Much of the strategy and management advice that business leaders turn to is unreliable or impractical. That’s because those who would guide us underestimate the power of chance. Gurus draw pointed lessons from companies whose outstanding results may be nothing more than random fluctuations. Executives speak proudly of corporate achievements that may be only lucky coincidences. Unfortunately, almost no one provides scientifically credible answers to every business leader’s basic questions about superior performance: Which companies are worth studying? What sets them apart? How can we follow their examples?
Frustrated by the lack of rigorous research, we undertook a statistical study of thousands of companies, and eventually identified several hundred among them that have done well enough for a long enough period of time to qualify as truly exceptional. Then we discovered something startling: The many and diverse choices that made certain companies great were consistent with just three seemingly elementary rules:
1. Better before cheaper—in other words, compete on differentiators other than price.
2. Revenue before cost—that is, prioritize increasing revenue over reducing costs.
3. There are no other rules—so change anything you must to follow Rules 1 and 2.
The rules don’t dictate specific behaviors; nor are they even general strategies. They’re foundational concepts on which companies have built greatness over many years. How did these organizations’ leaders come to adopt them? We have no idea—nor do we know whether the executives even followed them consciously. Nevertheless, the rules can be used to help today’s and tomorrow’s leaders increase the chances that their companies, too, will deliver decades of exceptional performance.
In our quest to identify top-performing companies and figure out why they were among the best, we spent almost two years developing and working through appropriate statistical methods and another three years identifying the behaviors common to the best.
We started by digging through Compustat’s database of more than 25,000 companies publicly traded in U.S. markets from 1966 to 2010. With the help of Andrew D. Henderson, of the University of Texas at Austin, we used quantile regression—which allowed us to strip out extraneous factors such as survivor bias, company size, and financial leverage—to rank companies according to their relative performance on return on assets (income divided by the book value of assets), a metric that reliably reflects managerial efforts rather than simply changes in investor expectations, which are the primary driver of shareholder returns.
Then we used advanced simulation techniques to determine which companies had achieved high performance long enough that the chance their results were due to luck was less than 10%. The qualifying length of time depended on life span: For example, to be a Miracle Worker, a company with 10 years of data had to have been in the top 10% for all of them, but a firm with 45 years of data needed just 16 years in the top 10%.
To figure out what made these companies special, we selected a Miracle Worker, a Long Runner, and an Average Joe in each of nine industries and then made pairwise comparisons among the three. In each comparison, we figured out how much of the ROA difference arose from each of the components of ROA—return on sales (ROS) and total asset turnover (TAT), aka asset utilization. Then we figured out how much of the ROS difference was due to differences in gross margins and a number of expense categories, which included R&D, SG&A (selling, general, and administrative), and several others (depreciation, extraordinary items, and so on). Similarly, we figured out how much of the TAT difference was due to current asset turnover and fixed asset turnover. We sought behaviors that could plausibly explain exceptional companies’ performance advantages, and where possible, we assessed the impact of those behaviors by creating financial models.