Ticket and tech analysis to find slackers
Right about the time I decided to leave the ticket/phone call support monkey life, I started getting some inspirations for new ways to interpret ticketing system data. I guess that putting in my notice to leave left me in a state where I didn't care what kind of uncomfortable truths these tools would display.
The last of these was something I called "onfire", as in "on fire", the kind of term you use when someone's really moving and is getting lots of things done. It was a way to visualize an entire shift worth of work by different techs. Here's how it worked.
I assigned arbitrary "point values" to actions taken in the ticketing system. The exact details are lost to the sands of time, but this is an approximate idea. You'd get 16 points for logging into the ticketing system at the beginning of your shift, 8 for a public comment to the customer, 4 for an internal private comment, and 2 for changing status on a ticket.
You start the day at midnight with 0 points. As events occur, your current score jumps up according to whatever you did in that minute of the day. There's also a "cooling off" effect, where you lose one point per minute no matter what, and this is the key.
The whole point of writing this was to see who was actually working and who was just being a lazy slacker. This tool made it painfully obvious.
Here are some examples.
This is a third shift tech who supposedly worked 4 days a week with 10 hour shifts. That means if he came in at 11, he should have worked until about 11 hours later, since there's an hour lunch in the middle. We miss the first hour of his shift since it's on the previous day, but the rest of it is shown here, and it's not a pretty sight.
Watch what happens when he goes idle: you just get that downward slope of blue. Just by looking at this, you can tell that this guy did nothing after about 4:30 that morning. Considering that his shift didn't end until 10 AM, that's pretty amazing!
Let's look at another.
This is another third shifter. He actually did so little work that it cooled off all the way back down to zero. You can see multiple hours at a time here where he's doing nothing at all. That's a lot of smoke breaks. Tough life, huh?
I bet at this point some of you are thinking that third shift just didn't have that much to do. While it's true that they had fewer new tickets and almost no phone calls, trust me when I said there was plenty of work to be done. Other tools showed lots and lots of tickets just sitting there all night.
Okay, enough picking on third shift. How about a first shifter?
Yep. This is someone who only touched the ticketing system between about 12:30 PM and 3 PM in an 8 hour shift. What was he doing the rest of the time? Who knows? It sure didn't involve working on tickets or phone calls!
Here's another first shifter:
1:30 PM to 4:30 PM with nothing happening, and you get paid for this? What a life!
So, finally, let's look at someone who didn't slack off as a matter of policy. This is a second shifter who was on a adjusted schedule that particular day and came in early.
The first thing you should notice is how squashed the data is. There's so much stuff being squeezed into those 200 vertical pixels that you can't even really see the jaggies in the graph. It's all squished in there, and even then, it basically keeps going up all night!
Once you appreciate the stark difference in that graph, now look back at the others and pay attention to the Y-axis labels. Tech #5 did so much work that it rescaled to a range of 0-2500. Nobody else even came close to that.
Despite all of this, did anything ever happen to the slackers? Nope. They were set for life since they had found an environment which didn't check and didn't care what they actually did. As long as they looked friendly and did a smattering of work, they were safe.
Stuff like this is why the people who really cared later burned out.
November 17, 2011: This post has an update.