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Once we finished determining the proper measures for unique customers, and their expectations, we looked at our other measure for usage. Another choice was the percentage of respondents who chose the Service Desk as their primary service provider in our annual survey. Table 10-6 shows the expectations we identified for the survey results.

Figure 10-11 shows the percentage of survey respondents who listed the Service Desk as their first choice when seeking assistance with their Information Technology problems.

Figure 10-11. Percentage of customers who preferred the Service Desk for assistance

Although the results for 2008 were not an anomaly, the results were so different from the following year that we had to investigate to find out why. The only determination of cause we could find was that in 2008 respondents were asked to pick their top three preferences. The Service Desk was listed as one of the top three choices for 59 percent of the respondents. The next two years the question only allowed one answer—the top choice/preference. You've seen how the charting can change the perception of the measures. This is another strong example of how measures can be totally different based on how the data is collected.

As mentioned, we could also add a measure on repeat customers. If we wanted to stick with percentages, we could produce a measure of the percent of repeat customers compared to the total customers for the given period. Again this could be on a three-, six-, or twelve-month cycle. The really good news is that this measure could be derived from the same data set that gave us the count of unique customers. Where unique customers were compared to total possible customers (customer base), repeat customers would be compared to the total customers for a given period.

The final category of measure we used was what most people think of as the first to collect—customer satisfaction surveys.

Customer Satisfaction

Again we were lucky since the data was already being collected, compiled, and analyzed for us. As mentioned, we had to decide which view of the data worked best. Basically we had to determine the measure to build from the data.

The department had been reporting this measure for a long time, but only as an average on the 5-point Likert scale. Our leadership wondered what to make of the average grade—how to interpret it. Being problem solvers, they didn't leave the solution to the department—instead they asked for benchmarks to compare the average to. They reasoned if they were better than the “national average” or the average of their competitors, they were doing well. In reality this would only tell them that they were doing better than the average. They could claim to be in the top half. If we were really lucky, we'd find the following:

Our peers were using the same questions that we were. The third-party provider had an impressive list of customers, but they did not have all of our peers or a monopoly on the service.

Our peers were using the same 5-point scale that we were.

Our peers were willing to share their average grades.

Our peers determined the average the same way—they could discount repeat customers within a short time frame, they could categorize their customers differently, or they could not include certain customers—like internal users of the Service Desk. Another difference could be if they surveyed only a sampling and we surveyed all users.

This magical alignment of stars was unlikely to happen, much less stay in alignment for an extended period of time. So, what we'd have instead as a benchmark would be a sampling of some of our peers. This was my first argument against using benchmarks to try and make the measures meaningful.

My second argument was that even when compared to a valid benchmark, it only showed how well we did versus the standard selected—not how well we were satisfying our customers. We could feasibly be at the “top of our class” and still be well below our customers' expectations. If you bring home a B- average