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Published 29 April 2013 12:57, Updated 01 May 2013 13:30
The devout may follow the 10 commandments in life but for long-term business success, there are but three.
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.
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 choices that made certain companies great were consistent with just three seemingly elementary rules.
The rules don’t dictate specific behaviours nor are they general strategies. They’re foundation concepts on which companies have built greatness over many years.
The impetus for our research was the increasing popularity over the past 30 years of “success 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’ 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 behaviour.
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 US exchanges at any time from 1966 to 2010. We measured performance using return on assets, a metric that reflects strong, stable performance.
We defined two categories of superior results: “Miracle Workers” fell in the top 10 percent 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 percent 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.”
A total of 174 companies qualified as Miracle Workers, and 170 qualified as Long Runners.
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.
We searched for behavioural differences that might explain the specific performance differences we had discerned. Where the data permitted, we built financial models to estimate the impact of these behavioural differences on performance.
Was customer focus the key? Nope. Innovation? Risk-taking? Nope and nope.
Then things got messy. We repeatedly tried and failed to isolate measurable behaviours that were consistently relevant. Was customer focus the key? Nope. Innovation? Risk-taking? Nope and nope. All these factors were associated with great, good or average performance in pretty much equal measure.
A useful explanatory frame began to emerge only after we shifted our emphasis away from what these companies did to hypotheses about how they thought.
That allowed us to see past what the exceptional companies were doing, which was endlessly variable, to how they apparently decided what to do, which proved highly consistent. The only factor that seemed to matter was their adherence to our rules.
Every company faces a choice: it can compete mainly by offering superior non-price benefits such as a great brand or excellent functionality or it can meet some minimal acceptable standard along these dimensions and try to attract customers with lower prices. Miracle Workers overwhelmingly adopt the former position. Average Joes typically compete on price. Long Runners show no clear tendency one way or the other.
We don’t mean to suggest that a company can afford to ignore its relative price position any more than one that competes through low prices can afford to ignore product quality.
We mean only that, in most cases, outstanding performance is caused by greater value and not by lower price. Companies seeking sustained, exceptional profitability should pursue strategies that are consistent with this rule and avoid those that aren’t.
Companies must not only create value but also capture it in the form of profits. By an overwhelming margin, exceptional companies garner superior profits by achieving higher revenue than their rivals, through either higher prices or greater volume. Very rarely is cost leadership a driver of superior profitability.
For eight of the nine Miracle Workers in our sample, revenue was the main driver of performance. Six of these eight relied mainly on higher prices to achieve their revenue levels; the other two relied largely or entirely on volume.
This rule underscores the uncomfortable (or liberating) truth that in the pursuit of exceptional profitability, everything but the first two rules should be on the table.
When considering all the other determinants of company performance – operational excellence, talent development, leadership style – we saw wide variation among companies of all performance types. There’s no doubt that these and other factors matter to corporate performance, but we couldn’t find consistent patterns of how they mattered.
More telling still, we found individual companies that had remained exceptional despite changing their approaches to a number of critical determinants of performance. The reason? The changes they made kept them aligned with the first two rules.
The absence of other rules doesn’t give you permission to shut down your thinking. You are still responsible for searching actively for ways to follow the rules in the face of what may be wrenching competitive change.
Companies don’t become truly great by reducing costs or assets; they earn their way to greatness.
Exceptional companies often, even typically, accept higher costs as the price of excellence. These organisations put significant resources, over long periods of time, into creating non-price value and generating higher revenue. When successful companies are led astray by the seeming certainties of short-run cost cutting or disinvestment, they are more likely to destroy what they most want to enhance.
Our retail grocery trio was intriguing – first because the Miracle Worker of the bunch, Weis, was a price-based competitor that captured profits through low costs but also because Whole Foods, a high-profile purveyor of organic products, clearly practises better before cheaper and revenue before cost, yet turns out to be an Average Joe.
During its more than 20 years as a public company, its ROA has sometimes been worse than the majority of companies’.
Whole Foods may very well be on route to becoming a Long Runner or a Miracle Worker but it might just as easily get submerged in a wave of competition.
Its high costs – sourcing specialty items is expensive, and the company provides high levels of customer service (“Excuse me, how do I cook quinoa?”) – have kept competitors at bay but with supply-chain costs falling, mainstream grocers are already starting to mimic its competitive position (you can get quinoa just about everywhere these days).
This example highlights an important limitation of our work: Following our rules doesn’t guarantee Miracle Worker status. Sometimes a non-price position isn’t worth the resources a company must devote to maintaining it. The rules can only tell you which hard problem you should try to solve. They can’t tell you how to solve it.
Don’t be misled by the simplicity of the rules. Long-term success in any industry is a rare and difficult achievement, and finding a workable strategy that stays within the rules requires enormous creativity and flexibility.
Take Merck and Eli Lilly: They have long histories and are well known as leading research-based drug companies but Merck was able to become a Miracle Worker while Eli Lilly remained a Long Runner. Why?
Past executives have told us that the primary driver of Merck’s superior performance was its research excellence, which yielded higher-value therapies.
It’s true that Merck was early among pharmaceutical companies to shift the focus of its research from chemistry-based screening – isolating new compounds and testing them in vivo to assess the effects – to biology-based “rational discovery”.
But Eli Lilly had great research too. And in any case, higher prices weren’t the main reason for Merck’s higher profitability: Barely a third of its ROA advantage can be attributed to superior gross margins.
We found that Merck’s profitability rested on better asset utilisation, which was a function of revenue growth through higher unit volumes. For example, Merck’s command of the mechanisms of action for compounds allowed scientists to develop variations on established drugs in order to attenuate side-effects or mitigate interactions with other drugs, making a compound’s basic therapeutic effects available to a larger population of patients.
As a result, Merck was introducing three times as many products across twice as many therapeutic areas yet enjoying economies of scope in discovery and manufacture that stemmed from similarities among core compounds.
The company was also a leader in international expansion, which further increased unit volumes and asset utilisation.
Both its global expansion and its greater product diversification were driven by demand, born of the value of Merck’s unique medicines. By 2010 its pharmaceutical business was more than twice the size of Eli Lilly’s – up from about 8 per cent larger in 1985.
Merck’s story shows that there are various routes to better before cheaper and revenue before cost, and they should all be under consideration all the time.
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 behaviours common to the best.
We started by digging through Compustat’s database of more than 25,000 companies publicly traded in US markets from 1966 to 2010.
With the help of Andrew D. Henderson, of the University of Texas, 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 percent.
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 percent for all of them but a firm with 45 years of data needed just 16 years in the top 10 percent.
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 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 and total asset turnover, aka asset utilisation. 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 research and development, 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 behaviour that could plausibly explain exceptional companies’ performance advantages and, where possible, we assessed the impact of those behaviours by creating financial models.
Michael E. Raynor is a director at Deloitte Services LP, and Mumtaz Ahmed is a principal with Deloitte Consulting LLP and the chief strategy officer for Deloitte LLP. They are the authors of The Three Rules: How Exceptional Companies Think, forthcoming from Portfolio.