I am reading Nate Silver’s book, The Signal and the Noise: Why So Many Predictions Fail – But Some Don’t. The book is resonating with me relative to the challenge of choosing the best operational measures and data that will help us effectively analyze, predict outcomes, and make improvements in customer services.
We thrive on measuring and “goaling” everything in customer services – response time, resolution time, customer satisfaction, productivity, and so much more. Why not – there are so many from which to choose. Implementing the right balance is important, as too few or too many measurements can be problematic.
Given any one number, and only one number, the probability of achieving a measurement goal may be very high, but without balance, the measurement may do more harm than good. For example, focus on resolution time alone may provide incentive to close service requests prematurely. While resolution time goals may be met, quality and customer satisfaction may suffer. Conversely, too many measurements can be overwhelming for managers. It helps to determine:
- the few critical metrics that effectively measure key elements of your processes
- who should focus on each number
- what numbers are critical vs. merely interesting
- a process for integrating results into strategic and operational planning efforts.
It is also important to consider various vantage points – customer, stockholder, partner, and employee. Ask where the “customer moments of truth” are and what you should monitor at these points in the process. Where does potential exist for defect creation and waste that will drive up cost? And in the service business, employees are a key part of success, so what is driving employee satisfaction?
During the analysis phase, another major risk enters the equation – choosing the numbers or suppositions that best fit your desired truth. Silver writes, “The instinctual shortcut that we take when we have ‘too much information’ is to engage with it selectively, picking out the parts we like and ignoring the remainder, making allies with those who have made the same choices and enemies of the rest.” This is particularly true with data coming from analytics of unstructured data. You may inadvertently set your parameters and search to discover what you believe to be true.
Certainly this topic is far more complex than what I can explore here, but for more I suggest taking a look at a recent post, The Not So Dreaded Annual Review: A Checklist for Data Success, by Frank Coleman.
I will close with a final thought. While the most recent advances in data analytics tools have been instrumental in helping us to continually improve the EMC service experience for our customers, all answers are not in the numbers. We are providing real people with a real service experience. Personal touch is crucial in fully understanding feedback. Make sure customers and employees have the opportunity to provide direct feedback and/or the opportunity to clarify. And then, ensure that you have an established process for incorporating this into your analysis and planning efforts.
