Metrics_ How to Improve Key Business Results - Martin Klubeck [87]
Of course, Speed to Resolve could also look better, since no cases would show taking the time it took to rework an issue. Without triangulation this could easily happen. Of course, I had an ace in the game. Even without triangulation, the manager of the department and her workforce were all believers in serving their customers. Not only providing the service, but doing so as well as possible. They were believers in continuous process improvement and in service excellence. Even without triangulation, I have total faith that this department would not chase the data.
But by using triangulation, and not looking at measures in isolation, it helps even the less committed departments stay true to the customer's viewpoint.
If we saw spikes in other categories because of false reporting in one category, we'd find anomalies that would require explanation. Besides these anomalies, Rework would be so low (or non-existent) that it too would be an anomaly. This is another reason (besides wanting to replicate successes) we investigate results that exceed expectations, as well as those that fail to meet them.
Usage
For Usage, we captured the number of unique customers each month and also ran it as a running total. Using the potential customer base, we were able to derive a percentage of unique customers using the Service Desk. I've heard arguments that some data is just impossible to get. In this case I enjoy turning to Douglas W. Hubbard's book, How to Measure Anything (Wiley, 2007). Not because it has examples of all possible measures, but because the methods offered give readers confidence that they can literally measure anything.
Unique customers should be able to be measured against the customer base, regardless of your service. If you are a national service desk—say, like Microsoft or Amazon.com or Sam's Club—the customer base can be still determined. As you know by now, exact (factual) numbers may not be obtainable, but getting a very good and meaningful estimate is very feasible. The customer base can be estimated by determining how many sales of the software in question were made. If Microsoft sold 150,000 copies of a title, how many calls, from unique customers, were received about that software? Amazon.com has information on the total number of customers it has. Same can be said for Sam's Club since it's a paid membership outlet. Walmart, McDonalds, and the neighborhood supermarket have a more difficult time.
In the case of Walmart and McDonalds, their national call center can use the marketing data on “number of customers.” Each has information that can be used. Not necessarily unique customers, but even so, they have a good idea of how many repeat customers they have and total customers, so the numbers can be derived.
The neighborhood supermarket can determine either the number of customers or consider the populace of the neighborhood (based on a determined radius using the store as the center) as the potential customer base.
In the case of the Service Desk, we had identifying information for each customer (when provided). Of course, this data was only as accurate as the analyst's capture of it (misspellings of names could be an issue) and the honesty (or willingness) of the customer to provide it. One hundred percent accuracy wasn't necessary though. Good data was good enough.
Sometimes “good” data is good enough.
Looking at the data over a three-year period showed that the customer base usage was pretty steady, and we felt more confident that anomalies would stand out. In Figure 9-5 you can see what we saw. The last year showed a steady increase. But not an unexpected one, since the last year showed a lot of new technologies, software, and hardware put into use.
Figure 9-5. Percentage of unique customers
As with the two previous measurement areas, Usage was represented in