Rules for improving the productivity of companies

MUCH OF THE STRATEGY and management advice that business leaders turn to is unreliable or im¬practical. That's because those who would guide us underestimate the power of chance. Gums draw pointed lessons from companies whose outstand¬ing results may be nothing more than random fluc¬tuations. 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 com¬panies 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 com¬panies, and eventually identified several hundred among them that have done well enough period of time to qualify as truly exceptional. Then we discovered something star-tling: The many and diverse choices that made certain companies great were consistent with just three seemingly el¬ementary rules:

1. Better before cheaper—in other words, com¬pete on differentiators other than price.

2. Revenue before cost—that is, prioritize increas¬ing 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 great¬ness 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.

Beyond Truisms

The impetus for our research was the increasing popularity over the past 30 years of "suc-cess study" business books and articles. Perhaps the most famous of these are Thomas Peters and Robert Waterman's In Search of Excellence (1982) and Jim Collins's Good to Great (2001), but there are many others. The problem with them is they don't give us any way to judge whether the companies they hold up as examples are indeed exceptional. Randomness can crown an average company king for a year, two years, even a decade, before performance reverts to the mean. If we can't be sure that the performance of companies mentioned in success studies was caused by more than just luck, we can't know whether to imitate their behaviors.

We tackled the randomness problem head-on. Finding what we assumed would be weak signals in noisy environments required a lot of data, so we began with the largest database we could find—the more than 25,000 companies that have traded on U.S. exchanges at any time from 1966 to 2010. We measured performance using return on assets (ROA), a metric that reflects strong, stable performance—unlike, say, total shareholder return, which may reflect the vagaries of the stock market and changes in investor expectations rather than fundamental company performance. We defined two categories of superior results: Miracle Workers fell in the top 10% of ROA for all 25,000 companies often enough that their performance was highly unlikely to have been a fluke; Long Runners fell in the top 20% to 40% and, again, did so consistently enough that luck was highly unlikely to have been the reason. We call the companies in both these categories exceptional performers. For comparison purposes, we also identified companies that were Average Joes. (See the sidebar "Finding the Signal in the Noise.")

A total of 174 companies qualified as Miracle Workers, and 170 qualified as Long Runners. That's the entire population of companies that separated themselves from the noise in this way. (It's probably worth mentioning that of the allegedly superior companies mentioned by 19 high-profile success studies we examined, barely 12% met our criteria, even for Long Runner status.)

Exceptional companies, it turns out, come in all shapes and sizes. 3M, with its leg-endary innovation and thousands of products in commercial and industrial markets, made the list, but so did WD-40 —a company built on a single, unpatented product that was designed to prevent corrosion on nuclear missiles and has since become most famous as the bane of squeaky hinges. The globally ubiquitous McDonald's proved lobe exceptional, but so did Luby's, a cafeteria chain, when it had only 43 locations (it has since grown to almost too). IBM qualified, and so did Syntel, even though at the time it was only 0.5% of Big Blue's size.

To understand what was behind superior performance, we identified trios in each of nine industries; each trio consisted of one company from each of our performance categories, carefully matched for years of overlap and relative size, with one from each of our three performance categories. We searched for behavioral differences that might explain the specific performance differences we had discerned. For instance, if a Miracle Worker's ROA advantage was driven primarily by superior gross margins, we looked for behaviors that might account for that. If asset utilization was salient, we looked for the behaviors that drove asset utilization. Where the data permitted, we built financial models to estimate the impact of these behavioral differences on performance. To illustrate: Heartland Express, the Miracle Worker in our trucking-industry trio, relied entirely on gross-margin advantage for its ROA edge, and its gross margins seemed to be a function of higher prices. By recalculating the company's financials without that premium, we satisfied ourselves that Heartland's pricing was a plausible explanation for its higher gross margins and thus its better ROA.

Then things got messy. We repeatedly tried and failed to isolate measurable behav-iors that were con¬sistently relevant. For example, at first it seemed that an M&A-shunning strategy might be driving excep¬tional results in the trucking industry; yet during one I5-year period, top-performing Heartland was also the most acquisitive. Nor could we conclude that a propensity toward M&A was a consistently positive factor in other industries, because in confectionery, for example, Wrigley, the Miracle Worker, and Rocky Mountain Chocolate Factory, the Average Joe, had grown organically, whereas Tootsie Roll, the Long Runner, largely bought its growth.

RULE 1

BETTER BEFORE CHEAPER

Was

...