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Episode 59 Building an Email Calculator with Michael Einstein part 3
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Content provided by Francis Wade. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Francis Wade or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
Ever longed for a tool that could give you feedback on the health of your email inbox? Listen in as I take on the challenge of creating one from scratch. Here in the third and final episode in this series, I continue working with Dr. Michael Einstein to create an email health calculator. We take the lessons learned from the prior discussion and start by listing a hierarchy of concerns. They are listed here from 1-5 in rank order. 1. How many days of stored email are accumulated? (read vs unread, subscribed vs non-subscribeds) 2. How old are these message? (read vs. unread) 3. How unique are these messages? (subscribed vs non-subscribeds) 4. How fast are they entering? (incoming email) 5. How complicated are they by being threaded? During the hiatus since the last episode, I drafted a weight for each measure and after playing with the tool we would be using, producing the following formula which we discussed in this episode. 0.25 x Left Behind Index (i.e. (Total messages in your inbox - unread messages-tagged, read messages)/incoming email each day) 0.20 x Number of Days Surprise Index (i.e. unread messages – unread subscriptions email)/incoming email each day) 0.20 x Total messages older than a day)/incoming mail each day 0.20 x (Average age of non-Subscription messages/days) 0.20 x ( .50 x Average age of Subscription messages/days) 0.10 x Incoming Email each day / messages removed per day 0.20 x Threaded messages The final input into the calculoid app used the following weights which were simply scaled so that they would sum to 1.0: Field 1 - 18% Left Behind Index [i.e. (Total messages in your inbox - unread messages-tagged, read messages)/incoming email each day)] Field 2 - 14% Number of Days Surprise Index [i.e. unread messages – unread subscriptions email)/incoming email each day] Field 3 - 14% Total messages older than a day)/incoming mail each day Field 4 - 14% Average age of non-Subscription messages/days Field 5 - 7% Average age of Subscription messages/days Field 6 - 11% Max(1, incoming email/150) Field 7 - 7% Incoming Email each day / messages removed per day Field 8 - 14% Threaded messages x #average active participants in each thread Want to support the work at 2Time Labs? Here's my Patreon Link. Patrons receive a number of benefits including early access and followup conversations related to work like this.
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30 episodes
MP3•Episode home
Manage episode 225910455 series 2468779
Content provided by Francis Wade. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Francis Wade or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
Ever longed for a tool that could give you feedback on the health of your email inbox? Listen in as I take on the challenge of creating one from scratch. Here in the third and final episode in this series, I continue working with Dr. Michael Einstein to create an email health calculator. We take the lessons learned from the prior discussion and start by listing a hierarchy of concerns. They are listed here from 1-5 in rank order. 1. How many days of stored email are accumulated? (read vs unread, subscribed vs non-subscribeds) 2. How old are these message? (read vs. unread) 3. How unique are these messages? (subscribed vs non-subscribeds) 4. How fast are they entering? (incoming email) 5. How complicated are they by being threaded? During the hiatus since the last episode, I drafted a weight for each measure and after playing with the tool we would be using, producing the following formula which we discussed in this episode. 0.25 x Left Behind Index (i.e. (Total messages in your inbox - unread messages-tagged, read messages)/incoming email each day) 0.20 x Number of Days Surprise Index (i.e. unread messages – unread subscriptions email)/incoming email each day) 0.20 x Total messages older than a day)/incoming mail each day 0.20 x (Average age of non-Subscription messages/days) 0.20 x ( .50 x Average age of Subscription messages/days) 0.10 x Incoming Email each day / messages removed per day 0.20 x Threaded messages The final input into the calculoid app used the following weights which were simply scaled so that they would sum to 1.0: Field 1 - 18% Left Behind Index [i.e. (Total messages in your inbox - unread messages-tagged, read messages)/incoming email each day)] Field 2 - 14% Number of Days Surprise Index [i.e. unread messages – unread subscriptions email)/incoming email each day] Field 3 - 14% Total messages older than a day)/incoming mail each day Field 4 - 14% Average age of non-Subscription messages/days Field 5 - 7% Average age of Subscription messages/days Field 6 - 11% Max(1, incoming email/150) Field 7 - 7% Incoming Email each day / messages removed per day Field 8 - 14% Threaded messages x #average active participants in each thread Want to support the work at 2Time Labs? Here's my Patreon Link. Patrons receive a number of benefits including early access and followup conversations related to work like this.
…
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30 episodes
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