Agile practitioners have been discussing technical debt for a while, using this term for the gap between the investment in software development and the business value derived from that same software.
After being introduced to the term and the concept behind it I kept wondering about the best way to implement a similar concept in the customer support environment.
If we accept the concept that customer support is responsible for generating or protecting customer value, then we can come to the conclusion that outstanding support debt at any point in time can be defined as “the total amount of customer value that is unrealized due to unresolved cases” at that specific point in time.
When we look at our open cases (aka backlog) we can reasonably assume that a case that’s been open for a longer period has a bigger negative impact on customer value than a case that’s been open for a shorter period. Therefore, we can safely accept the amount of time a case has been open as an approximation to its negative impact on customer value, and the total impact support has on total customer value can then be represented by the total age of all open cases (regular blog readers will note that this number is similar to the pain report, but has no weighting for the severity of the case).
So, now that we have this metric defined, what do we do with it?
First, we have to understand that this metric is a trailing indicator, showing the success we had in accomplishing two goals that are common to most support organizations:
- Reducing the number of cases opened
- Processing cases faster
And like most other metrics, its main value is in the way it changes over time and how it is associated with changes in business volume. The best option I have found for that is dividing the change in support debt by the change in revenue over a certain period. If the resulting number is lower than 1, then the organization is improving its performance, if it is greater than 1, it is not managing its workload well.
In a future post I will talk about the relationship between this metric and other commonly used ones. Stay tuned.
An open question remain. In what way ca we relate this number to financial value? Any ideas?