Hidden Flaws in Strategy

Hidden flaws in strategy

Charles Roxburgh

The McKinsey Quarterly, 2003 Number 2

After nearly 40 years, the theory of business strategy is well developed and widely disseminated. Pioneering work by academics such as Michael E. Porter and Henry Mintzberg has established a rich literature on good strategy. Most senior executives have been trained in its principles, and large corporations have their own skilled strategy departments.

Yet the business world remains littered with examples of bad strategies. Why? What makes chief executives back them when so much know-how is available? Flawed analysis, excessive ambition, greed, and other corporate vices are possible causes, but this article doesn't attempt to explore all of them. Rather, it looks at one contributing factor that affects every strategist: the human brain.

The brain is a wondrous organ. As scientists uncover more of its inner workings through brain-mapping techniques,1 our understanding of its astonishing abilities increases. But the brain isn't the rational calculating machine we sometimes imagine. Over the millennia of its evolution, it has developed shortcuts, simplifications, biases, and basic bad habits. Some of them may have helped early humans survive on the savannas of Africa ("if it looks like a wildebeest and everyone else is chasing it, it must be lunch"), but they create problems for us today. Equally, some of the brain's flaws may result from education and socialization rather than nature. But whatever the root cause, the brain can be a deceptive guide for rational decision making.

The basic assumption of modern economics--rationality--does not stack up against the evidence

These implications of the brain's inadequacies have been rigorously studied by social scientists and particularly by behavioral economists, who have found that the underlying assumption behind modern economics--human beings as purely rational economic decision makers--doesn't stack up against the evidence. As most of the theory underpinning business strategy is derived from the rational world of microeconomics, all strategists should be interested in behavioral economics.

Insights from behavioral economics have been used to explain bad decision making in the business world,2 and bad investment decision making in particular. Some private equity firms have successfully remodeled their investment processes to counteract the biases predicted by behavioral economics. Likewise, behavioral economics has been applied to personal finance,3 thereby providing an easier route to making money than any hot stock tip. However, the field hasn't permeated the day-to-day world of strategy formulation.

This article aims to help rectify that omission by highlighting eight4 insights from behavioral economics that best explain some examples of bad strategy. Each insight illustrates a common flaw that can draw us to the wrong conclusions and increase the risk of betting on bad strategy. All the examples come from a field with which I am familiar--European financial services--but equally good ones could be culled from any industry.

Several examples come from the dot-com era, a particularly rich period for students of bad strategy. But don't make the mistake of thinking that this was an era of unrepeatable strategic madness. Behavioral economics tells us that the mistakes made in the late 1990s were exactly the sorts of errors our brains are programmed to make--and will probably make again.

Flaw 1: Overconfidence

Our brains are programmed to make us feel overconfident. This can be a good thing; for instance, it requires great confidence to launch a new business. Only a few start-ups will become highly successful. The world would be duller and poorer if our brains didn't inspire great confidence in our own abilities. But there is a downside when it comes to formulating and judging strategy.

The brain is particularly overconfident of its ability to make accurate estimates. Behavioral economists often illustrate this point with simple quizzes: guess the weight of a fully laden jumbo jet or the length of the River Nile, say. Participants are asked to offer not a precise figure but rather a range in which they feel 90 percent confidence--for example, the Nile is between 2,000 and 10,000 miles long. Time and again, participants walk into the same trap: rather than playing safe with a wide range, they give a narrow one and miss the right answer. (I scored 0 out of 15 on such a test, which was one of the triggers of my interest in this field!) Most of us are unwilling and, in fact, unable to reveal our ignorance by specifying a very wide range. Unlike John Maynard Keynes, most of us prefer being precisely wrong rather than vaguely right.

We also tend to be overconfident of our own abilities.5 This is a particular problem for strategies based on assessments of core capabilities. Almost all financial institutions, for instance, believe their brands to be of "above-average" value.

Related to overconfidence is the problem of overoptimism. Other than professional pessimists such as financial regulators, we all tend to be optimistic, and our forecasts tend toward the rosier end of the spectrum. The twin problems of overconfidence and overoptimism can have dangerous consequences when it comes to developing strategies, as most of them are based on estimates of what may happen--too often on unrealistically precise and overoptimistic estimates of uncertainties.

One leading investment bank sensibly tested its strategy against a pessimistic scenario--the market conditions of 1994, when a downturn lasted about nine months--and built in some extra downturn. But this wasn't enough. The 1994 scenario looks rosy compared with current conditions, and the bank, along with its peers, is struggling to make dramatic cuts to its cost base. Other sectors, such as banking services for the affluent and on-line brokerages, are grappling with the same problem.

There are ways to counter the brain's overconfidence:

1. Test strategies under a much wider range of scenarios. But don't give managers a choice of three, as they are likely to play safe and pick the central one. For this reason, the pioneers of scenario planning at Royal Dutch/Shell always insisted on a final choice of two or four options.6

2. Add 20 to 25 percent more downside to the most pessimistic scenario.7 Given our optimism, the risk of getting pessimistic scenarios wrong is greater than that of getting the upside wrong. The Lloyd's of London insurance market--which has learned these lessons the hard, expensive way--makes