Metrics_ How to Improve Key Business Results - Martin Klubeck [84]
Upon further inspection, we found that the raw data included the length of the call (initiation time vs. abandoned time). This allowed us to pull another measure, as shown in Table 9-4.
The measure was charted in Figure 9-2. We looked at it compared to the total abandoned rate to see if it told a clearer story.
Figure 9-2. Percentage of abandoned calls less than 30 seconds in duration
As with all measures, a major question is how to communicate the measure (what graphical representation to use). In the case of Availability, we started with an Abandoned Call Rate in the form of Percentage of Calls Abandoned. When we added the more specific Calls Abandoned in Less Than 30 Seconds, we again used a percentage. We chose to show it in relation to the total or only show the Percentage of Abandoned Calls with the qualification that “abandoned” was defined as calls abandoned after 30 seconds.
After a year of looking at the measures in conjunction with improvements to the processes (including a shorter recording), the department chose to drop the Total Abandoned Rate and use only Abandoned Calls Less Than 30 Seconds. This was a better answer to the question of availability since it would allow for the following:
Wrong numbers
Questions answered/problems resolved by the automatic recording
Customers who changed their mind (They may have chosen to use the new e-mail or chat functions for assistance. Perhaps their problem solved itself while the customer was calling.)
While the assumption that a caller who didn't wait more than 30 seconds was not disappointed by the wait was only an assumption, it was believed that this would provide a more accurate account. This would have to be compared with the Speed (see the additions to the Speed measures) and the customer satisfaction measure of “timeliness.” Of course, the only ones filling out the survey were ones who stayed on the phone long enough to have their call answered.
With a solid start on Availability, let's look at Speed. Speed started as Time to Resolve, which was known to be a concern with customers. Not that the organization was deficient in this aspect, but that the customer cared about how long it took to resolve an issue.
Speed
We started with the open and close times for cases tracked in the trouble call tracking system. This data required human input. It required that the analyst be religious in his behaviors and adherence to the processes, procedures, and policies established around trouble-call tracking. If the manager were almost any other manager, I would have had to spend a considerable amount of time ensuring that the workforce understood that the information would not be misused and that it would be in the best interest of the department, each and every worker, and the manager for the data input into the system to be as accurate as possible. Regardless of the story that it told. If the analyst “fudged” the data so that it wouldn't “look so bad” or so that it “looked extra good,” the information would be rendered useless. Wrong decisions could be made.
In this case, I trusted the manager and only spent a minimal amount of time communicating at a staff meeting the importance of accuracy in the data and how the resulting measures and information could be used to improve processes—and would not reflect on individual performance. The key to this explanation was consistent with any of the measures and any of the units I worked with.
The data, then measures, then information, and finally metrics should not reflect the performance of an individual.
The metrics, moreover, did not reflect on how efficiently the department was run.
What the data, measures, information, and metrics did clearly reflect was the customers' perception of the service. Regardless of what the “truth” was, the department would benefit from knowing the customers' perception. This is especially true