Some of you might know that I went to graduate school. It’s an amazing experience, but incredibly intense. You are asked to gain a huge breadth of knowledge in your area of study and then dive deep in one small part of your specialty. The first two and a half years are focused on breadth, so we studied key thinkers and writers in our area. Often, there was so much reading that my cohort and I felt overwhelmed. I saw very different reactions to this workload over the years. Some people read as much as they could and stopped when they could. Some read parts of everything. Some dug deep in only one of the readings.
Support managers are often in this same situation. We have access to so many measures that we can easily become overwhelmed. One response is to look at all the measures. We hear stories about managers who look at dozens (even hundreds) of measures on a daily basis. It’s hard to make good decisions with so much data. The other, like my colleagues who dove deep into one of the readings, is to focus on one measure. But this can be as bad as looking at too many measures. Focusing on one measure too tightly won’t allow you to see the interrelationships with other measures. Here’s a brief story about an organization that had turned their focus on one very popular measure – case backlog.
One of our clients had identified backlog as a key problem for their teams. They had two different groups taking cases and one of the teams (the standard support team) was drowning in backlog, while the other (the premier support team) was handling their backlog and driving it down. One support director was lobbying for reallocation of some of the premier support team members to help out with backlog. We decided to model out the net effect of that reallocation (with mock data).
What we saw was that there was a short-term positive effect on the standard backlog when team members were reallocated from weeks 3-6, but the premier backlog grew and shortly after the premier team went back to their own work, the standard support backlog queue was almost back to where it was before. The net effect was a growth in the backlog over time, not a net positive effect.
When we modeled the same situation with a program to drive greater scalability (specifically a KM program), not reallocating any resources. We built in a 10% drop in productivity in week 2, then building to a 10% improvement in productivity by week 5 (with a smaller, 5% improvement in productivity for the premier support team, assuming that they take more complex cases). After 6 weeks, the premier support team has nearly cleared their backlog and the net effect was positive. At this point, a short-term reallocation would have a very positive effect.
But the net effect was only positive by addressing something other than backlog. By looking at backlog alone, the team would have made a decision that negatively affected the satisfaction of their premier customers and not addressed the underlying cause.
Metrics are never isolated. They depend on and interrelate to one another. As support managers, we need to identify a key set of measures that will drive real value for our company and build projects that drive these measures toward a common goal. That’s what we are building with TSIA, a common set of measures that is generally accepted and that can provide real guidance for support teams. We would love for you to join the effort and our Metrics LinkedIn group!