Building an Email Calculator: Part 3

A Podcast Series with Francis Wade of 2Time Labs

I recently had a series of podcast conversations with Francis Wade, a professional productivity practitioner on the topic of Email Overload. In this series we try to work through the key elements, and challenges, of building an Email Overload calculator. Here is the third and final episode of the three-part series. Listen in and let us know what you think!

In this third and final episode, Francis and I take the lessons learned from the prior discussion and start by listing a hierarchy of concerns.

Here are the key concerns in 1-5 Rank order:

How many days of stored email are accumulated? (read vs unread, subscribed vs non-subscribes)
How old are these message? (read vs. unread)
How unique are these messages? (subscribed vs non-subscribes)
How fast are they entering? (incoming email)
How complicated are they by being threaded?

During the hiatus since the last episode, Francis drafted a weight for each measure and after playing with the tool we would be using.

Here is the final formula and weights that 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 scaled to 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

Listen to the third and final episode and let us know if you think we have come-up with some good ideas!

Episode 57 Building an Email Calculator with Michael Einstein: Part 3

Francis Wade is a productivity practitioner and consultant. His firm Framework Consulting, focuses on improving productivity, strategy, and corporate culture. He also manages 2Time Labs, a web site dedicated to productivity and time management research.

He has also recently published his second book, "Perfect Time Based Productivity". This book focuses on providing a comprehensive set of productivity-oriented skills and the tools to assess and develop them. What's more, it allows for individuals to customize these skills to meet their own, unique needs.