What are the Steps in Decision Tree Analysis?

Steps to conduct a decision tree analysis

Decision trees provides a structured framework for making decisions by visually mapping out the potential outcomes and choices involved in a decision-making process. Decision trees are particularly useful when dealing with complex and uncertain situations, allowing decision-makers to weigh the pros and cons of different options systematically.

This article aims to take you through a step-by-step guide to understanding how to peform a decision tree analysis, so let's jump right in.

Step 1: Identify the problem

Every decision tree begins with a clear understanding of the problem at hand. Identify the goals and objectives, as well as the key variables and factors that will influence the decision. This step lays the foundation for the entire analysis. The more precise your problem definition, the better your decision tree will serve you.

Step 2: Begin to structure the decision tree

Once the problem is well-defined, the next step is to begin creating the decision tree. The tree starts with a decision node from which branches extend, representing different options. Further nodes are added to represent the potential outcomes of those options.

At this stage you won't have a clear idea as to how far each branch will extend, but by laying out the groundwork you'll begin to get a visual sense of how the decision tree will evolve. Add notes and other documents to serve as a reference as you continue to build you your decision tree.

Step 3: Identify decision alternatives

Continue building out the decision tree by listing all possible alternatives or courses of action available. Add these as branches stemming from the central decision node.

These alternatives represent the different paths or choices that can be taken in the decision-making process. Ensure that you include a comprehensive range of options and potential outcomes for each.

Step 4: Estimate payoffs or costs

Assign payoffs or costs to each outcome. These values represent the impact or consequences of each outcome on the overall decision. Consider both quantitative and qualitative factors when estimating payoffs or costs.

Step 5: Assign probabilities

Assigning probabilities to each potential outcome is crucial. These probabilities can be derived from historical data, market research, or expert judgment. They represent the likelihood of each outcome occurring, providing a quantitative basis for your decision-making process.

Step 6: Determine the potential outcomes

Each outcome has a value attached to it. This could be the potential financial gain or loss, the impact on customers, or any other metric that matters to your decision. These outcomes should cover both the positive and negative aspects, as well as any uncertainties or risks involved. Multiply each outcome value by its probability to calculate the expected value of each decision path.

Step 7: Analyze and select the best decision

Now comes the analysis. By adding up the expected values of each decision path, you can identify the most promising option. This decision point provides the highest expected value, giving you a data-driven recommendation for your strategic decision.

Step 6: Review and update the decision tree

Decision trees are not set in stone. As new information becomes available or circumstances change, your decision tree should evolve. You can perform a sensitivity analysis at this stage by testing key assumptions, probabilities, or payoffs. This step helps identify the robustness of the chosen decision and provides insights into potential areas of uncertainty or risk.

Practical example of decision tree analysis

Consider a software company deciding whether to develop a new product. They start by identifying the decision: whether to invest resources in developing a new project management software.

Next, they identify the decision alternatives, such as developing the new software in-house, outsourcing development to a third-party, or not pursuing the project at all.

The company then brainstorms all the potential outcomes. These may include successful development and launch of the software, project delays, budget overruns, market acceptance, and the possibility of the software becoming obsolete quickly.

The next step is to calculate probabilities based on factors they have insights into. For instance, there might be a high probability of successful development but a lower probability of meeting the original timeline.

The company next evaluates the financial and non-financial impacts of each outcome. Payoffs may include revenue from software sales, potential cost savings, or the opportunity to gain a competitive advantage.

With all this information gathered, the team can then create a decision tree with decision nodes for each alternative, chance nodes for possible outcomes, and end nodes for final results.

Now the team can analyze their decision tree and choose the alternative with the highest expected payoff. This might involve a trade-off between the potential financial gains, development risks, and time-to-market considerations.

Final thoughts

Understanding and implementing the steps in decision tree analysis can transform your decision-making process, providing clarity amidst complexity. It's a dynamic tool that requires regular updates and reviews but offers invaluable insights for strategic decisions.

Miro makes it easy to create a decision tree together with your team and make well-informed, strategic decisions.