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Article | Executive Pay Memo North America

Enhance your pay-for-performance narrative with predictive analytics

Executive Compensation
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By Torie Nilsen , Kenneth Kuk and Steve Kline | June 29, 2018

Pay-for-performance may receive unprecedented attention from shareholders and the public in 2018 and beyond, given the current climate, including the first ever disclosures of CEO pay ratios. Advanced predictive analytics can help companies balance investors’ expectations with reasonable goals that tie pay to results.

Throughout the year we publish pay-for-performance updates for the S&P 1500 and various sectors represented by the index. For most sectors, total shareholder return (TSR) performance was quite strong in 2017 while other key financial measures showed incremental improvement. So it shouldn’t be a surprise that this momentum sets the bar for even higher investor expectations for 2018. Given the current climate, including the first ever disclosures of CEO pay ratios, we expect pay-for-performance to receive unprecedented attention from shareholders and the public in 2018 and beyond.

Rising expectations are reinforcing the need to refine and sustain a strong pay-for-performance narrative, and challenging “check-the-box” thinking around pay-for-performance. Additionally, the emergence of disruptive technology is driving new competitive pressures for companies to reinvent their business models, casting more uncertainty on goal setting. Meanwhile, tax reform is filling corporate coffers and compelling business leaders to make bold new investments on which investors expect to see healthy returns.

In his recent letter to portfolio companies’ CEOs, BlackRock’s Larry Fink opines on a market of high returns and high anxiety that encourages short-term thinking at the expense of the long term (see “Laurence Fink’s letter – implications for executive compensation from BlackRock”, Executive Pay Memo, February, 28, 2018). While companies with a long-term vision will need to invest significantly in the current business environment, their shareholders also expect quarterly financial performance to meet their expectations, fueling an increased scrutiny on goal-setting practices by proxy advisors.

A compelling pay-for-performance narrative connects the dots between executive compensation and sustainable value creation for shareholders through thoughtful consideration of metric selection, weighting, goal setting, and payout potential. Traditional analytics will continue to play a role in this process, e.g., testing the historical alignment of pay (and recalibrating when it’s not aligned) and benchmarking incentive plan design as well as the calibration of goals and payouts. But it’s becoming apparent that future success will require enhancing the conventional approach with disruptive analytics. In particular, we see predictive analytics becoming increasingly common in testing what-if scenarios as companies seek to optimize incentive plan goals and payout potentials.

You may think predictive analytics is complex but this innovative forecasting technique has been around for over a decade…since FAS 123 first began to require stock option expensing and outlined how to account for market-based (e.g., relative TSR) plans. We understand proxy advisors are now beginning to consider the probabilities of goal achievement using predictive analytics in their assessments of executive compensation practices, and Willis Towers Watson’s Predictive Performance Model (PPM), developed in 2016, was first used to model and simulate the probability of future financial performance in order to optimize the calibration of incentive plan goals given the probable payout potential. Since then, PPM has helped to solve pay-for-performance challenges in many sectors (e.g., materials, industrials, consumer staples, retail, health care, technology and utilities) around the globe. Learn more by watching a brief video about PPM.

Predictive Performance Model

Willis Towers Watson’s proprietary Predictive Performance Model (PPM) models and simulates future financial performance to inform the calibration of incentive plan goals and payout potential.

Rising investor expectations mean that companies must build even stronger pay-for-performance narratives. As the need to set increasingly more challenging goals persists, new tools such as predictive analytics like PPM add a forward-looking perspective to traditional evaluation techniques such as peer practice reviews and pay-for-performance alignment analyses. A more sophisticated approach can help you to meet investors’ expectations and also avoid setting goals so high that leadership grows frustrated with middling pay for strong results.

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