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The right performance targets motivate executives, and the right tools make that possible

Executive Compensation
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By Yong Fei Tan | June 6, 2018

Goal setting can be a challenge, and boards of directors can best assess executive compensation if they have the right tools.

Most current executive compensation models and incentive plan designs are predicated on achieving performance against a certain set of performance targets. In many companies, the management team is tasked with setting these targets, which for incentive plan purposes, the remuneration committee then approves. Even in the best of times and in the most stable industries, it is difficult to predict a reasonable performance level.

As regional and global economic conditions become more unpredictable and technology disruption becomes a continuing expectation in all sectors, Asia-based companies are increasingly pushing into new geographic markets and developing new ways of doing business— all of which are making business forecasting more difficult. At the same time, remuneration committees are adding performance conditions to long-term incentive plans and extending the length of performance cycles on existing plans. Of course, setting appropriate performance goals over multiyear periods is vastly more difficult than doing so over a single year.

The reality, then, is that many boards of directors find it increasingly difficult to set meaningful performance targets and can often have contrasting views of expected business outcomes and corresponding financial performance compared with those of management. A reliable approach based on empirical data and observation can help facilitate performance goal discussions and highlight potential shortcomings of current target-setting approaches.

Leverage all tools and approaches

Since, the goal-setting process is becoming more difficult, board members should avail themselves of all useful tools and approaches — including Willis Towers Watson’s Predictive Performance Model (PPM). Companies that are successful in establishing appropriate performance goals rely on many tools and inputs, including past performance results and trends, industry trends and key peers’ performances, analysts’ and economists’ forecasts, PPM and similar approaches, and of course, management and board members’ insight and experience.

Willis Towers Watson has worked with our clients to develop a stochastic modeling tool for predicting future financial and share price performance that incorporates two well-known financial theories: the capital asset pricing model (CAPM) and the discounted cash flow (DCF) model. Our PPM works by gathering and analyzing a wealth of available performance information about a company to create projections of a range of future financial outcomes (both profit and loss and balance sheet results) and related or stand-alone share price outcomes. When compared against a set of targets, the PPM can provide probabilities of achieving financial, operational and stock price or market capitalization targets.

Using a golf analogy, if we could gather all available information about a particular upcoming putt (e.g., the golfer’s historical putt performance across a range of conditions, distance to the hole, condition of the green, slope of green), we could then simulate a range of probable putt outcomes and compute the probability of putting successfully into the hole as well as probabilities of missing the putt on either side of the hole.

Our PPM approach can be used to develop a range of outcomes for financial performance and assess how likely management is to achieve various performance targets and goals. The approach is independent and unbiased, which is especially important when executive compensation matters are discussed. Overall, the PPM supports the goal setting decision process in many ways:

  1. Ensures the efficacy of incentive plans
  2. Helps management and the board develop appropriate incentive goals
  3. Helps remuneration committees/boards understand and assess the relationship between incentive goals and plan payouts
  4. Ensures plan metrics and goals are aligned with the executive value proposition
  5. Improves alignment of pay governance, risk and pay for performance

How PPM works

Willis Towers Watson recently worked with an investment holding company in Singapore. At the holding company level, executive bonuses depend on yearly achievement of economic profit (EP) — a risk-adjusted profit measure that takes into account the cost of capital. For the past three years, bonus payouts had been zeroed out, as the company had not achieved the minimum threshold level of performance, even though the underlying operating profits were improving. Based on this history, there were concerns that performance goals were not appropriately set. Willis Towers Watson worked closely with the company’s finance and HR departments to gather information about the EP bonus plan design (e.g., plan objectives, plan mechanics, historical payouts, future EP projections and compensation data). We then modeled outcomes that provided the remuneration committee and management with useful insights to assist in their decision-making process.

Economic value back testing simulation for 2017

Figure of simulation result showing only a 34% probability of achieving threshold EP performance and virtually a 0% probability of achieving the EP target

The simulation result in the figure showed only a 34% probability of achieving threshold EP performance and virtually a 0% probability of achieving the EP target. While actual EP was lower than expected, it fell within the probabilistic ranges of 40% to 60%, an appropriate range for incentive plans. After reviewing these model results, and taking into account other data points such as analyst estimates, the remuneration committee agreed to revise EP targets moving forward to ensure the EP bonus plan continues to motivate executives. This was done by setting the EP performance goals such that the probability of achieving at least the threshold performance goal was closer to 90% and the probability of target achievement was between 40% and 60%. The maximum performance goal was also set at a challenging but achievable level, with approximately a 10% chance of achievement.

The result was a more motivated and focused executive team which bought into the results of the goal setting process. The board was also better able to understand any deviations from expected performance — both positive and negative — and was less likely to need to consider adjustments to goals or payouts, including special or discretionary plan payouts to address retention concerns and unhappy executives or to mitigate “windfall” payouts.

Based on the success of this approach at the holding company level, the company extended the new goal setting process to a number of the company’s portfolio companies. Even though these portfolio companies used different performance metrics (and had different performance and industry dynamics), the outcome of the goal-setting process was similar, including a more supportable rationale for performance goals and a better understanding of how the incentive plans operated and rewarded for various levels of performance.

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