Four Levels of Uncertainity

Strategy is all about defining a course of action for the future. Executives often make predictions about the future where they underestimate uncertainties. A McKinsey framework of four levels of uncertainty can be helpful to select the right set of strategic tools.




Level One: A predictable future.

This would apply to situations where sufficiently precise predictions can be made about key variables affecting a company’s markets and businesses (e.g. market demographics in a reasonably stable consumer goods sector). In this case, executives can apply the standard strategy tool kit (market segmentation, competitor’s costs and capacities, value chain analysis, Porter’s five forces model, etc.) to define an optimal course of action.



Level Two: Alternative futures.

Sometimes firms are faced with discrete scenarios, e.g. regulatory changes, significant actions of competitors, etc. It’s hard to predict which outcome will actually happen, although one can assign probabilities to various alternatives. The recommendation here is to develop strategic scenarios, and apply a decision analysis framework or a “real option” approach. Important is also to define trigger points, and monitor markets and competitors closely, in order to react quickly once some of these uncertainties are removed.



Level Three: A range of futures.

Unlike level two (where the outcome is either-or), in level three, a small number of variables define a broad range out outcomes, but the actual result may lie anywhere in between. An example would be a company entering an emerging market, where the consumer penetration rate could be very low or very high, or anywhere in between. Similar to level two, executives are advised here to develop a number of scenarios. It will be important to define these carefully, make sure they don’t overlap, and cover a reasonably broad range of outcomes.



Level Four: True ambiguity.

This type of uncertainty is actually quite rare. It may happen in cases of entirely new technologies (e.g. mobile internet applications), where technology adoption, platform prevalence, competitive landscape and revenue models are all up in the air. Strategy in this situation would be highly qualitative, based on the study of analogous markets and patterns.