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Normally, using a little investigative tenacity, I can create an initial range for the customers' expectations. Once I've analyzed enough data, and considered anything usable from satisfaction surveys, the norm is easily identified.

This norm provides insight to the stability of the processes and to the level of service normally provided. I use this as a guide to work with the department to create expectations. This is necessary because usually the customer base is just too large to query a complete representative body. After I've determined the norm, I ask the department if it seems right.

Figure 8-4 shows an example of data—call it trouble calls resolved within eight hours or less—collected over three calendar years.

Note: If you don't have historic data, you may not be able to conduct this exercise until you have collected enough data to compare to.

Figure 8-4. Sample data

When there is data for multiple years and several data points, it becomes easier to determine expectations. In the case of Figure 8-4, I would ask the customer if Year 1 was a “normal” year. Usually, the answer is that it was a below-par year for them. The poor results, compared to other years, were usually caused by a change in process, leadership, or system. Regardless of the reasons, I only want to know if, what took place during that year could be considered normal.

I then check Year 2 and Year 3. Chances are that these years were more normal and the department felt good about its overall performance during these periods.

Using only Years 2 and 3, I work with the department to see what the data is showing as “normal.” This is seen in Figure 8-5. I disregard Year 1, the “abnormal” year. Even if it had been an exemplary year—with results consistently close to 90 percent, I would remove it because that, too, would not be “normal.”

Figure 8-5. Two-year sample

At this point, I again ask the department to use their collective memory and tell me if the lows and highs were abnormal. Rather than try to get them to set a range, I just ask them about the extremes.

“In August of Year 2, you were below 75 percent. Was that normal?” If the answer is no, I will ask about March of that year.

“It seems that March is traditionally a ‘bad' month—is that right? Were the past two Marches abnormal for any reason?”

I will ask the same questions about August of Year 3, in which the unit reached its highest numbers, and then work my way down. You can also use statistical analysis for this purpose, but I find using the charts of the data much less intimidating to the department. Also, statistics give the impression that creating the range is not up to the department.

It is important to give the unit ownership of both the metric and the team's performance. By having the members of the department collaboratively determine the customers' expectations, you gain benefits from the start, as follows:

Ownership of both the processes and team performance levels

A common understanding of what the customers expect

An open discussion of the previous highs and lows—without negative or positive connotations. The department learns to view metrics as input, rather than drivers of consequence.

Figure 8-6 shows an example of what I estimate to be the performance norm for the unit.

Figure 8-6. Letting the data determine the norm

At this point, I would ask the department if the chart (Figure 8-6) seems correct.

“Would you agree that your customers' expectations lie between a 75 percent and an 85 percent rate of performance?” I usually get an affirmative nodding of heads. That isn't enough, though. I want to get as close to correct as possible, but I'm willing to accept what amounts to a good guess to start. I say this because regardless of where we set the expectations, we must stay flexible regarding the definition. We may obtain better feedback from the customer. We may change our processes in a way that the expectations would have to change (rarely do customers grow