From Aggregates to Insights: Drilling Down into Shrinkage and Occupancy Rates

One of the areas in reporting where I have noticed an increasing demand for robustness and accuracy is in analyzing shrinkage and occupancy rates. When I first started in Workforce Management (WFM), shrinkage and occupancy rates were primarily viewed at the business level. We would report metrics like "the contact center experienced a shrinkage rate of 25%, and the occupancy rate was around 80%." However, this trend has been evolving, as operational stakeholders now seek a more granular view of these metrics, particularly at the associate level.

This deeper analysis into shrinkage and occupancy rates at the associate level creates a distribution that highlights how each associate performs relative to the median. Such insights allow for the quick identification of outliers. Once outliers are identified, operational leaders can develop customized plans for individual associates to address the variance. These plans often involve two key considerations:

1. Is the variance explainable, or is it due to external factors?
2. Are there specific aspects of the associate’s workflow or behavior that could be adjusted to improve performance?

This approach is beneficial because leaders can provide tailored, actionable feedback to specific individuals rather than issuing generic messages to the entire front-line staff. Targeted communication not only avoids diluting the message but also empowers associates to make meaningful changes to their actions.

Recently, there has been a push to go even deeper—what I call a "triple-click"—into shrinkage and occupancy data. Operational teams now want to analyze these metrics at a more granular level, down to the unit of time for each associate. While some may view this level of detail as excessive, it reveals interesting patterns. For instance:

• Are associates with high shrinkage rates logging out at specific times to address client needs using workaround solutions?
• Do some associates experience consistently higher shrinkage rates throughout the day due to inadequate training, requiring additional coaching?
• Are higher shrinkage rates concentrated around breaks and lunches, suggesting associates are taking more time off than allotted?

Each of these scenarios would require a distinct and tailored intervention plan. Without including the time element in the analysis, these nuanced insights might be overlooked, potentially missing opportunities to address root causes effectively.

From a WFM perspective, interval-level occupancy data is particularly useful in push environments, where work items are automatically routed. This granular data enables schedulers to quickly identify and resolve gaps in shifts that could cause significant oscillations in occupancy rates. Moreover, fluctuating occupancy rates by interval can highlight potential routing challenges. For example, one group of associates may be overloaded while another is underutilized. These insights could uncover opportunities for cross-training or create changes to routing logic for better balance and efficiency.

The demand for granular analysis of shrinkage and occupancy rates reflects a broader shift toward data-driven decision-making in workforce management. While such detailed reporting may seem excessive, it offers invaluable insights that can enhance operational efficiency, improve associate performance, and optimize resource allocation.