Decision tree analysis: 5 steps with expected value

Have you ever made a decision knowing your choice would have major consequences? If you have, you know that it's especially difficult to determine the best course of action when you aren't sure what the outcomes will be.

Decision tree analysis can help you visualize the effects of your decisions so you can find the best course of action. In this article, we'll show you how to create a decision tree so you can use it throughout the project management process.

What is a decision tree?

A decision tree is a flowchart that maps out decisions and their possible outcomes. Decision tree analysis uses this visual tool to outline the potential costs, consequences, and expected values of complex choices, helping you identify the best course of action.

You can use a decision tree to:

• Calculate expected value: Assign probabilities and values to each outcome to quantify your options

• Compare choices: See all possible paths side by side to assess which decision makes the most sense

• Solve problems: Break down complex decisions into manageable, visual components

• Manage costs: Factor in expenses at each decision point to understand the true cost of each path

Decision tree symbols

A decision tree includes the following symbols:

• Alternative branches: Two lines that branch out from one decision, showing outcomes or decisions that stem from the initial choice

• Decision nodes: Squares that represent a decision being made; every decision tree starts with one

• Chance nodes: Circles that show multiple possible outcomes

• End nodes: Triangles that show a final outcome

A decision tree analysis combines these symbols with notes explaining your decisions and outcomes, along with any relevant values that show your profits or losses. You can manually draw your decision tree or use a flowchart tool to map it out digitally.

What is decision tree analysis used for?

Decision tree analysis works best as part of a data-driven decision-making approach, using quantitative data. The more numbers you can assign to costs, outcomes, and probabilities, the more accurate your analysis will be.

Common use cases for decision tree analysis include:

• Project selection: Compare potential projects to determine which will deliver the highest return

• Budget planning: Evaluate spending options and their likely financial outcomes

• Operations decisions: Choose between process changes by mapping costs and efficiency gains

• Resource allocation: Decide where to invest team time and money for maximum results

Types of decision trees

There are three main types of decision trees, and understanding them can help you choose the right approach for your needs:

• Classification trees: These trees categorize data into distinct groups or classes. In a business context, you might use a classification tree to determine whether a project should be approved, rejected, or sent for further review based on specific criteria.

• Regression trees: These trees predict continuous numerical values rather than categories. For example, a regression tree could help estimate a project's expected revenue based on variables such as budget, timeline, and team size.

• Decision trees for decision analysis: These trees help you evaluate choices by mapping decisions, chance events, and outcomes to calculate expected values and identify the best course of action.

For project management and strategic planning, decision trees are the most relevant for decision analysis. They help you weigh options, assess risk, and make data-informed choices about your projects.

How to create a decision tree

Follow these five steps to create a decision tree diagram to analyze uncertain outcomes and reach the most logical solution.

1. Start with your idea

Begin your diagram with one main idea or decision. You'll start your tree with a decision node, then add branches for each option you're considering.

For example, if you want to create an app but can't decide whether to build a new one or upgrade an existing one, use a decision tree to assess the possible outcomes of each.

In this case, the initial decision node is:

• Create an app

The three options, or branches, you're evaluating are:

• Building a new scheduling app

• Upgrading an existing scheduling app

• Building a team productivity app

2. Add chance and decision nodes

After adding your main idea to the tree, continue adding chance or decision nodes after each decision to expand your tree further. A chance node may need an alternative branch after it because there could be more than one possible outcome of that decision.

For example, if you decide to build a new scheduling app, there's a chance your revenue from it will be large if it's successful with customers. There's also a chance the app will be unsuccessful, which could result in a small revenue. Mapping both potential outcomes in your decision tree is key.

3. Expand until you reach end points

Keep adding chance and decision nodes to your decision tree until you can't expand the tree further. At this point, add end nodes to your tree to signify the completion of the tree creation process.

Once you've completed your tree, you can begin analyzing each decision.

4. Calculate tree values

Ideally, your decision tree will include quantitative data. The most common data used in decision trees is monetary value. For example, it'll cost your company a specific amount to build or upgrade an app. Writing these values in your tree under each decision can help you in the decision-making process.

Once you know the cost and probability of each outcome, you can calculate expected value using this formula:

• Expected value (EV) = (First possible outcome x Likelihood of outcome) + (Second possible outcome x Likelihood of outcome) - Cost

In other words, multiply each possible outcome by its probability, add those values together, then subtract your initial costs.

5. Evaluate outcomes

Once you have expected values for each decision, determine which option is best based on the risk you're willing to take. The highest expected value may not always be the right choice; a high potential reward often comes with greater project risk.

Keep in mind that expected value relies on estimation methods, not guaranteed predictions. It's up to you and your team to weigh these projections against your risk tolerance.

Pros and cons of decision tree analysis

Used properly, decision tree analysis can help you make better decisions, but it also has its drawbacks. As long as you understand the flaws associated with decision trees, you can reap the benefits of this decision-making tool.

Pros

When you're struggling with a complex decision and juggling a lot of data, decision trees can help you visualize the possible consequences or payoffs associated with each choice.

• Transparent: Decision trees provide a focused approach to group decision-making for you and your team. When you parse out each decision and calculate its expected value, you'll have a clear idea about which decision makes the most sense.

• Efficient: Decision trees require little time and few resources to create. Other decision-making tools like surveys, user testing, or prototypes can take months and a lot of money to complete.

• Flexible: If you come up with a new idea once you've created your tree, you can add that decision with little work. You can also add branches for possible outcomes if you gain information during your analysis.

Cons

Decision trees aren't perfect. Understanding their limitations helps you use them as part of a larger forecasting process.

• Complex: Decision trees can become unwieldy if you add too many branches. Keep your tree simple by using other tools, like a decision matrix, to narrow down options first, then apply decision tree analysis to your top choices.

• Unstable: It's important to keep the values within your decision tree stable so that your equations stay accurate. If you change even a small part of the data, the larger data can fall apart.

• Risky: Because the decision tree uses a probability algorithm, the expected value you calculate is an estimation, not an accurate prediction. If you don't sufficiently weigh the probability and payoffs of your outcomes, you could take on a lot of risk with the decision you choose.

Decision tree analysis example

In the decision tree analysis example below, you can see how you would map out your tree diagram if you were choosing between building or upgrading a new software app.

As the tree branches out, your outcomes involve large and small revenues, and your project costs are taken out of your expected values.

Decision nodes from this example:

• Build new scheduling app: $50K

• Upgrade existing scheduling app: $25K

• Build team productivity app: $75K

Chance nodes from this example:

• Large and small revenue for decision one: 40 and 55%

• Large and small revenue for decision two: 60 and 38%

• Large and small revenue for decision three: 55 and 45%

End nodes from this example:

• Potential profits for decision one: $200K or $150K

• Potential profits for decision two: $100K or $80K

• Potential profits for decision three: $250K or $200K

Although building a new team productivity app would cost the most for the team, the decision tree analysis shows that this project would also yield the most expected value for the company.

Make better decisions with decision trees

You can draw a decision tree by hand, but using decision tree software to map out possible solutions will make it easier to add various elements to your flowchart, make changes when needed, and calculate tree values. With Asana's Lucidchart integration, you can build a detailed diagram and share it with your team in a centralized project management tool.

Decision tree software will make you feel confident in your decision-making skills so you can successfully lead your team and manage projects. Ready to turn your analysis into action? Get started with Asana to keep your projects organized and your team aligned on the decisions that matter most.

Frequently asked questions about decision tree analysis