Video Insights: Exploring Branch Workforce Management - Forecasting
We’re excited to launch a new video series diving into key topics within the world of workforce management. In this first video, Robert Woolsey, a leader in workforce analytics, covers forecasting—why it’s important, best practices for managing the process, and some ideas to make your forecasts more accurate.
Top Tips for Accurate, Effective Branch Forecasting
Accurate forecasting is critical to both your short and long-term staffing and scheduling. For your week-to-week schedule creation, improved volume forecasts enable your auto-scheduler to better optimize your shift coverage and ensure better “bang for the buck” for your scheduled hours. But enhancing transaction accuracy—and specifically picking up on trends and customer migration patterns—is also key to right-size overall staffing levels over a longer time horizon. Ideally staffing levels are set to achieve a target average customer wait time, but service level calculations are sensitive to changes in transaction volumes, underscoring the need for an accurate forecast to support your network-level staffing strategy and planning.
Employing best practices in forecast management is key to ensuring your forecasts stay as accurate as possible over time. Setting up a regular cadence to review outliers and the largest deviations from your forecast is arguably the most impactful activity for a forecasting function. This process can help uncover both one-time and ongoing events that require either revising your historical transaction volumes or adding a new explanatory variable to the model. A quick check for significant weather or large local events for the impacted branches will inform you of the right course of action.
Creating an internal forum with key stakeholders is also vital to your forecasting process. This is a great opportunity to not only inform others about important changes and trends in the forecast, but also learn about any external overlays that might be necessary based on policy changes or other initiatives within the bank.
Don’t just settle for a “basic” forecasting solution – additional techniques should be explored to hone your accuracy even further. Forecasting in banking typically requires a large number of variables to get the best results. It’s important for your model to understand the day of the week, week of the month, government check days, paydays, and others. However, when these variables overlap on the same day, traditional forecasting models typically overstate what the actual transaction volume will be. New deep learning and neural network forecasting models can handle these situations more accurately with less time and work required.
It’s also worth exploring a top down approach to forecasting in your WFM function. Aggregating the transaction volumes across all customer channels—in-branch, mobile, ATM, and online—and then disaggregating just the in-branch portion can often yield improved results. Transactions in the aggregate tend to be much more stable, predictable, and can be forecasted with a higher level of accuracy.
To learn how Reflexis can help you improve branch forecasting, reach out to email@example.com to connect with Robert or one of our other banking experts!