We still collected the data and used statistical process control (W.E. Deming “Red Bead Experiment”) to establish upper and lower control limits on the errors in the guest experience process and then determined if it was “out of control”. Any effort to bring the process into control or to reduce the upper and lower control limits requires ownership investment. Couple statistical process control with the predictive data from experience memory, and one could almost do a discounted cash flow analysis to justify investment in iterative process improvements.

The dilemma we face revolves around the definition of good and bad. If a provider delivers services on time, with accurate billing and competitive rates, that does not necessarily interpolate into a single “good” experience; it’s simply an expectation of performance. However, a single failure along those lines will most likely fit the “bad” profile and have lasting impact on the customer’s perception. In a nutshell, the big picture experience/relationship may be “good”, but individual “bad” elements have a greater impact due to the sheer number of potential hurdles.

There’s a saying that one “ah, s**t” erases a thousand atta-boys. While that ratio is somewhat skewed, it gets the point across: service providers must take steps to accentuate positive experiences, and minimize negative ones. Sounds obvious, but sometimes falters in practice.