Four levels of uncertainty

Available strategically relevant information tends to fall into two categories. First, it is often possible to identify clear trends, such as market demographics, that can help define potential demand for a company’s future products or services. Second, if the right analyses are performed, many factors that are currently unknown to a company’s management are in fact knowable—for instance, performance attributes for current technologies, the elasticity of demand for certain stable categories of products, and competitors’ plans to expand capacity.

The uncertainty that remains after the best possible analysis has been undertaken is what we call residual uncertainty—for example, the outcome of an ongoing regulatory debate or the performance attributes of a technology still in development. But quite a bit can often be known despite this. In practice, we have found that the residual uncertainty facing most strategic-decision makers falls into one of four broad levels (Exhibit 1).

[Here are the first two of four levels.]

Level one: A clear enough future

The residual uncertainty is irrelevant to making strategic decisions at level one, so managers can develop a single forecast that is a sufficiently precise basis for their strategies. To help generate this usefully precise prediction of the future, managers can use the standard strategy tool kit: market research, analyses of competitors’ costs and capacity, value chain analysis, Michael Porter’s five-forces framework, and so on. A DCF model that incorporates those predictions can then be used to determine the value of alternative strategies.

Level two: Alternative futures

The future can be described as one of a few discrete scenarios at level two. Analysis can’t identify which outcome will actually come to pass, though it may help establish probabilities. Most important, some, if not all, elements of the strategy would change if the outcome were predictable.

Many businesses facing major regulatory or legislative change confront level two uncertainty. Consider US long-distance telephone providers in late 1995, as they began developing strategies for entering local telephone markets. Legislation that would fundamentally deregulate the industry was pending in Congress, and the broad form that new regulations would take was fairly clear to most industry observers. But whether the legislation was going to pass and how quickly it would be implemented if it did were still uncertain. No amount of analysis would allow the long-distance carriers to predict those outcomes, and the correct course of action—for example, the timing of investments in network infrastructure—depended on which one materialized.

In another common level two situation, the value of a strategy depends mainly on competitors’ strategies, which cannot yet be observed or predicted. For example, in oligopoly markets, such as those for pulp and paper, chemicals, and basic raw materials, the primary uncertainty is often competitors’ plans for expanding capacity. Economies of scale often dictate that any plant built would be quite large and would be likely to have a significant impact on industry prices and profitability. Therefore, any one company’s decision to build a plant is often contingent on competitors’ decisions. This is a classic level two situation: the possible outcomes are discrete and clear, and it is difficult to predict which will occur. The best strategy depends on which one does.

Here, managers must develop a set of discrete scenarios based on their understanding of how the key residual uncertainties might play out. Each scenario may require a different valuation model. Getting information that helps establish the relative probabilities of the alternative outcomes should be a high priority. After establishing an appropriate valuation model for—and determining the probability of—each possible outcome, the risks and returns of alternative strategies can be evaluated with a classic decision analysis framework. Particular attention should be paid to the likely paths the industry might take to reach the alternative futures, so that the company can determine which possible trigger points to monitor closely.