Optimizing Workforce Planning: The Missing Data from Routing Platforms
Every workforce management professional knows that a routing platform must have the capability to capture the final queue where a transaction ends. This data is essential for creating demand forecasts by aggregating historical workload actuals for each queue.
That being said, there are other critical routing platform data points that many systems either lack the capability to capture or fail to measure effectively. One of the most important yet often overlooked aspects is tracking the journey of transactions through multiple queues and workload distribution.
Tracking the Full Transaction Journey
Yes, the final queue indicates where a transaction ultimately ends, but a portion of total transactions will pass through multiple queues before resolution. Capturing only the final queue ignores insights into how transactions flow through the system. A more effective approach involves measuring the entry point of a transaction, the number of queues it passes through, and the time spent in each.
For example, consider a ticket that initially enters Queue "X" and remains there for 5 minutes before being forwarded to Queue "Y" for another 5 minutes. A limited routing platform might assign the total 10-minute handle time solely to the final queue, Queue "Y." As a result, when a capacity planner forecasts future demand, they will allocate the full 10 minutes of workload to Queue "Y," overestimating its requirements while underestimating the workload for Queue "X."
A more advanced routing platform will accurately distribute the 10-minute transaction time between Queue "X" and Queue "Y." This leads to a more balanced workload distribution, allowing capacity planners to minimize overestimations in the final queue and underestimations in previous queues.
Understanding Answering Groups and Workload Distribution
Once a transaction enters a queue, it is typically handled by a group of associates. However, not all associates have the same priority or availability. Capacity planners must forecast workload requirements by queue and then determine how many full-time equivalents or production hours are available in the primary answering group.
To accurately assess queue performance, planners need visibility into how much of the workload is handled by the primary answering group versus secondary or tertiary groups. For example, if a queue had 100 hours of workload yesterday, a basic routing platform might not report how these hours were distributed across different answering groups. The capacity planner would then assume that the primary group handled the full 100 hours, which could lead to miscalculations in staffing needs.
A better routing platform would provide a breakdown, showing that the primary group handled 70% (70 hours) while a secondary group handled 30% (30 hours). This insight enables planners to make informed decisions:
- Should we increase the capacity of the primary group to handle more of the queue workload?
- Since the secondary group is a primary group in another queue, are we underestimating its total workload across multiple queues?
Capacity Planning isn't just about tracking where a transaction ends—it's about understanding the entire journey. By capturing transaction flows across multiple queues and measuring workload distribution among answering groups, organizations can improve forecasting accuracy, optimize staffing, and prevent inefficiencies. Routing platforms that fail to measure these insights create blind spots, leading to misallocated resources and avoidable operational costs.
The future of workforce management lies in intelligent routing platforms that go beyond the basics. Companies that leverage this level of data granularity will position themselves ahead of the curve, ensuring their workforce is as efficient and agile as possible.