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• What is decision tree analysis? 5 steps ...

What is decision tree analysis? 5 steps to make better decisions

Decision tree analysis involves visually outlining the potential outcomes, costs, and consequences of a complex decision. These trees are particularly helpful for analyzing quantitative data and making a decision based on numbers. In this article, we’ll explain how to use a decision tree to calculate the expected value of each outcome and assess the best course of action. Plus, get an example of what a finished decision tree will look like.

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. 

What is a decision tree?

A decision tree is a flowchart that starts with one main idea and then branches out based on the consequences of your decisions. It’s called a “decision tree” because the model typically looks like a tree with branches. 

These trees are used for decision tree analysis, which involves visually outlining the potential outcomes, costs, and consequences of a complex decision. You can use a decision tree to calculate the expected value of each outcome based on the decisions and consequences that led to it. Then, by comparing the outcomes to one another, you can quickly assess the best course of action. You can also use a decision tree to solve problems, manage costs, and reveal opportunities. 

Decision tree symbols

A decision tree includes the following symbols:

Alternative branches: Alternative branches are two lines that branch out from one decision on your decision tree. These branches show two outcomes or decisions that stem from the initial decision on your tree.

Decision nodes: Decision nodes are squares and represent a decision being made on your tree. Every decision tree starts with a decision node. 

Chance nodes: Chance nodes are circles that show multiple possible outcomes.

End nodes: End nodes are triangles that show a final outcome.

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

What is decision tree analysis used for?

You can use decision tree analysis to make decisions in many areas including operations, budget planning, and project management . Where possible, include quantitative data and numbers to create an effective tree. The more data you have, the easier it will be for you to determine expected values and analyze solutions based on numbers. 

For example, if you’re trying to determine which project is most cost-effective, you can use a decision tree to analyze the potential outcomes of each project and choose the project that will most likely result in highest earnings. 

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 before adding single branches to the various decisions you’re deciding between.

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 deciding between 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 potential outcome for choosing that decision. 

For example, if you decide to build a new scheduling app, there’s a chance that your revenue from the app 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 of the decisions. 

4. Calculate tree values

Ideally, your decision tree will have quantitative data associated with it. The most common data used in decision trees is monetary value. 

For example, it’ll cost your company a specific amount of money to build or upgrade an app. It’ll also cost more or less money to create one app over another. Writing these values in your tree under each decision can help you in the decision-making process . 

You can also try to estimate expected value you’ll create, whether large or small, for each decision. Once you know the cost of each outcome and the probability 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 

Calculate the expected value by multiplying both possible outcomes by the likelihood that each outcome will occur and then adding those values. You’ll also need to subtract any initial costs from your total. 

5. Evaluate outcomes

Once you have your expected outcomes for each decision, determine which decision is best for you based on the amount of risk you’re willing to take. The highest expected value may not always be the one you want to go for. That’s because, even though it could result in a high reward, it also means taking on the highest level of project risk .  

Keep in mind that the expected value in decision tree analysis comes from a probability algorithm. It’s up to you and your team to determine how to best evaluate the outcomes of the tree.

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. 

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: The best part about decision trees is that they provide a focused approach to decision making for you and your team. When you parse out each decision and calculate their expected value, you’ll have a clear idea about which decision makes the most sense for you to move forward with. 

Efficient: Decision trees are efficient because they 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. A decision tree is a simple and efficient way to decide what to do. 

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

There are drawbacks to a decision tree that make it a less-than-perfect decision-making tool. By understanding these drawbacks, you can use your tree as part of a larger forecasting process.

Complex: While decision trees often come to definite end points, they can become complex if you add too many decisions to your tree. If your tree branches off in many directions, you may have a hard time keeping the tree under wraps and calculating your expected values. The best way to use a decision tree is to keep it simple so it doesn’t cause confusion or lose its benefits. This may mean using other decision-making tools to narrow down your options, then using a decision tree once you only have a few options left.

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 of each outcome. This means you must take these estimations with a grain of salt. 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 money for the team, the decision tree analysis shows that this project would also result in the most expected value for the company. 

Use a decision tree to find the best outcome

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.

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Blog Beginner Guides What is a Decision Tree? How to Make One with Examples

What is a Decision Tree? How to Make One with Examples

Written by: Rachel Cravit Nov 14, 2023

Ever wondered how to break down and examine your decisions so well that you can determine potential outcomes, assess various risks and ultimately predict your chances for success?

That’s where a decision tree comes in — it’s a handy diagram to improve your decision-making abilities and help prevent undesirable outcomes.

In this step-by-step guide, we’ll explain what a decision tree is, how you can visualize your decision-making process effectively using one and how you can make a decision tree easily using Venngage’s professionally designed tree diagram templates .

Click to jump ahead:

What is a decision tree?

• Decision tree symbols & meanings
• How to make a decision tree

Decision tree examples

Decision tree best practices.

• Why should you make a decision tree?

Decision tree FAQs

A decision tree is a specific type of flowchart (or flow chart) used to visualize the decision-making process by mapping out different courses of action, as well as their potential outcomes.

Decision trees are used in various fields, from finance and healthcare to marketing and computer science. They are particularly useful for complex decisions where there are multiple options and uncertain outcomes.

A decision tree helps you figure out your choice step-by-step. It does this by asking you smaller questions, one at a time. It’s like having a checklist to follow so you don’t miss anything.

Decision tree symbols and meaning

Take a look at this decision tree example. There are a few key sections that help the reader get to the final decision.

Decision trees typically consist of three different elements:

The top-level node represents the ultimate objective or big decision you’re trying to make.

Branches, which stem from the root, represent different options — or courses of action — that are available when making a particular decision. They are most commonly indicated with an arrow line and often include associated costs, as well as the likelihood to occur.

The leaf nodes — which are attached at the end of the branches — represent possible outcomes for each action. There are typically two types of leaf nodes: square leaf nodes , which indicate another decision to be made, and circle leaf nodes , which indicate a chance event or unknown outcome.

When formed together, these elements loosely resemble a tree, which is where the diagram gets its name. This gradient decision tree diagram example shows you how it works.

Although you can certainly make a case for Grandmother Willow’s age-old advice to “let your spirits guide you”, sometimes a more formalized and calculated approach is necessary. This is why decision trees are so effective.

Just so you know, some of our templates are free to use and some require a small monthly fee. Sign up is always free, as is access to Venngage’s online drag-and-drop editor. With Venngage’s decision tree maker , you can use multiple colors to represent different types of decisions and possible outcomes.

How to make a decision tree?

1. start with your overarching objective/ “big decision” at the top (root).

The overarching objective or decision you’re trying to make should be identified at the very top of your decision tree. This is the “root” of the entire diagram.

Hot Tip:  With Venngage, you can  make a decision tree by quickly adding in different shapes and lines without having to draw them from scratch.

2. Draw your arrows

Draw arrow lines for every possible course of action, stemming from the root. Include any costs associated with each action, as well as the likelihood for success.

3. Attach leaf nodes at the end of your branches

What are the results of each course of action? If it’s another decision to be made, draw a square leaf node. If the outcome is uncertain, draw a circular leaf node.

The decision tree example below includes numerous branches and leaves for each decision. This makes the process easier for the staff to understand and follow.

With Venngage for Business , you can access automatic template resizing, as well as 24/7 priority support, so your decision tree reflects your brand purpose.

4. Determine the odds of success of each decision point

When creating your decision tree, it’s important to do research, so you can accurately predict the likelihood for success. This research may involve examining industry data or assessing previous projects .

5. Evaluate risk vs reward

Calculating the expected value of each decision in tree helps you minimize risk and increase the likelihood of reaching a favorable outcome.

Take a look at this decision tree example by HubSpot, which evaluates whether to invest in a Facebook ad or Instagram sponsorship:

The decision tree is simple but includes all the information needed to effectively evaluate each option in this particular marketing campaign:

• The cost of a paid ad campaign on Facebook vs an Instagram sponsorship
• The predicted success and failure rates of both
• The expected value of both

Here’s the exact formula HubSpot developed to determine the value of each decision:

(Predicted Success Rate * Potential Amount of Money Earned) + (Potential Chance of Failure Rate * Amount of Money Lost) = Expected Value

You now know what a decision tree is and how to make one. You can get started by grabbing a pen and paper, or better yet, using an effective tool like Venngage to  make a diagram .

Project management decision tree

Managing a project involves a multitude of decisions, from resource allocation to task prioritization. A decision tree in project management might help decide whether a project should proceed to the next phase or be revised based on various criteria.

This type of decision trees help project managers assess whether the current resources are sufficient to meet the project requirements within the given timeline. It aids in making informed decisions about whether to proceed or make adjustments.

Product launch decision tree

Launching a product involves a series of strategic decisions, from market analysis to promotional tactics.

Use a decision tree to help determine whether the current market conditions, production capabilities and chosen marketing strategies align for a successful product launch. It encourages adaptability and strategic adjustments based on real-time factors.

Analysis decision tree

Decision trees are valuable in data analysis, helping analysts choose the most effective path based on various factors.

An analysis decision tree ensures that the chosen method aligns with the data type and analysis requirements, contributing to more accurate and meaningful results.

1. Keep it simple

Don’t overload your decision tree with text—otherwise, it will be cluttered and difficult to understand. Use clear, concise language to label your decision points.

2. Use data to predict the outcomes

When you’re making your decision tree, you’re going to have to do some guesswork. It’s fine to be uncertain—no one expects you to bust out a crystal ball. That being said, your decision tree will be much more useful if it considers actual data when determining possible outcomes.

A simple action plan flow chart like the decision tree example below will make it easier to come to the right decisions using data.

3. Use a professionally designed decision tree template

Using a professionally designed template can make your decision tree more appealing to clients, team members and stakeholders in your project. Venngage offers a Brand Kit feature, which makes it easy to incorporate your logo, colors and typography into your decision tree design.

Add your website when prompted and our editor will automatically extract your logo, colors, and fonts so you can apply them across your decision trees and other designs.

Along with the My Brand Kit feature,  Venngage for Business  offers brands a variety of convenient solutions including live  team collaboration .

Why should you make a decision tree? 

Now that you know exactly what a decision tree is, it’s time to consider why this type of flowchart is so effective for decision making. Decision trees, like problem-solving flowcharts , have several advantages due to their structured visual approach. Here are some of the perks:

1. Decision trees are flexible

Decision trees are non-linear, which means there’s a lot more flexibility to explore, plan and predict several possible outcomes to your decisions, regardless of when they actually occur.

For example, if you’re an HR professional, you can choose decision trees to help employees determine their ideal growth path based on skills, interests and traits, rather than timeline.

On the other hand, if you’re a product manager and are considering launching a new product line, you can use a decision tree flow chart to determine the best course of action.

Using a tool like Venngage’s drag-and-drop decision tree maker makes it easy to go back and edit your decision tree as new possibilities are explored.

2. Decision trees effectively communicate complex processes

Decision tree diagrams visually demonstrate cause-and-effect relationships, providing a simplified view of a potentially complicated process. Decision trees are also straightforward and easy to understand, even if you’ve never created one before.

The decision tree diagram template below is a good example of a complex process that’s easy to understand as it’s broken down and visualized in simpler steps:

Design tip: If you’d like to present your decision tree to others who may be involved in the process, a professionally designed Smart template can go a long way.

Venngage offers a selection of decision tree templates to choose from, and we’re always adding more to our templates library.

3. Decision trees are focused on probability and data, not emotions and bias

Although it can certainly be helpful to consult with others when making an important decision, relying too much on the opinions of your colleagues, friends or family can be risky. For starters, they may not have the entire picture. Their advice to you may also be influenced by their own personal biases, rather than concrete facts or probability.

Decision tree diagrams, on the contrary, provide a balanced view of the decision-making process, while calculating both risk and reward:

If you’re a real estate agent, decision trees could make a great addition to your real estate marketing efforts, especially since your clients are likely evaluating some major decisions.

A decision tree flow chart to help someone determine whether they should rent or buy, for example, would be a welcome piece of content on a real estate blog . You could also create a custom decision tree to help your clients determine which property is best for them, like the example below.

Related: Editable Genogram Templates & Why They Are Important for Documenting Family Info 

4. Decision trees clarify choices, risks, objectives and gains

One big advantage of decision trees is their predictive framework, which enables you to map out different possibilities and ultimately determine which course of action has the highest likelihood of success. This, in turn, helps to safeguard your decisions against unnecessary risks or undesirable outcomes.

Decision trees also prompt a more creative approach to the decision-making process. Caroline Forsey writes in HubSpot :

By visualizing different paths you might take, you might find a course of action you hadn’t considered before, or decide to merge paths to optimize your results.

Visualizing your decision-making process can also alleviate uncertainties and help you clarify your position, like in this decision tree example below.

5. Decision trees enable you to flesh out your ideas fully before sinking in valuable time and resources

Decision trees force you to apply a methodical and strategic approach to your decisions, rather than going with your gut or acting on impulse.

Decision trees can also fit in nicely with your growth strategy , since they enable you to quickly validate ideas for experiments.

How can a decision tree simplify your decision-making process?

Decision trees act like a roadmap to untangle complex choices. Here’s how they simplify decision-making:

• Breaking down complexities: Decision trees break down a big problem into a series of smaller, easier to handle questions. This step-by-step approach makes the whole process seem less daunting.
• Visual clarity: Decision trees show your options and outcomes visually, like a map. Using a decision tree makes it easy to figure out which option gives you the best outcome.
• Step-by-step analysis: Decision trees make sure you think about every question one step at a time. They keep you from making quick decisions and give you a chance to consider all your choices fairly.
• Considering uncertainty: Decision trees can factor in probabilities and risks across various paths. This helps you to weigh the upsides and downsides of each choice more clearly.

Overall, decision trees simplify decision-making by bringing structure, clarity and a systematic approach to the table.

What is a decision tree in data mining?

In data mining, a decision tree is a simple way to classify or predict outcomes. It’s like a flowchart where each step represents a decision based on data, leading to a final result.

Make better business decisions by using decision trees

Decision trees can dramatically increase your decision-making capabilities. The process of identifying your big decision (“root”), possible courses of action (“branches”) and potential outcomes (“leafs”)—as well as evaluating the risks, rewards and likelihood of success—will leave you with a birds-eye view of the decision-making process.

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What is Decision Tree? (With Case Study)

A decision tree is a pictorial description of a well-defined decision problem. A decision tree describes graphically the decisions to be made, the events that may occur, and the outcomes associated with combinations of decisions and events.

If probabilities are assigned to the events, and values are determined for each outcome by calculating the expected value of each available alternative. Decision trees are useful because they provide a clear, documentable and discussible model of either how the decision was made or how it will be made. A major goal of the analysis is to determine the best decisions.

Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in machine learning.

How to Construct and Decision Tree with Case Study

We can start from the root node which contains an attribute of Travel cost per km.

• If the travel cost per km is expensive, the person uses a car.
• If the travel cost per km is the standard price, the person uses a train.
• If the travel cost is cheap, the decision tree needs to ask the next question about the gender of the person. If the person is a male, then he uses a bus.
• If the gender is female, the decision tree needs to ask again how many cars she owns in her household.
• If she has no car, she uses a bus,
• Otherwise, she uses the train.

Edit this Diagram

Decision Tree Example: Vehicle Purchase Decision Tree

This is a decision tree example created with the Decision Tree tool. It shows what and how a purchase decision is made. Let’s take a path as an example – If the color of the vehicle is red and was launched after 2010, buy it.

Decision Tree Example – Entertainment Decision

Imagine you only ever do four things at the weekend:

• go shopping,
• watch a movie,
• play tennis or
• just stay in.

What you do depends on three things:

• the weather (windy, rainy or sunny);
• how much money you have (rich or poor) and
• whether your parents are visiting.

The decision for Tree Classification

• Rule 1: if my parents are visiting, we’ll go to the cinema.
• Rule 2: If they’re not visiting and it’s sunny, then I’ll play tennis, but
• Rule 3: if it’s windy, and I’m rich, then I’ll go shopping.
• Rule 3: If they’re not visiting, it’s windy and I’m poor, then I will go to the cinema.
• Rule 4: If they’re not visiting and it’s rainy, then I’ll stay in.

Now, you draw a flowchart which will enable you to read off your decision in the form of decision trees for the weekend decision choices would be as follows:

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What Is a Decision Tree?

Decision trees are flowchart graphs or diagrams that explore all potential decisions and their possible outcomes.

Table of Contents

In business, many decisions must be taken seriously because they have wide-reaching implications for the company, its employees, its customers or the public. For these decisions, it’s essential for business leaders to examine all of their options. One business decision-making tool is a decision tree.

Decision trees help professionals and company leaders make more informed choices by allowing them to weigh and visualize various options and outcomes. Here’s how to use a decision tree at your business.

What is a decision tree?

A decision tree is a flowchart graph or diagram that explores all potential decisions and their possible outcomes.

Each “branch” of the tree represents one of the decision’s potential outcomes. The branches can be extended when one alternative outcome leads to another decision you’ll need to make. Incorporated into each branch are the costs associated with each choice and the likelihood of its occurrence.

Because decision trees require numerous calculations, many businesses use dedicated decision tree software to help them with the process. Decision tree software helps businesses draw their trees, assign values and probabilities to each branch, and analyze each option. There are both free and paid versions of decision tree software available from vendors such as IBM, TreeAge, SmartDraw, Palisade, Angoss and Edraw.

How to draw a decision tree

Decision trees help business owners and leaders understand the risks and rewards associated with each decision. These include all possible outcomes, both positive and negative. Here’s how to create a decision tree.

• Start with a square box — representing the decision you must make — on the top or left side of a page.
• From that box, draw out each option — either down, if you’re starting at the top of the page, or to the right, if you’re starting on the left side. On each line, write the option.
• Draw a circle around any uncertain option, and draw a box around any option that requires a further decision, with lines going to additional outcomes.
• Continue this process until you’ve drawn as many possible outcomes and decisions as you believe can come from the original decision.

Once you’ve drawn the tree, analyze it to determine what each branch and outcome means. Assign values to the possible outcomes, and estimate the probability of each one. With those numbers, you can calculate which option is best.

The benefits of using a decision tree

Using a decision tree to explore and compare potential outcomes can provide many advantages.

• A decision tree makes it easy to understand and interpret information. A decision tree is easy for people to read, even for those who may not be well versed in statistical analysis .
• A decision tree is simple to prepare. As long as you have the information, creating a decision tree is more straightforward than other decision-making techniques.
• A decision tree reduces data cleaning. According to the Corporate Finance Institute , because less data cleaning is required after you’ve created the variables for the decision tree, “cases of missing values and outliers have less significance on the decision tree’s data.”

Examples of how businesses use decision trees

Decision trees can help companies make informed choices about a wide range of business areas, including the following: 

• Pricing products or services
• Promoting employees
• Offering training to employees
• Selling the business
• Hiring contract workers

Decision tree examples

To better understand how decision trees work, consider some examples. For instance, say you’re deciding between hiring a full-time employee and working with a contractor. For this decision tree, separate your two options into different boxes. Then, branch those out into various outcomes, including some benefits or downfalls you might experience from making that particular choice.  

Alternatively, create two separate decision trees — “Should I hire a full-time worker?” and “Should I hire a contract worker?” — and then dive deeper into each choice. Then, break down the question into two boxes: “Yes” and “No.” From there, explore the outcomes by asking further questions, like “Is this necessary for our company’s workload?” Then, add “Yes” and “No” boxes. Continue this until you’ve exhausted all options and solutions and have determined all possible outcomes. 

Using a decision tree in the workplace

Using a decision tree is a great way to make more informed decisions for your business. No matter how many options you’re weighing, breaking them down into different branches and boxes will allow you to understand any potential outcome that might benefit or harm your company. 

If you have a business idea or question regarding your business operations, consider turning to a decision tree as your solution. You can use a decision tree template or simply create your own decision tree using the above tips and examples.

Chad Brooks and Sean Peek contributed to this article.

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Decision Tree Examples: Problems With Solutions

On this page:

• What is decision tree? Definition.
• 5 solved simple examples of decision tree diagram (for business, financial, personal, and project management needs).
• Steps to creating a decision tree.

Let’s define it.

A decision tree is a diagram representation of possible solutions to a decision. It shows different outcomes from a set of decisions. The diagram is a widely used decision-making tool for analysis and planning.

The diagram starts with a box (or root), which branches off into several solutions. That’s way, it is called decision tree.

Decision trees are helpful for a variety of reasons. Not only they are easy-to-understand diagrams that support you ‘see’ your thoughts, but also because they provide a framework for estimating all possible alternatives.

In addition, decision trees help you manage the brainstorming process so you are able to consider the potential outcomes of a given choice.

Example 1: The Structure of Decision Tree

Let’s explain the decision tree structure with a simple example.

Each decision tree has 3 key parts:

• a root node
• leaf nodes, and

No matter what type is the decision tree, it starts with a specific decision. This decision is depicted with a box – the root node.

Root and leaf nodes hold questions or some criteria you have to answer. Commonly, nodes appear as a squares or circles. Squares depict decisions, while circles represent uncertain outcomes.

As you see in the example above, branches are lines that connect nodes, indicating the flow from question to answer.

Each node normally carries two or more nodes extending from it. If the leaf node results in the solution to the decision, the line is left empty.

How long should the decision trees be?

Now we are going to give more simple decision tree examples.

Example 2: Simple Personal Decision Tree Example

Let’s say you are wondering whether to quit your job or not. You have to consider some important points and questions. Here is an example of a decision tree in this case.

Download  the following decision tree in PDF

Now, let’s deep further and see decision tree examples in business and finance.

Example 3: Project Management Decision Tree Example

Imagine you are an IT project manager and you need to decide whether to start a particular project or not. You need to take into account important possible outcomes and consequences.

The decision tree examples, in this case, might look like the diagram below.

Download  the following decision tree diagram in PDF.

Don’t forget that in each decision tree, there is always a choice to do nothing!

Example 4: Financial Decision Tree Example

When it comes to the finance area, decision trees are a great tool to help you organize your thoughts and to consider different scenarios.

Let’s say you are wondering whether it’s worth to invest in new or old expensive machines. This is a classical financial situation. See the decision tree diagram example below.

Download it.

The above decision tree example representing the financial consequences of investing in old or new machines. It is quite obvious that buying new machines will bring us much more profit than buying old ones.

Need more decision tree diagram examples?

Example 5: Very Simple Desicion Tree Example

As we have the basis, let’ sum the steps for creating decision tree diagrams.

Steps for Creating Decision Trees:

1. Write the main decision.

Begin the decision tree by drawing a box (the root node) on 1 edge of your paper. Write the main decision on the box.

2. Draw the lines 

Draw line leading out from the box for each possible solution or action. Make at least 2, but better no more than 4 lines. Keep the lines as far apart as you can to enlarge the tree later.

3. Illustrate the outcomes of the solution at the end of each line.

A tip: It is a good practice here to draw a circle if the outcome is uncertain and to draw a square if the outcome leads to another problem.

4. Continue adding boxes and lines.

Continue until there are no more problems, and all lines have either uncertain outcome or blank ending.

5. Finish the tree.

The boxes that represent uncertain outcomes remain as they are.

A tip: A very good practice is to assign a score or a percentage chance of an outcome happening. For example, if you know for a certain situation there is 50% chance to happen, place that 50 % on the appropriate branch.

When you finish your decision tree, you’re ready to start analyzing the decisions and problems you face.

How to Create a Decision Tree?

In our IT world, it is a piece of cake to create decision trees. You have a plenty of different options. For example, you can use paid or free graphing software or free mind mapping software solutions such as:

• Silverdecisions

The above tools are popular online chart creators that allow you to build almost all types of graphs and diagrams from scratch.

Of course, you also might want to use Microsoft products such as:

And finally, you can use a piece of paper and a pen or a writing board.

Advantages and Disadvantages of Decision Trees:

Decision trees are powerful tools that can support decision making in different areas such as business, finance, risk management, project management, healthcare and etc. The trees are also widely used as root cause analysis tools and solutions.

As any other thing in this world, the decision tree has some pros and cons you should know.

Advantages:

• It is very easy to understand and interpret.
• The data for decision trees require minimal preparation.
• They force you to find many possible outcomes of a decision.
• Can be easily used with many other decision tools.
• Helps you to make the best decisions and best guesses on the basis of the information you have.
• Helps you to see the difference between controlled and uncontrolled events.
• Helps you estimate the likely results of one decision against another.

Disadvantages:

• Sometimes decision trees can become too complex.
• The outcomes of decisions may be based mainly on your expectations. This can lead to unrealistic decision trees.
• The diagrams can narrow your focus to critical decisions and objectives.

Conclusion:

The above decision tree examples aim to make you understand better the whole idea behind. As you see, the decision tree is a kind of probability tree that helps you to make a personal or business decision.

In addition, they show you a balanced picture of the risks and opportunities related to each possible decision.

If you need more examples, our posts fishbone diagram examples and Venn diagram examples might be of help.

About The Author

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

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Decision trees: Definition, analysis, and examples

Used in both marketing and machine learning, decision trees can help you choose the right course of action.

A decision tree is a versatile tool that can be applied to a wide range of problems. Decision trees are commonly used in business for analyzing customer data and making marketing decisions, but they can also be used in fields such as medicine, finance, and machine learning.

The most detailed decision trees can be incredibly complex, but simple decision trees are easy to create and interpret. They’re built around a series of yes/no questions that gradually narrow down your options until the most sensible decision is reached.

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What is a decision tree?

A decision tree is a flowchart-like diagram mapping out all of the potential solutions to a given problem. They’re often used by organizations to help determine the most optimal course of action by comparing all of the possible consequences of making a set of decisions.

For example, a decision tree could be used to help a company decide which city to move its headquarters to, or whether to open a satellite office . Decision trees are also a popular tool in machine learning, as they can be used to build predictive models. These types of decision trees can be used to make predictions, such as whether a customer will buy a product based on their previous purchase history.

What are decision tree nodes and symbols?

Decision trees are made up of various connected nodes and branches, expanding outward from an initial node. The three types of nodes are decision nodes, chance nodes, and outcome nodes. 

• Decision nodes are square-shaped and represent a point on the tree at which a decision can be made. For example: Should you host a barbecue on Saturday? 
• Chance nodes are circle-shaped and represent a point on the tree at which there are multiple uncertain consequences. For example: What are the chances that it will rain on Saturday?
• Outcome nodes are triangle-shaped and represent the final endpoint of a series of decisions. For example: A sudden downpour ruins your barbecue.

Connecting these nodes are the branches of the decision tree, which link decisions and chances to their potential consequences. Evaluating the best course of action is achieved by following branches to their logical endpoints, tallying up costs, risks, and benefits along each path, and rejecting any branches that lead to negative outcomes. 

How to make a decision tree step-by-step

You can use software tools or online collaboration platforms to create a decision tree, but all you really need is a whiteboard or a pen and paper.

• Draw your initial node. This square node represents the main decision you’re trying to make. For every possible action you can take at this point, draw a branch and label it with the name of that action. You can include additional information here, such as the financial cost of making that decision.
• Add nodes to the end of each branch. Now consider what happens in each labeled scenario. Would following that course of action lead to another decision point ? If so, add another square and repeat the process. If the decision leads to a chance outcome, draw a circle node and try to determine the possible outcomes and the probabilities of each one occurring. In our simplified barbecue example, that would be the chance of it raining on the day.
• Expand the tree until every endpoint is reached. Continue adding decision nodes, chance nodes, and branches until there are no more choices you can make. Then cap off each branch with an outcome node. This outcome node describes the end result of following that path and should include some kind of value or score so that comparisons can be made between each endpoint.

Decision tree advantages and disadvantages

Depending on when and how they’re used, decision trees can come with certain advantages and disadvantages:

• They’re clear and easy to understand. From an infographic point of view, a well-constructed decision tree can condense a huge amount of data into an accessible format that every member of the organization can understand. Marketing teams, for example, don’t need to know the nitty-gritty detail of the statistical analysis behind a potential decision. A decision tree cuts through the noise to bring people the information they need to determine the most effective course of action.
• They’re only as good as the underlying data. Decision trees aren’t a crystal ball. If you don’t have high-quality data to begin with, or you can’t determine the probabilities of certain chance nodes occuring, the tree becomes exponentially less reliable as it carries on. Biases in your data or incomplete data sets will also introduce inaccuracies.
• They’re quick to design and simple to refine. Decision trees don’t need to be overly complex to be useful. Mapping out your options on paper can be done in minutes and helps to bring clarity to the decision-making process.

The role of decision trees in data science

We’ve mostly focused on the use of decision trees in choosing the most effective course of action in business, but this type of informational mapping also has practical applications in data mining and machine learning.

In this context, decision trees aren’t used to manually determine some optimal course of action, but rather as a predictive model to automatically make observations about a given dataset. These algorithms take in enormous amounts of information and use a decision tree to derive accurate predictions about new data points. For example, consider using the medical data of thousands of hospital patients to predict the likelihood of a person developing a disease.

Types of decision trees

There are two main types of decision trees in data science :

• Classification trees. A classification tree is a decision tree where each endpoint node corresponds to a single label. For example, a classification tree could take a bank transaction, test it against known fraudulent transactions, and classify it as either “legitimate” or “fraudulent.”
• Regression trees. A regression tree is a decision tree where the values at the endpoint nodes are continuous rather than discrete. That is, the regression tree predicts a real-valued output rather than a class label—for example, predicting a person’s salary based on their age and occupation.

Related articles

Decision tree examples

Some examples of when you might use a decision tree include:

• Predicting whether a customer will leave (churn)
• Analyzing credit card data to identify fraudulent transactions
• Identifying which patients are at risk of developing a certain disease
• Forecasting stock market movements

Steve Hogarty is a writer and journalist based in London. He is the travel editor of City AM newspaper and the deputy editor of City AM Magazine , where his work focuses on technology, travel, and entertainment.

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Decision trees are graphical representations of alternative choices that can be made by a business, which enable the decision maker to identify the most suitable option in a particular circumstance.

For example, they will be used when oil and gas exploration companies have to decide whether to invest in a particular gas field or in choosing to allocate resources to exploit one gas field rather than another.

Decision trees are a helpful visual tool when it is possible to measure the probability of an event occurring and the likely financial outcomes of making a particular decision.

An oil exploration company has £100 million available in cash. It can invest the money in a bank at 10% yielding a return of £150 million over five years (ignore compound interest).

Alternatively, it can invest in an oil exploration project, of which there are currently two available.

If it invests in Project A there is a 0.5 chance of the project being a success yielding £200 million, and a 0.5 chance of the project failing to lead to a loss of £50 million. (over the five year period)

If it invests in Project B there is a 0.6 chance of the project being a success yielding £300 million and a 0.4 chance of the project failing to lead to a loss of £20 million. (over the five year period)

We can now work out the likely expected values:

Invest in bank : 1.0 x £150m

0.5 x 200 = £100m 

0.5 x -50 = £25m 

0.6 x 300 = £180m

0.4 x -20 = -£8m

You can see that Project B yields the best result. We can illustrate this information on a decision tree. We set out the tree initially by working from left to right, the decision fork is to invest, or go for Project A or B.

There are then chance forks where probabilities are involved. When we have set out the tree we can prune it back by cutting off the branches which yield the worst results.

This leaves us with the final expected value – £172m which we put in the box at the start of the diagram.

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Issue Tree: What It Is & How It Helps You Pass Case Interviews

• Last Updated February, 2024

Former McKinsey Consultant

One of the keys to nailing a case interview is to demonstrate that you can quickly structure your thoughts and that you’re a good communicator. In this article, we want to help you add another communication tool to your skill set: issue trees.  

Definition: Issue trees are visual diagrams that you can use to break down a larger problem or question into several smaller questions. You can use the issue tree as a structure for your consulting case interview. 

In this article, we’ll discuss:

• Why issue trees are important in consulting case interviews
• How to build your own issue trees & decision trees
• Provide issue tree examples
• Tell you how to incorporate issue trees & decision trees into your case prep
• Provide 6 tips for creating an issue tree

Let’s get started!

Table of Contents

What Is an Issue Tree?

Why issue trees are important in consulting case interviews.

How to Build Your Own Issue Trees & Decision Trees

How to Use an Issue Tree to Pass Your Consulting Interview

Issue tree examples.

How to Incorporate Issue Trees & Decision Trees into Your Case Prep

6 Tips for Creating Great Issue Trees

Help with case study interview prep.

Issue trees, also known as decision trees, are used in consulting to help the team:

• Identify key issues in complex business problems
• Lead their discussions
• Determine how the work and resources should be allocated to solve the problem
• Ultimately lead to identifying a solution

They look like a (horizontal) tree: they flow from the top of the tree on the left to smaller branches on the right. We’ll talk you through the process of putting the right questions at the top of the issues tree, and how to ask questions to get to the root causes on the right side of the page. 

What about just using the simple outline structure you’ve been using to structure cases? Outlines are great. Issue trees are next level. 

Outlines and issue trees both organize the questions you’re looking to address. Issue trees also help you communicate more effectively because they visually show relationships between the initial high-level questions and root cause questions. 

When you’ve completed your issue tree, you should have a complete set of the most important areas to explore in order to answer your client’s questions.

In consulting, you’re going to be using a lot of visual frameworks to communicate ideas because it’s easier to communicate with clients and get them on board if there are visual cues for them to anchor on. In the case interview, using an issue tree is your chance to show the interviewer that you are comfortable using a visual framework. 

The issue tree also allows the interviewer to feel confident that your thought process is well-structured, that you’ve covered all key aspects of the case, and that you understand how various components are related to the problem you’re trying to solve. 

There’s another reason to use an issue tree in your cases. You can show off two of your skills in one structure

• Mastery of business or conceptual theories and how they work together,
• How you apply unique insights to the problem.

For example, in a consumer goods profitability case – let’s say the retail beer market – you obviously want to dig into the structure of revenue: price and volume. 

That shows you know the profitability framework and revenue drivers. 

But anyone can memorize revenue drivers.  

You can show your own creativity and how you connect concepts to reality. For example, when discussing price, you can ask a few more detailed questions to show that you understand the underlying drivers of price : 

• Distribution channels: Are we looking at beer prices in the grocery store or in bars? 
• Product mix: Are we looking at domestic, mass-produced beers or craft beers? 

These deeper layers are kind of common sense, but when you connect revenue to price to price drivers, it shows that you understand how to relate information back to price and ultimately back to revenue. 

Now let’s look at how to build an issues tree.

How to Build Your Own Issue Trees & Decision Trees

There are many different types of issue trees and decision trees. The most common in case interviews include a series of questions that will help you answer your client’s main questions. 

You can alternately frame the issue tree as a series of hypothesis statements. You typically won’t be able to do this as soon as you’re presented with the case question by your interviewer, but can ask questions during the opening section of the case that will help you identify hypotheses to test.

How to Use Questions to Build an Issue Tree

At this stage, we’re trying to understand the root causes of your client’s problem. 

What’s a root cause? It’s the underlying reason why something has happened. 

There are a few different ways to get to the underlying cause. In our issue trees, we use two types of questions: 

• Can we grow profits to $100 million per year? 
• Can we grow market share to over 5%?
• What types of beer does the client sell? 
• How fast is the beer market growing?

In case interviews, it’s best to use hypothesis questions for the first 2 layers, if you can.

By the 3rd or 4th layer of your trees, it’s best to use open questions to encourage exploration and avoid closing yourself off from possible answers. 

A Step-by-step Guide to Building Your Own Issue Trees and Decision Trees

• Start with a blank piece of paper, oriented horizontally.
• Note key facts. About one inch from the left side of the page, draw a vertical line down the page. To the left of this line, jot down key facts during the case introduction.
• Take about 30 seconds to think about how you want to break down the problem. If you start writing before you think it through, you may run out of space while you’re diving into a specific branch of the tree,
• 1st layer : write down the question or problem you are trying to address framed as a Yes/No question, 
• Revenue and expense, supply and demand,
• 3 C’s (Customer, Competitor, Company),
• 4 P’s (Product, Price, Promotion, Place),
• 3rd layer : write another 2 – 4 questions for each 2nd layer box and start to use open-ended questions, 
• 4th layer : You won’t always need a 4th layer at this stage. If you do, keep the questions open-ended, 
• Tip: Make sure what you’re adding is relevant and useful to the case, or else you might simply distract the interviewer with color that doesn’t add value. 
• Is your issue tree MECE? MECE stands for mutually exclusive and collectively exhausting . It means you should cover all key points on the topic and that there should not be overlap between the points. For more on this, see our article on MECE .
• Is there a good distribution of sub-questions under each point? If there are too many sub-questions under one branch and none beneath another, you may need to even this out either by adding more sub-questions, or double checking if you have the right branches.

Now that we know how to build a tree, let’s look at how to use it in your consulting interview.

During your case interview, you should use your issue tree to organize your thinking and as a communication tool.

Nail the case & fit interview with strategies from former MBB Interviewers that have helped 89.6% of our clients pass the case interview.

Using an Issue Tree to Organize Your Thoughts

Once you’ve confirmed the problem you’re trying to solve and asked any clarifying questions, you will want to structure the problem. This is the time to use an issue tree!

Spend some time – this could be even a few minutes – to organize your thoughts on paper as we described above. You have options for how to organize your issue tree:

• Boxes vs. no boxes: You don’t have to draw boxes around your questions. It does add a layer of professionalism but don’t sweat it if it stresses you out.
• Headlines vs. full text
• You can write out the full question: Will revenue grow more than 5%?
• You can write out a shorthand reminder for yourself: Rev > 5%?

Using Issue Trees as a Communication Tool

Issue trees are communications tools, not a script. You should not read directly from your tree for five minutes. 

Top tips on how to use your issue tree in a discussion:

• Use the issue tree to prompt you through different parts of the discussion, 
• Start on the left, and work your way across and down the page. Avoid hopping all over the page,
• Communicate using ordinal numbers, such as “We have 4 branches to look at. For the first branch, I want to understand…,” to make it easier for the interviewer to follow you as you walk him through your tree,
• Refer to the issue tree when you want to see what to discuss next and in case you forgot anything you had written down,
• Focus on the interviewer as you discuss the details of each question, and
• Refer back to it as you present your recommendation to ensure you hit all key points.

Some cases, especially in second round interviews, don’t easily lend themselves to issues trees. Don’t sweat it – go back to the outline structure and knock your case out of the park. 

When you use an issue tree, you’re demonstrating next-level communication skills. 

Let’s dive into some examples

You can use them to examine almost any problem.

Issue Tree Example 1:

Let’s start with a traditional case question: 

Your client manufactures tools for mechanics and auto services companies. They have experienced rising costs in their plant in Bulgaria, and are wondering if they should close the plant and move the production to one of their other facilities. 

Our 1st layer of the tree is the client’s question:

Should our client shut down its only tool manufacturing plant in Bulgaria?

Our 2nd layer of the tree will have three boxes:

Financial considerations

Operational considerations.

• Branding considerations

The hypothesis question we want to ask is: 

Will the company be better off financially if they close the plant?   

There are 5 key financial areas to explore in the 3rd layer:

• Plant operating expenses
• One-time costs if the plant is closed
• Distribution expenses
• Government incentives
• What are the variable costs per unit?
• Example color commentary you could make when you discuss this branch of the tree: perhaps labor has gotten very expensive and that is driving the company to consider this move.
• What are the facilities costs of the plant?
• Example of color commentary: Perhaps land/space was cheap when the plant was opened but no longer is. 
• What are the costs associated with closing the plant?
• What are severance costs?
• If the equipment needs to be moved, what are the moving costs?
• What are distribution costs today? 
• What would distribution cost in the future be if production is sourced from another plant?
• Will there be different tariffs if the product comes from another country?
• What incentives does the government offer to manufacturers?
• Is the Bulgarian government offering any incentives to the company to stay in Bulgaria?
• Do the incentives go toward existing operations or must they go toward new operations? 
• Are there restrictions that come along with accepting the government incentives? 
• Color commentary: The government may require that the company maintain a payroll with a certain number of employees or open for a certain number of shifts in order to receive incentives, etc. 
• Are there any other tax benefits due to operating in Bulgaria?
• Will we lose sales in Bulgaria if we do not make the product there? 
• Will we lose sales elsewhere if we do not produce the product in Bulgaria?

Could the company produce the tools elsewhere?  

• Are there any plants that could absorb all the capacity needed? 
• Could capacity be distributed to multiple plants?
• How soon could the other plants ramp up?
• Where are the products sold?
• How would relocation of manufacturing affect lead-time on delivery? 
• Would a distribution facility be required to meet lead-times required by customers?

Brand considerations

The hypothesis question we want to ask is:

Would the brand suffer if manufacturing was moved to another country? 

• How much bad press do we expect? 
• Will the savings from closing the plant be sufficient to offset any impact on the brand?

There you have it, a complete issue tree for this case discussion!

Note how this issue tree is uneven. There are so many more branches to explore in the financial considerations section. That’s okay as long as you’ve covered each branch sufficiently.

As a way to recap your structure, you can even note to your interviewer that the final decision will mainly revolve on financial considerations, but they will be weighed against operational and brand considerations. 

Let’s look at another case that might be more typical in a second round interview.

Issue Tree Example 2:

Sometimes in your second round interviews, partners can get bored with cases they’ve given a million times. So just like Law & Order, your case interview might be ripped from the headlines. Let’s take that approach for this example:

Why was there no toilet paper on the shelves of North American retailers during the first wave of the COVID-19 pandemic? 

Let’s dive into an issue tree for this question. 

If you’re lucky, like we are here, you can layer in a business framework. 

Don’t force a framework if nothing comes to mind. You want to practice structuring enough cases that you’re not gonna miss an obvious framework like this one. 

Our 1st level of the tree will include the question: 

Was demand for toilet paper actually greater than supply in North America during the first wave of the COVID-19 pandemic? 

Our 2nd layer of the tree will have two boxes:

Toilet Paper Demand

• Toilet Paper Supply 

Was there a demand shock for toilet paper in North America?

There are three areas to explore in the 3rd layer:

• Toilet paper usage
• Consumer shopping behavior
• Alternatives for toilet paper
• How many rolls per week did one household typically use?
• How much more time were people spending at home during the first wave of the pandemic vs. at work?
• How did that impact toilet paper usage?
• Superstar commentary: If you recognize that people are home and awake almost double the normal amount of time, you could assert here that toilet paper used at home may have doubled!
• How often do Americans typically buy toilet paper at a retailer?
• Did the number of visits increase during the first wave of the pandemic?
• How did the lockdowns (and anticipation of the lockdowns) impact shopping behaviors? 
• What role did panic buying play?
• Superstar commentary: While long-term demand for TP didn’t change, demand for commercial TP (used in businesses and retail stores) dropped substantially because everyone was self-isolating. This was more than offset by the fact that home use of TP probably doubled and actual demand more than doubled due to panic buying.

When you look back at the above, you can see that residential toilet paper usage likely doubled, and purchases increased because of panic buying. Even though we don’t have hard data, we were able to ask the right set of questions and articulate that this probably was a demand shock.

Again – this is a partner-level case question. They want to see how you think but also be engaged in an interesting conversation.

There’s one more question to address on the demand side. 

• Alternatives for toilet paper: Are there adequate alternatives to toilet paper? 

The answer is no, and this is a great way to transition to the supply chain. 

Can we meet the increased demand by making changes in the supply chain? Let’s take a look at the supply chain for toilet paper.

Toilet Paper Supply

Was there a supply shortage for toilet paper in North America?

Let’s evaluate our options on the supply side. In the 3rd layer, we’re going to look at: 

• Forecasting
• Raw materials and other inputs
• Manufacturing capacity
• Inventory positions
• Shipping 
• Retailer stocking programs
• How do toilet paper suppliers forecast toilet paper demand?
• How much toilet paper do suppliers produce in a year?
• Superstar commentary: Toilet paper demand has likely been very stable over the past several decades. It’s unlikely that manufacturers have accounted for a spike in demand. 
• What are the main inputs to toilet paper?
• Is there sufficient supply of pulp to accommodate demand surges? 
• Were there any barriers to pulp importation due to COVID (e.g., lack of supply due to sick employees or reduced manpower due to social distancing)?
• Are there any other inputs to toilet paper? 
• What is the utilization of toilet paper manufacturing plants?
• What is the capacity of toilet paper manufacturing plants? What does it take to ramp up capacity?
• Can capacity be transferred easily from machines that make TP for the home market vs. the commercial market?
• Are there other facilities that could be repurposed to produce toilet paper? 
• Most US manufacturers utilize “just-in-time” manufacturing, meaning they only make as much as they need in the near-term, and do not stockpile extra products. It’s likely that toilet paper is made on an as-needed basis, and that manufacturers would have to redirect capacity from something else in order to ramp up capacity. 
• Sounds easy, but it’s actually very difficult to repurpose other manufacturing lines to produce toilet paper. Even commercial toilet paper machines can’t easily be repurposed to produce residential TP. Manufacturers had a hard time keeping up. 
• Should more inventory be increased at the manufacturing plants?
• Should more inventory be increased at retailer warehouses and stores?
• Superstar commentary: Given the increase in sales of all grocery products, it’s also likely the shipping capacity got very tight. And who’s to say that toilet paper is more important than Double Stuffed Oreos? (Not me!) 

Looks like you’ve got a pretty comprehensive set of questions and hypotheses to explore. Let your interviewer guide you through the supply questions.

Now that you’ve learned about issue trees and reviewed these examples, what should you do next?

How to Incorporate Issue Trees & Decision Trees into Your Case Prep

Let’s talk about how you can get good at issue trees. 

• Become fluent in using issue trees for basic frameworks. Here are some basic business frameworks that are helpful to know. Keep them in mind so you can build off them quickly rather than building every one from scratch.
• Practice. When I was doing case prep, I spent time just practicing issue tree structure. I didn’t finish the cases, just created the trees. Once I was confident with issue trees, I moved on to full cases. 
• Get creative. Issue trees can be applied to things other than consulting cases. Apply them to decisions you need to make in your daily life.

So now we’ve covered all things related to issue trees! Let’s wrap it up with our top tips.

1. Practice writing out issue trees for all the basic frameworks. You want to be fluent in issue trees for profitability, supply and demand, and other basic business frameworks.

2. take time to envision your framework before you start writing. 60-90 seconds is good because you want to leave enough time for your analysis. don’t stress about the silence while you’re building your framework. it will be worth it to make sure your approach is solid., 3. make it mece. you want the interviewer to feel confident that you have all bases covered and that you will approach the problem in an organized way., 4. aim for 3 – 4 layers in your issue tree. if you only have 2 layers, you may not be going deep enough to hit key issues. if you go any deeper, you will be in the weeds of the problem., 5. use your issue tree as a communication tool. get comfortable shifting your focus back and forth between the issue tree (to make sure you are covering all your points) and your interviewer (to communicate your analysis and recommendations)., 6. leverage the issue tree throughout the interview. it’s a tool that should let you shine in the case interview by helping you stay on track as you work toward a recommendation and remind you of the question you need to answer..

In this article, we’ve covered:

• What Issue Trees Are,
• Why Issue Trees Are Important in Consulting Case Interviews,
• How to Build Your Own Issue Trees and Decision Trees,
• How to Use them to Pass Your Consulting Interview,
• Issue Tree Examples,
• How to Incorporate Issue Trees & Decision Trees into your Case Prep, and
• 6 Tips for Creating Issue Trees.

Still Have Questions?

If you have more questions about creating issue trees, leave them in the comments below. One of My Consulting Offer’s case coaches will answer them.

Other people studying issue trees found the following pages helpful:

• Our Ultimate Guide to Case Study Prep
• MECE – Multually Exclusive, Collectively Exhausting
• The Hypothesis-driven Approach
• Answer First Communication
• Hypothesis Tree
• The Profitability Framework
• Thanks for turning to My Consulting Offer for advice on case study interview prep. My Consulting Offer has helped almost 89.6% of the people we’ve worked with get a job in management consulting. We want you to be successful in your consulting interviews too. For example, here is how Jeff was able to improve his casing and get an offer from McKinsey.

5 thoughts on “Issue Tree: What It Is & How It Helps You Crack Case Interviews”

Thanks so much! We’re glad the article was helpful!

Here are some thoughts on your questions.

1. Is an issue tree always about identifying root causes of a problem (as some call the Why Tree), or does it generally also include possible solutions (the How Tree)?

McKinsey thinks of Issue Trees as the breakdown of the root causes of a problem and Decision Trees as the possible solutions that should be examined to solve the problem. These are separate, but we didn’t want to write a book on it fearing we’d lose people, so we went with the term Issue Trees as an umbrella term for both.

The key issue is ensuring the mutually exclusive, collectively exhausting categorization in each.

2. If an Issue Tree is a combination of the Why and How trees, is there a one-to-one and MECE discipline between the root cause and solution options.

Another good question!

I’d say the Issue Tree covers the root causes and the Decision Tree the solutions if you want to be perfectly clear with your terms. There could be a one-to-one, MECE correspondence between the branches on your Issue Tree and Decision Tree, but there doesn’t have to be.

Example 1 – no correspondence:

Let’s consider our Issue Tree Example of the Plant Closure Case. Our high-level branches were: • Will the company be better of financially if they close the plant? • Could the company produce tools elsewhere? • Will the brand suffer if manufacturing is moved to another country?

Imagine a team was tasked with answering these questions in order to determine if the plant should close, and then following up with a recommendation on the new location for tool production and creating a plan for the move.

You would not need a Decision Tree branch for the first bullet. Once you’ve done the financial calculation and determined that the company would be better off closing the plant, that branch of analysis is finished.

Your Decision Tree could have 2 branches corresponding to the remaining Issue Tree 2 branches: • Where should the company relocate the productions from the closed plant. This might have sub-branches such as Plant A, Plant B, and Greenfield. • How could the company minimize the negative impact of shutting the plant. This might include sub-branches such as severance packages for displaced employees, clear communication of the reasons behind the plant closure decision, and a new brand awareness campaign.

Example 2 – one-to-one correspondence:

Let’s try a different example. Imagine there was a need to reduce costs at a corporate level for NextGen Games, a software company, by 10%. Your Issue Tree could breakdown the cost reduction ideas by allocating the savings goal down to the business units and the corporate overhead.

• How can the PC gaming division save 10% or more in costs? • How can the console gaming division save 10% or more in costs? • How can the mobile phone gaming division save 10% or more in costs? • How can the cloud gaming division save 10% or more in costs? • How can 10% or more be saved in corporate overhead?

The Issue Tree diving into NextGen’s cost problem would have further detail on the cost reduction options that would allow each group to meet the target with the least disruption to its business. These could include a travel freeze, putting off capital expenditures, or even a headcount reduction.

The Decision Tree for implementing NextGen’s cost problem could have the same 1st level branches, a one-to-one correspondence. But the lower-level branches would focus on the savings ideas that were applicable to each part of the business.

I hope this helps!

Best of luck with your problem-solving!

Is there a specific software/app you find is best for creating issue trees?

Powerpoint’s hierarchy “smart chart” is a drag.

Ideally you’d have expand/collapse functionality.

Curious as to what you prefer

At MCO, we mostly hand draw issue trees because when you’re in a case interview (or coaching people for case interviews), “quick and dirty” is the only option.

When I was at McKinsey, we used PowerPoint, but to be fair, we had help with creating the slides. When I was editing slides myself, I’d pull a page from an old deck with an issue tree that had a similar scale. That way, I wouldn’t need to mess a lot with the number/size of boxes.

Do other readers have software they like better for issue trees? Let us know!

How can i create an issue tree for an insurance company that is restructuring and repositioning

Hi, Yusufu!

Great question! I’d start by asking what the goal of the restructuring is. For instance, if the goal was increased profitability because profits have been falling gradually over time, I might structure my issue tree around the profitability formula.

Profitability = revenues – costs = (price x quantity) – (fixed costs + variable costs)

Then I’d start digging down into each.

This would be a good direction if the problem was firm-wide, across products and business units. The company’s fixed costs might be getting bloated.

But if there’s a new competitor, a new product, or some big change in the market that’s hurting performance, I might break the issue tree down by product lines or business units, or even just focus on the one or two areas where the insurance company is having the biggest problem.

For instance, if the insurance company is facing rising costs due the change in climate/claims due to severity of storms, I’d focus in increased costs from storm-related damage in each product line or business unit.

I hope that helps! Good luck with your issue tree!

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What Is a Decision Tree and How Is It Used?

Costs. Benefits. Probabilities. For data analysts (or just human beings!) these concepts are key to our daily decision-making. Realize it or not, you’re constantly weighing up each one; from deciding which brand of detergent to buy, to the best plan of action for your business. For those working in data analytics and machine learning, we can formalize this thinking process into an algorithm known as a ‘decision tree.’

But what exactly is a decision tree? This post provides a short introduction to the concept of decision trees, how they work, and how you can use them to sort complex data in a logical, visual way. Whether you’re a newly-qualified data analyst or just curious about the field, by the end of this post you should be well-placed to explore the concept in more depth.

We’ll cover the following:

• What is a decision tree?
• What are the different parts of a decision tree?
• An example of a simple decision tree
• Pros and cons of decision trees
• What are decision trees used for?
• Decision trees in summary

1. What is a decision tree?

In its simplest form, a decision tree is a type of flowchart that shows a clear pathway to a decision. In terms of data analytics, it is a type of algorithm that includes conditional ‘control’ statements to classify data. A decision tree starts at a single point (or ‘node’) which then branches (or ‘splits’) in two or more directions. Each branch offers different possible outcomes, incorporating a variety of decisions and chance events until a final outcome is achieved. When shown visually, their appearance is tree-like…hence the name!

Decision trees are extremely useful for data analytics and machine learning because they break down complex data into more manageable parts. They’re often used in these fields for prediction analysis, data classification, and regression. Don’t worry if this all sounds a bit abstract—we’ll provide some examples below to help clear things up. First though, let’s look at the different aspects that make up a decision tree.

2. What are the different parts of a decision tree?

Decision trees can deal with complex data, which is part of what makes them useful. However, this doesn’t mean that they are difficult to understand. At their core, all decision trees ultimately consist of just three key parts, or ‘nodes’:

• Decision nodes: Representing a decision (typically shown with a square)
• Chance nodes: Representing probability or uncertainty (typically denoted by a circle)
• End nodes: Representing an outcome (typically shown with a triangle)

Connecting these different nodes are what we call ‘branches’. Nodes and branches can be used over and over again in any number of combinations to create trees of various complexity. Let’s see how these parts look before we add any data.

Luckily, a lot of decision tree terminology follows the tree analogy, which makes it much easier to remember! Some other terms you might come across will include:

In the diagram above, the blue decision node is what we call a ‘root node.’ This is always the first node in the path. It is the node from which all other decision, chance, and end nodes eventually branch.

In the diagram above, the lilac end nodes are what we call ‘leaf nodes.’ These show the end of a decision path (or outcome). You can always identify a leaf node because it doesn’t split, or branch any further. Just like a real leaf!

Internal nodes

Between the root node and the leaf nodes, we can have any number of internal nodes. These can include decisions and chance nodes (for simplicity, this diagram only uses chance nodes). It’s easy to identify an internal node—each one has branches of its own while also connecting to a previous node.

Branching or ‘splitting’ is what we call it when any node divides into two or more sub-nodes. These sub-nodes can be another internal node, or they can lead to an outcome (a leaf/ end node.)

Sometimes decision trees can grow quite complex. In these cases, they can end up giving too much weight to irrelevant data. To avoid this problem, we can remove certain nodes using a process known as ‘pruning’. Pruning is exactly what it sounds like—if the tree grows branches we don’t need, we simply cut them off. Easy!

3. An example of a simple decision tree

Now that we’ve covered the basics, let’s see how a decision tree might look. We’ll keep it really simple. Let’s say that we’re trying to classify what options are available to us if we are hungry. We might show this as follows:

In this diagram, our different options are laid out in a clear, visual way. Decision nodes are navy blue, chance nodes are light blue, and end nodes are purple. It is easy for anybody to understand and to see the possible outcomes.

However, let’s not forget: our aim was to classify what to do in the event of being hungry. By including options for what to do in the event of not being hungry, we’ve overcomplicated our decision tree. Cluttering a tree in this way is a common problem, especially when dealing with large amounts of data. It often results in the algorithm extracting meaning from irrelevant information. This is known as overfitting. One option to fix overfitting is simply to prune the tree:

As you can see, the focus of our decision tree is now much clearer. By removing the irrelevant information (i.e. what to do if we’re not hungry) our outcomes are focused on the goal we’re aiming for. This is one example of a pitfall that decision trees can fall into, and how to get around it. However, there are several pros and cons for decision trees. Let’s touch on these next.

4. Pros and cons of decision trees

Used effectively, decision trees are very powerful tools. Nevertheless, like any algorithm, they’re not suited to every situation. Here are some key advantages and disadvantages of decision trees.

Advantages of decision trees

• Good for interpreting data in a highly visual way.
• Good for handling a combination of numerical and non-numerical data.
• Easy to define rules, e.g. ‘yes, no, if, then, else…’
• Requires minimal preparation or data cleaning before use.
• Great way to choose between best, worst, and likely case scenarios.
• Can be easily combined with other decision-making techniques.

Disadvantages of decision trees

• Overfitting (where a model interprets meaning from irrelevant data) can become a problem if a decision tree’s design is too complex.
• They are not well-suited to continuous variables (i.e. variables which can have more than one value, or a spectrum of values).
• In predictive analysis, calculations can quickly grow cumbersome, especially when a decision path includes many chance variables.
• When using an imbalanced dataset (i.e. where one class of data dominates over another) it is easy for outcomes to be biased in favor of the dominant class.
• Generally, decision trees provide lower prediction accuracy compared to other predictive algorithms.

5. What are decision trees used for?

Despite their drawbacks, decision trees are still a powerful and popular tool. They’re commonly used by data analysts to carry out predictive analysis (e.g. to develop operations strategies in businesses). They’re also a popular tool for machine learning and artificial intelligence, where they’re used as training algorithms for supervised learning (i.e. categorizing data based on different tests, such as ‘yes’ or ‘no’ classifiers.)

Broadly, decision trees are used in a wide range of industries, to solve many types of problems. Because of their flexibility, they’re used in sectors from technology and health to financial planning. Examples include:

• A technology business evaluating expansion opportunities based on analysis of past sales data.
• A toy company deciding where to target its limited advertising budget, based on what demographic data suggests customers are likely to buy.
• Banks and mortgage providers using historical data to predict how likely it is that a borrower will default on their payments.
• Emergency room triage might use decision trees to prioritize patient care (based on factors such as age, gender, symptoms, etc.)
• Automated telephone systems guiding you to the outcome you need, e.g. ‘For option A, press 1. For option B, press 2’, and so on.

As you can see, there many uses for decision trees!

6. Decision trees in summary

Decision trees are straightforward to understand, yet excellent for complex datasets. This makes them a highly versatile tool. Let’s summarize:

• Decision trees are composed of three main parts—decision nodes (denoting choice), chance nodes (denoting probability), and end nodes (denoting outcomes).
• Decision trees can be used to deal with complex datasets, and can be pruned if necessary to avoid overfitting.
• Despite having many benefits, decision trees are not suited to all types of data, e.g. continuous variables or imbalanced datasets.
• They are popular in data analytics and machine learning, with practical applications across sectors from health, to finance, and technology.

Now you’ve learned the basics, you’re ready to explore this versatile algorithm in more depth. We’ll cover some useful applications of decision trees in more detail in future posts.

New to data analytics? Get a hands-on introduction to the field with this free data analytics short course , and check out some more tools of the trade:

• What exactly is Poisson distribution? An expert explains
• What is logistic regression?
• What is a pivot table?

Decision Tree (CART) – Retail Case Study Example (Part 5)

Greedy Decision Tree – by Roopam

This article is a continuation of the retail case study example we have been working on for the last few weeks. You can find the previous 4 parts of the case at the following links:

Part 1:  Introduction Part 2:  Problem Definition Part 3:  EDA Part 4:  Association Analysis

In this article, we will discuss a type of decision tree called classification and regression tree (CART) to develop a quick & dirty model for the same case study example. But before that let us explore the essence of..

Decision Trees

Let’s accept, we all do this before we pick a slice of pizza from the box: we quickly analyze the size of the piece, and proportions of toppings. In this quick optimization, you mostly look for the biggest slice with the maximum amount of your favorite toppings (and possibly avoid your least favorite ones). Hence, I would rather not call this little boy, shown in the picture, greedy. He is just trying to cut his birthday cake to maximize his preferred taste.  The cake has his favorite topping i.e red cherries, and not so favorite green apples in equal proportions (50-50). He needs to make a clean cut with just two strokes of the knife otherwise, the guests at his party won’t appreciate his messy use of the knife. With a complete deftness and using the built-in decision tree in his brain, this boy cuts the perfect piece to savor his taste. Let us have a look at his artistry:

Decision Tree Cake – The CART Algorithm

He started with equal proportions of red and green (50%-50%). Remember, he wanted the most number of reds and least number of greens on his piece. His slice , a quarter of the cake, has 71% reds and 29% greens. Not bad! This is precisely how decision tree algorithms operate. Like the above problem, the CART algorithm tries to cut/split the root node (the full cake) into just two pieces (no more). However, there are other decision tree algorithms we will discuss in the next article, capable of splitting the root node into many more pieces.

I must point out that though we are using discrete data (such as red cherries and green apples) for the decision tree in this article, CART is equally capable of splitting continuous data such as age, distance etc. Let us explore more about CART decision tree algorithm.

Classification and Regression Tree (CART)

I find algorithms extremely fascinating be it Google’s PageRank algorithm, Alan Turing’s cryptography algorithms, or several machine-learning algorithms. To me, algorithms are a mirror of structured thinking expressed through logic. For instance, the CART algorithm is an extension of the process that happened inside the brain of the little boy while splitting his birthday cake. He was trying to cut the largest piece for himself with maximum cherries and least green apples. In this problem he had two objectives:

• Cut the largest piece with a clean cut
• Maximize the number of cherries on this piece while keeping green apples at lowest

The CART decision tree algorithm is an effort to abide with the above two objectives. The following equation is a representation of a combination of the two objectives. Don’t get intimidated by this equation, it is actually quite simple; you will realize it after we will have  solved an example in the next segment.

For our example, k=0,1 in above equation are 0=green apples & 1=red cherries. Remember, for our case study with marketing campaigns, k=0,1 will become responded (r) and not-responded (nr) customers. Similarly, for our banking case study & credit scoring articles ( link ) they will become loan defaulters & non-defaulters. However, the philosophy of decision tree and the CART will remain the same for all these examples and much more practical classification problems.

Let me define some important terminologies for the CART decision tree algorithm before explaining the components of the above equation for the goodness of split.

The CART Decision Tree Terminologies

The following is the definition of the components in the above equation for the goodness of split.

I hope you have realized, the largest value of the product of Ψ(Large Piece) and ‘Ψ(Pick Cherries) called the goodness of split will generate the best decision tree for our purpose. Things will get much clearer when we will solve an example for our retail case study example using CART decision tree.

Retail Case – Decision Tree (CART)

Back to our retail case study example, where you are the Chief Analytics Officer & Business Strategy Head at an online shopping store called DresSMart Inc. that specializes in apparel and clothing. In this case example, your effort is to improve a future campaign’s performance. To meet this objective, you are analyzing data from an earlier campaign where direct mailing product catalogs were sent to hundred thousand customers from the complete customer base of over a couple of million customers.  The overall response rate for this campaign was 4.2%.

You have divided the total hundred thousand solicited customers into three categories based on their past 3 months activities before the campaign. The following is the distribution of the same. Here, the success rate is the percentage of customers responded (r) to the campaigns out of total solicited customers.

As you know, CART decision tree algorithm splits the root node into just two child nodes. Hence for this data, CART can form three combinations of binary trees as shown in the table below. We need to figure out  which is the best split among these 3 combinations. The results for the same are shown in the table below.

Let me help you out with the calculation of each column for the above tree. We will use the first row (i.e left node: Low and right node: Medium+High) for the following calculations and then you could do the rest of the calculations yourself. To start with we have calculated P L and P R  in the following way:

Now the calculation for Ψ(Large Piece) is simple as shown below:

Now, let’s come to the second part of the equation that is Ψ(Pick Cherries). Remember, r represents responded and nr  represents  not-responded customers for our campaign’s example.

You may want to calculate the other two terms (i.e r: P(k|R), and nr: P(k|R)) yourself before plugging them in the following equation to get the value for Ψ(Pick Cherries).

This leaves us with one last calculation for the last column i.e.  goodness of split which is:

The final task now is to find the maximum value for goodness of split in the last column. This will produce the following decision tree through the CART algorithm with   Low on the left node, and Medium+High on the right node.

Decision Tree – The CART Algorithm Final Result

This is an important business insight as well that people with higher activity tend to respond better to campaigns. I agree it was clear from the first table at the top as well, but we have learned the science of creating decision tree using the CART algorithm in the process. This is extremely useful when you are dealing with a large dataset and want to create decision tree through recursive partitioning.

Sign-off Note

OK, next time while choosing that pizza slice, remember the evolutionary decision tree that helps you maximize your chances for the best slice. Once in a while, you may want to leave that best slice for someone else – I bet you will feel equally good!

In the next article, we will extend this concept of binary child node decision tree through the CART algorithm to more than two nodes decision tree through other algorithms. See you soon!

23 thoughts on “ Decision Tree (CART) – Retail Case Study Example (Part 5) ”

Still digesting this but I think perhaps you made a mistake in the 2nd table, on the bottom row:

Low+high Medium 0.3 0.7

Shouldn’t the 0.3 and the 0.7 the other way round?

Also you say P(k|R) has count of L record counts as the denominator – presumably you meant R? 😉

Thank you for reading this article so carefully! Have corrected the typos.

Hi Roopam ,if i have a variable through which my binary target variable was derived, in that case the second term of equation will be more than 1(Because P(k=0|L)=|1-0| & P(k=1|R)=|0-1|). But i guess that is not correct.in that case what will be the calculation for goodness of split for the variable?

Hi, I hope I am getting you question right. In case of the perfect split (i.e. 50-50% data in either child node, and all the 0s in one node and 1s in other), the value of 2P L P R i.e. Ψ(large piece) is 1/2, and Ψ(pick cherries) has value of 2. Hence the maximum value that the goodness of split metric can take for the CART algorithm is 1. Is this what you meant?

Thanks for your prompt reply.yes Ψ(pick cherries) has value of 2. Hence the maximum value that the goodness of split metric can take for the CART algorithm is 1.so in this situation what will be the split calculation?

Hi, the calculation for goodness of fit will remain the same. However, in practice, analysts need to ensure that such a variable should not be used as a predictor variable for model building. For such cases remember the old adage ‘If it seems too good to be true, it probably is’.

you have given such a well detailed article on CART. Thanks so much. If I can nit pick on the size of pie I argue that P(L) P(R) is more to make the split even. Taking two combinations P(L)=0.7, P(R)=0.3, and P(L)=0.5, P(R)=0.5 will have higher P(L)P(R) for the second combination as it is even sized split. Excellent article. Thanks!

Hi Bharath, thanks for the kind words. You are absolutely right, the CART algorithm optimizes both the pieces simultaneously through Ψ(large piece), hence the best case scenario is 50-50% data split.

Thanks for your post Roopam.

The way you explain the Cart algorithm in awesome I loved the greedy boy example

Another excellent post. Love the cake analogy!

It would very informative and educational to describe classificatio algorithms (Decision Trees techniques (C4.5, CART, Random Forests, Logistic Regression, Support Vector Machine) presenting the essence of the algorithms (without the math whiich sholud be left behind the scene and handled by the software) and where each can be best applied. Then analyze a case and comparing the results. Also use the Ensemble technique to get the best answer.

Hi I am a fresher to this topic and i am enjoying reading your case studies.I want some clarification in this CART analysis.In the first table we have taken 3 categories like low,medium,high in 1st column(activity in the last quarter ),why do we need to take it as 3 categories , I understand that we need to compare goodness of split to do a decision tree so we can’t take less than 3 and we can take more than three categories ,Am I right ?Please help me in understanding .

We don’t need to take 3 categories for decision trees. It was used to make the case study example easy to understand. Decision trees can easily incorporate multiple continuous variables (like height, income etc.) and multi-category variables (like type of cars, cities etc.). Also, we could use binary variables (just 2 categories i.e. male / female).

Thanku Roopam for clearing my doubt.

wuld be great i fyou could share the data sets

Thank you so much for all the wonderful articles. These are very useful for some one like me who are new the modeling world. I really appreciate you spending lot of time to post these articles. Thanks a lot.

I have few questions on this article. I can see that ” you have divided the total hundred thousand solicited customers into three categories based on their past 3 months activities before the campaign”. How do we choose that category? Is this the result of EDA to understand the variables that have the high correlation to the customer response rate or something else? In other words, how do we choose the variables to use in regression tree?

Another question, why do we have to use CART? we can use even the logistic regression to identify the potential customers who would respond using the 10 deciles breakdown and cut off point and lift and gain chart, right?

I think, article is not telling us the best product to offer to a customer, right? it is to identify the potential respondents and to estimate the potential profits. Is there any model to identify the best product to offer based on their past behaviors?

To answer your first question, these categories where created to simplify the explanation for this article. In practical situations, you will most likely have a numeric value for activity of each customer e.g. 12 visits. You could create classes from these numeric data either based on a business definition or through analytical methods. Multi-node decision trees such as CHAID or C5.0 are some of the analytical methods to create grouped data from numeric data. In this case we started with these classes as raw data so you could assume that a predefined business rule is applied on this data.

To answer your next question, decision trees are often the simplest way to understand relationship between predictor and response variables. They are easy to explain to the business teams. Logistic regression and other machine learning algorithms are not as easy to explain. That’s the reason decision tree, despite their lower predictive power, are still extremely popular within consulting and data science community. Moreover, a bunch of decision trees together in random forest algorithm always perform at par, if not better, with any other machine learning algorithm.

Yes, this a response model to optimize likelihood of purchase. Customer need based models are created by identification of life stage and life style of customers. This requires segmenting customers based on their life style / stage, and identification of right products for each segment.

Hi !! please can you help me by giving me a simple example of process CART about a binary decision tres classification and another example of a binary decision tree regression !!

What happened when there are more than 1 case with the same goodness value (and at their highest value) ? Which one should we choose?

Well documented article.I was hoping if you could help me understand the derivation of goodness of split formula,as in whats the logic behind the formula. Why are we using 2PLPR and not 4PLPR or something likewise?

Hi Thanks for your article. Could you please explain how the success rate 4.2 % & 5.8 %(shown in fig) is calculated.

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Decision Trees

Last updated 22 Mar 2021

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A decision tree is a mathematical model used to help managers make decisions.

• A decision tree uses estimates and probabilities to calculate likely outcomes.
• A decision tree helps to decide whether the net gain from a decision is worthwhile.

Let's look at an example of how a decision tree is constructed. We'll use the following data:

A decision tree starts with a decision to be made and the options that can be taken. Don't forget that there is always an option to decide to do nothing !

The first task is to add possible outcomes to the tree (note: circles represent uncertain outcomes)

Next we add in the associated costs, outcome probabilities and financial results for each outcome.

These probabilities are particularly important to the outcome of a decision tree.

Probability is

• The percentage chance or possibility that an event will occur
• Ranges between 1 (100%) and 0
• If all the outcomes of an event are considered, the total probability must add up to 1

Finally we complete the maths in the model by calculating:

Expected value:

The financial value of an outcome calculated by multiplying the estimated financial effect by its probability

The value to be gained from taking a decision.

Net gain is calculated by adding together the expected value of each outcome and deducting the costs associated with the decision.

Let's look at the calculations. What do they suggest is the best option?

Option: Launch loyalty card:

High sales: (0.6 x £1,000,000) = £600,000

Low sales: (0.4 x £750,000) = £300,000

Total expected value = £900,000

Net gain: £900,000 - £500,000 = £400,000

Option: Cut prices:

High sales: (0.8 x £800,000) = £640,000

Low sales: (0.2 x £500,000) = £100,000

Total expected value = £740,000

Net gain: £740,000 - £300,000 = £440,000

Both options indicate a positive net gain, suggesting that either would be better than doing nothing.

However, cutting prices has a slightly higher net gain & looks the best option of the two considered.

BENEFITS OF USING DECISION TREES

• Choices are set out in a logical way
• Potential options & choices are considered at the same time
• Use of probabilities enables the “risk” of the options to be addressed
• Likely costs are considered as well as potential benefits
• Easy to understand & tangible results

DRAWBACKS OF USING DECISION TREES

• Probabilities are just estimates – always prone to error
• Uses quantitative data only – ignores qualitative aspects of decisions
• Assignment of probabilities and expected values prone to bias
• Decision-making technique doesn’t necessarily reduce the amount of risk
• Scientific decision-making
• Decision tree
• Decision-Making

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Using decision tree consulting to build mece hypotheses.

How To Use Decision Tree in Consulting to Build MECE Hypotheses

In today’s post, we will explain the steps to build hypotheses in a more effective, methodical, and, for a lack of a better word, a more MECE ( mutually exclusive and collectively exhaustive ) way, using a decision tree .

When we do cases with candidates, even our own clients, what always surprises us is how messy their hypotheses can be.

It’s almost as if people are just throwing out ideas they have without any real understanding of how to create a structure, with the help of a decision tree , to ensure the hypotheses derived from the structure are on point and MECE .

I think most people are primarily right-brain thinkers by nature. That means that they will throw out an idea first and then decide if it solves the problem they are trying to address. They are basically brainstorming in the traditional sense of the word, but in a very messy way without priorities and a link to the issue. This type of thinking can often lead to big breakthrough insights but it will not work during consulting case interviews or during consulting engagements as consultants are expected to think in a structured way.

And what I find with most hypotheses is that they are very ill-considered, they have poor structure beneath them and most importantly they are not collectively exhaustive nor are they mutually exclusive (MECE) .

And by their very nature hypotheses are difficult to make mutually exclusive or collectively exhaustive.

Think about it. You develop these ideas and then you have to explain why the problem exists, which is hard on its own. And then you have to compare each hypothesis with the next hypothesis you develop to make sure you have listed every possible hypothesis .

You also have to make sure the issue you covered in one hypothesis doesn’t overlap with another hypothesis . Trying to package issues into hypotheses while trying to get all the issues listed and in the right order is naturally going to be very difficult.

This is not something that is taught in MBA programs or any training programs that we know of outside of leading management consulting firms.

This video  teaches this entire process below in great detail.

This additional video  teaches comprehensive McKinsey hypotheses – based case interview approach. This is necessary to show the firm you can hit the ground running and add immediate value. Those interviewing for Deloitte S&O, Roland Berger, McKinsey Implementation, etc. are strongly advised to watch both videos.

Hypothesis Based Consulting vs. a Decision Tree Approach – Which approach Does MBB prefer?

Leading management consulting firms, whether it is BCG, McKinsey or Bain (collectively called MBB ), like hypothesis-based consulting . This is also called the answer -first approach. The answer being the hypothesis.

However, BCG tends to also accept the decision tree -leading-to- hypotheses approach to solving cases. We also have had candidates who interviewed at McKinsey and used a decision tree approach to solve the case and did well. They basically did not go into formal hypotheses .

The approach of using a decision tree is usually less appropriate at Bain where they tend to be quite frigid in wanting hypotheses upfront.

At McKinsey, it depends on how well you use the decision tree approach . If you use it poorly they would probably think you aren’t capable of developing hypotheses . That is why you avoided the hypotheses in the first place. And at BCG it is again like at McKinsey. They are not adamant they want hypotheses . They are okay with the decision tree approach as long as you use it effectively to arrive at the likely problem.

And in fact, if you use the decision tree approach very well, they generally would be very happy with the technique.

You can also avoid decision trees to build hypotheses , but I am yet to see anyone build neat and logical hypotheses without using a decision tree. Even corporate strategy partners we work with to develop our training do not do this.

An Effective Technique to Build Hypotheses Using a Decision Tree – The Best of Both Worlds

So what I want to talk you through today is a very effective technique we teach all of our clients in terms of how to build hypotheses that are MECE, by using a decision tree .

In our strategy training programs we teach, in-depth, how to go through the entire process from defining the issue, all the way through structuring the problem, developing hypotheses , building an analysis plan, conducting analyses, synthesizing and providing the recommendation. In The Consulting Offer training program (consulting case interview training program where we help real candidates get offers from top firms), we teach the part of this process applicable to case interviews .

We get a lot of questions about how to use this technique well and how to adopt it for case interviews with consulting firms so this post should provide some clarification.

The technique we teach candidates is to develop a key question upfront – define the problem (step one in the exhibit below). Then from your key question brainstorm out the sub-drivers of the key question, which gives you the first-level branches of your decision tree .

At a very high level, the strategy engagement structure can be simplified into 6 basic steps, keeping in mind that it is an iterative process (shown in the exhibit below). Structuring the problem (developing a decision tree ) fits within step 2 of this process and developing hypotheses fits within step 3, as shown in the diagram below.

For each sub-driver/branch of the first level of the decision tree , brainstorm the drivers of that particular driver. This part of the approach is called structuring the problem or brainstorming (refer to step 2 in the exhibit above called structure the problem). Each level of drivers/ branches of the decision tree must be mutually exclusive and collectively exhaustive (MECE).

All the drivers/ branches are collectively called the framework/structure for the case. 

Finally, when you complete the decision tree , the branches must be prioritized and hypotheses are developed ONLY for the prioritized branches . You can sometimes solve a case without hypotheses because the drivers are so specific and point out the problem. So, you only use hypotheses if the decision tree is not generating an answer quickly.

Hypotheses , for the prioritized branches /drivers, should be worded as follows:

• Event causing the observable phenomenon…
• Observable phenomenon…
• Event caused by an observable phenomenon…

An example of a properly structured hypothesis is below:

The development of decision trees and hypotheses are the core skills behind strategy consulting.

In an actual consulting study, the team comes up with analyses necessary to test the hypotheses when the hypotheses are developed. They then build the work plan, conduct the analyses, synthesize the findings, and present the final recommendation to the client.

All well-planned studies work this way. If you are in a study that does not follow this approach, you are almost certainly doing unnecessary analyses.

Let’s look at an example of applying the technique of developing hypotheses using a decision tree .

Let’s assume I gave you a case whereby I told you that a famous French restaurant, a single restaurant in downtown Manhattan, faced a steep drop in profits over the last three years. Their profitability went from something like $10,000 a day to about $1,000 – $2,000 a day and they think it has a lot to do with the changes in their opening times, the menu, the clientele they serve, and so on. 

And most of all, they think the drop in profitability is driven primarily by the change in working hours. They went from being open during lunch and dinner to opening throughout the day from 10 am to 1 am. They want you to solve the case.

Help them address the problem. Maybe try to solve this case before reading the solution below.

The way we would teach candidates to apply hypotheses with the decision tree approach is to start by taking some time to think about clarifying questions . Then come back once you’ve got your clarifying questions .

Now, you may have no clarifying questions , but if you do, always take some time to think about it.

A clarifying question is a question to understand the information provided to you. It is NOT to dig for more new information to solve the case. It is to understand what you have. If you ask clarifying questions to gain new information without understanding and using the information already provided, the interviewer will wonder what the value of providing you with new information is if you could not use the information initially offered.

You could ask the interviewer, “ Is it possible for me to go through my clarifying questions ? I have four of them and they could help me develop my structure. Or, would you prefer to see my structure upfront knowing full well that my clarifying questions , if answered, may change my structure a little bit .”

That is a good technique because it gives the interviewer an option with regards to which approach they prefer and the opportunity to guide you.

Let’s assume the interviewer said, “ It’s okay. Ask your clarifying questions . ”

You can go ahead. Ask no more than four. If you come up with additional questions during this discussion you can say, “ I asked the four but two more came up based on the conversation we had. ” Most of the time the interviewer will allow you to ask it. But don’t go into 7, 8, or 10 questions. Don’t try to solve the case. That is for later. You want to merely understand what you have been given.

The clarifying questions are not there to solve the case. They are used to identify the key question .

Then you would take the information from the clarifying questions and rephrase the initial problem statement to say, “ Okay, I’m going to paraphrase what you’ve given to me. We need to figure out how can a French restaurant located in downtown New York went from $1,000-$2,000 of profits to $10,000 of profits without altering its menu and without changing the cuisine it offers.”

Assume that not altering its menu and the cuisine it offers are the answers to the two of the clarifying questions . You then have to build in the information you received by asking clarifying questions.

Please do not present the key question without using the answers to your clarifying questions. If you did that, what would be the point of even asking clarifying questions ? They would be wasted since you are not using them to narrow down the problem statement.

Narrowing down the problem statement makes the case really easy to solve . Most candidates struggle in a case since they do not understand the problem statement.

It must be noted that Bain and McKinsey tend to have very clear problem statements and this step may not be needed. BCG tends to have broader questions so this step may be needed. In general, if the problem statement is vague, you want to narrow it down.

Next, you could say, “ What drives profitability? Well, clearly it would be revenue and cost . And what are the drivers of revenue and cost ? The drivers of revenue are different revenue streams. So it’s food, alcoholic beverages, and non-alcoholic beverages. It will also be the time of the day that the restaurant is open. The drivers of the cost will be fixed and variable cost. ”

What many candidates do is they would simply ask the clarifying questions upfront and throw in hypotheses . Don’t do that. Your hypotheses would be too vague at this point .

Develop your key question . Develop your decision tree to the second level of branches .

The first level of branches would be revenue minus cost . The second level of branches would be the drivers of revenue and the drivers of cost.

Once you have the drivers of revenue and the drivers of cost , you can develop a hypothesis for each prioritized branch you think is important to solve the case. Not all the branches will be important. Use your judgment and the information provided in the case to prioritize the branches .

Develop a hypothesis for the food revenue stream, the nonalcoholic beverage revenue stream, the alcoholic beverage revenue stream, and the hours when the restaurant is open. Then develop hypotheses for fixed cost and variable cost . That is, assuming you wanted to prioritize them all. You could just as easily have prioritized fewer branches .

Let’s go through some hypotheses . We would say, “ Since the restaurant is open longer hours, they may have alienated some clientele, attracted new clientele, and also incurred higher cost , which is not compensated by higher revenue. That is one hypothesis .

This steep drop in revenue is probably driven by the fact that there is a different clientele coming in which is demanding different prices .”

On the alcoholic beverage side we would come up with a similar hypothesis , and on the fixed as well as variable cost side.

“ Let’s look at the variable cost side. I would hypothesize that it is possible that although variable costs have decreased due to the drop in revenue, it has not decreased sufficiently to compensate for the drop in revenue.

On the fixed cost side, I would hypothesize that due to the longer operating hours, our fixed cost may have increased to carry the longer operating hours. ”

Notice how specific hypotheses for the sub-drivers are. They are more useful than throwing out drivers for revenue.

Your hypotheses don’t require all three parts as in the image above, but they MUST be tightly linked to the issue in that one branch . This prevents overlap with other branches .

If you build your hypotheses off the branches of the decision tree , you maximize your chances to build useful hypotheses because you will have to make sure that your decision tree is mutually exclusive and collectively exhaustive .

So if you build your hypotheses off your decision tree and if you did a thorough job, your hypotheses by default would be collectively exhaustive. And if your decision tree is mutually exclusive , your hypotheses would also be mutually exclusive .

And obviously, your hypotheses are dependent upon the information they have given you in the case and the clarifying information you have collected when you asked clarifying questions upfront.

This is an effective and simple technique to build hypotheses in a mutually exclusive and collectively exhaustive way. If you just throw hypotheses out without deriving them from a decision tree you will have no way of knowing whether they made sense or whether they are MECE.

Our clients are trained to do all of this in 60 – 120 seconds flat. That is pretty fast and would only work if you understand the process . This video teaches this entire process above in great detail.

This additional video teaches comprehensive McKinsey hypotheses based case interview approach. This is necessary to show the firm you can hit the ground running and add immediate value. Those interviewing for Deloitte S&O, Roland Berger, McKinsey Implementation, etc. are strongly advised to watch both videos.

How To Apply Hypotheses With The Decision Tree Technique At MBB

When you get to a McKinsey interview you follow the process above, but you don’t need to show the interviewer the entire process. That is key. With the McKinsey interviewer or Bain interviewer, you don’t tell them what your key question is, because for McKinsey and Bain the key question in the case is very obvious. The clarifying questions are largely redundant because they tend to give you the key question very clearly and upfront. Therefore, there is no reason to narrow it down or rephrase it.

The case is not conceptually difficult as, for example , a BCG case.

Therefore, for McKinsey and Bain, you build out your decision tree as we taught you above. Yet, you don’t discuss your key question , your clarifying questions , or your decision tree . What you do is you build your key question and your decision tree purely to help you develop a framework and then based on prioritized branches of your decision tree , develop hypotheses .

Therefore, just explain your hypotheses and very briefly how you created them.

To recap, in McKinsey and Bain interviews they are not going to see your key question . They may want to see your approach, but what you really want to show them is your hypotheses .

Setting Out The Alternatives

Problems result in several alternatives, and it is essential to think about these alternatives before tackling the issues. Setting out the alternatives is like simply stating the obvious. Also, acknowledging that specific facts and events can exist in a circumstance will assist in the problem-solving process when employing the decision tree approach. 

For instance, a team head of an organization wants to hold a get-together at his home with his team members. It is the rainy season, and he is faced with uncertainty about the weather conditions. On the one hand, he wants to hold it on the lawn in the front of his home, so everyone can be comfortable and enjoy the fresh air. So, he is considering what the weather will be like and how it will affect the party. 

Based on considerations, holding it in the open has the following probabilities:

• Pleasant weather without rain, offering everyone luxury and utmost comfort.
• A rain-filled day, ruining the event and leaving everyone disappointed.

Alternatively, hosting the party inside the house has the following odds:

• A rain-free day leaving everyone wishing that they had used the lawn.
• A rain-filled day, leaving everyone happy, comfortable and satisfied that they had made the right choice.

We listed the alternatives to this condition. We stated the possibilities that could happen in this circumstance. We didn’t have to give complicated hypotheses . Just as we set out the alternatives for this problem, we can also do the same for complex cases by using decision trees . To solve difficult situations, we need to remember that decision trees comprise several junctions and subsidiaries. While the junctions represent alternative decisions, the subsidiaries depict the possible hypothetical outcomes of each decision. 

Symbols like squares and circles can represent decisions on decision trees, while double lines, colors, single lines, etc., can be used to represent subsidiaries. However big your decision tree is, it must have and combine these components:

• Alternative actionable choices 
• Probable outcomes of every chosen action

Most times, these results are partly influenced by either fate or some condition that cannot be controlled. We must note that the above-listed components must be blended to give realistic results and keep our clients on the right path.

The Decision-Event Methodology

To aid your clients in building MECE hypotheses , you need to use the decision-event methodology effectively. It is an effective way of handling situations that require more than one decision stage. The instance we used above has just one action stage, meaning that one decision path did not lead to another junction of decision choices. We cited it to establish the primary principles for building complicated decision trees .

Consider a more complex situation where a majority shareholder wants to approve an upgrade project for a product. The board of directors believes that approving an upgrade will give the firm an advantage over its competitors. However, on the other hand, forfeiting the upgrade might mean that the brand may lose its position to its market alternatives.  So, the first crossroad is to decide if the upgrade needs to be done or not. Hence, a decision tree.  

Assuming they are doing a decision tree , if they kill the upgrade idea, this path will end at two subsidiaries:

• Competitors can introduce their upgrades, sending this firm down the ranking chart.
• Competitors do not introduce an upgrade, preserving this firm’s market position.

However, if they decide to go on with the upgrade, there will be two new junctions:

• Project success
• Project failure

The board does not have to seriously consider the failure path because the hypothetical outcomes will be like those of the scrapped idea , with an additional possibility of trying again. However, the project’s success will lead them to either shelving or commercially producing the upgraded product. The former choice might not be favorable because they may eventually release the upgrade after their competitors have introduced theirs. The latter can either yield an all-around market expansion or a struggle with competitors.

The wise thing to do in this situation is to analyze the necessary actions and outcomes and those that have significant consequences. For the firm to make informed decisions, it needs to apply the following strategies:

• Identifying actions and options at every junction.
• Identifying areas that pose ambivalence and the kind or level of the varying outcome at every stage.
• Calculating the necessary values when analyzing, especially the possibilities of various outcomes and the expenses and profits attached to every action and result.
• Using a decision tree to deliberate what the project’s financial situation will look like while examining the production decisions. The fixed and variable costs must be within the safe spending capacity of the company. Also, they have to be realistic when compared with the expected value returns at the end of every fiscal year.

Using Decision Making Trees in Critical Decision Making

Decision-making trees can play a vital role when a company needs to make a particular decision . Properly executed decision trees guide decision makers to arrive at an effective final decision that maximizes chances for the most profitable financial return, sustainability, and competitive advantage. As expected, every person holding a vital position in the firm will have varying opinions that will likely be conflicting – people like the capital suppliers, major idea contributors, decision-makers, data analysts, and other board members that have a say in the company. If these ideological differences are not checked and ironed out carefully and critically, the decision-makers, investors, information suppliers, and data analysts will judge the case, data importance, essence of analysis, and canon of success in ways that do not agree with one another.

For instance, the firm’s shareholders may handle a significant investment as one with several unpredictable outcomes. That investment might threaten a middle-level manager, including his job and entire career. Others will have a lot to benefit from the investment if it works and little to lose if it fails. In essence, the level of risk staked at every individual affects their presumptions and decision strategies.

Hence, to avoid the negative consequences of the political reasoning of every individual, the central deciding personnel need to make the following evaluations:

• What are the things at risk? Is it the net profit or equity value , the business’ strength and life span, job security, or the possibility of a profitable career?
• Who is affected by the risk? Is it only the shareholders or the company’s managerial body, staff, or the entire community? (Even if they are all affected by the risk, they may bear it differently).
• What is the nature of the risk that the affected parties suffer from ? Is it general or unique? (While shareholders may bear their risks in one form, other parties might bear their risks differently). 
• To what extent does the risk affect the company and the general economy? Is it a one-off or a lifetime risk? Is it successive? Can it be insured? Is the risk consequential to a unit in the company, the entire company, the sector of operation, or the country’s economy?

The above-listed evaluations can help the board make informed decisions. While the decision tree will not completely solve the problems, it will give the management an avenue to choose a course of action to facilitate the firm’s goals. This is a big advantage of using decision trees for decision-making. 

Adopting The Issue Trees Framework

Another name for the decision tree is the issue tree. As we discussed, for some management consulting firms like McKinsey, their approach and framework for handling problems is the issue tree. For your management consulting interview, you might need to adopt the issue tree framework to answer questions on case interviews . Issue trees make the problem-solving process more accurate and straightforward. Although this system is not adequately taught in business institutes, consultants are often required to be equipped with the knowledge for utmost excellence in their careers. Those who know how to use issue trees are at an advantage in acing their case interviews . They know how to arrange their thoughts within a short time and present themselves as excellent communicators. 

In essence, these solution trees aid consulting teams in seamlessly achieving the following:

• Discovering significant problems in every complicated business case in a structured way.
• Gaining the advantage of leading their dialogues in a structured way.
• Deciding how they should set every job and resource for practical problem-solving.
• Finding an actionable solution to every problem in a structured way.

Issue trees can be referred to as logic trees. In some cases, they are referred to as why trees or solution trees . Why trees help you understand why a problem is occurring. They are illustrations used to divide complex business questions into comprehensible bits. Issue trees are often effective in handling case interviews . They are like horizontal trees that flow from the top left side to the right. They are broken into more straightforward queries as they flow towards the answer (the right).

You might be thinking, “But outlines are productive.” Yeah, that is true. Outlines are fine, but issue trees are more effective. Outlines and issue trees help effectively arrange the queries you want to tackle. However, issue trees aid in effective communication because they clearly reveal the connections between the original complex queries and the deduced root-cause queries. After completing an issue tree , the most relevant areas that will be useful for solving the client’s puzzles must have shown up.

An example of this is the profitability tree. The profitability tree explores the different ways a business can maximize profit. It starts from the key question on the left side and breaks it down into revenue and cost, and then further breaks down these components into more detail. 

Let’s Explore Some Issue Tree Examples And Case Studies

Issue trees are adequate for handling almost any kind of case. Let us take a few issue tree examples and see how trees can be used to tackle problems.

We will pick our first example from a regular case query:

“Your client manufactures and distributes plastics. They are finding it more expensive to run their production plant in Belgium; hence, they consider shutting down the plant and relocating to their facility in Germany.”

The first stage of the issue tree is the client’s query, 

“Should they close their plastic producing plant in Belgium?”

The second stage of the issue tree will be divided into three layers:

• The financial aspect
• The operational aspect
• Overall considerations of the brand

Let’s go in-depth into each of these layers.

The Financial Aspect

Considering the financial aspect of the brand, the hypothetical question to ask is, 

“Will the firm be positively or negatively affected after shutting down the plant?”

Under the financial aspect, there are five major areas to consider. They are:

• The plant’s operating costs
• The one-off expenses that will come with closing the plant
• The supply costs
• Government benefits

The Plant’s Operating Costs

The plant’s operating costs open into the fourth stage, which encompasses variable and fixed costs .

• Variable Costs : What is the actual variable cost for every unit? (NOTE: You can consider the high transportation or labor cost as factors that could be causing the move intention).
• Fixed Costs : What are the plant’s facility expenses? (NOTE: A considerable situation could be the increased cost of purchasing lands and structures).

The One-Off Expenses

Under the one-off costs , we can raise three queries:

• What expenses will come with shutting down the plant in Belgium?
• What are the severance expenses?
• What is the price of moving equipment from the abandoned site to another place?

The Supply Costs

• How much will it cost to supply products with the present economy?
• If production is moved to Germany, what will be the future supply chain cost ?
• Will the tariffs change if production happens from Germany?

Government Benefits

• Does the German government offer incentives to companies in their states?
• What kind of incentives does the government give?
• Will the incentives favor existing projects or new ones?
• Are the incentives’ terms and conditions difficult to keep?
• What are government subsidies due for operation in Belgium? 
• Will the company still make sales and maintain its market position in Belgium if they are still producing there?
• Will they lose customers in other countries if they relocate the plant to Germany?
• What is the expected value the company is looking to have after relocation?

The Operational Aspect

The hypothetical question for this aspect is,

“Can the firm manufacture plastics in a different location?”

In this aspect, the significant areas to analyze are:

• The company’s operational capability in other places
• The effects on supply 

The Company’s Operational Capability In Other Places

• Can the firm’s operational capability be spread across several plants?
• Are there plants available to take up every needed capability?
• How long will it take other plants and facilities to step up to demands?

The Effects On Supply

• What locations are the goods being supplied to?
• What kind of effect will the relocation lay on lead-time product supplies?
• Will there be a need for a supply facility to keep up with customer-demanded lead times regularly?
• How much effect will the relocation lay on lead-time product supplies? 

Overall Considerations On The Brand

Under the brand considerations, our hypothetical question will be:

“What will happen to the firm if it relocates its plant to Germany?”

There are three major categories to analyze under this hypothesis question:

Will The Company Suffer If It Relocates?  

• To what extent will it suffer?
• How long will the brand suffer?

Will It Maintain Its Market Stance?

• What are the possibilities of experiencing a rise and fall over time?
• How long will it maintain its stance?
• Will its maintained market stance give it an edge or a loss to its competitors?

Will The Firm Grow After Relocating?

• What will be the company’s growth margin after relocating?
• How consistent will the company’s growth be?

At this point, we have successfully established an issue tree for this business problem. We can deduce that this issue tree is not even. There are several areas to examine under the financial aspect. No matter the number of branches you cover, ensure that every element is adequately considered.

Furthermore, you can hint to your interviewer that the end decision will be centered around the financial aspects. However, they will be matched against the brand and operational aspects.

Let’s look at another issue tree example . Let us consider another business problem that can pop up during a second session interview. 

In second session interviews, the structure can strategically change, making the business problem sound more realistic. Here we go:

“Why was there a liquor scarcity on retail shelves in the U.S during the early stage of the Covid-19 pandemic?”

Now, to solve this one, our first stage on the issue tree is the query:

“Was there a higher liquor demand than supply during the early stage of the pandemic?”

Then, the second stage of the tree will be divided into two layers:

• Liquor demand
• Liquor supply

Liquor Demand

The hypothetical question for this layer is:

“Did the demand for liquor exist in the U.S?”

This question takes us to the next stage, the third stage under liquor demand. The third stage comprises three layers:

• Liquor usage
• The behavior of consumers
• Liquor alternatives

Liquor Usage

Speaking of liquor usage, the center point of consideration is how the consumption of liquor changed during the first Covid-19 season.

• How much liquor did each customer consume every week?
• How much time did people spend at home compared to their offices and public places?
• How did that influence the demand levels?
• What conditions caused the increase in liquor consumption? (NOTE: You can speculate that boredom and uncertainty caused the spike in alcohol intake).

  The Behavior of Consumers

The behavior of consumers towards shopping was different at the early stage of the pandemic. How did it change? Hence, these queries.

• What was the liquor shopping rate of Americans before the Covid-19 pandemic?
• Did the shopping rate rise at the beginning of the pandemic?
• How much impact did the lockdown and its anticipation have on shoppers’ behaviors?
• What impact did panic purchases have on the demand levels?
• To what extent did the panic purchase affect the demand?

Looking at the liquor demand queries above, we can see that the consumption hiked because of panic purchases and boredom at home. Although there was no factual information, we could still coin vital, relevant, and resourceful questions to solve the problem.

Liquor Alternatives

There are some alternatives for liquor. However, they might not be widely adopted as substitutes. Hence, these queries:

• How many people opted for liquor alternatives?
• Were the alternatives as affordable as liquor? 
• By what margin was the cost of the alternatives different from liquor?
• What was the demand level for these substitutes?

Now let us head to the supply stage.

Liquor Supply

The hypothetical question for this stage is:

“Did the U.S experience a liquor supply deficit?”

This query takes us to the third stage under liquor supply. The third stage comprises five layers:

• Market prediction 
• Production capability

Distribution

• Inventory status
• Raw materials and resources

Market Prediction

Did liquor producers predict the rise in demand? Hence, these questions:

• How do liquor producers expect market demand?
• How much liquor is manufactured annually?

Production Capability

Do liquor producers have the ability to contain demand spikes?

• How much capacity do liquor producing plants have?
• How much time and resources are needed to improve plant capacities?
• Are there places that could be redesigned to manufacture liquor?

How well can the distribution sector handle hikes in demand? 

• Is there an effective distribution system for raw materials and finished products?
• What difficulties do distributors experience when shipping or supplying?

Inventory Status

Is there a need for an increment in inventory to handle the increased demands?

• Is there a need to add inventory at production plants?
• Should there be an inventory concentration at warehouses and shops?

Raw Materials and Resources

What amount of resources are available to be supplied to producers to handle the demand hike?

• What are the primary raw materials for liquor production?
• Is there enough supply of resources and raw materials?
• What kind of hindrance prevented the importation of raw materials and human resources? E.g. reduced labor due to ailments and Covid-19 safety precautions.

Now we have an exhaustive list of case questions to aid our client. This is how trees can be very instrumental in solving business cases and complex problems.

Let us look at techniques we can use to create great issue trees .  

Ideas And Best Practices For Creating Actionable Issue Trees

These ideas will make you effective, productive, and fast when you are creating issue trees. Remember, when it comes to case interviews , you don’t have all day. So, you need to come up with potential solutions to the case question within a limited time frame. These best practices will surely come in handy for your consulting interviews . 

Constantly Write Out An Issue Tree For Basic Frameworks

Writing out issue trees for every essential framework helps your fluency. This habitual practice helps your flow in foundational business frameworks like supply, profitability, demand, etc.

Spend Enough Time Reflecting On Your Framework Before You Analyze

You can take as long as two minutes to ponder on your framework before moving on to the analysis. You don’t have to be anxious about how your client will feel during your silent period. The beautiful thing is that your result will turn out to be very helpful.

Ensure It Is Mutually Exclusive And Collectively Exhaustive

By making it MECE, you make your client trust that you have the bases for tackling complex problems and that you will address the problem in a structured manner. So, after creating your issue tree , ask yourself, is my issue tree MECE ? If it isn’t, you might want to revisit your issue tree to ensure that all key points are covered and there are no overlapping points. 

Ensure You Create More Than Two Stages In Your Tree

You may not address the case question substantially if you just handle it in two layers. Remember that one of the aims is to break down the complex question into simple successive units. However, avoid having too many stages so you don’t get interlocked in the issue.

Let Your Issue Tree Be An Effective Communication Medium

Ensure to keep constant communication with your interviewer or client when creating your logic trees . This way, you won’t miss out on vital details, opportunities, and intuitive ideas . You need to keep a consistent focus on the tree to address every point, and at the same time, you have to relate your thought process and potential solutions to your interviewer from time to time. 

Make Effective Use Of The Issue Tree Throughout The Discussion

The issue tree keeps you effective and guided during your case interview. If you fail to leverage it, you may miss relevant points that will lead to practical solutions.

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Building simple models: A case study with decision trees

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Building correctly-sized models is a central challenge for induction algorithms. Many approaches to decision tree induction fail this challenge. Under a broad range of circumstances, these approaches exhibit a nearly linear relationship between training set size and tree size, even after accuracy has ceased to increase. These algorithms fail to adjust for the statistical effects of comparing multiple subtrees. Adjusting for these effects produces trees with little or no excess structure.

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Jensen, D., Oates, T., Cohen, P.R. (1997). Building simple models: A case study with decision trees. In: Liu, X., Cohen, P., Berthold, M. (eds) Advances in Intelligent Data Analysis Reasoning about Data. IDA 1997. Lecture Notes in Computer Science, vol 1280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0052842

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