Ticketing Environments and Contact Center Occupancy Challenges

In a synchronized setting like phone and chat support, occupancy reflects the capacity of the contact center. You can easily determine occupancy by assessing the hours of demand and supply in this context. Calculating supply hours in a ticketing system is a bit more complex but still intriguing.

In the phone and chat channel, all interactions flow through a single pipeline and are then assigned to available associates with the most suitable skills to assist the client. This underlying logic ensures workload balance, even during peak periods, as transactions are automatically assigned to associates. To compute actual occupancy in this environment, it's a straightforward calculation of the time an associate spends dealing with a client during and after a call, divided by the total time the associate was working during that period

However, in a ticketing system, tickets can exist in multiple views that associates must review to decide which ones they can address. Since tickets are not automatically assigned, the time associates spend searching for tickets to work on is not tracked. Consequently, one could argue that the occupancy calculation in a predominantly ticket-based environment is underestimated.

There are additional features in ticketing systems, such as guided mode, where the system attempts to assign associates the next available ticket automatically. Nevertheless, some technical challenges persist. First, associates may skip the tickets assigned to them, and the time spent doing so is not readily available as a metric. Therefore, merely adding up the total handling time of a ticket and dividing it by the available time the associate had for work still underestimates occupancy. Second, macros are commonly used in ticketing systems. These are automated actions that can provide canned responses to clients or route tickets to other queues. Depending on how these macros are executed within the business setup, the time it takes to run them is not considered as work time. Again, this results in an underestimation of occupancy.

Given these system constraints, you cannot simply calculate the workload hours of a contact center by multiplying the total number of tickets by the average handling time. Instead, you need to examine additional key performance indicators (KPIs) such as skip rates for groups handling tickets and the percentage of processes that are automated. While most of these instances may not take more than a minute individually, in a contact center dealing with millions of tickets annually, those minutes quickly accumulate. Therefore, in ticketing environment, I see occupancy not as symptom of a contact center’s capacity but a vital input to calculate a contact center’s capacity.