Setting financial incentive goals is one of the most complex executive compensation challenges faced by compensation committees and management teams. Striking the right balance between challenge and attainability is critical for driving engagement, performance and long-term shareholder value creation.
This article builds on historical payouts, one of the factors Zach Georgeson considered in his article on a framework for effective goal setting. Specifically, we examine whether S&P 1500 CEO annual incentive payouts align with commonly cited statistics around the payout distribution. We also explore whether organizations should universally calibrate incentive goals to a bell-shaped normal distribution or if variations based on the company’s unique compensation strategy are more appropriate.
Discussions around incentive plan calibration typically center on three key performance levels: threshold, target and maximum. A widely accepted rule of thumb suggests that the probability of achieving these levels should follow a structured pattern:
Figure 1 illustrates the typical distribution.
of the bell, target (50% to 60%) being at the center / top of the bell, and maximum (10% to 20%) being on the far-right side of the bell.
Source: Research and analysis conducted by the Global Executive Compensation Analytics Team (GECAT), 2025.
This probability-based approach ensures that goals (i.e., threshold, target, maximum) are challenging yet attainable. Proper calibration helps avoid two significant risks:
Using a bell-shaped distribution model, organizations can balance motivation and risk management, ensuring that incentives reward strong performance without encouraging reckless decision making.
We analyzed 10 years of CEO annual incentive payouts across the S&P 1500 to validate this framework. Figure 2 plots a cumulative distribution graph, which reflects how often annual incentive payouts exceed a certain percentage of target.
The findings closely align with the expected probability ranges:
These findings closely align with the anticipated probabilities: 80% to 90% for threshold; 50% to 60% for target; and 10% to 20% for maximum performance. Even after excluding 2020 and 2021 — years with pandemic-related anomalies — the results were consistent, further reinforcing the model’s validity.
These findings suggest that many organizations naturally align with the normal curve, whether by design or as an outcome of their financial planning processes.
The short answer: Not necessarily.
While the normal curve serves as a useful guideline, organizations should consider their unique compensation strategy when calibrating goals rather than blindly adhering to a fixed distribution model. Compensation strategies vary based on company culture, risk appetite, industry, operational objectives and business maturity. For example:
Ultimately, the key question is: Does your company’s strategy prioritize stable payouts with minimal variance or a higher-risk, higher-reward structure with more volatility? Your answer should align with the organization’s long-term strategic plan and risk tolerance.
Consider an organization with an earnings per share (EPS) growth goal and an asymmetric payout curve, offering a steeper slope (and therefore bigger rewards) for outperformance than penalizing for under-performance, as shown in Figure 3.
How do you determine if this structure is appropriate? We recommend evaluating past payout distributions against actual performance results:
Figure 4 reflects an example of EPS performance over the course of 10 years.
Consider the same company as in Figure 4, where threshold was achieved six times, target five times and maximum four times in the past decade:
Goal setting in executive incentive plans is both an art and a science. While a probability-based approach provides a structured framework, it must be tailored to each organization’s strategy, culture and risk tolerance.
For this reason, we also recommend reviewing the other inputs from Zach Georgeson’s article: balancing internal data (e.g., budgets, forecasts, long-term strategies) with external benchmarks (e.g., peer performance, analyst/market expectations).
By analyzing historical payouts, benchmarking against normal distribution models and aligning with long-term business objectives, companies can design effective incentive plans that drive performance, manage risk and sustain executive engagement.
A version of this article appeared in Workspan on Apr. 17, 2025. All rights reserved, reprinted with permission.
