Michael J. Mauboussin

About a dozen years ago, when I was working for a large financial services firm, one of the senior executives asked me to take on a project to better understand the company’s profitability. I was in the equity division, which generated fees and commissions by catering to investment managers and sought to maximize revenues by providing high-quality research, responsive trading, and coveted initial public offerings. While we had hundreds of clients, one mutual fund company was our largest. We shuttled our researchers to visit with its analysts and portfolio managers, dedicated capital to ensure that its trades were executed smoothly, and recognized its importance in the allocation of IPOs. We were committed to keeping the 800-pound gorilla happy.

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Reprint: R1210B 
When it comes to assessing performance, business executives can be a lot like old-time baseball scouts, who have been around so long that they&#8217;ve developed a gut feel for which statistics matter most. But as Michael Lewis describes in Moneyball, the Oakland Athletics discovered that the metric the team&#8217;s scouts used to choose players had nothing to do with whether those players would score runs. They had been measuring the wrong thing, and executives may be making the same mistake. 
Theory and empirical research show only a shaky connection between value creation and two of the most popular performance measures: earnings per share (EPS) growth and sales growth. Yet executives cling to those metrics because they are overconfident in their intuition, they misattribute the causes of events, and they do not escape the pull of the status quo. 
The most useful statistics reliably reveal cause and effect. They have two defining characteristics: They are persistent, showing that the outcome of a given action at one time will be similar to the outcome of the same action at another time, and they are predictive&#8212;that is, there is a causal relationship between the action the statistic measures and the desired outcome. 
To choose the right statistics, you must define your governing objective, assess the financial and nonfinancial drivers of that objective, and figure out which employee activities support those drivers. You must also regularly reevaluate your metrics. The drivers of value creation change, and so must your statistics.

The True Measures of Success

Reprint: R1210B When it comes to assessing performance, business executives can be a lot like old-time baseball scouts, who have been around so long that they’ve developed a gut feel for which statistics matter most. But as Michael Lewis describes in Moneyball, the Oakland Athletics discovered that the metric the team’s scouts used to choose players had nothing to do with whether those players would score runs. They had been measuring the wrong thing, and executives may be making the same mistake. Theory and empirical research show only a shaky connection between value creation and two of the most popular performance measures: earnings per share (EPS) growth and sales growth. Yet executives cling to those metrics because they are overconfident in their intuition, they misattribute the causes of events, and they do not escape the pull of the status quo. The most useful statistics reliably reveal cause and effect. They have two defining characteristics: They are persistent, showing that the outcome of a given action at one time will be similar to the outcome of the same action at another time, and they are predictive —that is, there is a causal relationship between the action the statistic measures and the desired outcome. To choose the right statistics, you must define your governing objective, assess the financial and nonfinancial drivers of that objective, and figure out which employee activities support those drivers. You must also regularly reevaluate your metrics. The drivers of value creation change, and so must your statistics.

The True Measures of Success

Summary.

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