One of the major problem with getting marketing emails is that we lack good feedback mechanisms to incentivize companies whom we do give our emails because we do want to get some information not to spam us with other information that we don’t want to receive. At the moment we have two options to punish companies who abuse the relationship. We can click on “mark as spam” or we can unsubscribe. The first version is a punishment as it means that more emails of the company end up in spam folders. Unfortunately, the company usually doesn’t know the specific email for which it is punished and thus can’t effectively improve their behavior. Unsubscription does work as a specific punishment but we can only use it we we want to stop getting all emails from the company.
We could have a better system:
A plugin that let’s us rate the emails we are getting on a 5 point scale.
Once we rate a few emails we can have a machine learning algorithm that predicts our rating and allows us to filter out emails with predicted scores that are under a specified threshold
The company that provides the plugin for free can sell access to the scoring data to email marketers who care about whether customers welcome their messages.
Marketers are already getting much of this data via click through rates and open rates. They care much less about “how much you like an email” and much more about “how much an email is likely to make you buy in the future”.
The problem of course, is that people who aren’t buyers being annoyed by the email is a negative externality. It doesn’t affect the marketer’s bottom line at all if someone who was never a buyer gets annoyed. It slightly effects them if someone who was a potenjtial buyer gets annoyed, but only if that causes them not to buy in the future (which is reflected in CTR and Open rates).
The only way to have marketers not take advantage of a free marketing channel is to better align incentives. One way to do that would be to make it not free, as jacobjacob talked about in another thread. Collective spam filters like in gmail also provide a slight incentive for this, as messages being marked as spam will cause them to be marked as spam in customers’ inboxes as well. As you said this isn’t perfect because marketers don’t know WHICH messages are being marked as spam, but in general this feels decently solved, for instance most email marketing platforms have a “spam score” that will tell you if you’re likely to be filtered to the spam filter before you send, using the data THEY have on which messages are marked as spam.
In the end they do care about the fact that people buy, but the fact that marketers care about metrics like open rates suggest that it’s useful for them to have more information.
A lot of emails are send out as a form of content marketing where the goal of the company is to create a trusted relationship which can be later monetized. In those cases it’s not easy to measure the effects of an email on sales months down the road.
The fact that the marketing platforms have a spam score doesn’t mean that the spam score accurately captures the spamminess when it comes to how annoying the email is to customers.
One of the major problem with getting marketing emails is that we lack good feedback mechanisms to incentivize companies whom we do give our emails because we do want to get some information not to spam us with other information that we don’t want to receive.
At the moment we have two options to punish companies who abuse the relationship. We can click on “mark as spam” or we can unsubscribe.
The first version is a punishment as it means that more emails of the company end up in spam folders. Unfortunately, the company usually doesn’t know the specific email for which it is punished and thus can’t effectively improve their behavior.
Unsubscription does work as a specific punishment but we can only use it we we want to stop getting all emails from the company.
We could have a better system:
A plugin that let’s us rate the emails we are getting on a 5 point scale.
Once we rate a few emails we can have a machine learning algorithm that predicts our rating and allows us to filter out emails with predicted scores that are under a specified threshold
The company that provides the plugin for free can sell access to the scoring data to email marketers who care about whether customers welcome their messages.
Marketers are already getting much of this data via click through rates and open rates. They care much less about “how much you like an email” and much more about “how much an email is likely to make you buy in the future”.
The problem of course, is that people who aren’t buyers being annoyed by the email is a negative externality. It doesn’t affect the marketer’s bottom line at all if someone who was never a buyer gets annoyed. It slightly effects them if someone who was a potenjtial buyer gets annoyed, but only if that causes them not to buy in the future (which is reflected in CTR and Open rates).
The only way to have marketers not take advantage of a free marketing channel is to better align incentives. One way to do that would be to make it not free, as jacobjacob talked about in another thread. Collective spam filters like in gmail also provide a slight incentive for this, as messages being marked as spam will cause them to be marked as spam in customers’ inboxes as well. As you said this isn’t perfect because marketers don’t know WHICH messages are being marked as spam, but in general this feels decently solved, for instance most email marketing platforms have a “spam score” that will tell you if you’re likely to be filtered to the spam filter before you send, using the data THEY have on which messages are marked as spam.
Minor note: Jacobian and jacobjacob are different people
Whoops, edited.
In the end they do care about the fact that people buy, but the fact that marketers care about metrics like open rates suggest that it’s useful for them to have more information.
A lot of emails are send out as a form of content marketing where the goal of the company is to create a trusted relationship which can be later monetized. In those cases it’s not easy to measure the effects of an email on sales months down the road.
The fact that the marketing platforms have a spam score doesn’t mean that the spam score accurately captures the spamminess when it comes to how annoying the email is to customers.
On a higher level, email clients could make the “mark as spam” button send information to the sender.
I think because of marketing and branding reasons that’s not a valid move for the companies that produce most email clients.