What is Decision Tree Analysis? Learn Top 5 Steps to Better Decisions
Expenses. Benefits. Probabilities. For financial analysts, these three concepts are crucial in their day-to-day decision-making. Essentially, professionals constantly weigh the pros and cons while determining the best course of action for their businesses. To formalize this thinking process into an algorithm, financial experts have coined the term called decision tree analysis.
So, what exactly is a decision tree? Simply put, a decision tree is a logical and visual representation of the impact of your decision. This guide will explain how to create a decision tree to help you foolproof financial decision-making for your company.
What is Decision Tree Analysis?
According to Investopedia, Decision Analysis (DA) is a systematic, quantitative, and visual approach to help businesses make strategic business decisions. This process of decision analysis is performed using a decision tree, which is typically a flowchart that starts with a main idea and then branches out depending on the consequences of a business decision.
Thereafter, decision trees are employed in decision tree analysis, which entails graphically illustrating the probable results, expenses, and effects of a business decision. Based on the choices and results that lead to each outcome, the optimal course of action is determined by comparing the outcomes to one another.
The decision tree analysis technique is used by both small and large corporations while making various decisions related to management, operations, marketing, capital investments, or strategic choices.
Importance of Decision Tree Analysis
Business owners and financial decision-makers can employ a decision tree to consider the various alternative decisions and assess the potential repercussions of each one. Decision tree analysis is important due to the following reasons:
1. Helps to Determine the Level of Risk Involved in Each Decision
Before making a final decision, financial analysts can measure how changing one variable in the decision-making process impacts others. This will help you to identify which areas require more research or information. Besides, data from decision trees can also be used to build predictive models or to analyze an expected outcome.
2. How Particular Occurrences Unfold in the Context of Other Events
It’s easy to determine how different decisions and their possible outcomes interact with one another by analyzing decision tree diagrams.
3. Concentrate Your Efforts
The most effective steps for achieving the desired and final outcome are demonstrated in decision trees, which can help professionals to focus their efforts on the areas that need more work.
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Steps to Create Decision Trees
Decision trees help finance experts make better decisions by allowing them to understand what they are stepping into before committing a huge sum of money or resources. To analyze uncertain outcomes and reach the most logical solution, you can create decision trees by using the following five steps:
Step 1: Start With One Idea
Design a decision tree diagram with one main idea. To start building a tree, analysts should add a decision node first before adding single branches to the various decisions they are deciding between.
For instance, let’s consider an example of creating a financial plan for a business. In this case, the initial decision node is to create a financial plan and the two options—or branches—are:
- Build a new plan
- Upgrade the existing plan
Step 2: Add Chance and Decision Nodes
After including the primary idea in the tree, continue to add chance or decision nodes after each decision to expand the tree further. A chance node usually needs an alternative branch to be added after it because there could be more than one outcome for making that decision.
For instance, if you decide to build a new financial plan, there is a chance that the plan will help you mitigate financial risks successfully. However, there is also a chance that the plan is unable to fulfill your risk management goals. Mapping both potential outcomes in your decision tree is important.
Step 3: Expand the Tree Till the End Points
Keep adding chance and decision nodes to each branch of the decision tree until you reach endpoints and cannot expand the tree any further. At this point, add end nodes to your tree to highlight the completion of the tree creation process.
Step 4: Calculate Tree Values
Once you’ve completed adding the nodes and chances to your tree, you can begin analyzing the outcomes of each decision. Ideally, the decision tree will contain the monetary values of each of your decisions. For instance, it’ll cost your company a specific sum of money to build or upgrade a financial plan. It’ll also cost more or less money to create a new plan versus upgrading the existing one. Writing these values in your tree under the decision nodes and branches will help you make better decisions.
Moreover, you can determine the estimated value you’ll create for each decision in your tree mathematically. Once you are aware of the cost of each outcome and the chances that it will occur, you can calculate the expected value of each outcome using the following formula:
Expected value (EV) = (First possible outcome x Likelihood of outcome) + (Second possible outcome x Likelihood of outcome) – Cost
Step 5: Analyze Outcomes
Once you determine the numerical values for the expected outcomes of each decision, you can easily evaluate which financial decision is best for your business by looking at the figures. However, the highest expected value may not always be the one you should go for. That’s because, even though this decision could yield a high reward, it also means taking on the highest financial risks. Keep in mind that the expected value in decision tree analysis comes from a probability algorithm. It’s up to you and your finance team to determine how to evaluate the decision tree outcomes best.
Pros And Cons Of Decision Tree Analysis
When used properly, decision tree analysis can help make better decisions, but it also has some drawbacks. Let’s outline some of the advantages of decision tree analysis to understand the term better.
Advantages of Decision Tree Analysis
1. Transparent
Decision trees provide professionals and teams with a targeted way to make decisions. This helps select between options that make the most sense for them by breaking down each choice and determining its expected value.
2. Efficiency
As decision trees can be created quickly and with few resources, they are considered more efficient when compared to prototyping and user testing, which require considerable time and money.
3. Flexible
Unlike linear models such as logistic regression, decision trees are flexible due to their non-linear nature. Since it is possible to include a new decision at any stage by adding an extra branch, decision trees offer more flexibility to explore, plan and predict among several possible outcomes without disrupting the fundamental tree structure. Decision trees are flexible in the sense that if one can come up with a new idea after creating the tree, still you can includes that decision very easily and with little effort by adding a new branch for a possible outcome.
Disadvantages of Decision Tree Analysis
1. Complex
While decision trees have definite endpoints, they can become complicated when too many decisions are involved. If the tree branches are in various directions, may find it tough to keep the tree under wraps and calculate expected values.
2. Risky
Since decision trees rely on probability algorithms, the expected value calculated is an estimation and not an accurate value of the outcome. Due to this accuracy gap, there are a lot of risks involved in any decisions made.
Decision Tree Analysis Example
While this article focuses on the importance of decision tree analysis in finance, this concept can be utilized in other sectors as well. For instance, a marketer can employ decision tree analysis to determine which style of advertising will yield the best results. In this case, the decision tree can include print media and online advertising as the two options. By analyzing how each option influences sales among the target audience, the marketer can determine the best way to create an ad campaign without exceeding the allotted budget.
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