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I ask, “So, would the customer be surprised if you delivered over 85 percent? “ If I get a yes, I ask the logical follow up question, “How did the customer react in February of Year 2 and August of Year 3?” And, “How did you achieve these results?” I'd ask the same questions about March of Year 3 and August of Year 2. Depending on the answers I might readjust the expectations.

I mentioned that you could use statistical analysis. Let's look at an example. Figure 8-7 shows the same data analyzed using Microsoft Excel's Data Analysis Add-in tools.

Figure 8-7. Histogram of the two-year sample

Based on the histogram, my guess of 75 to 85 percent would seem acceptable. More exact may be greater than 75 percent and less than or equal to 85 percent. If we look at a simpler view of the data spread, we find that the range offered is quite reasonable and probable. But again, the main test will be to check our ideas against customer feedback.

A simple frequency chart of the same data, shown in Figure 8-8, tests the visual interpretation and affirms the guess.

Figure 8-8. Frequency chart of two-year sample

Recap

The existing schools of thought on performance measures—specifically the concepts of targets, thresholds, and using measurements as goals—should be replaced by a collaborative approach that gives ownership of service and product quality to the workforce. This is accomplished by determining customer expectations.

Expectations are almost always a range.

Meeting expectations is what you want to do on a regular basis.

Failing to meet expectations is an anomaly that should be investigated to determine causes.

Exceeding expectations is an anomaly that should be investigated to determine causes.

Positive and negative anomalies should both be of concern to the organization. Whether you are exceeding expectations or failing to meet expectations, you need to investigate further.

When setting expectations, look to the data to help identify the norm. The question then becomes, “Is the norm equal to the customers' expectations?” If yes, then the data should reflect that. The purpose is to be able to identify the anomalies that fall out of the norm or outside of the range of expected behavior.

Treasure anomalies—because anomalies are where your metrics earn their return on investment. The main reason to adjust the range of expectations is to ensure that you are properly identifying the anomalies.

Conclusion

The old school-of-thought doesn't work. Using metrics as a form of motivation falls closer to manipulation than collaboration. You'll need to develop a solid rapport with your workforce and fully team with them to use metrics properly. Setting stretch goals, targets, thresholds, using measures as goals, and rewarding reaching a measure as an incentive are examples of using the wrong tool for the job.

Meeting customer expectations is the real goal. Anomalies to the expectations can provide useful information.

Expectations provide a clear context and are the key to open the doors to improvement.

Creating and Interpreting the Metrics Report Card

I struggled for a long time trying to decide how to introduce the Report Card to you. I debated if I should present it as another tool (like the Answer Key), or offer it as a methodology. What I settled on was to offer it as a real-life example of how metrics, when used within the constructs I've offered, can evolve and take shape.

I will relate to you the three-year journey to develop a viable metric program for an organization in which I had to clearly articulate the overall health of the organization and the health of each of its core services. I made mistakes along the way, learning, changing, testing, and trying. I will share with you the journey of discovery and the final destination I arrived at. Hopefully you can learn from my mistakes and benefit from the final product as either a template or example for your use.

The top-level executive asked a straightforward question of our CIO, “How healthy is your