s some major open source projects are deepening theirs roots in order to filter spams. Though, a spammer has no face, we can sort them on the technical base. Many open source projects, like Spam Assassin, assumes some of the key features of spammers, like,
Definitely in today’s world above mention points are not sufficient to filter spammer, that’s why ESP’s are spending a lot of money and resources just to filter them more efficiently, but these points indeed help us to filter a majority of candidates. ESP’s are open for every possible solution to filter spam emails so even they are taking advantages of these open source projects.
ESP’s are doing a lot to filter spams but we don’t know how exactly are they implementing it ? Though, Open Source Projects help us to filter spams but only on some extent. ESP’s, like Gmail, understand their user better than anyone. They know
As mentioned above, ESP’s are using learning based algorithms to filter spams. A mail could be ham for one user and spam for another depending upon individuals interest.
So for now it is pointless to say, as an Email Marketing company, an email spam or ham as a whole on the basis of individual’s interest. Instead we have to focus on the person, who is the source of these mails, the Sender. We need to classify users in two categories,
That’s why it generates the need to develop tools to filter could be spam mails.
As mentioned above, it is impractical to tag a mail spam on the basis of individual’s interest because interest varies person to person. So we have to focus on the source of mails, i.e., person who is shooting these mails, The Sender.
On the basis of behavior of sender, mails can be classified spam or ham. As a Email Marketing company, we can analyze
But what if a sender has by mistake chosen a wrong route, or blacklisted IP, or text-image ratio, or broken HTML design ? We assume that he has done this without any wrong-will so he should get a chance to correct them. Here comes the need of Spam Test Feature, where he can run a test before scheduling this campaign and do the needful.
Here at Sarv, we have incorporated the benefits of Open Source Projects and designed our own tools to classify spam from ham. System runs a vast array of tests and assign individual scores to them. In the end sum of these scores defines the final category of email
Individual Test may give Negative or Positive score.
We analyze the design, content, route, IPs, domains and other related information of the email. Bellow we have listed some of the key tests that we run on campaigns,