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Big reason reward frameworks fail

The calibration of the pay-off profile in executive incentives often fails to take into account the range of likely outcomes, leaving participants and boards unhappy


The majority of publicly listed companies, and many large privately held businesses, include an “at risk” incentive component in their remuneration framework for key management personnel. The theoretical basis, effectiveness and design of such incentives is a broad topic, so in this post I’d like to focus in on how such systems are calibrated as this is a common source of problems.


Calibrating the pay-off profile of an incentive design


In using the term ‘calibration’ I’m referring to how much the incentive plan rewards the participant for variations in performance. It’s useful to picture this relationship as a pay-off profile on a chart, where the x-axis shows variations of performance and the y-axis shows the corresponding pay-off or reward, as shown by the dark black line and red circles on the graph below.

The example above comes from the Point Value app, which you can view for free here.


In the app the x-axis is scaled as a generic % performance outcome. In practice your company’s incentive design will apply a specific performance measure, some popular ones being:

  • TSR or relative TSR

  • Growth in sales, profits or earnings

  • Return ratios, such as ROE, ROIC

  • Other industry specific measures

(*The decision as to a suitable measure is beyond the scope of this post)

Having advised public companies on incentive design since 2003 one recurring issue I’ve seen many times is that the range between threshold and stretch targets is too narrow.

Consider the example in the graph above where the calibration of the pay-off profile shows that the participant will:

  • Receive zero for performances below the threshold of 10%,

  • 50% of their incentive reward for reaching the threshold point,

  • 100% of their reward for reaching or exceeding the stretch performance at 15%, and

  • An amount described by the sloping straight line for results between the threshold and stretch targets.

This shape of pay-off profile is very common, albeit that each organisation uses its own performance measure with threshold and stretch targets. The common theme though is the distance between the threshold and the stretch target often only covers a limited part of the actual range of potential outcomes.


The potential performance outcomes are depicted on the chart by the light blue histogram, which graphically indicates the range and relative likelihood of performance outcomes. In this example performance is likely to fall between 0% and 30%, with the mode being 8%.


On the app you can change this curve by entering your own worst, most likely, and best cases.


The coloured % range bands at the top of the chart show the proportion of times performance would theoretically lie below threshold, above the target, or on the sloped line between the two. Notice in this example only 27.8% of outcomes are expected to lie between the threshold and stretch targets.


This means that in over 70% of cases the participant will receive either zero or 100%. In other words this calibration tends to yield binary results, 0% or 100%. Of course this is acceptable occasionally, however over time this calibration will lead to management and/or board discomfort and is indicative of a ‘broken’ system.


Natural questions arise such as:

  • Why have such a complicated design if the results are binary?

  • If the original purpose of the system was to incentivise management how effective can it really be when only a small proportion of outcomes fall on the sloped part of the line where the pay-off is affected by performance?

  • Is the reward system providing value for the time, money and ‘personal capital’ invested?

Such questions lead to dissatisfaction with the system and an inevitable and expensive exercise in redesigning the framework, and difficult conversations at management and board level.


Instead, please consider carefully the calibration of the pay-off profile when the system is being designed. Numbers like 10% and 15% can sound reasonable as targets, but it’s important to consider the supporting evidence. The position and distance between the threshold and stretch targets should reflect the expected distribution and volatility of performance results.


A view on this can be formed by:

  • Quantitative: Consider the historical performance of your company and that of other companies in the industry. Also look at potential future scenarios. Simulation (such as our app) can also provide insight.

  • Qualitative: Between key management and the board it’s likely there is a wealth of experience as to how the business is likely to perform (the ups and downs). Use structured discussions to draw out the expectations for the worst and best cases, rather than pin the calibration only on the budget target or an average.

Finally, the decision regarding the calibration of targets does not sit in isolation. A robust calibration for a plan which will stand the test of time should consider other elements within your remuneration framework and more broadly your strategy planning process. By adopting an incentive system you are implying to key personnel that certain things (performance measures) are fundamental, therefore naturally you should ensure that these measures and targets are truly aligned to the future of your business.


Conclusion. Thank you for reading to the end of this post. I hope you’ve found this article and the interactive calibration app useful. If you’d like to continue this line of thinking it would be great to hear from you, please get in touch or leave a comment.



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