Meaningful Metrics - using data to predict customer satisfaction

A field services company was struggling to improve their customer satisfaction related to missed service appointments.  Yet, when my client engaged their technician team, they heard quite a different story.  According to the data the technician team maintained, they rarely missed an appointment.  They were very proud, in fact, that they made over 99% of their appointments. On the other hand, the customer facing team said that missed appointments were the #1 problem customers experienced.  Who was right?

We found they were both “right.”  How is this possible? Because they were each evaluating the data differently.  One might say they were using completely different measuring sticks.

First, they were evaluating different data sets.  The customer facing team was tracking the number of customer complaints related to missed appointments. Given the number of scheduled appointments each day (over 10,000), even a small percentage of missed appointments would lead to a relatively high number of complaints from the perspective of the people receiving the complaint calls.  The technician team only measured the number of missed appointments that were their “fault.” Fault was defined subjectively and independently at each field office, and tracked on a manual spreadsheet that was sent in to management once a month.

Second, the team did not have a common definition of a missed appointment.  The technician team applied a subjective evaluation of whether it was their “fault” that the appointment was missed.  The customer complaints occurred when the customer perceived their appointment had been missed (or they thought it had been missed).  Neither measure was an accurate depiction of the issue.

These flawed metrics ultimately caused the team to spend more time arguing about the data, and less time working to solve the problem.  If we cannot agree that there is a problem, we will most likely not engage in fixing it.  We helped the client address this by taking a few key steps:

1. We conducted a customer survey to determine the customer’s definition of a missed appointment.  Ultimately, the customer defines expectations!
2. We developed automated reporting and an agreed upon coding and corrective action process.
3. The team set a goal of 99.999% pick-up completion based upon customer expectations (survey).
4. The customer facing and operations team reviewed the reports, daily, weekly and monthly. The data was leveraged to support Plan-Do-Check-Adjust (PDCA) problem solving over the next several months.
5. The process of collecting, analyzing and problem solving became standard work for the teams.

The results were outstanding! Missed appointments went from 7.8% to .5% in a matter of a few months. By standardizing the process, they were able to sustain the gains.  Customer satisfaction improved 77% as measured by the client’s customer satisfaction survey.

The keys to creating meaningful metrics:

1. Align on the definition and significance of the metric
2. Develop a standard process for collecting accurate data
3. Set a goal to determine target performance
4. Engage teams in PDCA Problem Solving to improve performance
5. Standardize the new process to sustain the gain

If you would like to learn more about how you can leverage meaningful metrics in your organization, please contact John Blaseos at john.blaseos@averdus.com or visit our website www.averdus.com