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The secret's out (in a small way) and Google is happily sharing a top line look at the processes and algorithms that go together to make up what the company describes as “One of the largest and most user facing applications of ML (machine learning) at Google”, namely the Priority Inbox.
So how can we use this knowledge to ensure better inbox placement in Gmail? And is this news to us anyway?
For many years, those within the email industry have watched the growth of engagement metrics used in the filtering of emails. This was mostly restricted to the observation of results coupled with reading between the lines of ESP best practice guides and feedback from sender support.
We also had the reputation gurus like Return Path to help steer senders down the right road of relevant emails and good reputation, but we have never before had an ISP lay bare any part of their filtering processes for all to see.
So why has Google done it?
I think the answer is that Google is pretty pleased with itself, and wants to show how much thought goes into its user experience.
I think it also wants to show the sending community and marketers that it is not the bad guy. Google is trying to demonstrate the time and effort it puts into ensuring that, if an email is really important to someone, it gets to the top of the pile!
It is also sharing with the wider ISP community too; sharing ideas that might ultimately help to improve email account filtering across the board.
I know there are those that decry this sort of priority filtering, but with the volumes of opt in emails due to increase considerably over the next few years, steps need to be taken to ensure the viability of the email channel.
How does Gmail’s Priority Inbox learn what’s important? And how can marketers use this information?
Gmail’s Priority Inbox uses certain features to decide what emails are important to the recipient,
There are two levels of learning from features. The first is global, allowing the product to be effective out of the box, as it uses aggregated information from the global pool of data. The other and more influential is user level behaviour and manual labelling.
The features themselves include social, content, thread and label.
“Social Features are based on the degree of interaction between sender and recipient”. It looks at the amount of emails the recipient has received and compares this with how many are opened or clicked. Which is good? Isn’t it?
How can marketers use this feature to improve email marketing? It should be fairly simple to segment out people who are no longer opening your emails, and reduce the mailing frequency to these users, or even try another channel. The message here is only to send emails to people who are still listening to you.
Content features look at what the email contains (e.g. headers or terms in the subject line) and measures this against how the recipient has responded to similar content in the past. This feature also looks at the speed which the user responds to the email based on the content included.
What does this mean for marketers? Well, if you are a good mailer, and get high response rates from your campaigns, your content signatures will be more positive than negative.
It sounds very much like a sort of Bayesian process (haven’t got space to explain, but worth a Google) and again this should reward the good mailer, who segments well and gets good response.
This looks at an email thread (who was it started by , did the recipient reply). A marketing message will not generally be replied to. This could be used by Gmail to differentiate what a personal email looks like as opposed to a commercial one. Ideas to use this? Thinking caps on!
These are the labels the recipient places on the messages, and are by far the most powerful of all the features. This is due to the weighting applied to the different metrics, which places anything the user sets as being the most significant element when choosing how to prioritise your messages in the future.
Why’s this of interest to marketers? Well, if you’ve any chance of being labelled by a user, then you have to ensure you email is relevant and of interest, and something they would want to label.
Does it work?
As I mentioned before, the objective of Priority Inbox is to improve the user experience and this seems to have been borne out in research carried out on Google employees.
According to Google, Priority Inbox users spent 6% less time reading email overall and 13% time reading less important email. Google found they were also more confident to bulk archive or delete email. This almost sounds like the Google employees who used Priority Inbox, were more efficient at work too!
This is the face of email for the future. The objective has moved from trying to “not” look like spam, to becoming one of sending important or interesting emails to recipients; ones they welcome and ones they read.
If you would like to read the paper, here is the link to the Google blog.