The true measure of success pdf

Like feeling stuck on a rollercoaster you no longer enjoy. So you can… Jump off the metrics rollercoaster, by responding to signals. Learn how to identify statistically meaningful signals in a metric.

To respond just right. Or perhaps, not at all. Learn a better way to manage your measures. With clarity and precision, Mark Graban steps through the process of collecting and analyzing the kind of data you focus on — activities that add value for your customers rather than activities that are just wasted motion.

And, how often? And, can we predict our future performance? And, when do we react? When do we ignore? When do we improve? How would you feel if you could answer these questions for your business? Measures of Success shows you how. And… Prove it all. In seconds. With charts and other visuals to show , rather than tell. Key takeaways for Measures of Success. Do manage the work.

Metrics are the result of your work. Improving work improves results. Two data points are not a trend. Not usually. A dozen or more? You betcha. Data only has meaning when compared over time. Context tells the story. A chart tells a better story than a list of numbers. Fluctuations occur for every metric. Process behavior charts show what to respond to, what to ignore.

Measure often. Weekly over monthly. Daily, even better. Respond thoughtfully, versus react mindlessly. Share this book Feedback Email the Author s. Foreword by Donald J. Wheeler, Ph. Practicing Lean. Mark Graban. Lean Veterinary Practice Management. Reflections From Lean Leaders. Best of Lean Blog Lean Blog: Sports. Do Well. Do Good. Learn more about writing on Leanpub.

Free Updates. Finally, executives like most people would rather stay the course than face the risks that come with change. The status quo bias derives in part from our well-documented tendency to avoid a loss even if we could achieve a big gain. A business consequence of this bias is that even when performance drivers change—as they invariably do—executives often resist abandoning existing metrics in favor of more-suitable ones.

Take the case of a subscription business such as a wireless telephone provider. For a new entrant to the market, the acquisition rate of new customers is the most important performance metric.

But as the company matures, its emphasis should probably shift from adding customers to better managing the ones it has by, for instance, selling them additional services or reducing churn. The pull of the status quo, however, can inhibit such a shift, and so executives end up managing the business with stale statistics. To determine which statistics are useful, you must ask two basic questions.

First, what is your objective? In sports, it is to win games. Second, what factors will help you achieve that objective? If your goal is to increase shareholder value, which activities lead to that outcome? These 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.

Statistics that assess activities requiring skill are persistent. For example, if you measured the performance of a trained sprinter running meters on two consecutive days, you would expect to see similar times. Persistent statistics reflect performance that an individual or organization can reliably control through the application of skill, and so they expose causal relationships. Think of persistence as occurring on a continuum. At one extreme the outcome being measured is the product of pure skill, as it was with the sprinter, and is very persistent.

At the other, it is due to luck, so persistence is low. When you spin a roulette wheel, the outcomes are random; what happens on the first spin provides no clue about what will happen on the next. The former statistic reliably links a cause the ability to get on base with an effect scoring runs. It is also more persistent than batting average because it incorporates more factors—including the ability to get walked—that reflect skill. All this seems like common sense, right?

Yet companies often rely on statistics that are neither very persistent nor predictive. Because these widely used metrics do not reveal cause and effect, they have little bearing on strategy or even on the broader goal of earning a sufficient return on investment. Consider this: Most corporations seek to maximize the value of their shares over the long term. Practically speaking, this means that every dollar a company invests should generate more than one dollar in value.

What statistics, then, should executives use to guide them in this value creation? A survey of executive compensation by Frederic W. Researchers at Stanford Graduate School of Business came to the same conclusion. And a survey of financial executives by finance professors John Graham, Campbell Harvey, and Shiva Rajgopal found that nearly two-thirds of companies placed EPS first in a ranking of the most important performance measures reported to outsiders. Sales revenue and sales growth also rated highly for measuring performance and for communicating externally.

But will EPS growth actually create value for shareholders? Not necessarily. Earnings growth and value creation can coincide, but it is also possible to increase EPS while destroying value. EPS growth is good for a company that earns high returns on invested capital, neutral for a company with returns equal to the cost of capital, and bad for companies with returns below the cost of capital.

Despite this, many companies slavishly seek to deliver EPS growth, even at the expense of value creation. The survey by Graham and his colleagues found that the majority of companies were willing to sacrifice long-term economic value in order to deliver short-term earnings.

Theory and empirical research tell us that the causal relationship between EPS growth and value creation is tenuous at best. Similar research reveals that sales growth also has a shaky connection to shareholder value. The most useful statistics are persistent they show that the outcome of an action at one time will be similar to the outcome of the same action at another time and predictive they link cause and effect, predicting the outcome being measured. The values need not be equal to produce a perfect correlation; any straight line will do.

The closer to 1. The closer to zero, the less persistent and predictive the statistic. The figures above show the coefficient of correlation for EPS growth and sales growth for more than large nonfinancial companies in the United States. The compounded annual growth rates from to , on the horizontal axes, are compared with the rates from to , on the vertical axes. If EPS and sales growth were highly persistent and, therefore, dependent on factors the company could control, the points would cluster tightly on a straight line.

This is consistent with the results of large-scale studies. In the figures above, adjusted EPS growth and sales growth are on the horizontal axes. Privacy Policy.

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