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Outcome Tree

An outcome tree is an exercise for modeling potential outcomes and probabilities of those potential outcomes. Outcome trees help do three things:

  • Align on value by being clear on the desired outcome of the decision
  • Defining the potential outcomes helps measure risk vs expected value
  • Build rigor in documenting your expected outcomes to compare with the actual outcome in order to make better decisions in the future

Whenever you make a key decision or take a key action, write down what you expect will happen. Nine or 12 months later, compare the actual results with your expectations… Practiced consistently, this simple method will show you within a fairly short period of time, maybe two or three years, where your strengths lie—and this is the most important thing to know. - Peter Drucker

How to build an Outcome Tree

Building an outcome tree is quite simple. The goal is to understand the possible outcomes of the decision in order to have a conversation about confidence and risk.

If you want to take it a step further, the outcome tree can help you understand how to measure success, and even more importantly, put measures in place that could help provide leading indicators of a potential negative outcome.

1) Identify the potential outcomes

The best place to start is by simply listing out potential outcomes. This can be done as a stream of consciousness or prompted by questions. For example, asking "What area all the potential positive outcomes?" and conversely, "What are the potential negative outcomes?".

What often happens is that the outcomes you want or expect tend to come to mind first. As this list starts to move into negative outcomes, interesting conversations start to emerge. If we decide to build a new feature, it's a very real possibility (no matter how much we think it won't be the case) that no one will use it. If that happens, then what? How might we defend against that outcome?

Building out the visual tree is helpful to see the different tracks potential outcomes can take. Maybe it's simple, but maybe there are domino effects as well.


Keep in mind the goal here is to drive conversations that lead to better risk mitigation and eventually better decision making - this is not meant to be an exhaustive list.

2) Understand the probability of the potential outcomes

After we build out the list, the next step is to group our potential outcomes into ones we believe are positive, negative or neutral. We could take this a step further and rate them on a five star scale to add some depth. For example, we may have two negative outcomes, but one may be a far worse outcome than the other.

For each potential outcome, give it a 'probability' for the likelihood this outcome will occur. Again, this activity is to drive conversation, so roughly right is okay. We can simply say 'high', 'low' or even 'very low'. If we're talking about a critical decision, maybe we do take the time to give percentages to these probabilities based on some research or insight.

Nevertheless, with each of these, have a conversation around why we believe the probability is what it is. Do we believe the positive outcomes are high due to bias? Do we have information that supports our assumptions? If not, think about whether or not all the risks are really known. This may influence your confidence score.


3) Measure, learn and adapt

Over time, decisions need to be revisited to log the actual outcome and retrospect on the process and expectations against reality. If a negative outcome occurred, did we predict it as a possibility? Were our assumptions of the risk or probability wrong?

Much of the value of outcome trees come from the activity of logging the process before the actual outcome happens. Once the actual outcome happens, we're subject to hindsight bias. Looking back, we (and others) believe the outcome was obvious all along. Documenting that process helps to adapt the process.

“What makes a decision great is not that it has a great outcome. A great decision is the result of a good process..." - Annie Duke

In the inverse, a bad outcome doesn't necessarily mean a bad decision was made; plenty of factors are involved, including luck. What determines a good or bad decision is based on the process built around that decision to accept or mitigate risk. In the tree above we can see that not a single outcome is certain, we're looking to accept risk relative to the expected value.

Logging the process, even if simple at first, and learning from the outcomes is the single most powerful way to make consistently better decisions as a team or organization.