This PPT slide, part of the 34-slide People Productivity Toolkit PowerPoint presentation, presents a framework for an incentive system that integrates external and internal metrics to assess performance. It is structured into 2 main categories: external metrics and internal metrics, each with specific data points, logic, and factors of influence.
External metrics include Total Shareholder Return (TSR) and Total Business Return (TBR), which focus on quantifiable outcomes such as changes in share prices and dividends. These metrics are essential for understanding how external market conditions affect overall business performance. The internal metrics, on the other hand, delve deeper into the company’s operational effectiveness. They include internal TBR, Fundamental-value CVA, Internal IVA, and ?CVA, which reflect a more nuanced view of value creation based on internal assessments and financial modeling.
The logic behind the internal metrics emphasizes the importance of fundamental values derived from external data, suggesting that a more accurate picture of performance can be achieved through internal modeling. For instance, the Fundamental-value CVA represents residual income calculated from these internal values, while Internal IVA focuses on value added through capitalized ?CVA.
The slide also hints at the potential for bonus pools to be combined with existing metrics, indicating a flexible approach to performance incentives. This integration of metrics allows for a more comprehensive evaluation of both external influences and internal operational success.
Overall, this framework encourages organizations to adopt a dual approach to performance measurement, balancing external market realities with internal operational insights. The insights provided can help in refining incentive structures to align better with both market and internal performance drivers.
This slide is part of the People Productivity Toolkit PowerPoint presentation.
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Human Resources Workplace Productivity Performance Measurement Value Creation Financial Modeling
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