This page documents hypothesis for metrics dips and techniques for evaluating them.
Additionally it includes specific actions that might be used to shore lagging metrics up.
Examined historical achievement by area to see if a specific type of tickets is behind the dip in performance
Took a look at historical and current ticket volume to see if there are any outliers (e.g. a large increase in tickets over the past period)
Peered into the ratio of tickets / engineer over the past year
Shaping Actions
If ticket volume is too high:
Determine what is driving that volume. If it's:
a specific problem type, investigate automation or clarifying workflows to increase efficiency for these problem types.
a specific incident, shape macros or workflows to increase efficiency, consider assigning a specific set of engineers to focus on these types of tickets.
a lack of headcount, focus on hiring and shape onboarding to increase the speed at which engineers can answer certain ticket problem types.
a general increase, focus on "burst mode" workflows; arrange groups of individuals to focus on first responses and setting customer expecations.
A time-consuming workflow is causing a dip in performance
Examined capacity loss by looking at effort toward L&R tickets that reduced effort in engineer experience areas
Compared effort toward L&R tickets with effort in general queues for engineers with experience in those queues (e.g L&R ticket effort is ~10% of total SaaS ticket effort)
Examined "worst case" capacity loss by assuming 100% of effort/productivity taken by this queue.
Shaping Actions
If dedicating team-members to a specific set of tickets has reduced capacity:
hire
look for efficiency gains through better workflows, processes and automation
determine if there are any gains to be made through engineering effort and communicate the impact
