Navigating the Nuances in Demand Forecasting in Workforce Management
When people ask me how to forecast demand, what used to be a straightforward question has evolved into a more nuanced inquiry. Initially, I would simply multiply the total number of transactions by the average handle time of each transaction to determine the necessary labor time for a capacity plan. However, as I gained experience, I realized the complexity involved in accurately answering this question. This week, I will elaborate on the concept of forecasting transaction volume.
Transaction Volume
Transaction volume is a critical variable in forecasting, but its definition and the extent to which it should be forecast can vary. A synchronous environment, such as chat, provides a clear example of the various stages in a client’s journey that can be considered for forecasting:
1. The number of visitors to the website or app.
2. The number of visitors encountering a chat icon.
3. The number of visitors clicking on the chat icon.
4. The number of visitors requiring agent intervention after not being supported by a chatbot.
5. The number of visitors needing agent support who did not abandon the chat before an agent could respond.
6. The number of visitors who were true positives that an agent could successfully assist.
In this scenario, there are six points in the client journey where forecasting can be applied. The primary goal remains to generate an output that feeds into a capacity plan to calculate labor hours. Points five and six are particularly crucial as they directly influence labor requirements.
However, forecasting other points in the client journey can be beneficial. It enables the operations department to gain deeper insights into the client experience and seek support from other departments. This collaboration can help reduce overall labor hours or enhance the client journey.
Conversely, forecasting numerous variables can lead to a buildup of assumptions, introducing variance into the forecasts. The main challenge lies in the fluctuating full-time equivalent (FTE) staff needed to service clients. Significant variances can cause downstream issues in scheduling and real-time monitoring. Moreover, they place additional pressure on the contact center’s support departments, like talent acquisition, training, and HR, as they must adjust their staffing to align with the swings in FTE requirements.
This complexity is magnified when considering multiple channels and client segments, highlighting the importance of having an experienced forecaster who can balance the effort required to forecast multiple transaction points against the potential benefits of such forecasting.
Forecasting demand in a dynamic environment like a contact center requires a deep understanding of both the technical aspects and the strategic implications. An experienced forecaster must navigate the complexities of multiple transaction points, balancing precision with practicality to optimize both labor resources and client satisfaction.