Occupancy is a term commonly used in contact centers and is a critical variable in calculating the right staff required to deliver service level. Within workforce management (WFM) and Erlang-C calculations, there is a theoretical maximum value for occupancy, tied back to three elements:
Your service level objective,
The size of your organization supporting the calls in the group or segment, and
In multi-skilled environments, your skill template configuration
Before considering whether you are calculating occupancy correctly, let's first understand the meaning of occupancy in contact centers. The simplest way to think of occupancy also referred to as utilization, is the percent of the time your call centers are actively engaged in a call. This percentage is generally calculated by adding up all the time the agent is engaged in a call divided by the total time logged in the phone switch. Most organizations calculate this by adding all ACD time (talk time, hold time, and after call work, or wrap) and then dividing by the total time logged in to the ACD. All other time in different states (aux time for training, breaks, coaching) is captured in shrink and does not factor into your occupancy calculation.
Occupancy has a "maximum" value which is first tied to your service level objective.
When we consider how high we can set occupancy in calculating our staff, we must first directly consider our service level objective itself. Service level objectives and occupancy are inversely related; the higher your service level target (ie 90% of calls answered within 30 seconds), the lower your maximum occupancy rate must be. As your service level objectives increase, we must maintain a higher level of idle agents to answer the next call within the service level objective.
To illustrate this, we take a call center servicing 220 calls with an average handle time of 300 seconds over a 30 minute interval. When we first examine the staff required and occupancy required to achieve a 60% service level, we find we need 40 agents, and they will experience an occupancy rate of ~ 92%.
As we increase our service level objective from 60% within 30 seconds to 95% within 30 seconds, we see our occupancy rate drops from ~92% to just under 80%. We also see we need 46 agents to achieve the 95% service level goal:
Maximum occupancy is directly related to the size of the organization serving the call queue
When work is shared across larger pools of agents in a contact center, your maximum occupancy can be increased. Shared pools of work drive greater efficiencies and require fewer agents to service the work than if the same work is segmented. To demonstrate this, let's take three groups servicing the same 100 calls with an average handle time of 300 seconds. If these groups operate independently all attempting to achieve an 80% service level:
3 independent groups would each require 21 agents and achieve a 79% occupancy rate, but
1 consolidated group would require 56 agents and achieve an 89% occupancy rate:
Increased occupancy through pooling of resources, in this case, saves seven agents and increases occupancy or utilization by 10% points.
Multi-skilled environments impact your maximum occupancy assumptions
If your contact center leverages a skill template, and all agents in a pool do not carry all skills throughout your time horizon, the maximum occupancy achieved must be adjusted downward. Many environments change the skills agents carry for numerous reasons throughout the month:
Agents became trained in a skill they were not proficient in, having a skill added
Agents may be reallocated to different call types, removing skills from their profile
Service level objectives may require a group to reduce skills and over-deliver on a segment
In our previous two examples, we can easily calculate what the maximum occupancy assumptions could be to feed forecast staffing models. But when skills are added or subtracted from an agent, occupancy assumptions for a call group can become very difficult to validate. In this case, the best method to adjust your maximum occupancy assumption is to examine specific weeks or months where you have most closely aligned to your service level objective, and examine the actual historical occupancy values obtained. This will give you a base set of assumptions to apply against the mathematical maximum occupancy (based on service level objective and size of organization), allowing you to adjust downward your occupancy assumptions.
By validating historical maximum occupancy achieved when achieving service level, you can protect the organization from under-staffing based on a bad occupancy calculation. However, make sure you are leveraging the historical occupancy values for weeks or months where service level was very close to your service level objective. If you under-achieved service level, your occupancy will be overstated, and inversely, if you over-achieved service level, the occupancy for that period will be understated.