This post really isn't meant to be interesting; it's more of a time-stamp on my work for today. (I can't show graphs, because the data is not public.)
I and my intern team are responsible for all the response letters in our office, and I track and graph how we're doing in terms of open responses and lists.
Number of open contacts used to be my primary metric - how many people do we still need to respond to? I also track a few other things (like how many lists we still have to write letters for), but these are mostly proxy measures to show me extra dimensions of the task.
I recently added another measure - the average "age" of every currently-open contact. In other words, for all our open contacts, how long has each contact been open? I should know this.
But I soon realized this measure, while useful, is hardly enough on its own. If I close half our contacts, but I close the more recent half, the average age of open contacts will go up, which misrepresents the accomplishment of closing so many contacts. What I really want to know is the sum of all the open contacts, weighted by how long they've been open. I call these the "open-days" (just as a "man-hour" is one hour of time worked by one person). We should track "open-days", which will give me a composite measure of (1) how many contacts we have and (2) how long those contacts have been open.
So I started tracking this. But today, I did something better - I used the reports from our constituent records system to reconstruct the open-days measurement over the past year, by looking at how many currently-closed contacts were open at certain points in the past. It took a lot of hours of excel crunching, but it's all in place now! And it all confirms that we're at an all-time low in terms of open-days, open contacts, and everything else.
I also created a measurement of how long contacts from each month stay open before being closed. This is similar to the average age of our currently open contacts. Instead, though, it's the average age of all the contacts that came in a certain month. It gives an idea of whether we're improving or not in our response time. Unfortunately, recent months' data isn't yet reliable because some currently open contacts will get older before we close them, so the numbers for the last three months (which are quite low, which is good) will rise. I'll have to recalculate these numbers every so often to adjust them. But it looks like we're improving on this front as well!
I and my intern team are responsible for all the response letters in our office, and I track and graph how we're doing in terms of open responses and lists.
Number of open contacts used to be my primary metric - how many people do we still need to respond to? I also track a few other things (like how many lists we still have to write letters for), but these are mostly proxy measures to show me extra dimensions of the task.
I recently added another measure - the average "age" of every currently-open contact. In other words, for all our open contacts, how long has each contact been open? I should know this.
But I soon realized this measure, while useful, is hardly enough on its own. If I close half our contacts, but I close the more recent half, the average age of open contacts will go up, which misrepresents the accomplishment of closing so many contacts. What I really want to know is the sum of all the open contacts, weighted by how long they've been open. I call these the "open-days" (just as a "man-hour" is one hour of time worked by one person). We should track "open-days", which will give me a composite measure of (1) how many contacts we have and (2) how long those contacts have been open.
So I started tracking this. But today, I did something better - I used the reports from our constituent records system to reconstruct the open-days measurement over the past year, by looking at how many currently-closed contacts were open at certain points in the past. It took a lot of hours of excel crunching, but it's all in place now! And it all confirms that we're at an all-time low in terms of open-days, open contacts, and everything else.
I also created a measurement of how long contacts from each month stay open before being closed. This is similar to the average age of our currently open contacts. Instead, though, it's the average age of all the contacts that came in a certain month. It gives an idea of whether we're improving or not in our response time. Unfortunately, recent months' data isn't yet reliable because some currently open contacts will get older before we close them, so the numbers for the last three months (which are quite low, which is good) will rise. I'll have to recalculate these numbers every so often to adjust them. But it looks like we're improving on this front as well!
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