hypothesis tree template

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Hypothesis Tree Template

Managing Editor September 13, 2018

A hypothesis tree takes a problem statement and comprehensively disaggregates potential solutions. A hypothesis tree is a great tool to use any time you are trying to better understand a problem and/or opportunities.

Most projects at strategy consulting firms start with the team spending a few hours brainstorming and aligning on the hypothesis tree for the defined problem statement. The hypothesis tree helps a team scope out the scale of the problem and potential solutions, build context, prioritize some of the analysis and facts needed, and communicate the problem solving structure and path.

To get you going on creating hypothesis trees , download the free and editable Hypothesis Tree PowerPoint Worksheet.

Take a large and important problem your team or company is trying to solve.

Create a problem statement. You can utilize the problem statement module to do this.

Once you have a clear problem statement, disaggregate potential solutions into 2-5 major hypotheses . Make sure they are MECE and at the same level of specificity. Next, tree out sub-hypotheses or ideas that would drive the major hypotheses. Next, list out the analyses or fact base you need to prove or disprove the hypotheses or determine the magnitude of the ideas.

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Consulting Hypothesis Tree: Everything You Need to Know

• Last Updated May, 2024

A hypothesis tree is a powerful problem-solving framework used by consultants. It takes your hypothesis, your best guess at the solution to your client’s problem, and breaks it down into smaller parts to prove or disprove. With a hypothesis tree, you can focus on what’s important without getting bogged down in details.

Are you feeling overwhelmed during a complex case interview? Try using a hypothesis tree! It’ll help you communicate your insights more effectively, increasing your chances of acing the case.

In this article, we’ll discuss:

• What a hypothesis tree is, and why it’s important in consulting interviews
• Differences between a hypothesis tree vs. an issue tree
• The structure of a hypothesis tree and how to construct one
• A hypothesis tree example
• Our 4 tips for using a hypothesis tree effectively in consulting interviews

Let’s get started!

Definition of a Hypothesis Tree and Why It's Important

6 steps to build a hypothesis tree, hypothesis tree example, 4 tips for using a hypothesis tree in your interview, limitations to using a hypothesis tree, other consulting concepts related to hypothesis trees.

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A hypothesis tree is a tool consultants use to tackle complex problems by organizing potential insights around a central hypothesis. It provides a structured framework for solving problems by forming sub-hypotheses that, if true, support the central hypothesis. This allows consultants to explore problems more effectively and communicate their insights.

Mastering the hypothesis tree can help you stand out in your case interview. It enables you to showcase your problem-solving skills and critical thinking ability by presenting insights and hypotheses in a concise and organized manner. This helps you avoid getting overwhelmed by the complexity of the client’s problem.

Hypothesis trees are not limited to consulting interviews; they are an essential tool in real-world consulting projects! At the beginning of a project, the partner in charge or the manager will create a hypothesis tree to scope the problem, identify potential solutions, and assign project roles. Acting as a “north star,” a hypothesis tree gives a clear direction for the team, aligning their efforts toward solving the problem. Throughout the project, the team can adapt and refine the hypothesis tree as new information emerges.

The terms “hypothesis tree” and “issue tree” are often used interchangeably in consulting. However, it’s important to understand their key differences.

Differences Between a Hypothesis Tree vs. an Issue Tree

A hypothesis tree is less flexible as it is based on a predetermined hypothesis or set of hypotheses. In contrast, an issue tree can be more flexible in its approach to breaking down a problem and identifying potential solutions.

Let’s look at a client problem and high-level solution frameworks to illustrate the differences: TelCo wants to expand to a new geography. How can we help our client determine their market entry strategy?

If you were to start building a hypothesis tree to explore  this, your hypothesis tree might include:

Hypothesis: TelCo should enter the new market.

• It has immense potential and is growing rapidly.
• The expansion is forecasted to be profitable as the costs to operate the service in the new market are low.
• There are few large competitors, and our product has a competitive advantage.
• How attractive is the new market? What is the growth outlook? What is the profitability forecast for this new market?
• What are the different customer segments?
• How is our client’s service differentiated from local competitors?

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

Here are the 6 steps to build a hypothesis tree. Practice doing these in your mock case interviews!

1. Understand the Problem

Before building a hypothesis tree, you need to understand the problem thoroughly. Gather all the information and data related to the problem. In a case interview, ask clarifying questions after the interviewer has delivered the case problem to help you build a better hypothesis.

2. Brainstorm

Brainstorm and generate as many hypotheses as possible that could solve the problem. Ensure that the hypotheses are MECE. In your interview, you can ask for a few moments to write down your brainstorming before communicating them in a structured way.

3. Organize the Hypotheses

Once you have brainstormed, organize your thoughts into a structured hierarchy. Each hypothesis should be represented as a separate branch in the hierarchy, with supporting hypotheses below.

4. Evaluate the Hypotheses

Evaluate each hypothesis based on its feasibility, relevance, and potential impact on the problem. Eliminate any hypotheses that are unlikely to be valid or don’t provide significant value to the analysis. During your interview, focus on the highest likelihood solutions first. You will not have the time to go through all your hypotheses.

5. Test the Hypotheses

Test your central hypothesis by confirming or refuting each of the sub-hypotheses. If you need data to do this, ask your interviewer for it. Analyze any information you receive and interpret its impact on your hypothesis before moving on. Does it confirm or refute it?

If it refutes your hypothesis, don’t worry. That doesn’t mean you’ve botched your case interview. You just need to pivot to a new hypothesis based on this information.

6. Refine the Hypotheses

Refine the hypothesis tree as you learn more from data or exhibits. You might need to adjust your hypothesis or the structure of the hypothesis tree based on what you learn.

Let’s go back to the TelCo market entry example from earlier. 

Hypothesis : TelCo should enter the Indian market and provide internet service. 

Market Opportunity : The Indian market is attractive to TelCo.

• The Indian telecommunications market is growing rapidly, and there is room for another provider.
• Margins are higher than in TelCo’s other markets.
• The target customer segments are urban and rural areas with high population densities.
• The competition is low, and there is an opportunity for a new provider for customers who need reliable and affordable service.

Operational Capabilities : The company has the capacity and resources to operate in India.

• TelCo can leverage its existing expertise and technology to gain a competitive advantage.
• TelCo should build out its Indian operations to minimize costs and maximize efficiency.
• TelCo should consider investing in existing local infrastructure to ensure reliable service delivery.
• TelCo can explore alliances with technology content providers to offer value-added services to customers.

Regulatory Environment : The local regulators approve of a new provider entering the market.

• TelCo must ensure compliance with Indian telecommunications regulations.
• TelCo should also be aware of any upcoming regulatory changes that may impact its business operations.

Overall, this hypothesis tree can help guide the analysis and process to conclude if TelCo should enter the Indian market.

1. Develop Common Industry Knowledge

By familiarizing yourself with common industry problems and solutions, you can build a foundation of high-level industry knowledge to help you form relevant hypotheses during your case interviews. 

For example, in the mining industry, problems often revolve around declining profitability and extraction quality. Solutions may include reducing waste, optimizing resources, and exploring new sites. 

In retail banking, declining customer satisfaction and retention are common problems. Potential solutions are improving customer service, simplifying communication, and optimizing digital solutions.

Consulting club case books like this one from the Fuqua School of Business frequently have industry overviews you can refer to. 

2. Practice Building Hypothesis Trees

Building a hypothesis tree requires practice. Look for opportunities to practice generating hypotheses in everyday situations, such as when reading news articles or listening to podcasts. This will help you develop your ability to structure your thoughts and ideas quickly and naturally.

3. Use Frameworks to Guide Building a Hypothesis Tree

Remember, you can reference common business frameworks, such as the profitability formula, as inputs to your hypothesis. Use frameworks as a starting point, but don’t be afraid to deviate from them if it leads to a better hypothesis tree.

Interviewers expect candidates to tailor their approach to the specific client situation. Try to think outside the box and consider new perspectives that may not fit neatly into a framework. 

For an overview of common concepts, we have an article on Case Interview Frameworks .

4. Embrace Flexibility

Don’t be afraid to pivot your hypotheses and adjust your approach based on new data or insights. This demonstrates professionalism and openness to feedback, which are highly valued traits in consulting.

Although hypothesis trees are a helpful tool for problem-solving, they have limitations. 

The team’s expertise and understanding of the problem are crucial to generating a complete and accurate hypothesis. Relying on a hypothesis tree poses the risk of confirmation bias, as the team may unconsciously favor a solution based on past experiences. This is particularly risky in rapidly evolving industries, such as healthcare technology, where solutions that have worked for past clients may no longer be relevant due to regulatory changes.

A hypothesis tree can also be inflexible in incorporating new information mid-project. It may accidentally limit creativity if teams potentially overlook alternative solutions. 

It’s important to be aware of these limitations and use a hypothesis tree with other problem-solving methods.

Several concepts in consulting are related to hypothesis trees. They all provide a structure for problem-solving and analysis. Each has its unique strengths and applications, and consultants may use a combination of these concepts depending on the specific needs of the problem.

Let’s look at some concepts:

• Issue Trees : As mentioned earlier in the article, issue trees are similar to hypothesis trees, but instead of starting with a hypothesis, they start with a problem and break it down into smaller, more manageable issues. Issue trees are often used to identify a problem’s root cause and to prioritize which sub-issues to focus on. If you want to learn more, we have a detailed explanation of Issue Trees .
• MECE Structure : MECE stands for mutually exclusive, collectively exhaustive. It is used to organize information and ensure that all possible options are considered. It is often used in conjunction with a hypothesis tree to ensure that all potential hypotheses are considered and that there is no overlap in the analysis. For an overview of the MECE Case Structure , check out our article.
• Pyramid Principle : This is a communication framework for structuring presentations, such as case interviews. It starts with a hypothesis and three to four key arguments, each with supporting evidence. You can use it throughout the case for structuring and communicating ideas, such as at the beginning of a case interview to synthesize your thoughts or when  brainstorming ideas in a structured way. To better understand why this tool is valuable, we have a deep dive into The Pyramid Principle .
• Hypothesis-Driven Approach : This is an approach to problem-solving where consultants begin by forming a hypothesis after understanding the client’s problem and high-level range of possibilities. Then, they gather data to test the initial hypothesis. If the data disproves the hypothesis, the consultants repeat the process with the next best hypothesis. To see more examples, read our article on how to apply a Hypothesis-Driven Approach .

– – – – – – –

In this article, we’ve covered:

• Understanding the purpose of a hypothesis tree
• What is different about a hypothesis tree vs. issue tree?
• How to build a hypothesis tree
• 4 tips on how to successfully use a hypothesis tree in your consulting case interview
• Other consulting concepts that are related to hypothesis trees

Still have questions?

If you have more questions about building a hypothesis tree, leave them in the comments below. One of My Consulting Offer’s case coaches will answer them.

Other people interested in the hypothesis tree found the following pages helpful:

• Our Ultimate Guide to Case Interview Prep
• Issue Trees
• Hypothesis-Driven Approach
• MECE Case Structure

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Crafting Cases

The Definitive Guide to Issue Trees

Introduction, issue trees: the secret to think like a mckinsey consultant and always have a clear, easy way to solve any problem.

Ask any McKinsey consultant what’s the #1 thing you should learn in order to solve problems like they do and you’re gonna get the same answer over and over again:

“You’ve gotta learn to create Issue Trees.”

Issue Trees (also known as “Logic Trees” and “Hypothesis Trees”) are THE most fundamental tool to structure and solve problems in a systematic way.

Mastering them is a requirement if you want to get a job in a top consulting firm, such as McKinsey, Bain and BCG.

But even if you’re not applying for a job at these firms, they’re a powerful tool for any job that requires you to solve problems .

In fact, Issue Trees are the main tool top management consultants use to solve the toughest multi-billion dollar problems their clients have.

This guide will teach you how to create and use Issue Trees.  

I will give a focus on case interviews  but you can use this skill in any other problem solving activity. I personally use it everyday at work.

(Which means what you’ll learn here is gonna be useful for far more than merely getting a job.)

About the author

I’m Bruno Nogueira.

I’m an ex-McKinsey consultant and I have learned to think using issue trees the hard way.

There were no good resources to learn this back when I was applying for the job.

Even within McKinsey there was no formal training. People just expected you to “get it” on the job.

After leaving the Firm, I’ve spent a few years coaching people to get a job in consulting, and I learned to teach this skill the only way possible: by actually teaching it!

Along with my partner Julio, I have taught 1000’s of people to break down problems in a structured way using issue trees.

And today I’m gonna teach you  how to do this.

In this guide you'll learn:

Issue Tree Fundamentals

Three Techniques To Build Issue Trees

Six Principles For AMAZING Issue Trees

Issue Tree Examples

Common Mistakes and Questions

How To Practice Issue Trees

BONUS CHAPTER

Applying Issue Trees On The Job

Issue trees are the blueprint of how McKinsey (and other) consultants think.

They make your thinking process more rigorous and much, much more clear.

Unfortunately they didn’t teach you this well enough (if at all) in school.

They don’t even teach this in most Business Schools.

But if you learn to harness their power, you’re set to case interview success (and a career where every problem can be easily solved).

How I learned about Issue Trees

A bit of a personal story first…

I first learned about Issue Trees from a friend who was working in management consulting. It was back when I was applying for a job at McKinsey, Bain and BCG.

This friend told me Issue Trees were the #1 thing I had to learn in order to do well on the interview and land a top consulting job.

And so, the first thing I did was to look for examples of Issue Trees.

And I found stuff like this…

Not exactly rocket science, right?

But then I thought… “Alright,  what if my problem is not a profit problem?  Or what if I need to dig a little deeper than that?”

It didn’t take me long to find people on the internet telling me that you could use Issue Trees to solve any  problem!

Here’s how they illustrated this important point:

Let’s be honest with ourselves here… This is NOT the best way to teach something!

And so I kept looking around. 

I wanted to see realistic examples of real Issue Trees consultants use to solve their client’s problems.

And if I was lucky, I hoped to find some explanation on why each example was structured the way it was.

Here’s the kind of stuff I found looking up on Google again:

And now I was left wondering how to get from Point A (the simple profit Issue Tree from the beginning of this orange box) to Point B (the behemoth you see above).

And I also wondered if getting to this behemoth was actually the kind of thing I wanted in the first place. Would it help me in a real interview?

So I gave up on the internet and decided to learn Issue Trees from those who know it best: real consultants. That’s who I learned to build Issue Trees from.

But I know that most people don’t have access to real consultants with the time to teach them things. 

And it never stopped bothering me the fact that the internet had no decent resource to teach people of a skill that I use multiple times a day (and even make a living out of).

This is why I wrote this guide.

The 4 things you need to "get" to understand Issue Trees

Before we jump into the nitty-gritty of how to create and use your Issue Trees, I want to give you a high-level view. This high-level view is what we’ll cover in this chapter.

I’m gonna show you four ways to look at Issue Trees so you can get an intuitive understanding of them.

And I’m gonna show you that through an example of a realistic Issue Tree. 

They are a "map" of your problem

The first thing you need to know about Issue Trees is that they’re nothing more than a “map” of the problem.

Not just any map, but a clear  and rigorous  map. 

We’re gonna achieve those two goals by using a principle called “MECE”. (Don’t worry about it now, we’re gonna get you covered later on).

So suppose you’re an executive in a Telecom Company in charge of B2C mobile services (that is, cell phone services for regular people like you and me).

Imagine you have a client retention problem. That means too many clients are unsubscribing for your services/plans. 

How would you figure out what’s causing this problem?

Well, a smart executive would build a “map” of all the possible things that might be going on. This map is your Issue Tree and “the things that might be going on” are your hypotheses.

I’ll show you one of these, but before I do that, I will ask you to do one 15-second task:

**Action step: grab a piece of paper and make a list of all the hypotheses that pop-up into your head of why customers might be unsubscribing for this Telco’s mobile services.**

Now, take a look at this Issue Tree.

If I did my job right, every hypothesis you had fits one of the “buckets” in this tree.

How do I know that?

Well, I used the MECE principle I mentioned above to build this tree. This means every “part” of the problem is here and that each “part” is different/independent from each other. 

We’re gonna get back to this later.

The second thing to notice is that there are probably whole categories of problems you didn’t even think of when you wrote out your list of hypotheses.

You’ve probably thought about customers hiring a competitor service because they hate us for a variety of reasons (unreliable service, poor customer service) and you’ve probably thought about them leaving us because they were lured by competition somehow (low prices, free phones).

And if you’re savvy on the telecom industry, you might have even though about customers moving to pre-paid services.

But if my intuition is good, you have probably forgotten about at least a couple of categories within the “They’re being forced out” branch. 

For example, you might’ve forgotten to think that they may be cancelling subscriptions on purpose because they’re leaving a market.

Simple – I’ve done thousands of cases with hundreds of candidates to consulting jobs and most people forget about those.

The third thing to notice is that I didn’t even mention any specific hypotheses that you might have written on your piece of paper, things such as:

• We’ve increased our prices and our competitors have dropped theirs
• There were failures in our billing provider and a bunch of people were overcharged and got mad at us
• Our network was down for several days due to a problem within our IT systems, leaving people offline
• A problem in the banking system caused us not to receive several payments, which triggered subscriptions to be cancelled automatically

But still, all of these hypotheses (and thousands of others) would fit into one of the eight categories at the right-end of the Issue Tree.

All of this is to say that an Issue Tree is a map of the problem you have to solve.

Just like a good map it covers the whole problem area (you wouldn’t want a map of just a part of the territory you’re exploring).

And just like a good map, it doesn’t go into the slightest details (the specific hypotheses), but focuses on the broad aspects of your problem  (the categories).

No adventurer should explore a territory without a good map.

And no smart problem solver should start solving a problem without a good Issue Tree.

Issue Trees are the tool for "dividing and conquering"

Issue Trees are more than a mere map. They’re a very useful one at that.

For those of you who are not warfare strategy geeks like me, “divide and conquer” is a military strategy based on attacking not the whole of the enemy’s forces at once, but instead, separating them and dealing with a part of their forces one at a time.

It’s much easier to deal with one cockroach a hundred times than with a hundred cockroaches at once (sorry for the nasty imagery for all cockroachofobics out there).

Anyway, this strategy goes back into the times of Sun Tzu (the ancient Chinese philosopher who wrote “Art of War”).

And it so happens that this “divide and conquer” strategy is not only good for dealing with military opponents, but also GREAT for dealing with Big, Hairy, Complex problems.

It’s very difficult to deal with a “customer retention problem” like our Telco Executive is facing right now without making this problem more specific first.

But if you try making it more specific without the help of an Issue Tree (or a “problem map”), you’re gonna end up with one of two things:

(1) An incomplete list of possible hypotheses (like the one you probably wrote down on your piece of paper)

(2) A HUGE list with hundreds, even thousands of hypotheses (which, at the end of the day, you don’t even know if it’s complete anyway)

Issue Trees allow you to divide the problem and work on it one part at a time.

Or, if you’re a Telco Executive like our friend from point #1, you can delegate this work to other people now that the problem is neatly divided.

Here’s an example of how you can divide the problem into tasks and delegate its parts:

On the left side are the 8 buckets at the end of our Issue Tree. These are the eight potential problem areas.

And in orange are the six tasks our executive must do to know what’s causing the problem. 

Many of them are actually just requests to other people within the company because when you use “divide and conquer” you get to give work to other people (which by the way, it’s a great way to grow your career quickly).

Depending on what they find Task #1, you may be able to stop there. Or you may need to do all 6 tasks and then some more as you find new, unexpected information.

Now, I know that this Telco Executive doesn’t seem like a really good professional when I put the Issue Tree and the tasks that way. He doesn’t even know the basics about what’s going on in his company!

But let’s pretend for a second that he was just hired and he’s not at fault for not knowing his company’s basic numbers.

Or that he’s actually a management consultant instead of an executive, and that he was hired to give this company’s executives an unbiased perspective of why customers are leaving.

Now things make more sense!

But the point is that the Issue Tree allows you to create a plan to solve the problem, just like a map allows you to create a route to get from Point A to Point B.

Issue Trees are excellent for prioritization

Not only Issue Trees let you have a “map” of the problem and help you create a “route” on how to solve it, they also give you the ability to anticipate a lot of stuff that could happen along that route.

And anticipation = prioritization.

(Or 80/20, for those of you who love the buzzwords).

Because Issue Trees lay out the underlying structure  of your problem, they help you with two things:

(1) Get data structured in a way that helps you quickly find out where the problem is

(2) Anticipate what happened with a moderate to high degree of confidence even before you get data.

Let’s tackle each of these individually.

(1) Issue trees help you get data structured in a way that’s helpful to prioritize the problem.

Suppose you’re the Telco Executive and you’ve built your Issue Tree.

Remember how his Task #1 was to ask the Business Intelligence unit of his company for hard data about what’s going on?

Let’s assume they came back with the data below – how would you prioritize the problem?

The way I see it:

Of the 6.5 thousand extra people who unsubscribed this year compared to last year, the vast majority came (4.5) from a system failure. This is not acceptable and this should be the area this executive should tackle first.

But there’s also another area that calls my attention: our biggest source of customer churn – them going to competitors – has increased from 7k per year to 10k per year.

This person (and the company) has two different problems, and getting data in a structured format via the Issue Tree makes this very clear.

(2) Issue Trees help you make a really good guess of what might be going on even before you get any data

Suppose this company’s Business Intelligence division is not that intelligent and has no data to provide.

In fact, suppose this company has such a problem with data gathering that they can’t get structured data for pretty much anything.

This would make this problem a nightmare to solve.

With no structured data, this exec (or his subordinates) would need to do a lot of legwork to test each category of hypotheses:

• To know if customers are hiring a competitor service, we’d need to call a large sample of them and ask
• To know if a problem in our processes caused customers’ subscriptions to be accidentally cancelled, we’d need to map out all our processes that could’ve caused that and evaluate each one individually

You get the idea!

But Issue Trees are a map of the problem. And as any good map, we can use it to see what parts of the terrain seem to be more important than others.

Here’s an example of how to do that even if you have no data:

Obviously you need to use logical reasoning and a bunch of assumptions to prioritize one of these categories as more likely than others. 

But in the absence of data that’s actually the best way to work!

So if I were this executive and there was no data, I’d try to work smart and start testing the most likely hypotheses.

This means I’d give more priority to the ones related to customers leaving us willingly. 

It customers were being forced out we’d have crazy call centers full of customer complaints and the executive would probably know about it already. We’d probably have some lawsuits already!

I won’t go into the weeds of how to prioritize as we already cover that in our courses (including our free 7-day course on case interview fundamentals) but for now it’s cool to know that Issue Trees are the tool  that enables you to prioritize effectively because it gives you a clear map of the problem.

You can have "problem trees" and "solution trees"

Last thing about Issue Trees that you must know to grasp what they are even before we can go into the specifics on how to build them is that you can have “Problem Trees” and “Solution Trees”.

Or, as I like to call them, “Why Trees” and “How Trees” .

“Why Trees”, also known as “Hypothesis Trees” are the one we’ve been working with so far.

You have a PROBLEM and you want to know WHY it’s happening. Then you create a tree with all categories of HYPOTHESES of why it happened.

Just like we did with our executive trying to fix the customer retention problem he is facing.

(By the way, this is why you can call them “problem trees”, “why trees” or “hypotheses trees”.)

But you can also use Issue Trees to map out SOLUTIONS.

This makes them really useful.

A consultant who can figure out what’s causing a problem every single time is a pretty good asset to the team.

But to have a consultant that not only can do that, but who can also figure out the best solutions to those problems every single time  is even better!

So let me show you how a “Solution tree” or a “How tree” is different from a “Problem tree”. 

Suppose our Telco Executive character did NOT have a customer retention problem. Everything is fine and clients aren’t unsubscribing from this company’s services more than the normal rate.

But, naturally, they still have some level of customer churn.

Let’s say that they want to make that level even better than it is today.

And then the executive team gets together for a meeting to “brainstorm” some ideas on how to reduce customer churn rates so they can grow revenues more.

What most people in this meeting are doing is to throw ideas on a whiteboard.

• “Hey, perhaps we can improve our customer service.”
• “Hey, maybe we should offer faster internet.”
• “Hey, what if we put people into long-term contracts?”

But our Telco Executive is smarter than that. He has learned how to make Issue Trees with his friend, a McKinsey consultant. And he puts his learnings into practice.

**Action step: grab a piece of paper and build an Issue Tree with the CATEGORIES of potential ideas/solutions  this company could have to improve their customer retention.**

Now, word of warning: this “solution Issue Tree” is NOT perfect.

If you try, you can probably come up with an idea that could improve customer retention that doesn’t fit any of these categories.

And the reason for that is that it’s much harder to map out all types of possible solutions to a problem than to map out all types of possible causes to a problem.

But in case you do come up with an idea that doesn’t fit any of these categories, you can easily abstract what “type” of solution is this and then create a category for it.

Now, you might be thinking – “Bruno, why do I want to use Issue Trees for mapping out types of solutions? Why not just Brainstorm freely?”

There are three reasons for that:

(1) Your ideas are gonna be way more organized

This helps you communicate your ideas with others.

And it also helps you organize everyone’s ideas into a coherent whole.

And then better prioritize those ideas and even “divide and conquer” the implementation of them. You know, all the good stuff Issue Trees allow you to do.

(2) Creativity from constraints

This is counter-intuitive, but bear with me.

There’s significant research showing that having some constraints make people MORE creative, not less. (You can see some of the core ideas here ,  here and here .)

And we know that intuitively!

Well, try to create a short story in your head.

Nothing comes to mind, right?

Now try to create a short story that involves an English guy, a French woman, a train trip and a few bottles of wine.

It’s actually easier  to do the second, even though there are many more constraints.

Now, if I ask you to generate ideas on how to improve customer retention in a Telco company you’ll probably be able to generate 5-7 ideas until they start to become scarce.

But if I ask you to generate ideas on how to improve customer service in a Telco you’ll also  be able to generate 5-7 ideas until they become scarce. Even though improving customer service is just a sub-set of the things you can do to improve customer retention.

And then I could ask you to generate ideas on how to make it financially costly to unsubscribe and you might be able to give me a few ideas as well.

Each of the last two questions was a branch of our issue tree from above.

And because our Issue Tree above has 7 different branches, if you’re able to generate 5 ideas for each, that’s 35 ideas!

I’ve never met a person that can generate that many ideas with just the prompt question (how to improve customer retention) and without building an Issue Tree first.

Our brains seem to get confused with that many ideas.

But if you add structure (forced constraints), you can think freely about each part without worrying about missing something.

Which leads me to the 3rd reason why you will want to use “solution Issue Trees” whenever you need to brainstorm ideas…

(3) They force you to see whole categories of ideas you wouldn’t have seen before.

This takes a bit of practice, but once you’re able to see how each category fits the whole, you might see parts of the whole that you weren’t even seeing before.

Take the “Make it costly to unsubscribe” category for example.

When I came up with it, I was thinking about financial costs. You know, contracts and stuff.

But when I saw the word “financial” coming up in my mind, I immediately noticed that there could also be “non-financial” costs, such as having to go to a physical retail store to cancel the service or losing your dear phone number that you had for 8 years and all your friends and business connections have.

I didn’t have these “non-financial costs” ideas before I create the category for them.

Which is another big advantage for using Issue Trees to come up with solutions for your problems. You can see the larger picture.

So, what’s our take away from all this?

Simple. Issue Trees are a “map” to your problem that help you prioritize what’s important and “divide and conquer” to solve it more effectively. 

And you can use them to map out solutions as well.

Oh, and by the way, I almost forgot…

One really powerful thing you can do is to use “Problem Trees” to find the problem and once you found it, use a “Solution Tree” on your newfound problem.

So, remember how we used a “Why Tree” to find out that one of our Telco Executive’s problems was that his customers were leaving to the competitor?

Now we could use a “How Tree” to figure out potential solutions to stop our customers from switching to the competitors even though they don’t really like us and the competitor is offering a better offer than we are.

I’ll leave this Issue Tree for you to build.

And you’ll be able to build it using the techniques you’ll learn in the next chapter!

Three Techniques to Build Issue Trees

You can have all the theory in the world, but if you don’t put it into practice you’re not gonna solve any of the world’s toughest problems (nor get a job offer at McKinsey, BCG or Bain).

In this chapter we’ll go deeply into the mechanics of how to build quality Issue Trees.

More specifically, we’ll go through three practical techniques that you will be able to apply in your next case interview or executive meeting to structure any problem.

The structure of an Issue Tree

Issue Trees are a “problem structuring” tool.

That means you can structure problems using them.

But even Issue Trees have an underlying structure to them. It gets a bit “meta” and abstract, but the point is that every Issue Tree shares some similarities with other Issue Trees.

Knowing these common characteristics is the starting point to being able to successfully build them, so I’m gonna go over that in this short section.

And I’ll be quick, I promise.

(Note: I’m gonna give names to some stuff so that you and I can talk more effectively over the rest of the guide, but you don’t have to memorize those names nor use them in case interviews.)

So we seem to always keep coming to this MECE thing, don’t we?

We have a whole article series on that , and I highly recommend you to go through it. 

You can do so right now and then come back to this guide or you can read this guide first and then go there to understand how to make each part of your Issue Tree MECE.

Now, I don’t want to break your reading flow here…

So, before you open a new tab on your browser and get into another rabbit hole, let me explain what MECE is in simple terms.

MECE means Mutually Exclusive, Collectively Exhaustive and it is a basic principle of organizing ideas that was popularized by ex-McKinsey Barbara Minto (from the book on the Pyramid Principle, you might have heard of that) but  goes back to the ideas of Aristotle  (yes, the greek one!).

It basically means your reasoning has no gaps (Collectively Exhaustive, all parts together exhaust the whole) and no overlaps (Mutually Exclusive, one part is different and independent from the other).

Easy, right?

Well, kind of. Most problems out there are harder than drawing rectangles. 

So, to give you a better idea of how to apply the MECE principle to a business problem, here’s an image from our article on  The 5 Ways to be MECE  of different MECE ways to break down the same problem:

No need to worry about understanding this whole image right now, but the idea behind it is that (i) there are 5 types of ways to break down the problem in the image’s title (or any other problem) in a MECE way, and (ii) you can build different structures within each type.

An Issue Tree is built using a lot of these MECE structures. You also need to know how to pick among different options when you find more than one way to break down a problem..

I’m gonna link to the article on the 5 Ways to be MECE again  because it’s the best way to learn about MECE in a practical way. Instead of a bunch of theory, I show actual techniques you can apply right now to any problem in that article.

Anyway, enough with MECE. Let’s jump into the actual techniques to build Issue Trees.

Technique #1: Create a Math Tree

Math Equations are ALWAYS MECE.

Equations have no gaps and no overlaps (otherwise they wouldn’t be equations).

Which is why I used rectangles within rectangles to explain MECE above. Rectangles are huge in mathematics if I remember my high school math right.

Anyway, one easy way to create MECE trees is to take advantage of that and ALWAYS break down the next level using a math equation.

Obviously you can only do that if your problem is numerical.

But since most business problems are  numerical, we’re in luck!

I’m gonna show you how to do this in a “slideshow” kind of way because I wanna show you in a very step-by-step fashion, so be prepared to click on the arrow button more than a few times:

Creating math trees as a way to create Issue Trees isn’t hard at all once you get some practice.

But some of its nuances can be deceiving. Most people see them done and think they can easily do it, but it all goes downhill when they actually grab a piece of paper and attempt to do these trees.

So, here are four methods to actually create your “mini-equations” to break down each bucket:

#1. Use a proven formula

Most of the time you don’t need to reinvent the wheel.

If you know a formula that fits the problem well, just use it!

The most common one here is the classical Profits = Revenues – Costs, but there are others as you can see on the image below…

You don’t need to memorize any formulas for your case interviews, as you can use the other methods and they will work.

But knowing some of these, especially the most basic ones does help a lot.

#2. The "Dimensional Analysis" method

This one’s my favorite!

Just find one direct “driver” of the variable you want to break down – a driver is a “fundamental cause” for that variable.

For example, one direct “driver” or “cause” of revenues is the “# of customers” you have. If you get more customers, these new customers  directly cause your revenues to increase.

Then, use dimensional analysis to find its mathematical complement. If you want “REVENUES” and you have “# OF CUSTOMERS”, you need to multiply that by REVENUE/CUSTOMER.

Just like in your high school physics class, customers on the numerator will cancel out with customers on the denominator and you’ll be left with REVENUES as a metric – exactly the one you’re aiming for.

This method is amazing because it lets you break down almost any metric into a formula really quickly – the only thing to be careful with is to not lose meaning in the process and end up with a formula that is mathematically right but doesn’t make any sense to actual human beings.

#3. The Funnel method

This works wonders when the target metric is a percentage or is the end result of a funnel.

Take one example from e-commerce: Conversion Rate.

This is the % of visitors in your website that buy from you. How can you break that down?

Simple, you multiply the steps of the funnel from visitor to buyer.

Funnels are everywhere: Sales, Product Development, Process Optimization. 

All you have to do is to find these funnels and then break them into stages.

#4. Use a sum of segments

This is my least favorite method because it doesn’t go too much into the structure of the problem, but simply slices it out.

However, it can be useful.

For example, if you’re working with a conglomerate and their profits are down, it might be useful to segment that conglomerate into its different businesses.

Or if you’re trying to understand a company’s market share drop in a certain category, it might be useful to just break it down into the market shares of its product lines.

If you’ve read  the article on the 5 Ways to be MECE  and you’ve been paying attention, you might have noticed that method #1, “Using a proven formula” and #2, “Dimensional Analysis” will get you an Algebra Structure. 

Method #3, “The Funnel Method” will get you a Process Structure. 

Finally, method #4, “Sum of segments” will get you a Segmentation type of structure.

If you haven’t read the article, don’t worry about these names – they are some of the ways to be MECE we teach there. I’m just helping the folks who did read it already to make the connections.

So, summing up. You can use any of these four methods to create a “mini equation” and you combine these “mini equations” to create a “Math Tree”, which is the first technique to build and Issue Tree.

And it’s a technique that works great with numerical variables, but doesn’t really work if you have a different type of problem to solve.

So, to tackle non-numerical problems – or even to make better  Issue Trees for numerical problems – let’s move on to the most powerful technique in your Issue Tree toolkit: layering the 5 Ways to be MECE.

Technique #2: Layering the 5 Ways to be MECE

Technique #1 works great because math is ALWAYS MECE and because creating equations isn’t too hard.

But not every problem is numerical and can be structured using equations alone.

And even to those problems that are numerical, doing a Math Tree isn’t always the best way to go about structuring them.

Here’s where Technique #2 comes in – instead of layering “mini equations” on top of each other, we’re gonna layer “mini MECE structures” on top of each other, regardless of them being equations or not.

Remember, we were confident to use math equations to build Issue Trees because they are always MECE. But from first principles what we need is MECE structure, not necessarily mathematical ones.

And where are we gonna find these “mini MECE structures”? 

Easy, with the 5 Ways to be MECE. These are 5 specific techniques we’ve developed that guarantee a MECE structure.

I’ll make your life easier in case you want to read about that now and link to  the article  we wrote about them.

But here’s a quick recap:

The process of building Issue Trees by layering the 5 Ways to be MECE is itself very very similar to the process to create Math Trees.

Step #1: Define the problem specifically  (no need to be a numerical variable here).

Step #2: Break down the first layer using one of the 5 Ways to be MECE.

Step #3: Get to the 2nd (and 3rd, and 4th) layers by breaking down each bucket into another “mini MECE structure” that comes from the 5 Ways to be MECE as well.

I’ll show you the exact process to create an Issue Tree by layering the 5 Ways to be MECE through the example below:

Layering the 5 Ways to be MECE is my go-to method to create Issue Trees and break down problems or finding solutions.

I use it every day of my life, either on paper or just in my head.

And I used to use it everyday when I worked at McKinsey as well (even though I was doing it unconsciously – no one there had explicitly told me there were five  ways to be MECE).

Now, let me address one thing that comes up often… One thing that may have crossed your mind as you were going through the three steps above regarding the Issue Tree is “well, but this is so obvious” .

That thought may have crossed your head in each break-down of a bucket or just when looking at the whole Issue Tree.

And here’s my take on it: a well-structured problem SHOULD look obvious – at least in hindsight .

How Elon Musk changes the world structuring problems in "obvious" ways

(I swear to you it’s interesting, but you can skip this green box if you want and/or understand why MECE Issue Trees are super important even when they’re “obvious”)

You’ve probably heard of Elon.

In case you haven’t, he’s this guy…

And he’s created these companies…

So, the guy basically transformed the payments industry, the automotive industry, the aerospace industry and is transforming the tunneling and the solar power industry.

But how does he do that?

Well, anyone who does that much has many “secret sauces”, but one of the special things Musk has is to think things from first principles.

In this fantastic blog post  (from one of my favorite blogs), a guy who had access to Musk breaks down exactly how he thinks.

But let’s analyze one specific instance: how he came up with “The Boring Company”, a company that was created to dig tunnels more efficiently and solve the traffic problem in Los Angeles.

There are two underlying logics to the company:

Simple logic, but a really strong reasoning about why tunnels are probably the best way to solve the traffic problem.

(And it actually is the only way that’s ever worked so far – demand for roads keep increasing no matter how many Uber rides people take, building more roads doesn’t seem to make a difference in most cities and no one’s ever been able to make flying cars… But most people in large cities take the subway/metro system every single day.)

Notice that we’re basically dividing the problem into supply and demand and then dividing “road” capacity into on ground, flying and underground. 

There’s no rocket science here (pun intended).

Alright, but there’s still a problem with tunnels: they’re expensive to make. So, is it possible to make them cheaper? Here comes Elon’s Logic #2 to build The Boring Company:

Again, no rocket science here (although a bit of tunneling science).

If you want to understand better how Musk thinks, I recommend  this article  and  this TED Talk .

Now, onto what matters for us: 

(1) Most traffic specialists know that trying to reduce demand is an uphill battle and that expanding road capacity is mostly fruitless.

(2) Most people in the auto/aerospace industry know that flying cars are a very far away dream

(3) Most people in the tunneling industry understand the cost drivers of a tunnel.

And yet, no one looked at the big picture and questioned things from first principles.

You need an Issue Tree to do that, even if it’s an obvious one.

I’m not saying Elon Musk draws Issue Trees for a living, but I know  he has them in his head because he talks like he has one – I “took” both trees I showed you above from his own words.

Takeaway from the green box: Issue Trees are “obvious” because they’re drawn from first principles.

And this means if you want to think from first principles, draw Issue Trees.

Like what you read so far? Share the guide on you favorite social media!

Technique #3: creating decision trees.

In the realm of Microsoft Excel, the most basic kind of logic you can do is using math operators. That is,  adding, subtracting, multiplying and dividing.

If you wanna go a step further you can use what they call “boolean operators”: AND functions, OR functions and so on.

And if you want to go a third step further, you can use “conditional operators”, the most famous of which are IF functions.

Decision Trees are basically regular Issue Trees with “conditional operators”, IF-THEN functions.

Now, let me translate into plain English for all the non-Excel nerds out there…

(Or should I say “future Excel nerds? I mean, this is a site for aspiring management consultants!)

When you do a Math Tree, the only way you have to relate the variables to each other is through math symbols. E.g.: Revenues = Price * Quantity. There is a mathematical relationship among everything in your Issue Tree.

It is great to have math because math is always MECE, but it is also limiting. What about everything that can’t fit an equation?

Enter Technique #2: Layering the 5 Ways to be MECE.

If you pay attention to it, everything that’s not in a mathematical relationship in that technique is joined logically by “AND” or “OR” relationships.

For example, we can find better employees ‘at the schools we already recruit in’ OR ‘in new schools’.

Another example, we can make new recruits better before their first project ‘by training them before they start’ AND/OR ‘as soon as they start working for us’.

Decision Trees are just like regular Issue Trees but they add another layer of logic to it: IF-THEN statements.

I won’t go into too much detail on how to build them because (1) it’s an advanced skill to be able to anticipate all the if-then logic required to take a decision before you even start exploring the problem, and (2) you don’t need to be able to do this to get a job at McKinsey, BCG or Bain if you can use the other two techniques well.

But I will give you a simple example below so you can see what I mean.

And if you want to learn more about this,  here’s a timeless article from Harvard Business Review on Decision Trees.

There are also different types of decision trees.

For example, you can create a decision tree for an investment opportunity that considers the probabilities of different events to happen in order to calculate the expected value (there’s an example of this in the HBR article I’ve shared above).

Or you can create decision trees for WHY and HOW problems where you use IF-THEN statements to say where would you focus and prioritize if certain conditions applied.

(An example of the last phrase is this: in a case on “How should a restaurant grow revenues”, you can say that IF it has lines/too much demand, THEN you would focus on increasing capacity through expansion or increased productivity, and that IF if doesn’t have enough demand, THEN you would focus in customer acquisition and retention initiatives.)

Decision Trees can get really complicated even for simple decisions, so I would NOT recommend you to start learning with them. 

Focus on Techniques #1 and #2 to solve WHY and HOW problems.

For “decision-making”-type problems, we recommend you to learn Conceptual Frameworks first. We teach how to structure these problems using Conceptual Frameworks in our free course on case interview fundamentals.

Want to learn to structure any case?

Issue trees aren’t the only technique to structure business problems.

Join our FREE 7-day course on case interviews to learn other techniques so you can structure any case, solve any problem and impress your interviewer!

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Six Principles for AMAZING Issue Trees

Man does not live by bread alone.

And Issue Trees need more than being “technically correct”

If Issue Trees had a “soul”, it would live in the six principles outlined in this short chapter.

In fact, if you follow the principles from this chapter, you don’t even need to use any of the three techniques I showed you on the last chapter.

And if you MASTER these principles, you might be able to come up with your own techniques. 

(And if you do come up with a “fourth technique”, please shoot me an e-mail telling me about it).

Separate different problems early on

Some restaurants that want to grow revenues should work on getting more clients. Others have too much demand and should work on expanding their operations to handle that and sell more.

Most companies that have employee attrition problem have some problem that makes people wanna leave their jobs. Others are just firing too many people.

And a violence crisis in a country could be caused by criminals. But it could very well be caused by a really violent police system as well.

The common factor between the last three situations is that each one could be caused by two COMPLETELY DIFFERENT PROBLEMS.

Separate them early on your Issue Tree because trying to fix the two things together will only lead to confusion. Not good.

Build each part ONE AT A TIME

Most people who see a huge Issue Tree for the first time are overwhelmed.

Of course they are! 

They see this huge logical structure (that takes time to digest) and wonder if they’ll be able to do the same when they need to.

What they’re missing is that these trees are built one step at a time .

First you get the problem question and your only concern  is to define it well.

Then your only concern  is to break it down into a first layer.

Then you get each bucket from the first layer and your only concern  should be to break each down into a “mini MECE structure”.

One bite at a time, you will eat the whole metaphorical elephant.

Each part must be MECE

I’ve talked about MECE before in this article, but I’ll do it one last time.

ME = Mutually Exclusive =  No overlaps  between the parts of your structure = your structure is as clear as the blue sky for another person to understand.

CE = Collectively Exhaustive =  No gaps  in the way you break each part of your structure down = your structure is rigorously correct.

MECE is tough for most people, but you can use  the 5 Ways to be MECE  as a checklist of structures you can use to be MECE. 

That means it’s not gonna be as hard for you and you have more chances of getting the offer than the other people. Good for you!

Each part must be relevant and add INSIGHT to the problem

There are many MECE ways to break down any problem.

Choose the one that’s more relevant. The one that adds more insight to the problem.

For example, one of the Issue Trees from Chapter 2 was about improving the quality of new recruits in a consulting firm. Within “making the selection better”, I could’ve broken it down into “Stages 1, 2, 3” and so on of the selection process. 

That would’ve been “technically correct” and “MECE”, but it would bring absolutely no insight to the table. 

Because it wouldn’t be problem-specific .

Here are two resources to help you make your structures more insightful and problem-specific:

The first is  a Youtube video on how to make better revenue trees.  It shows how to create more insightful revenue trees but you can apply the same principles to any type of Issue Tree.

The second is “The Toothbrush Test”, a numerical measure so you can get a proxy of how insightful one structure is compared to another. You can watch the video  here  or read the article  here .

Each part must be eliminative and help you FOCUS to the problem

An Issue Tree that is built in a way that allows you to ELIMINATE all the non-problems and focus on the one thing that’s driving the issue is way more useful than one that does not allow you to do that.

Say you’re a soft drinks company concerned that you’re selling less soda.

Here are two ways to structure the first layer of that Issue Tree:

(1) Drop in general soda consumption OR Drop in market share

(2) Customers less willing to buy product OR Competition getting stronger OR Company doing poor marketing or supply chain OR Distribution channels not exposing our product

Which one’s better?

Well, according to this fifth principle, (1) is better because it allows you to get data and eliminate a whole branch (unless the problem comes from both, of course).

Eliminative Issue Trees help you FOCUS the problem and waste less time (that means more 80/20 for you).

The key to be eliminative is to make each bucket FALSIFIABLE. 

Falsifiable means you can find a test that, given a certain result , guarantees that the problem is not on that bucket.

This falsifiability is what makes Issue Trees “hypothesis testing” structures. If you want to be a hypothesis-driven problem solver you need to include falsifiability in your structures whenever you can.

However, this does not mean every single structure  you create must follow this principle.

There are times where falsifiability is impossible , and that means you should focus your efforts in being the most insightful as you can (Principle #4).

It is usually in these situations where you’ll want to use a qualitative, conceptual framework. You can learn more about this in the free course we offer on case interview fundamentals. In the Frameworks module of the course we will show you exactly when to use conceptual frameworks and how to create them.by 

Clarify what you need in each layer you build

You might be shy, but hey, overcome that shyness!

You don’t need to do guesswork to build your structures. You can ask first.

Actually, doing guesswork when you could’ve asked a simple question and eliminated confusion will harm your performance.

Say you’re breaking down how a consulting firm could hire better junior consultants. You’re trying to break down how they select candidates, but you’re not sure how their recruiting process is currently like…

Say to your interviewer: 

“Hey, I want to break it down into the stages of the selection process but I don’t know what those stages are. Here’s what’s on my mind… Does it make sense or did I miss something?”

If you’re doing Issue Trees to solve a problem in your work, this principle is even more important. You can’t structure what you don’t understand, so when in doubt ask questions and understand it better!

Sometimes these principles will enter in conflict with one another.

You might need to choose between being more eliminative and being more insightful.

You might feel in doubt of whether you should be fully exhaustive (MECE) or just add the relevant stuff.

And when principles enter in conflict, experience and judgement are here to save the day. 

Seeing real examples of real people that know what they’re doing making Issue Trees to solve case interview problems is invaluable to get that experience.

Which is why I will show you in-depth examples in the next chapter, including videos of me going through the thought process of building Issue Trees with you.

Watch and practice real case interview structures!

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When I was preparing for my case interview I looked for good Issue Tree examples all around.

I found none .

I don’t want you to go through the same, so here I’m gonna go all in and not only show you great Issue Trees but also show you, in video, how I think through each step of building them.

I’ll show you everything that goes through my mind as well as the specific nuances that make them great.

I will use different examples so you can see how the principles and techniques apply to different types of situations.

And I will do exactly what I’d do in a real case interview on when solving a real problem at work.

The only thing I’ll avoid doing is using Decision Trees.

Because it’s much much harder to get to a MECE result using them, let alone explain why it’s MECE. I’d be only showing off instead of actually helping you learn how the principles apply and what makes a great Issue Tree. 

Not my style!

Example #1 - Airline fuel costs surge

This first example is of a fairly easy case question that would lead many well-prepared candidates to failure.

It’s funny how some problems can be easy  to real consultants and yet hard  even for candidates who have done 50+ cases.

Here’s why this happens: the business problem isn’t hard to solve from a first principles perspective (which is how good consultants tend to think) but they’re a bit unusual or too specific to an industry. 

Most candidates who haven’t internalized the principles of solving problems well feel overwhelmed when they get a case completely unrelated to anything they’ve seen before.

Even worse is when this problem doesn’t fit the half a dozen frameworks these candidates have memorized by heart.

Here’s a video of this first example. I highly recommend you to try to structure this Issue Tree by pausing the video right after I clarify the case question and then compare your structure and your thinking process with mine.

If you don’t have access to audio or can’t watch a video right now, you’ll be able to keep reading and grasp the main insights as well, although I highly recommend you come back to watch this later!

So, what’s interesting about this Issue Tree example is that I have structured the first two layers of the tree as a Math Tree (Technique #1) and then I used the “Opposite Words” technique and the “Conceptual Frameworks” technique to build layers 3 and 4.

You can do that too!

Here’s the whole Issue Tree if you weren’t able to watch the video: 

There were three main take aways from this structure:

Takeaway #1: Break down a numerical problem mathematically as long as the math remains meaningful/insightful – then get more layers using qualitative “mini-MECE-structures”

As with most thing problem-solving related, this is not a rule written in stone.

There are a few numerical problems that are best structured with a qualitative structure. And you don’t always need to do the qualitative layers afterwards.

But usually the best way to break down a math problem initially is to break it down into an equation first, as you’ll be able to quantify how each driver contributed to the problem.

And usually the equation alone won’t be enough to bring you to the meaningful stuff. 

In this case, for example, if we were only mathematical in our structuring we would have missed important elements that show real world business intuition, such as “maintenance”, “aircraft weight” and “mix of aircraft in the fleet”.

Takeaway #2: Stop each branch when it can reasonably  explain the source of the problem

I have stopped some parts of my tree in Layer 2, other parts in Layer 3 and others in Layer 4.

How did I make this call?

A lot of people have asked me this in the past: how can I know that my Issue Tree is done? How many layers do I need?

The rule of thumb is to stop when your buckets can reasonably explain the problem.

For example, on Layer 2 you have a bucket which is “# of trips flown has risen”. This can reasonably explain why fuel costs might have risen. It’s pretty logical – if you fly more trips, your fuel costs will rise as well.

Now, one could ask “why has the # of trips flown risen” and if that’s the actual problem going on, I as a consultant would want to know that. But that’s getting granular, you don’t need to go that far unless the problem is proven to be there.

If I told my mom or someone on the street that an airline’s fuel costs have risen because the # of trips have risen, they’d accept the answer and probably not question it further (and they certainly would tell me I’m a weirdo for caring about an airline’s fuel costs).

Now, if I told my mom or a random guy on the street that fuel costs have risen because liters of fuel per km flown have risen they would: (1) think I’m really really weird, and (2) not take that answer as it is.

Even if I used more accessible language and said that this airline’s fuel efficiency was down, they’d still ask me “why is it down”? (That is, assuming my mom is actually interested about airlines).

If I had stopped that branch on the 2nd layer, I wouldn’t be telling the whole story. 

And so I went a level deeper.

Now, on the 3rd layer if I say that fuel efficiency is down because we’re using less efficient types of aircraft, most people would be satisfied with that answer. I can stop the Issue Tree here.

But in the case we’re flying the same aircraft, most people would NOT be satisfied. They’d be like “Hey, you’re telling me you’re less fuel efficient even though we’re flying the same aircraft? How come?”

And so we dig a level deeper on that one. Maybe the aircrafts are flying with more weight. Or we’re doing less maintenance. Or we’re flying at lower altitude and facing denser atmosphere. Or our pilots are changing speed all the time. 

Most people would take any of those as sufficient answer. Which means we don’t need to dig a level deeper.

Takeaway #3: You can still go deeper in the buckets you need

If the last take away gives you an idea on where to stop structuring the Issue Tree, this one gives you permission to dig deeper than that.

Say your interviewer tells you the problem is that this airlines is flying their planes heavier and asks why that might be. Well, weight was at the end of our tree, right? But we can still investigate the reasons behind that increased weight.

Here I would segment the things that add weight to airplanes into their categories: people, cargo, equipment, fuel itself (we may be flying with excess fuel and thus spending more fuel to carry fuel itself).

Or say that the interviewer tells you that fuel prices have gone up even though we’re buying the same product from the same supplier. 

Why that might be happening?

Well, either this supplier’s cost has gone up (because crude oil is up in price, for example) or their margins are higher (because we’re not negotiating as well, for example). We could dig deeper into each one of these factors if need be.

The point here is that even though you need somewhere to stop your Issue Tree (otherwise you’d spend the whole day building 15 layers), you also need to be aware that you can go as deep as you need to in the specific parts of your structure that the problem really is.

You find where the problem really is by getting data, numerical or not, for each part of your structure.

Example #2 - Overwhelmed consultant productivity

Real consultants have their own personal problems to solve as well.

And often time they will solve them with Issue Trees!

They’re a great way to see what your options are.

So before you look into this example, I want you to do an exercise:

**Action step: grab a piece of paper and write down all ideas you have to become more productive in case you were overwhelmed with work as a consultant**

What you’ll see from this exercise is that if you just “freely brainstorm” ideas to improve productivity on paper, you’ll end up with a huge list of (probably) unconnected action steps that are hard to estimate impact and to prioritize.

But if you had built an Issue Tree to organize those ideas , you’d get something much closer to an actual system to improve productivity.

Here’s what I mean by that:

This tree is solving a more qualitative problem than Example #1, but the techniques still work.

You define the problem really specifically at first.

And then you layer different “mini MECE structures” using the techniques from the 5 Ways to be MECE.

Here’s the final Issue Tree in case you couldn’t watch the video:

Of course your tree can still be different than this one and still be correct.

How do you know if it’s correct or not?

Well, simple: are you adhering to the key principles? Are you using the techniques I have shown you in this guide?

If so, your Issue Tree is good to go!

Example #3 - Help a government solve illiteracy in children

This is an interesting example because it focuses specifically on Principle #1: Separating different problems early on.

In fact, the whole Issue Tree is built by separating different problems over and over again.

Because the problem to be solved has many different possible root-causes that are completely different from each other.

Once you watch the video, you’ll see that the way the Issue Tree is constructed in a very intuitive way. 

However, give this problem to most people and they aren’t able to structure it. They’ll spit out ideas and hypotheses without order nor an overarching logic.

Check it out how to help a government solve illiteracy in its children that go to public schools:

If you couldn’t watch the video, I’ll put an image of the Issue Tree bellow.

Notice how each layer is basically the previous bucket divided into two completely distinct problems.

The value of building Issue Trees like this is that you get a map of all types of possible root-causes. It’s also pretty easy to do so!

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Common mistakes and questions.

I’ve helped hundreds of people learn to build Issue Trees.

In the process I’ve seen them making thousands of Issue Trees. And probably somewhere north of tens of thousands of mistakes.

Making mistakes if part of the learning process.

But you don’t have to make all those mistakes yourself because you can learn from theirs!

In this chapter I will show you the most common mistakes people make (with real Issue Trees, from real candidates) and also answer some of the most common questions that arise as you learn to build them.

What you can learn from the key mistakes of real Issue Trees from real candidates

When I first wrote the  5 Ways to be MECE article  I had a little challenge in the end of it.

I challenged people to send me a structure for a specific business problem that could happen in a case interview: 

“Imagine you’re doing a project with Amazon and they’re complaining about a surge in theft in their warehouses – what could be causing this surge in theft?”

And so I got dozens and dozens of real Issue Trees from real candidates for the same problem.

What’s fascinating is that all these candidates had three things in common:

(1) They were having trouble with creating MECE structures for their cases (or else why would you read a huge guide on how to be MECE?).

(2) They had just read a huge guide with different techniques to be MECE and instructions on how to build Issue Trees using these techniques.

(3) They were dedicated enough to take my challenge, spend 10-20 minutes building their best Issue Trees and sending them to me.

Still, even with all those things going for them, most of their Issue Trees had mistakes. Mistakes you and I can learn from.

So in this section I’m gonna show you their trees, point out their key mistakes and show you the feedback I sent them.

#1 - Anastasia and the sin of ignoring problem definition

The first Issue Tree I wanna show you was sent by Anastasia.

Here it is:

Seems like a quite good Issue Tree, right? 

I mean, it describes quite well the process of a warehouse.

Well, not quite.

There are a few mistakes that this Issue Tree makes in terms of MECEness, some parts could be more insightful, etc. But the most important mistake here is that Anastasia ignored the specificity of the problem.

Much of this Issue Tree isn’t about theft – it is about losing items in general. So she’s talking about damage, negligence, machine mistake, etc.

Go back to the image above and click the right arrow to see all the areas of this tree that are not about theft at all.

Most of the tree is not talking about theft at all!

What that means is that she’s talking a lot about things unrelated to the problem and leaving a lot of important things out. It also implies that she wasn’t listening to the problem.

This is the #1 thing I’d tell Anastasia to focus on and the #1 thing I’d tell you to make sure you’re not messing up.

Now, Anastasia’s structure also has a #2 thing that I’d tell her to focus on if problem definition weren’t a problem: look for root causes. 

While she makes an excellent description of how the warehousing process is and thus is able to map out where  the problem might be, she never talks about the why.

You know, things like security systems and lack of penalties and having warehouses in areas with a lot of crime. The types of things you might expect for a WHY question…

#2 - How Anne messed up with layer ordering

This Issue Tree is actually quite good!

But it has three main mistakes. Can you guess what they are?

Well, I gave you the main one in the title.

Anne’s first layer shouldn’t be a first layer. 

Because geographical location is not all that important. The different geographical areas of the problem aren’t the most relevant way to break it down.

What’s more, even if it were, why divide by continent? Why not small vs. big cities? Or low income vs. high income areas? Or high-crime vs. low-crime areas?

Anyway, I think it’s an excellent idea to mention that you’d like to see in which warehouses is the problem more prevalent. But what I would’ve done is to put that as a side note to an Issue Tree that actually digs into the potential causes of the problem, not as the main course.

She could’ve done an Issue Tree of causes for one  warehouse and then said at the end: “and then I’m gonna look at these causes for all warehouses we have, segmented by geographical area, warehouse size, how old they are, etc”.

And what would this Issue Tree that digs into the potential causes look like? 

Well, very much like Anne’s example Issue Tree for American warehouses (which I guess she would replicate for other continents as well).

Now, you might be thinking: what are the other two mistakes she made?

Well, one is that she offered solutions to each root-cause of the problem. That’s not a mistake in itself. In fact, I loved it. But the problem is that she was a bit too early on that – she should’ve gone a layer deeper into the why  each thing happened.

Keep in mind the case question was a WHY question and not a HOW question. 

And what she did was to suggest, for example, that if internal thieves who had the intention of stealing were responsible for the surge in theft, then they should run better checks.

What she should’ve done instead was to say that if that was the cause, then that caused happened because (a) they’ve stopped doing background checks, (b) background checks have worsened in quality or (c) background checks were never good at stopping that but that was never a problem beforehand. And then perhaps dig even deeper into the cause.

But she offered solutions before she got to the root cause, and that may hurt because she may be solving the wrong problem.

And the last mistake she did was one related to problem definition.

Everything she mentioned was related to the amount  of theft. But we don’t know if that’s the problem. It’s not clear on the case question (on purpose). Maybe the problem is the value  stolen.

So, she would’ve done much better by showing that in her structure. Maybe there are more thefts (in which case her issue tree is valid) and maybe the amount stolen per theft is higher (and because she didn’t consider this, she missed a whole part of the problem).

#3 - Guillaume and the "aggregator fallacy"

There are many problems with the Issue Tree below, for instance:

• A regional segmentation early on when that’s not a really relevant factor to explain the problem (as in Mistake #2)
• This regional segmentation isn’t even MECE (there are emerging countries in Europe and he forgot all developed countries in Asia)
• A lack of $ value of theft (again, as in Mistake #2)
• The way he breaks down a process structure to explain a surge in # of thefts per warehouse isn’t very insightful/relevant

But I want to call your attention to one other mistake which is related to causal effects. I call it “the aggregator fallacy”.

Can you spot it?

Let me ask you one thing… If the number of gas stations raise in a city by 2X in a year, will sales of gas increase by 2X as well?

Will they even increase by 10 or 20%?

Not necessarily!

More gas stations don’t drive  more demand for fuel (unless there’s very few, high priced gas stations in town, but let’s leave extreme scenarios aside).

Yes, there might be 2X the number of gas stations because demand skyrocketed. But it could also be the case that gas stations were a really profitable business and entrepreneurs entered this market even thought there was no increase in demand. 

It could also be the case that some people who don’t know what they’re doing entered the market even though demand didn’t increase and profits weren’t that high (and everyone’s losing money now).

So if you were to find out if demand for gas increased in a town one MECE structure you could use is “# of gas stations * avg. amount of gas sold per station”, but that wouldn’t be the best one.

Because # of gas stations don’t drive  demand – more cars and more usage per car does.

The same thing is happening with Guillaume’s structure. 

More warehouses don’t drive more theft. They don’t cause  more theft.

Say, for example if Amazon had restructured their operations and they had switched from 10 huge warehouses to 100 smaller ones, with the goal of having faster delivery. Would it be ok for theft to increase 10X? Would it even be ok for it to increase by 50 or 100%?

Probably not, right? 

Amazon’s carrying the same number of items, they have roughly the same number of employees (considering internal theft) and if they have their security systems in place, they’re not necessarily more attractive to external burglars (if anything, it’s harder to steal a smaller warehouse than a huge one).

More warehouses shouldn’t cause more thefts. The warehouse is not a driver of stealing just as the gas station is not a driver of demand for gas.

The warehouse and the gas station are merely aggregators  of something. The warehouse aggregates products to be shipped (or stolen) and the gas station aggregates fuel to be sold (or not sold in case of a flat demand).

Which is why I call this mistake “the aggregator fallacy” – thinking that because the aggregator has increased that it has caused your problem.

Instead, try to build your Issue Trees with some causal relationship in mind. In the case of the gas station problem, that’d be “# of cars * fuel used per car”.

In the Amazon theft case, you could use “# of products in the warehouse * theft rate” if you assume that more products cause more demand for burglars or “avg. crime rate where Amazon warehouses are located * % of those crimes that are in Amazon’s warehouse” in case you assume that overall crime rate is a given and you can only control your exposure to it.

#4 - Jimi, the unMECE

Again many problems with this Tree. 

You can mistake-hunt later at your own pace, so I’ll just point out to the ONE FATAL MISTAKE YOU SHOULD NEVER MAKE:

Jimi wasn’t MECE on the first layer of his Issue Tree.

In part because he insisted on using a conceptual framework (the hardest of the 5 Ways to be MECE) without needing to do it (as a theft problem is a numerical problem).

In part because he didn’t know how to create a MECE conceptual framework (as we teach in our courses).

And this would’ve gotten Jimi rejected from a real case interview at McKinsey, BCG, Bain or any other firm.

And it would probably get him fired if he was in charge of Amazon’s warehouses.

Don’t be like Jimi.

Always be MECE (and especially so on the first layer)!

#5 - Was Natalia rejected due to a simple mistake?

I actually like this Issue Tree quite a bit.

It’s well built, although there are a couple of problems.

And it’s interesting because Natalia, the lady who built this tree had been rejected from a Bain and a BCG final round before. She was preparing to try again. That means she was good enough to actually get to the final round but made some mistakes that prevented her to get the offer.

Maybe her mistakes were showing in her Issue Tree? 

Perhaps… Let’s take a look:

There are two great mistakes with this tree.

One we’ve talked before – Natalia went for a conceptual structure to break down the “Warehouse facility factors” bucket and had trouble building it. There’s overlap between “Security” and “Information Confidentiality”. Also, there are many things not considered here (including theft caused by internal employees).

But the one mistake I wanna call your attention to is much less obvious. It’s more a nuance than a mistake.

It is on the first layer.

The way she build it is much better than many alternatives: there’s external factors (crime) and internal factors (the warehouse itself).

HOWEVER, it’s really really tough to test  which one is causing the surge in theft. These things look measurable but they’re not really.

Because measuring overall crime is a pain. And getting that data, an even higher pain.

Just to give you an example: what crime data should we consider to prove/disprove the fact that external crime has risen? Should it be overall criminal incidents? Thefts only? Warehouse thefts, specifically?

Also, how regional should the data be? Neighborhood? City? State?

And because you can’t measure “warehouse facility factors”, it’s hard to exclude a whole branch of the tree. Which means this tree is not very “eliminative”, because the factors in the first branch aren’t falsifiable.

Now, I’m being really picky here just to make a critical point to you. 

Maybe in a real interview Natalia would’ve been able to come up with a test that would reliably eliminate a whole branch. 

And maybe the problem could be solved without that kind of rigorous testing (e.g. maybe they completely switched their security personnel and had security holes in the process, so the cause would be obvious).

But if the situation was harder, more nuanced it would be tough to Natalia to actually diagnose the issue.

And whether she would be able to actually do it in real life is the #1 question in the interviewer’s mind.

Her first layer is not bad, but there are other MECE structures as insightful as this one that would also be more testable, more falsifiable.

And in a final round that could make all the difference.

Commonly Asked Questions

Learning from the mistakes of others is a great way to accelerate your learning curve!

But still, you might have some questions in your head.

Here are some of the questions I have been asked about Issue Trees throughout the years (and the best answers I have to those)…

Issue Trees are one structuring technique but they’re not the only one.

So there are actually two questions within this one: (1) How do I know if I should use a structure to solve the problem and (2) How do I know if I should use an Issue Tree or another technique.

Great questions!

Let’s start with #1…

You should use a structure to solve a problem, well, when you want to solve it in a structured way.

And when’s that? 

Well, whenever you want to be able to foresee the steps to the solution of the problem. 

That is, when you must have a due date of when the problem’s going to be solved (which is whenever you have a boss or a client, for example) or when you want to distribute the problem for other people to solve it (your employees or an outsourced company, for example).

That means almost always, especially in the professional world, where people have bosses, employees and clients.

Question #2 is a bit trickier to answer…

There are other structuring techniques – ways to break down the problem – that you can use. So, when to use Issue Trees and when to use the others?

Basically there are two scenarios: either you want to split the problem into components of the problem, or you want to look at the problem from different angles/points of view  without actually splitting it.

If the first, use an Issue Tree; if the last, use another tool (such as a conceptual framework, as we teach in our free course on case interviews).

How to know which one you want is a bit more complicated and would take an article on its own to explain. 

If you want the full details, check out our free course that you can find in our homepage or throughout this article, but here’s the long story short: if you want focus, efficiency and logic onto a well-defined problem use an Issue Tree and if you want awareness and insight onto a messy problem, use a tool like a conceptual framework.

A lot of people who teach case interviews say you should start with a hypothesis.

And they say that because MBB consulting firms (MBB stands for McKinsey, BCG and Bain) work in a hypothesis-driven approach. That means they come up with hypotheses and test them to find the truth (much like in the scientific method).

Being hypothesis-driven is tricky because you also have to be structured and MECE. 

So, how do you make your hypotheses MECE?

Well, one way some people figured out is to build a MECE tree and just throw the word hypothesis around. If it were in a case investigating why profits have fallen, this would sound something like this:

“My hypothesis if that profits have fallen because sales are down. To know if that’s true we need to look at sales and costs.”

Notice how there’s ZERO value add to using the word “hypothesis” in the phrase above. If the guy had just asked for sales and cost data he’d ask the same questions, do the same analysis and reach the same conclusion.

If you just want to use the word hypothesis like that, go for it, but there’s absolutely no need to do it. If your buckets are MECE and  testable with data, you can just lay out your Issue Tree with no “hypothesis” and test the buckets.

However if you can’t make your structure MECE/testable, you might need to use a hypothesis, but it’s a completely different type hypothesis than the one I’ve shown you above. Instead of being just a random guess with the word hypothesis on it, it must have a structure which we teach in the “Hypothesis Testing” module from our free course.

Great question, glad you asked that!

Clarifying questions are the questions you use to define the problem so you can create your structure / Issue Tree.

You use them to understand the problem better.

If the answer to a question you ask could potentially lead you to solve the problem then the question is a part of the structure of the problem and should be within your Issue Tree.

Drawing Issue Trees on paper is good practice whether you’re in a case interview, helping a client or solving your own problems.

The reason for that is that having it on paper makes it easier to communicate the ideas and frees up space in your mind so you can actually think about each part of the problem.

Not drawing the tree is kind of like memorizing a map – it’s helpful, but the whole purpose of the map is to be there when you need it without you having to know anything by heart.

But drawing does take a bit of time and in answering certain questions in case interviews, interviewers want you to be quick and may even ask you not to use paper . THIS DOES NOT MEAN YOU’RE ALLOWED TO BE UNSTRUCTURED.

It basically means they want to see if you can be structured and communicate your ideas in a structured way even when you don’t have a lot of time to think through a structure and draw it on paper.

Issue trees are a representation of how a consultant thinks. That means consultants think in Issue Trees . 

They communicate using these trees as the underlying structure of the ideas they’re thinking through.

So if you don’t have time at all to think, you don’t have to draw your Issue Tree on paper, but you still must communicate as if you were going through one.

This is a super common question, and a highly context dependent one.

If you’re in an interview and it’s a more conversational, back-and-forth style, you should use less layers and get data so you know where to focus on (and dig deeper on that one).

If you’re in a more structured rigid interview format without a lot of back-and-forth, you should use more layers and they may never give you data.

The first scenario will typically happen at BCG and the second at McKinsey. Other firms will depend more on office / interviewer.

But this is not a rule. I’ve gotten the first scenario at McKinsey (final rounds) and the second at BCG. This means you’ll have to feel the situation a bit, or even ask the interviewer what they prefer.

But there’s a rule of thumb: no less than 2 layers and no more than 5 layers, regardless of format.

Because with just one layer you’re not really structuring the problem. You’re not showing a map of the situation. And with more than 5 layers the time it takes to build each layer grows while the value each layer brings diminishes. Your interviewer can always ask you to dig deeper in a certain bucket if they want you to (and they often do).

That’s true!

Drivers are “underlying causes”, and Levers are “potential things you can do to fix the situation”.

You use drivers for WHY problems and Levers for HOW problems.

If you build a good WHY tree and a good HOW tree for the same problem you’ll see the similarities and differences between drivers and levers (and you can actually go back to Item #4 in Chapter 1, where I did just that).

Simple example: if costs in a factory have increased and you want to decrease them, “material costs” could be a driver of the problem AND a lever to solve it, “taxes” could be a driver but not a lever (because you can’t change it) and outsourcing could be a lever to solve it but not a driver of the problem.

Drivers must be potential causes to the problem and Levers should be under your control.

If each part is MECE, your structure is MECE.

To know if each part is MECE, read  the 5 Ways to be MECE .

And to know if your conceptual framework is MECE, check out our free course on case interview fundamentals.

Also, don’t obsess too much. There’s usually a bit  of overlap between areas and no framework is FULLY exhaustive. You want to aim for “as MECE as possible”, not perfection.

Take their hint and go do it!

Interviewers are there to help you. If they tell you the problem is elsewhere, it probably is.

That doesn’t mean there’s absolutely nothing  happening in the parts of the structure you were working on, but it does mean that they want to test your problem solving skills in the other part, not in the one you’re at.

If you got stuck, it’s either building  your issue tree or using  your issue tree.

If you got stuck building  your issue tree, that means you need more and better practice. There’s a whole section on how to practice in this guide (and it’s the part that’s coming next).

If you’re in the interview already, however, there’s no time left to practice. So, what do you do?

My advice: keep it simple.

Take a breath, rethink the case and create a very simple, down-to-earth structure that can solve the problem. Not a good time to be sophisticated and elaborate when you’re stuck.

Now, if you already have your tree and you got stuck using it, here’s what you should do:

Eliminate as many parts of your tree as possible and find out everything that is NOT a part of the problem .

It’s much easier to say something is not a problem than to say for certain that something else is.

Use this process of elimination to your favor. Doctors use it all the time to save people’s lives (they call it a differential diagnosis) and you can too to save your own butt in your interviews.

How to Practice Issue Trees

Practice makes perfect.

Or, as a teacher used to say, “Practice makes permanent”.

(Which means poor practice is worse than no practice).

You can have all the theory in the world, you can have seen all the examples and still not be able to perform when the time to use this tool comes.

Which means that reading this guide is useless if you don’t apply it into practice.

In this chapter, I’ll show you how.

4 ways to practice Issue Trees

I could just tell you to go practice Issue Trees.

But then this chapter wouldn’t exist!

Just kidding 🙂

Here’s the thing, telling people to go practice Issue Trees is what we did when we started our case interview coaching practice.

But it didn’t really work.

Most people would just memorize  the common profit trees you see out there and try to apply them to different problems. The problem with that is that they weren’t building their ability to create  new trees for new problems.

Other people would feel stuck. They’d get bogged down into the details and be afraid to do it wrong and waste their time. Or they wouldn’t know where to start.

So what did we do?

Over time we created different techniques for people to practice trees. Each one has a different function and they’re synergistic – the more techniques you use, the more you’ll learn.

Here are my four favorite ones:

As you can see there is a logic for the four types of practice I will suggest. (And yes, as a former consultant I can’t get over with 2×2 matrices.)

Case-specific practice  is important because this type of practice is very targeted to what you’ll find in your case interviews.

But you also need more generic day-to-day practice  because that will train your mind to always think in a structured way . Even when you’re in the bus. Even when you’re hanging out with your family. Even when the interviewer asks you that informal question about the time where you studied abroad.

On the vertical axis, you’ll find the type of problem you will be practicing with.

You need to practice with real problems you’ve tried to solve before  because you are (or were) emotionally invested in them. You know nuances about them that you wouldn’t know about a random problem and you care (or have cared) about solving them. That gives you the rigor and confidence to structure problems with all the nuances and details they need.

But you also need to practice with hypothetical problems , problems you’ve never considered before. Why? Because that gives you the flexibility and confidence to structure any  problem, even those you have never seen before! 

It helps you be more creative and trains you to face the unknown. What’s the point of learning to structure problems if you can’t face new problems, after all?

Using the four techniques I’ll show you, you will get all four types of practice. 

Actually, because this is a 2×2 matrix, practicing with three of these techniques should be enough to get you really good at this, so if you don’t like any of these, feel free to skip one of them if you want.

Practice #1: Creating "deep trees"

The first type of practice is that of creating very deep Issue Trees for hypothetical problems, simulating one you would do in a case interview if you had 20-30 minutes to think or one you would do in a real project.

The process is rather simple:

(1) Think of a problem (business or public sector) that someone might have to solve. It could be a WHY problem or a HOW problem.

(2) Create a multilayered Issue Tree to solve the problem. Aim for at least 6 layers and try to create even more than that as you get more practice.

What you’ll notice is that the first few layers are going to be quite easy, especially if the problem you chose to structure is a common one.

However, as you go deeper you’ll find that it gets harder and harder.

Because when you get deep into your Issue Tree you must deal with much more specific problems, problems that you might have never considered in your life before.

The deepest layers are the ones that teach you the most.  

Everyone knows how to break down “profits” in a MECE way. Few people can break down “improving customer retention” in a MECE way. Even fewer can find a MECE structure on how to increase customer friction to leave to a competitor.

This exercise works wonders because most cases start really broad but they eventually get to really specific issues, such as “increasing customer friction to leave”, “outsourcing job tasks”, “reducing perceived purchase risks” and things like that.

Here’s an example of a “deep tree” for the “How to reduce costs in a widget manufacturing plant?” problem:

Hey, I’m the first to say this tree isn’t perfect, especially in the last couple layers. It’s really hard to create MECE structures to “buying terms and conditions” and other specific things like that.

And I only covered the “material costs” part, otherwise it wouldn’t fit the screen.

But I wanted to show you one example just do you could see how deep you should go when doing this kind of practice.

Practice #2: Restructuring past cases

Remember the last case you did? The one you messed up on the initial structure?

How much better would your structure be if you had 20-30 minutes to do it?

There’s a simple way to find out…

Restructure that case with as much time as you want!

This is a really good way to practice Issue Trees because (1) you internalize what you’ve learned in the case and (2) you can structure it with unlimited time and without being nervous.

Plus, let’s be honest, you keep telling yourself that your structures aren’t as good as they could be because you don’t have a lot of time to build them and you’re nervous.

But is that really the case?

Try it out!

This practice is as simple as the name suggests, but there is ONE NUANCE…

You will  feel tempted to overemphasize the parts of the case your interviewer directed you to and underemphasize other areas.

So, for example, if you had a profitability case and the case ended up being about cutting labor costs in a telecom company, you will tend to make your structure much more robust in the labor costs part than in the rest of the tree.

DON’T DO THAT.

Instead, build a robust tree all around.

Maybe this case was about labor costs, but the next one could be on infrastructure costs and the one after that could be on pricing. Build a robust structure all around that simulates what you would’ve done had the interview gone in any of those directions.

Be prepared for every situation.

Practice #3: Solving real work problems

Got a problem at work?

Work like a consultant and build an Issue Tree first and foremost!

Have to hit a certain target in an organization you work at or collaborate with?

Break that metric down into an Issue Tree and find the best lever to focus on.

Have a school assignment?

Try to build an Issue Tree for it.

By doing these things you will incorporate Issue Trees in your daily work and study. 

Sometimes I even create them as I read a book to better organize its ideas. And as I do that, I end up with the whole structure and all the important ideas of a book in just one page.

Practice #4: Creating "mental trees"

Remember I said you can do 3 out of the 4 types of practices in this chapter and still do fine?

Well, don’t skip this one.

Mental trees exercise a different muscle than the other practices, because it happens all in your head.

It’s kind of like mental math but for Issue Trees.

And it’s a skill that every consultant can do , and so should you.

So what are “mental trees”?

It’s simple. As you go through your day you will notice things. You will be curious about things. You will wonder how to fix certain problems or why they happen in the first place.

You’ll have questions such as:

• “How could this restaurant generate more demand?”
• “What could the city do to improve its transport system?”
• “Why is the doctor always late for the appointment?”
• “What will TV networks do to generate more revenue now that everyone’s on Youtube and Instagram?”

And as you have these questions, use these opportunities to create Issue Trees in your head.

Not huge ones, 2 or 3 layers is fine.

But do that and try to keep them in your head as you generate hypotheses for each bucket. At first this is gonna be really hard, but once you get the hang of it it will be a breeze.

And once it’s easy, you’ll be able to use Issue Trees whenever you need them.

This practice is especially important for final rounds because partners will often tell you to discuss a problem without using paper. (And they do expect you to structure it).

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Applying issue trees on the job.

If you’ve read this far, you’ve learned how to use the most versatile tool in solving business (and many other) problems.

And if you’re like me, you want to now maximize the value you got from learning this!

Issue Trees can help you be a better problem solver, but also to present your ideas better, to bring more and better insights and even to be a better manager.

In this chapter I’ll show you 5 direct, on-the-job applications of Issue Trees that you can use if you’re a consultant, if you work in industry and even if you have started your own business.

Issue Trees can be used in every facet of your job

Before we even jump into examples of direct applications of how to use Issue Trees on the job, let me make a bold claim: Issue Trees can be used in every  facet of your job.

You know that saying about how everything looks like a nail to the guy who has a hammer?

Well, don’t think of Issue Trees as hammers. 

They’re more like Swiss army knives or Microsoft Excel. It’s a tool with many functions.

And you can use it as a consultant, but also as an executive, as an entrepreneur and more. I once taught my dad who is a doctor how to use it and he’s now better able to explain his thought process and diagnostics to his patients.

Why am I telling you all this?

Just so you know that the 5 on-the-job applications I’m about to show you are some  of the things you can do with Issue Trees. 

With a bit of creativity you can do much more.

Application #1 - As a map to solve a specific problem

If you’ve spent any time at all as a knowledge worker in your career (that’s most analyst and management positions at most companies), you know how it feels to be stuck with a problem.

Most business problems start with a very simple, almost trivial, question, but as you dig deeper you start seeing all the nuances you feel overwhelmed. 

It’s very different from the experience of solving a problem in business school, where all the information you’ll need (and all the info you’ll get) is in a neat 10-20 page case.

Anyway… When you feel overwhelmed, when you feel like there’s too much nuance to handle and when you feel like there’s so many directions to go what you need is a map. A high-level view of the problem with its distinct parts laid out in front of you so you can put numbers, hypotheses and plans to act in each part.

What you need is an Issue Tree.

Years ago I worked in a Venture Capital firm here in Brazil. They had just entered the market and wanted to invest in e-commerce.

My task was to figure out what types of e-commerce businesses would thrive in the country so they could invest well. Would it be auto-parts? Maybe fashion? Or perhaps food delivery?

It was an overwhelming task for me. There’s so many things you can do with e-commerce.

So what I did was to build two Issue Trees. One with our options and another with the high level criteria I’d want to see in each option for it to become a successful e-commerce.

Something like this:

Now, the real trees I did were a bit more sophisticated than this. They had:

• More layers and a more MECE structure for the verticals
• Other criteria for success not shown here
• Prioritization so we could find the most important information first and eliminate whole verticals quickly

But you can get the idea… I got both of these trees and put into a spreadsheet and now I had a map of the problem that I could work on.

Because I used Issue Trees to create this map, I assured that the thinking was clear and rigorous, that I would be able to work efficiently by eliminating bad options quickly and that I’d bring insight to the table.

It also removed all overwhelm and made my work much more efficient. I no longer had to consider all the factors at once in my head. All I had to do now was to fill out a table with the best information I could get and see the results.

Application #2 - As a guide to brainstorm solutions

Brainstorming solutions to common business problems is a nervous activity.

Everyone wants to show the best solution, and people want to show common sense AS WELL AS creativity. It’s a tough spot to be in.

On top of that, people typically brainstorm solutions to problems that are urgent and critical (why fix what’s not broken?) and this is usually done in meetings, which adds to the pressure.

But that’s not all… In most meetings, solution generation happens in a haphazard way – completely different ideas are mentioned in the spam of a few minutes and it’s hard to even evaluate which are the best ones.

The result? The best solutions rarely win and it’s common that people don’t even reach a consensus on which should be implemented.

So, what’s the antidote?

You guessed it: Issue Trees.

If you have a solution generating meeting (or if you’re doing it by yourself) and you can find a HOW tree that reaches consensus (not actual solutions, but the structure of the problem) at the beginning of the meeting, you can then lead the discussion forward, helping people generate solutions for each bucket of your tree and then prioritizing those in an organized fashion.

Also, doing it this way tends to bring out more, better ideas – for the same reason why dividing the problem brings more creativity in case interviews. It’s easier to get 5 ideas per bucket than 40 for the problem as a whole.

I’ve been to both kinds of solution-generating meetings. One feels like a pointless chaos and the other gives you certainty that the problem will be solved from minute one.

Application #3 - As a way to structure a presentation

Structuring a presentation is the kind of thing that gets most people CRAZY.

You have to consider your audience, how to capture and keep attention, storytelling, getting your point across quickly and being to the point and so many other conflicting goals.

But here’s a simple way to do it: use the Issue Tree of the problem as a basis to how your presentation is organized.

This works because your Issue Tree is a map of your problem. And maps are great ways to make people understand a complex thing with simplicity and accuracy.

Let me show you an example of how to do this…

Remember the Telco executive from Chapter 1 that had a problem because his customers were unsubscribing from their services? I’ll help you remember it, it’s been a while…

Now, imagine he had to present what’s happening to the executive committee. It needed to be a short and to the point presentation that was compelling as well.

Not a full solution to the problem, but a presentation showing what happened.

What would you do in his place?

Here’s what I’d do:

Slide 1: A chart showing the high level problem (overall unsubscriptions have raised from 10.5 to 17 thousand clients, with an increase of 2,000 from clients willing to unsubscribe and 4,500 from clients being forced out). 

I’d also add something that pointed out that the cause of the clients being forced out (the main problem) was a problem in the systems.

In other words, Slide 1 would be “High-level view” + “root-cause of main problem”. Everything the committee needs to understand the situation.

Slide 2: A chart detailing the root-cause of the main problem, with all details needed to understand why it happened. This would include numbers and qualitative things about that system problem.

Slide 3: A chart showing that even though we only lost 2,000 extra customers because they wanted out, we actually lost 3,000 to competition. I’d show the numbers (2nd Layer at “They wanted to unsubscribe” bucket) and show that there is potential there.

Slide 4: I’d turn back to the system’s problems and start talking about solutions. I’d show what was done, what is being done and what’s next to prevent it from happening again.

Slide 5: I’d show next steps to understand how to retain more customers vs. competition. This is a less urgent problem so I’d leave it at that.

That’s it, simple and straightforward.

And it all comes because I have a simple and straightforward Issue Tree that helps me solve and explain the problem in simple and straightforward ways.

Application #4 - As a guide to research best practices

We’ve all had that hurried boss that passes through your desk and casually mentions: “Hey, you should try to find some best practices around X”.

X can be anything he or she is concerned about: doing better presentations, sharing internal documents, improving productivity at work, getting more clients.

And the problem with that is that it’s really really hard to research that. If you just type “best practices for X” in google, chances are you’ll get some really generic, obvious tips.

One thing I’ve learned to do at McKinsey was to research best practices for each component  of X. So instead of looking for best practices around “getting more clients”, I could research best practices to “get more leads” and “increasing conversion rate”.

And then I could break down those components even further and look for best practices for each sub-component.

Guess what’s the tool you need to get all the components in a logical manner? Yes, Issue Trees!

A normal best practice for X’s sub-sub-component usually is a great insight to improve X, so by simply doing this exercise you will come off way ahead of your peers as the go-to person for insights on how to improve your company.

Application #5 - As a way to generate KPIs and indicators

In case you don’t know the lingo, KPIs are you “Key Performance Indicators”.

They’re a business’ dashboard. The numbers you have to look at to see how healthy your business is.

But how do you create KPIs?

Well, in three simple steps:

1) You define your goals

2) Your break down your goals into the sub-components that must be true for you to achieve them

3) You figure out indicators for each prioritized sub-component. (Without the “prioritized” part, these indicators wouldn’t be “Key”)

So for example,  if you’re studying for consulting interviews and you want to see how your preparation is going , here’s an example of how to create KPIs you can track:

Each bullet point could be a KPI. Some of these are numbers to track, others are Yes/No KPIs.

I am not saying nor implying every candidate should use all these KPIs to prepare, but notice how nuanced you can get when you use a MECE Issue Tree to create KPIs.

Most candidates just track the # of cases they did, without even caring for the quality of those. 

No wonder why most get rejected. 

It’s like a company that just tracks how many products it has sold without concerning about margins, customer retention rates, customer satisfaction, quality control and so on.

You can get any Issue Tree from this article and transform it into a list of KPIs to track within each important bucket. 

There’s certainly an art on which ones are better to track (because you don’t want to end up with 35 different KPIs) but just generating them out of a MECE Issue Tree allows you to have at least one indicator to every important part of the problem, leaving no blind spots in your master dashboard.

What's next?

Issue Trees are one, but not the only  tool MBB consultants use to solve their client’s problems.

There are actually 6 types of questions interviewers ask in case interviews, to test on the 6 most important tasks consultants perform in real client work. 

You can learn about those questions and the specific tools, techniques and strategies management consultants from McKinsey, BCG and Bain use to solve business problems by joining our free course on case interviews!

By joining our course, you’ll get access to:

• Step-by-step methods to solve the 6 (and only six) types of questions you can get in case interviews
• The “Landscape Technique” to create conceptual frameworks from scratch (this is the technique you need when Issue Trees fail to help you)
• Tons of practice drills so you can apply your knowledge

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Resources >

Mckinsey approach to problem solving, a guide to the 7-step mckinsey problem solving process.

McKinsey and Company is recognized for its rigorous approach to problem solving. They train their consultants on their seven-step process that anyone can learn.

This resource guides you through that process, largely informed by the McKinsey Staff Paper 66. It also includes a PowerPoint Toolkit with slide templates of each step of the process that you can download and customize for your own use.

In this guide you'll learn:

Overview of the mckinsey approach to problem solving, problem solving process, problem definition.

• Problem Statement

Stakeholder Analysis Worksheet

Structure the problem, hypothesis trees, issue trees, analyses and workplan, synthesize findings, craft recommendations, communicate, distinctiveness practices, harness the power of collaboration, sources and additional reading, request the mckinsey approach to problem solving.

Problem solving — finding the optimal solution to a given business opportunity or challenge — is the very heart of how consultants create client impact, and considered the most important skill for success at McKinsey.

The characteristic “McKinsey method” of problem solving is a structured, inductive approach that can be used to solve any problem. Using this standardized process saves us from reinventing the problem-solving wheel, and allows for greater focus on distinctiveness in the solution. Every new McKinsey associate must learn this method on his or her first day with the firm.

There are four fundamental disciplines of the McKinsey method:

1. Problem definition

A thorough understanding and crisp definition of the problem.

2. The problem-solving process

Structuring the problem, prioritizing the issues, planning analyses, conducting analyses, synthesizing findings, and developing recommendations.

3. Distinctiveness practices

Constructing alternative perspectives; identifying relationships; distilling the essence of an issue, analysis, or recommendation; and staying ahead of others in the problem-solving process.

4. Collaboratio n

Actively seeking out client, customer, and supplier perspectives, as well as internal and external expert insight and knowledge.

Once the problem has been defined, the problem-solving process proceeds with a series of steps:

• Structure the problem
• Prioritize the issues
• Plan analyses
• Conduct analyses
• Synthesize findings
• Develop recommendations

Not all problems require strict adherence to the process. Some steps may be truncated, such as when specific knowledge or analogies from other industries make it possible to construct hypotheses and associated workplans earlier than their formal place in the process. Nonetheless, it remains important to be capable of executing every step in the basic process.

When confronted with a new and complex problem, this process establishes a path to defining and disaggregating the problem in a way that will allow the team to move to a solution. The process also ensures nothing is missed and concentrates efforts on the highest-impact areas. Adhering to the process gives the client clear steps to follow, building confidence, credibility, and long-term capability.

The most important step in your entire project is to first carefully define the problem. The problem definition will serve the guide all of the team’s work, so it is critical to ensure that all key stakeholders agree that it is the right problem to be solving.

The problem definition will serve the guide all of the team’s work, so it is critical to ensure that all key stakeholders agree that it is the right problem to be solving.

There are often dozens of issues that a team could focus on, and it is often not obvious how to define the problem.

In any real-life situation, there are many possible problem statements. Your choice of problem statement will serve to constrain the range of possible solutions.

Constraints can be a good thing (e.g., limit solutions to actions within the available budget.) And constraints can be a bad thing (e.g., eliminating the possibility of creative ideas.) So choose wisely.

The problem statement may ignore many issues to focus on the priority that should be addressed. The problem statement should be phrased as a question, such that the answer will be the solution.

Example scenario – A family on Friday evening :

A mother, a father, and their two teenage children have all arrived home on a Friday at 6 p.m. The family has not prepared dinner for Friday evening. The daughter has lacrosse practice on Saturday and an essay to write for English class due on Monday. The son has theatre rehearsal on both Saturday and Sunday and will need one parent to drive him to the high school both days, though he can get a ride home with a friend.

The family dog, a poodle, must be taken to the groomer on Saturday morning. The mother will need to spend time this weekend working on assignments for her finance class she is taking as part of her Executive MBA. The father plans to go on a 100-mile bike ride, which he can do either Saturday or Sunday. The family has two cars, but one is at the body shop. They are trying to save money to pay for an addition to their house.

Potential problem definitions – A family on Friday evening :

The problem definition should not be vague, without clear measures of success. Rather, it should be a SMART definition:

• Action-oriented

Given one set of facts, it is possible to come up with many possible problem statements. The choice of problem statement constrains the range of possible solutions.

Before starting to solve the problem, the family first needs to agree on what problem they want to solve.

• What should the family do for dinner on Friday night?
• How can the family schedule their activities this weekend to accomplish everything planned given that they only have one vehicle available?
• How can the family increase income or reduce expenses to allow them to save $75K over the next 12 months to pay for the planned addition to their house?

Problem Statement Worksheet

This is a helpful tool to use to clearly define the problem. There are often dozens of issues that a team could focus on, and it is often not obvious how to define the problem. In any real-life situation, there are many possible problem statements. Your choice of problem statement will serve to constrain the range of possible solutions.

• Use a question . The problem statement should be phrased as a question, such that the answer will be the solution. Make the question SMART: specific, measurable, action-oriented, relevant, and time-bound. Example: “How can XYZ Bank close the $100 million profitability gap in two years?”
• Context . What are the internal and external situations and complications facing the client, such as industry trends, relative position within the industry, capability gaps, financial flexibility, and so on?
• Success criteria . Understand how the client and the team define success and failure. In addition to any quantitative measures identified in the basic question, identify other important quantitative or qualitative measures of success, including timing of impact, visibility of improvement, client capability building required, necessary mindset shifts, and so on.
• Scope and constraints . Scope most commonly covers the markets or segments of interest, whereas constraints govern restrictions on the nature of solutions within those markets or segments.
• Stakeholders . Explore who really makes the decisions — who decides, who can help, and who can block.
• Key sources of insight . What best-practice expertise, knowledge, and engagement approaches already exist? What knowledge from the client, suppliers, and customers needs to be accessed? Be as specific as possible: who, what, when, how, and why.

In completing the Problem Statement Worksheet, you are prompted to define the key stakeholders.

As you become involved in the problem-solving process, you should expand the question of key stakeholders to include what the team wants from them and what they want from the team, their values and motivations (helpful and unhelpful), and the communications mechanisms that will be most effective for each of them.

Using the Stakeholder Analysis Worksheet allows you to comprehensively identify:

• Stakeholders
• What you need from them
• Where they are
• What they need from you

The two most helpful techniques for rigorously structuring any problem are hypothesis trees and issue trees. Each of these techniques disaggregates the primary question into a cascade of issues or hypotheses that, when addressed, will together answer the primary question.

A hypothesis tree might break down the same question into two or more hypotheses. 

The aim at this stage is to structure the problem into discrete, mutually exclusive pieces that are small enough to yield to analysis and that, taken together, are collectively exhaustive.

Articulating the problem as hypotheses, rather than issues, is the preferred approach because it leads to a more focused analysis of the problem. Questions to ask include:

• Is it testable – can you prove or disprove it?
• It is open to debate? If it cannot be wrong, it is simply a statement of fact and unlikely to produce keen insight.
• If you reversed your hypothesis – literally, hypothesized that the exact opposite were true – would you care about the difference it would make to your overall logic?
• If you shared your hypothesis with the CEO, would it sound naive or obvious?
• Does it point directly to an action or actions that the client might take?

Quickly developing a powerful hypothesis tree enables us to develop solutions more rapidly that will have real impact. This can sometimes seem premature to clients, who might find the “solution” reached too quickly and want to see the analysis behind it.

Take care to explain the approach (most important, that a hypothesis is not an answer) and its benefits (that a good hypothesis is the basis of a proven means of successful problem solving and avoids “boiling the ocean”).

Example: Alpha Manufacturing, Inc.

Problem Statement: How can Alpha increase EBITDA by $13M (to $50M) by 2025?

The hypotheses might be:

• Alpha can add $125M revenues by expanding to new customers, adding $8M of EBITDA
• Alpha can reduce costs to improve EBITDA by $5M

These hypotheses will be further disaggregated into subsidiary hypotheses at the next level of the tree.

Often, the team has insufficient knowledge to build a complete hypothesis tree at the start of an engagement. In these cases, it is best to begin by structuring the problem using an issue tree.

An issue tree is best set out as a series of open questions in sentence form. For example, “How can the client minimize its tax burden?” is more useful than “Tax.” Open questions – those that begin with what, how, or why– produce deeper insights than closed ones. In some cases, an issue tree can be sharpened by toggling between issue and hypothesis – working forward from an issue to identify the hypothesis, and back from the hypothesis to sharpen the relevant open question.

Once the problem has been structured, the next step is to prioritize the issues or hypotheses on which the team will focus its work. When prioritizing, it is common to use a two-by-two matrix – e.g., a matrix featuring “impact” and “ease of impact” as the two axes.

Applying some of these prioritization criteria will knock out portions of the issue tree altogether. Consider testing the issues against them all, albeit quickly, to help drive the prioritization process.

Once the criteria are defined, prioritizing should be straightforward: Simply map the issues to the framework and focus on those that score highest against the criteria.

As the team conducts analysis and learns more about the problem and the potential solution, make sure to revisit the prioritization matrix so as to remain focused on the highest-priority issues.

The issues might be:

• How can Alpha increase revenue?
• How can Alpha reduce cost?

Each of these issues is then further broken down into deeper insights to solutions.

If the prioritization has been carried out effectively, the team will have clarified the key issues or hypotheses that must be subjected to analysis. The aim of these analyses is to prove the hypotheses true or false, or to develop useful perspectives on each key issue. Now the task is to design an effective and efficient workplan for conducting the analyses.

Transforming the prioritized problem structure into a workplan involves two main tasks:

• Define the blocks of work that need to be undertaken. Articulate as clearly as possible the desired end products and the analysis necessary to produce them, and estimate the resources and time required.
• Sequence the work blocks in a way that matches the available resources to the need to deliver against key engagement milestones (e.g., important meetings, progress reviews), as well as to the overall pacing of the engagement (i.e., weekly or twice-weekly meetings, and so on).

A good workplan will detail the following for each issue or hypothesis: analyses, end products, sources, and timing and responsibility. Developing the workplan takes time; doing it well requires working through the definition of each element of the workplan in a rigorous and methodical fashion.

It’s useful to match the workplan to three horizons:

• What is expected at the end of the engagement
• What is expected at key progress reviews
• What is due at daily and/or weekly team meetings

The detail in the workplan will typically be greater for the near term (the next week) than for the long term (the study horizon), especially early in a new engagement when considerable ambiguity about the end state remains.

Here are three different templates for a workplan:

This is the most difficult element of the problem-solving process. After a period of being immersed in the details, it is crucial to step back and distinguish the important from the merely interesting. Distinctive problem solvers seek the essence of the story that will underpin a crisp recommendation for action.

Although synthesis appears, formally speaking, as the penultimate step in the process, it should happen throughout. Ideally, after you have made almost any analytical progress, you should attempt to articulate the “Day 1” or “Week 1” answer. Continue to synthesize as you go along. This will remind the team of the question you are trying to answer, assist prioritization, highlight the logical links of the emerging solution, and ensure that you have a story ready to articulate at all times during the study.

McKinsey’s primary tool for synthesizing is the pyramid principle. Essentially, this principle asserts that every synthesis should explain a single concept, per the “governing thought.” The supporting ideas in the synthesis form a thought hierarchy proceeding in a logical structure from the most detailed facts to the governing thought, ruthlessly excluding the interesting but irrelevant.

While this hierarchy can be laid out as a tree (like with issue and hypothesis trees), the best problem solvers capture it by creating dot-dash storylines — the Pyramid Structure for Grouping Arguments.

Pyramid Structure for Grouping Arguments

• Focus on action. Articulate the thoughts at each level of the pyramid as declarative sentences, not as topics. For example, “expansion” is a topic; “We need to expand into the European market” is a declarative sentence.
• Use storylines. PowerPoint is poor at highlighting logical connections, therefore is not a good tool for synthesis. A storyline will clarify elements that may be ambiguous in the PowerPoint presentation.
• Keep the emerging storyline visible. Many teams find that posting the storyline or story- board on the team-room wall helps keep the thinking focused. It also helps in bringing the client along.
• Use the situation-complication-resolution structure. The situation is the reason there is action to be taken. The com- plication is why the situation needs thinking through – typically an industry or client challenge. The resolution is the answer.
• Down the pyramid: does each governing thought pose a single question that is answered completely by the group of boxes below it?
• Across: is each level within the pyramid MECE?
• Up: does each group of boxes, taken together, provide one answer – one “so what?” – that is essentially the governing thought above it?
• Test the solution. What would it mean if your hypotheses all came true?

It is at this point that we address the client’s questions: “What do I do, and how do I do it?” This means not offering actionable recommendations, along with a plan and client commitment for implementation.

The essence of this step is to translate the overall solution into the actions required to deliver sustained impact. A pragmatic action plan should include:

• Relevant initiatives, along with a clear sequence, timing, and mapping of activities required
• Clear owners for each initiative
• Key success factors and the challenges involved in delivering on the initiatives

Crucial questions to ask as you build recommendations for organizational change are:

• Does each person who needs to change (from the CEO to the front line) understand what he or she needs to change and why, and is he or she committed to it?
• Are key leaders and role models throughout the organization personally committed to behaving differently?
• Has the client set in place the necessary formal mechanisms to reinforce the desired change?
• Does the client have the skills and confidence to behave in the desired new way?

Once the recommendations have been crafted in the problem-solving process, it’s vital to effectively communicate those findings and recommendations.

An executive summary is a great slide to use for this. See more on executive summary slides, including 30 templates, at our Ultimate Guide to Executive Summary Slides .

Great problem solvers identify unique disruptions and discontinuities, novel insights, and step-out opportunities that lead to truly distinctive impact. This is done by applying a number of practices throughout the problem-solving process to help develop these insights.

Expand: Construct multiple perspectives

Identifying alternative ways of looking at the problem expands the range of possibilities, opens you up to innovative ideas, and allows you to formulate more powerful hypotheses. Questions that help here include:

• What changes if I think from the perspective of a customer, or a supplier, or a frontline employee, or a competitor?
• How have other industries viewed and addressed this same problem?
• What would it mean if the client sought to run the company like a low-cost airline or a cosmetics manufacturer?

Link: Identify relationships

Strong problem solvers discern connections and recognize patterns in two different ways:

• They seek out the ways in which different problem elements – issues, hypotheses, analyses, work elements, findings, answers, and recommendations – relate to one another.
• They use these relationships throughout the basic problem-solving process to identify efficient problem-solving approaches, novel solutions, and more powerful syntheses.

Distill: Find the essence

Cutting through complexity to identify the heart of the problem and its solution is a critical skill.

• Identify the critical problem elements. Are there some issues, approaches, or options that can be eliminated completely because they won’t make a significant difference to the solution?
• Consider how complex the different elements are and how long it will take to complete them. Wherever possible, quickly advance simpler parts of the problem that can inform more complex or time-consuming elements.

Lead: Stay ahead/step back

Without getting ahead of the client, you cannot be distinctive. Paradoxically, to get ahead – and stay ahead – it is often necessary to step back from the problem to validate or revalidate the approach and the solution.

• Spend time thinking one or more steps ahead of the client and team.
• Constantly check and challenge the rigor of the underlying data and analysis.
• Stress-test the whole emerging recommendation
• Challenge the solution against a set of hurdles. Does it satisfy the criteria for success as set out on the Problem Statement Worksheet?

No matter how skilled, knowledgeable, or experienced you are, you will never create the most distinctive solution on your own. The best problem solvers know how to leverage the power of their team, clients, the Firm, and outside parties. Seeking the right expertise at the right time, and leveraging it in the right way, are ultimately how we bring distinctiveness to our work, how we maximize efficiency, and how we learn.

When solving a problem, it is important to ask, “Have I accessed all the sources of insight that are available?” Here are the sources you should consider:

• Your core team
• The client’s suppliers and customers
• Internal experts and knowledge
• External sources of knowledge
• Communications specialists

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Hypotheses trees

Problem-solving

July 27, 2022

"The first principle is that you must not fool yourself, and you are the easiest person to fool." –Richard Feynman, Nobel Prize-winning physicist

This statement rings true for teams trying to solve complex problems. Business owners with experience in specific fields may think they know how to solve a problem—especially if they have encountered a similar issue in the past.

Leaders will usually form a hypothesis around a core problem and use it as a foundation to solve it. This can either speed up the process and solve the problem in a single meeting or lead to confirmation bias. If the latter happens, the wrong issues will be targeted and the problem won't be solved correctly. 

This piece will look at the power—and the pitfalls—of using hypothesis trees to solve problems. 

Table of contents:

Hypothesis trees: the basics, how a flawed hypothesis brought down blockbuster, the three common pitfalls with using hypotheses to solve business problems, expert corner: rob jenks' pros and cons of using hypotheses to solve problems, coming soon..

A hypothesis tree is a powerful tool for top-down problem-solving in business. 

It's used to help teams target a problem and break it down into sub-hypotheses to make it easier to solve. Think of hypothesis trees as a shortcut—teams create a theory about what's causing the problem based on their experience and use it to form a hypothesis on how to go forward. 

Hypothesis trees are famously taught in McKinsey Mind as a way to solve a problem in a single meeting. For McKinsey analysts, hypothesis problem-solving usually follows three simple steps: 

• A hypothesis is formed about the problem and how to solve it
• Data is gathered to prove (or disprove) the hypothesis by comparing results
• The original hypothesis is updated when the data has been analyzed, and the opinion is proven or discarded

Teams may also use hypothesis trees to get an even better idea if a hypothesis solves a problem. These trees have three main characteristics: 

• A primary hypothesis to answer the question 
• Structured layers (sub-hypotheses) to add nuance to the hypothesis (e.g. the first layer contains supporting data for the hypothesis)
• Each layer and issue uses M.E.C.E to examine and support the previous hypothesis

For teams using hypothesis trees, there are two huge benefits. 

First, they reduce the time taken to solve a question as the discussion starts with an answer that gets proven or disproven. Second, teams are forced to give an educated guess upfront to the hypothesis, which captures their experience and insight to speed up the problem-solving process.  

Let's bring a hypothesis tree to life by examining the core question Blockbuster CEO James Keyes faced when he tried to resurrect the business in the 2000s. 

Keyes asked himself: How can Blockbuster increase the profitability of its stores?

The primary hypothesis he worked with was Blockbuster's existing strategy was working, and doubling down on it would increase store profitability. This hypothesis was reached as a result of team experience and their understanding of the core problem. 

With the primary hypothesis defined, Blockbuster created three supporting hypotheses to save money and increase profitability: 

• Reduce investments in Blockbuster online
• Maximize cash flow by increasing late fees 
• Add impulse purchase products and stores

It is easy to see how Keyes assumed data and facts to reinforce his assertions with these three additional hypotheses in place. 

For example, the hypothesis behind adding impulse purchase products and stores was based on the data that customers spend an average of 20 minutes in the store when picking out a film. Blockbuster assumed customers would be tempted to buy candy and popcorn within this timeframe. 

The danger of this example is the initial hypothesis—it's flawed. Blockbuster's existing strategy wasn't aligned with shifting customer expectations (going from physical DVDs to streaming), not to mention the fatal blow of cutting investment in Blockbuster online. 

The incorrect hypothesis devastated Blockbuster and the company went bankrupt in 2010 .

Using hypothesis trees for problem-solving can save time and draw out your team's expertise—but they also come with great responsibility. 

A common problem with hypothesis trees is creep . As teams lean on their expertise and experience to create hypotheses, they are exposed to narrow core question definitions and limited alignment. 

Then comes the biggest risk to hypothesis trees— confirmation bias . 

Let's use an example—congestion in the streets of Los Angeles—to highlight the pitfalls with hypothesis trees in problem-solving.  

The problem's primary hypothesis could focus on reducing the costs of tunnel boring and moving traffic underground. However, that hypothesis also represents a very narrow view. So, another hypothesis could be investing in self-driving vehicles above-ground and public transportation, or encouraging businesses to let their employees work from home to reduce traffic. 

Teams must also consider limiting team alignment. Since the hypothesis is forced early on, it doesn't let the data speak. One hypothesis must be picked, and it limits opinions or data on other hypotheses being discussed and can impact the team's ability to solve the problem successfully. 

Finally, hypothesis trees are prone to confirmation bias. Humans have over 180 cognitive biases that our brains use to guide decision-making. But psychology experts say these biases are bad for business, as they can lead to incorrect decision-making, and teams can default to embracing group think. Hypothesis trees start with the strongest solution in our brain, and we are wired to believe it's correct because of our experience and expertise—which can be a disaster for business. 

Rob Jenks is the senior vice president of Corporate Strategy at Tanium , a cybersecurity and systems management company.

His role has no room for bias, and every decision he makes has to be rooted in data and evidence. 

According to Jenks, to overcome a structural problem it's best to break it down into parts and find bite-sized chunks that can be solved. When you piece it back together, the chunks will either prove (or disprove) the hypothesis you've been working on. 

Jenks says it's important to remember a hypothesis is a provisional answer and a theory that needs to be investigated. 

"The reason it works so well is that it makes the solution transparent and when you identify that last leaf on the hypothesis tree, you realize the subpart of the problem often is trivial," he says. 

"I remember from my McKinsey days. We would always ask teams literally on the first day— what's the answer to this problem? What's your day one answer? What you're really saying is: what's your hypothesis? "

Even when a hypothesis is identified, Jenks says the pros and cons of using hypothesis trees to solve problems can't be forgotten. 

The downside to using hypothesis trees, Jenks says, is you simply could be wrong. 

"We might end up going down a path that's driven by the hypothesis that ends up being completely wrong," he says. "You can waste a lot of time as hypothesis-driven problem-solving is sometimes just confirmation bias."

Hypothesis trees can be a very effective and rapid method of solving problems. 

"When you have an answer in mind, It's much easier (and more concrete) to then think about it and how to prove the hypothesis," Jenks says. "It dramatically accelerates the top-down structuring of problems, and it focuses on the acceleration to the answer and it cuts off extraneous work."

Use hypothesis trees wisely to speed up your problem-solving

Hypothesis trees are a powerful tool to speed up problem-solving—if they're used correctly. 

Hypotheses are born out of expertise and experience. Backed up with evidence and data, they give businesses a head start when solving problems and can significantly speed up the process. There's a reason firms like McKinsey use them in daily interactions with their clients— they work . 

However, there's a downfall to relying on hypotheses to solve problems—the human brain. Our brains are wired in cognitive bias. If we think we know the answer to a question or problem, we can forget that we need to back it up with facts. Missing this step when using hypothesis trees can lead to incorrect problem-solving and wasted time. 

Our advice is to use hypothesis trees as they were designed: as a provisional answer to give your team a head-start to solve complex problems.

Now you know all about hypotheses trees—but have you heard about issue trees?  Learn more.

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Module 4: Developing hypotheses

Outline of module summary, learning objectives, and downloadable course materials

In this module we will help you develop hypotheses to clarify your thinking and synthesise recommendations efficiently, using tools such as the hypothesis tree.

Learning objectives:

• Recognise the importance of constantly articulating and developing hypotheses
• Understand how to use a hypothesis tree to do this
• Hypothesis Tree - PowerPoint Template
• Hypothesis Tree - Google Slides Template

"Thanks so much for a brilliant course. I've been applying hypothesis trees all over the place!"

Previous Module:

Module 3: Engaging stakeholders

Next Module:

Module 5: Gathering data and conducting analysis

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Issue Trees – What Are They and How Do You Use Them?

Issue trees are a useful approach to breaking down a problem statement into component parts that can more easily be acted upon. In consulting teams, it’s often done in the first couple of weeks of a project. It enables the team to structure the project in a way that people can be assigned to specific “workstreams” and that the team can align their hypotheses to make predictions about which elements might have the biggest impact.

I like the definition that McKinsey Mind uses for issue trees:

The issue tree, a species of logic tree in which each branch of the tree is an issue or question, bridges the gap between structure and hypothesis. Every issue generated by a framework will likely be reducible to sub issues, and these in turn may break down further. An issue tree is simply the laying out of issues and subissues into a MECE visual progression. By answering the questions in the issue tree, you can very quickly determine the validity of your hypothesis.

It’s a good definition but it’s also chock-full of jargon and we’re not a fan of jargon (or at least egregious uses of it) here are StrategyU The simplest way of thinking about an issue tree is as a way of breaking down a complex problem into many possible explanations of what is going wrong.

What Do They Look Like And How Do You Use Them ?

Issue trees are often created visually in PowerPoint but can also be in the form of financial models. The type of issue tree we are concerned about are the ones that help us structure our central problem or mission. For example, most companies are focused on increasing profitability. We might frame this “problem” in the form of a question, “Company profitability is declining, what are the ways to improve it?”

We can then start to brainstorm different ways that profitability might be increased. At the higher levels, you want to be as broad as possible such that you can break the tree down further and get more specific the deeper you go. You’ll also want to try to use MECE . Our initial issue tree might look like this:

From there we can go deeper. What are the different ways we can increase revenue. It’s best to just start listing ideas and then start thinking about how to synthesize them, organize them, and yes, make sure they are MECE!

You might develop the next leg of your tree:

This tree is not perfect and the answers at the lowest level are not collectively exhaustive for all the possibilities for increasing revenue and decreasing costs. However, for a specific company, these may be the relevant issues, meaning that they are the ones that you are able to invest money on, tweak and that might have a positive impact.

The next step is to develop analyses or experiments that you can perform to validate or quantify how much impact can be generated by focusing in each of these areas

How Issue Trees Are Linked With Problem Solving

At StrategyU we are fans of the SCQA process to define problems and develop hypotheses. This approach enables us to have a rigorous problem-solving approach to business problems instead of starting with the solution in mind from the beginning. This approach works best when you are open-minded and flexible. The first test of the issue tree is when you are doing the initial research and analysis after you structure the problem. This is step two of the consulting process :

At this point, you will likely get some quick feedback on your initial problem statement such as:

• Have we defined the problem appropriately or are there deeper issues?
• Have we identified the relevant issues and areas in which we can make a difference?
• What kind of initial tests have we done are are we designing to confirm if the issues and questions are right?

This is a frustrating, iterative process and within a consulting team, you are often revisiting the issue tree and problem statement over and over again throughout a project.

How To Use This In Your Company

You should have a good understanding of the “levers” that help your company continue to grow, increase its profitability, and improve over time. Spend long enough in any company and you start to realize that there are a narrow set of metrics everyone makes decisions around. Except unless you’ve mapped this out explicitly, there will likely be many different definitions and interpretations of what you are optimizing for.

Using a template like follows and coming up with the high-level issues and areas within the business you are focusing on can be clarifying. You can also add specific types of analyses and information that you use to help you solve or improve in these areas:

This can also be rolled out across your org chart. Let’s imagine a company realizes that it doesn’t have much room to lower costs anymore and it wants to focus exclusively on increasing revenue. They can do this in two ways (assuming they aren’t adding new products). They can increase the price per order or they can increase volume. They may when want to break this down into different sub-issues.

In reality, you’d want to collect a lot of data and verify that the way you are breaking things down is correct. The numbers often surprise companies. They realize that an area of focus (increasing # of customers, for example) is not as big of an impact on the bottom line as other areas.

The only way to figure this out is to map out all of the possibilities of your issues and then validate them with real data.

This is the same thing that consulting teams do when they work for companies.

In my course, Think Like A Strategy Consultant, you have to complete an issue tree for a case example featuring Facebook’s transition from desktop to mobile and I’ll walk you through the process step-by-step which also providing you feedback if you want. Learn more here .

Do you have a toolkit for business problem solving? I created Think Like a Strategy Consultant as an online course to make the tools of strategy consultants accessible to driven professionals, executives, and consultants. This course teaches you how to synthesize information into compelling insights, structure your information in ways that help you solve problems, and develop presentations that resonate at the C-Level. Click here to learn more or if you are interested in getting started now, enroll in the self-paced version ($497) or hands-on coaching version ($997). Both versions include lifetime access and all future updates.

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SCQH & Issue Trees

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Introduction

Situation, Complication, Question, Hypothesis (SCQH) is a simple and powerful problem-solving tool. It is especially useful for strategy and is part of the standard training in leading strategy consultancies such as McKinsey.

It can be used in a number of ways, from telling stories to structuring research programmes to planning projects.

It is sometimes written as SCQA, for Answer , but it is usually helpful to treat the last component as a Hypothesis , which can then be tested.

It describes a problem (situation, complication), frames a question about what to do, and finally offers a solution in the form of the hypothesis.

The hypothesis is optional. In some cases, you will only have a question at the start of your work and a hypothesis will only come later (once you’ve done work on your question).

An SCQH does two things: provides clarity on the problem (and solution) and aligns the group on that. This second factor is often as important as the first.

An SCQH is best created in a small group of people, say maximum 7-8 (but you can do more). Once you have the SCQH you can share with wider and wider groups as needed.

Allow between 2h and several days to create an SCQH depending the scale of the issue and the size of the group. The process will be faster the smaller the group and the more experience people have with the process.

Tip SCQ(H) is connected to the Minto Pyramid . Another intro here .

Structure of an SCQH

A SCQH has four parts:

• Situation: where are we now, what’s the context.
• Complication: what’s the problem, what’s not working.
• Question: what do we need to work out, what can we do?
• Hypothesis: what we propose to do to solve the question.

Strictly each SCQH item gets one sentence though this can be relaxed to being a short paragraph. This succinctness forces one to keep things simple and really distill things down.

• Situation: we’re a company making baths and we have been losing money (100k per year) the last two years.
• Complication: if we keep losing money at this rate we’ll be bankrupt in 2 years time and we don’t have any new products ready that will change the situation.
• Question: what new product can we develop and market in the next two years that will generate at least 1m in revenues and at least 100k in profit per year?
• Hypothesis: in the next 18 months we will develop and launch a new enamel bath targeted at the high-end market.

For a long time we have been …

Start by telling your audience something they already know. This helps establishes relevance. As soon as they are asking themselves “I know this – why are you telling me?” you have them hooked. You now have an opening for the Complication.

Typical situations are “we have a task to perform”, “we have a problem” and “we took an action”.

Tip Situations should be factual. They aren’t about what’s wrong. “The walls of our apartment are white,” is a situation, whereas, “I don’t like the white walls of our apartment,” is not.

Example situation: we’re a company making baths and we have been losing money (100k per year) the last two years.

Complication

“Recently the situation has changed…”

What happened next? And specifically, what’s the problem with the situation. The Complication creates tension in the story you’re telling. This triggers the Question you will ask.

Typical complications: “something is stopping us performing the task”, “we [don’t] know the solution to the problem”, “a solution to the problem has been suggested but we don't know if it will work” and “the action we took did not work”.

Often at the start you won’t be clear what is situation and what is complication. That’s fine, just put whatever comes up down

Example complication: if we keep losing money at this rate we’ll be bankrupt in 2 years time and we don’t have any new products ready that will change the situation.

“So what should we do?”

The Question arises logically from the Complication and leads into the Answer.

Typical questions: “what should we do?”, “how do we implement the solution?”, “is it the right solution?” and “why didn’t the action work?”

Example question: what new product can we develop and market in the next two years that will generate at least 1m in revenues and at least 100k in profit per year?

“We need to…”

The Answer to the Question is the substance of your main point. Summarise it first – completing your introduction – then break it down into details and write the main body of your presentations.’

NB: The answer is better thought of as a hypothesis in research-based scenarios.

Example hypothesis: we will develop and launch within the next 18 months a new enamel bath targeted at the high-end market

Example 1: Butcher

Situation: we’re a small family butchers in a medium-sized UK market town and we've lost so many customers (and with them, revenue) over the past few years that we've been considering closing down.

Complication: a major supermarket chain has just announced it is going to build a new store (complete with a butchers counter) on the outskirts of town.

Question: should we keep going and if not what's the best way to close down and make best use of our remaining assets?

Hypothesis: we will sell our 150-year old sausage recipe (our bestseller) to a new local farm shop and organise local press coverage, in which we will also mention that our last stock items will be selling at a discount on Saturday.

Example 2: iMed

This is a real-life SCQH for iMed . Note that this SCQH took a group of 4 people around 2 days to produce with another 2 days spent on the issue and hypothesis tree.

Situation: Medicines are expensive to research and cheap to make and millions of people need them; meanwhile funding mechanisms are not directly linked to health impact, profits are based on prices, and the existence of monopoly patents supports prices well above the cost of manufacture.

Complication: Monopoly patents fund innovation through high prices, creating an inevitable tension between access and innovation; and currently denying access to medicines for millions of people through inflated prices and lack of innovation in non-profitable areas, and failing to incentivise for health impact or efficiency of research and manufacture.

Question: What funding mechanisms can replace the tension between innovation and access inherent in the current [patent] system; incentivising innovation based on cost effective health impact, providing incentives for innovation as high as today, and providing access at close to the cost of manufacture.

Hypothesis: The best resolution to the tension between access and innovation is a remuneration rights model that removes the dilemma and offers incentives for both innovation and access; it provides a free market, state-independent mechanism resourced by the state and philanthropists that incentivises innovations via remuneration based on health impact, on condition that the innovations are free to use and unrestricted, allowing for competition in manufacturing and therefore lower prices for medicines whilst providing incentives for innovators at a similar level to today.

Issue and Hypothesis Trees

The SCQH alone is very powerful. But you can take the SCQH a step further and turn it into a complete planning and implementation tool using issue and hypothesis trees.

Issue tree: expands the question into a series of sub-questions Hypothesis tree: expands the solution statement into a series of sub-statements

Issue Trees

The Question can be broken down into an Issue Tree. This should be a mutually exclusive, collectively exhaustive (MECE) logic tree from left to right, outlining all the questions you need to answer for your question to be answered.

Issue trees are a great way of working out what is causing a certain problem. They are the starting point for solving a problem, since you can’t solve a problem if you don’t know what the problem actually is!

The theory behind creating issue trees is that they give you a scaffold or framework within which to brainstorm. To explain why this is important, consider the following example: someone gives you 30 seconds to make up a story in your head. This is actually quite difficult, since the possibilities are endless. It would be much easier if they framed your story for you - for example, they tell you it must be a story about a dog that travels to Paris by himself, and involves a baguette, a policeman and the Eiffel Tower.

Issue trees work in much the same way. Once you have a basic framework down on paper, fleshing out the rest becomes much easier. The hardest part of the issue tree is creating this framework.

Let’s consider an example. We want to create an issue tree to answer the following question: “why did our leather goods company make less money in 2020 compared to 2019?”. This question goes in a box on the left hand side of our page and forms the ‘tree trunk’. Note that the question itself is quite specific - it doesn’t just ask ‘why is our company making less money’, but adds some detail about the company and the specific time period in which less money was earned.

We then have to add on two (or possibly three) branches that split our question into the main areas in which the problem could be found. These branches should be MECE (mutually exclusive, collectively exhaustive). Mutually exclusive in the sense that the branches are completely distinct and do not overlap in any way. Collectively exhaustive in the sense that all possible causes of our problem that exist come under the umbrella of one branch. See the '5 Ways to be MECE guide' below for a full summary.

Let’s look at the example below:

In this example, ‘factors within our control’ and ‘factors outside of our control’ are completely distinct from each other. Moreover, there is no cause of our problem that is not covered by these two ‘umbrella’ branches. Making sure that these two branches are MECE is the most important part of the process because it ensures that no possible causes of our problem can slip through the cracks even before the real brainstorming begins.

We now need to add another layer of branches, and these also need to be MECE.

We now have a solid framework down on paper in which all possible areas in which the cause of our problem could be found are laid out. It’s time to start fleshing out ideas. Your finished issue tree might look like this:

Many people find it difficult to know where to stop iterating branches. The best way to know whether to stop is to work out whether you can now discredit any of the options based on facts or statistics. For example, in the branch on economic factors, with a little market research you should be able to work out whether there’s a recession or market uncertainty. Likewise, possible causes such as a rise in veganism, whether there are more competitors on the market or if people are shopping less are all factors that can be either confirmed or discredited with statistics. Sometimes, you might need to gather these statistics yourself. For example, to find out whether your staff are demotivated, you might carry out a survey of attitudes.

A great thing about issue trees is that you can also guess which possible causes are very unlikely before you start to gather information or statistics. For example, if there was a technical problem with your website or your staff were being rude to customers, you would probably have received lots of complaints by now. Going through your issue tree and writing down ‘likely’ or ‘unlikely’ next to each possible cause can help you prioritise which areas to look into first.

In this sense, issue trees have most impact when used early on in the problem solving process, when you know little about the problem. Since issue trees disaggregate problems into smaller pieces, they also make it easier to divide the work needed to get to the bottom of an issue between teams.

Some branches on the above issue tree could be looked into in more detail. For example, if you think it likely that the problem lies with your company finances, and suspect you are overspending, you might want to expand these branches. For example:

iMed Issue Tree

For a real-life example, see the iMed issue tree below.

Issue Tree Template Spreadsheet

We have created a Google Sheets SCQH Issue Tree Template .

Tip The sheet is set up for an issue tree of depth 3. If you have greater depth, just add more columns to the left of the green line.

Here's an explanation of the different columns:

• What material is needed? A literal description of what the output should be (e.g. a list, a 1-pager, etc.) or, more broadly, a definition of "done" for this item. For example, if the question were " Who are our competitors? ", the " What material is needed " might say " A list of our competitors with brief description ".
• Value : An estimate of business value. We recommend 1-13 Fibonacci.
• Status : Completion status 0-100%.
• Owner : Who owns answering this.
• By when : When this will be completed by.
• Output : The output(s) answering this question. Usually a link to outputs e.g. others docs, spreadsheets, etc. If the answer is short, it can be written directly.
• Notes / TODO : Self-explanatory. This will often be used sparingly and detailed planning work on answering something will be in a separate project management system.

Hypothesis Trees

In a similar fashion, the Hypothesis can be broken down into a Hypothesis tree. This should be a MECE logic tree from top to bottom, outlining all the hypotheses you need to prove for your hypothesis to be accepted or rejected.

iMed Hypothesis Tree

For a real-life example of hypothesis trees, see the iMed tree:

5 Ways to be MECE

This summary is taken from Crafting Cases .

MECE = Mutually Exclusive, Collectively Exhaustive

Algebraic Structures

Maths equations. E.g. Profits = Revenues - Costs

Process Structures

Beginning, middle, end. Each step of the problem is part of the structure.

Conceptual/Qualitative Frameworks

3Cs of strategy (customers, company, and competition). 4Ps of marketing (product, pricing, placement, and promotion).

Segmentations

Slice up the problem into segments: don’t give you root cause, but do give you an idea of where the problem lies.

Opposite words

Supply vs demand Internal vs external Help generates structure.

Usage: short-term numerical problems.

If your problem revolves around a metric, e.g.

• market shares
• customer evasion rates
• production efficiency

Then you can break it down into its components e.g.:

• Revenue = Price * Quantity
• Profit = Revenue - Cost
• Customer evasion = customers we stopped serving + customers who moved to competition + customers who stopped using this type of service

Guarantees MECEness.

Where algebraic structures don’t work:

Qualitative problems

Long term strategic questions: M&As, Market Entries, Long-term growth strategies…

Only use on problems that use predictable process e.g. manufacturing

Beginning, middle end. E.g. The cost of manufacturing a widget has risen. Break the manufacturing process down into its constituent parts, then see whether the costs of each part has risen.

Conceptual Frameworks

Great for qualitative, long-term problems, where quantitative data is not available.

Conceptual frameworks are structures based on categories of concepts. Examples: 3Cs, 4Ps, Porter’s 5 forces. More examples: People Process Systems – simple organisational problems can be pinned to a problem with one or more of each. Trust Equation – Credibility + Reliability + Intimacy + (lack of) Self-orientation

Risk 1: Not knowing a framework well enough for your specific situation.

Risk 2: Not being able to adapt the chosen framework to the specifics of your situation.

Risk 3: Not seeing how your framework connects with other potential structures.

Be flexible when using structures, don’t be afraid to mix and match structures. Think about how different structures could work together.

Use as a complement to another structure, when there is reason to think one segment has different behaviour than others, or when testing for “the mix effect”

Examples of segmentation:

• by age group (0-20, 21-40, 41-60, 61+)
• by gender (male, female)
• By product line
• By type of customer

Segmentations help create structure, but only generate insight if you’ve chosen the right segmentation criterion.

Also useful in identifying “mix effects”.

Mix effect: Average performance changes not because underlying performance changes, but because performance is different across segments and the weights of the segments have varied through time.

In this example, the change in distribution channel was the cause behind the drop in average prices, as opposed to a drop in prices themselves. However, the segmentation criterion that would reveal this is far from obvious. Instead of changing distribution channel, it could have been a change in package size (where bulk is cheaper per unit), or that the company is growing in countries where their products cost less. You must guess and hope you’ve got the correct criterion.

Segmentations best complement other structures, because they rarely generate insight on their own.

Opposite Words

Opposite word pairs aren’t that insightful, but helps structure any problem.

• Supply and demand
• Financial and non-financial
• Strategic and operational
• External and internal
• Short term and long term
• Buy and sell

Use as a deeper layer of another structure.

Creating Issue Trees with MECE

Being MECE is good because you can structure any case in a way that will lead to a solution.

Tying up these five techniques is a way of creating issue trees from scratch.

• Break down a problem into a MECE structure.
• Pick each part of your first layer, and break it down again, using the same or a different technique.

Notes on the above issue tree

Starting with the algebraic structure is good because:

• Leads to the insight that the problem could be with selling devices as well as capsules
• Makes the problem quantifiable, so that half of the tree could be ignored with a little bit of data

Within the machine share, there’s a segmentation for further quantification, but the 3Cs framework in addition in order to analyse why. The 3Cs is good because each segment has a different demand and competition.

The following issue tree is bad, even though it’s MECE.

It’s bad because:

• The initial breakdown doesn’t focus on the nature of problem nor bring insight
• Could have been mentioned once if it were being used to compare Nespresso’s marketing against its competitors (working with them in comparison, not in isolation)

More info here

How to practice:

• Pick a case
• Find as many ways to break it down as you can.
• Pick one breakdown to start your issue tree. Prioritise according to insightfulness and efficiency. a. Insight: showing fundamental characteristics of the problem. b. Efficiency: prioritise or eliminate parts of your structure with little bits of data c. If in doubt, pick another framework and compare the differences.
• Build the rest of the tree by breaking down each bucket of the first layer.
• Evaluate your structure. a. Best is to ask a consultant. b. Second best is to practice with cases for which you have good answers c. Third is to assess against the principles of a good structure

And remember:

• Did I pick the technique that will bring me the most insight to do the first break-down? The first layer of your structure is critical because it determines the rest.
• Am I missing anything? One way to test this is to google the topic of the case (in this case “warehouse theft”), read a couple of articles or news about it, list down the issues/ideas/hypotheses that come up in those articles and see if there’s a place for each in your structure. If there’s not, it’s probably not MECE.
• Try explaining your structure to someone else (a friend, your romantic partner or even your dog will do!) and see if you sound like a human being going through your structure. If you don’t, it’s probably too complex or too “buzzwordy”.

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• Create your first decision tree from a template or blank canvas or import a document.
• Add shapes, connect lines, and write text.
• Learn how to adjust styling and formatting within your decision tree.
• Locate what you need with Feature Find.
• Share your board with your team to start collaborating.

Decision tree diagram templates

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Project management decision tree

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Make decision trees on any device with ease. Lucidchart helps teams collaborate in real time from anywhere in the world. Enhance productivity while working together with your team to achieve your goals.

Lucidchart brings clarity to the complex. Intelligent diagramming allows you to quickly visualize projects and processes from start to finish.

Drive alignment with your team by collaborating in the same space. Quickly map out processes and ideas together in real time so that you never miss a beat.

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What is a decision tree diagram?

A decision tree diagram is a map of the possible outcomes of a series of related choices. It allows an individual or organization to weigh possible actions against one another based on cost, probability, and benefits. They can be used to drive informal discussion or to map out an algorithm that predicts the best choice mathematically.

Advantages to using decision tree diagrams

Comprehensive.

Decision trees invite you to look at all the possible outcomes of a choice, so you’re able to better understand the risks and consequences of your decisions.

Decision trees don’t rely on formulas. They’re easy to understand and easily shared with others to get their input. This can help you gain buy-in from stakeholders.

Use decision tree diagrams to get your questions answered. They can be simple or complex to adapt to your needs.

Reduce bias

Decision tree diagrams help eliminate the emotions involved in making a decision, letting you properly weigh the results of one decision against another.

Decision trees work with the data you already have. If there are gaps in the data, you can identify where you’ll need more information.

Cost effective

Making a decision tree diagram is free and doesn’t require advanced training. There are plenty of free templates available.

Frequently asked questions about decision trees

There are three different types of nodes: chance nodes, decision nodes, and end nodes. A chance node, represented by a circle, shows the probabilities of certain results. A decision node, represented by a square, shows a decision to be made, and an end node, represented by a triangle, shows the outcome of a decision path.

Decision trees help you evaluate your options. Decision trees are excellent tools for helping you to choose between several courses of action. They provide a highly effective structure within which you can lay out options and investigate the possible outcomes of choosing those options.

Decision trees are composed of three main parts—decision nodes (denoting choice), chance nodes (denoting probability), and end nodes (denoting outcomes).

Flowcharts are used to describe the tasks involved in a process, which could include multiple decisions along the way. Decision trees are designed for a single decision or classification.

Yes! Our template gallery offers several decision tree templates, which can help you create a decision tree online. In the Lucidchart editor, type “decision tree” in the template search bar and select from the examples provided.

Yes! Use formulas in Lucidchart to calculate more accurate outcomes. When applied, these formulas will automatically adjust potential costs or values as you branch out your decision tree.

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• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1. ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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McCombes, S. (2023, November 20). How to Write a Strong Hypothesis | Steps & Examples. Scribbr. Retrieved September 16, 2024, from https://www.scribbr.com/methodology/hypothesis/

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The Hypothesis Prioritization Canvas

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Over the past 10 years we’ve been lucky to have a tremendous amount of content, practice and experience shared to help us build and design better products, services and businesses.  One of the core concepts being adopted broadly from this body of work is the hypothesis — a tactical, testable statement used to help us frame our ideas in a way that encourages experimentation, learning and discovery. The idea is that we write our ideas, not as requirements, but as our best guesses for how to deliver value and with clear success criteria to tell us whether our idea was valuable and we delivered it in a compelling way.

While there are many templates, the one I’ve been teaching for the past few years looks like this:

We believe [this outcome] will be achieved if [these users] attain [a benefit] with [this solution/feature/idea].

I like this template because the act of filling it out is the first test of the hypothesis. If you and your team can’t complete this template in a way that you believe that’s a good indication you shouldn’t be working on that idea. But, assuming you’ve come up with some good ideas, you end up creating a new challenge for the team.

So many hypotheses, so little (discovery) time

If you only have one hypothesis to test it’s clear where to spend the time you have to do discovery work . If you have many hypotheses, how do you decide where your precious discovery hours should be spent? Which hypotheses should be tested? Which ones should be de-prioritised or just thrown away? To help answer this question I’ve put together the Hypothesis Prioritisation Canvas. This relatively simple tool and a companion to the Lean UX Canvas can help facilitate an objective conversation with your team and stakeholders to determine which hypotheses will get your attention and which won’t. Let’s take a closer look at the canvas.

When should we use this canvas?

If you’re familiar with the Lean UX Canvas, the Hypothesis Prioritisation Canvas (HPC) comes into play between Box 6 (writing hypotheses) and Box 7 (choosing the most important thing to learn next). If you’re not familiar with it, the HPC comes into play once you’ve assembled a backlog of hypotheses. You’ve identified an opportunity or problem to solve, declared your assumptions and have come up with ideas to capitalise on the opportunity or solve the problem.

What kinds of hypotheses work with this canvas?

The HPC is designed to work with any hypothesis you come up with. It can work with tactical, feature-level hypotheses as well as business model hypotheses and everything in between.

How do we use the canvas?

The canvas is a simple matrix. The horizontal axis measures your assessment of the risk of each hypothesis. This is a team activity and is the collective best guess of the people assembled of how risky this idea is to the system, product, service or business.  The challenge with assessing risk is that every hypothesis is different. Because of this, your risk assessment will be contextual to the hypothesis you’re considering. For example, you may have to integrate modern technology with a legacy back end system. In this case the risk is technical. You may be reimagining how consumers shop in your store which is risky to your customer’s experience. Maybe you’re considering moving into an adjacent market after years focusing on a different target audience. The risk here is market viability and sustainability. Every hypothesis needs to be considered individually.

The vertical axis measures perceived value. The key word here is “perceived.” Because this is a hypothesis, a guess, the value we imagine our ideas will have is exactly that, imagined. It won’t be until a scalable, sustainable version of the idea launches that we’ll know whether it lives up to our expectations. At this point we can only guess the impact the idea will have on our business if we design and implement it well.

We take each hypothesis we’ve created to solve a specific business problem and map it onto the HPC’s matrix. Once we’ve completed this process, we assess where each hypothesis landed.

Box 1 — Test

Any hypothesis that falls into this box is one we should test. Based on what we know right now this is a hypothesis with the chance of having significant impact on our business. However, if we get it wrong it also stands the chance of doing damage to our brand, our budget or our market opportunity. Our discovery time is always precious. These are the hypotheses that deserve that time, attention, experimentation and learning.

Box 2 — Ship & Measure

High value, low risk hypotheses don’t require discovery work. These are ideas that have a high level of confidence and, based on our experience and expertise, stand a good chance of impacting the business in a positive way. We build these ideas. However, we don’t just set and forget these solutions. We ship them and then measure their performance. We want to ensure they live up to our expectations.

Box 3 — Don’t test. Usually don’t build.

This is, perhaps, the least clear quadrant because there are ideas that may fall here that have value despite the “low value” indication on the matrix. To be clear, hypotheses in Box 3 don’t get tested. In most cases they don’t get built either however there will be times where ideas land in this box that we need to build a successful business but that won’t differentiate us in the market. For example, if you’re going to do any kind of commerce online you’ll need a payment system. In most cases, how you collect payment is not going to differentiate you in the market. These types of ideas often end up in Box 3. They’re table stakes. We have to have them to operate but they won’t make us successful on their own. In these cases we build them, ensure they work well for our customers but don’t do extensive discovery on them prior to launch.

Box 4 — Discard

Hypotheses that we deem to have low value and high risk are thrown away. Not only do we not do discovery on them, we don’t build them either. These are ideas that came up in our brainstorm that we’ve not realised won’t add the value we’re seeking.

Ultimately the value of the HPC will be realised if and how your team uses it. Take it out for a spin. It’s intended to be a team activity. Let me know how it works for you, where it can be improved and whether you find it useful or not.

I’m excited to hear your feedback.

P.S. — Lots of new events posted on the Events page now. Join me in person in 2020.

Jeff Gothelf’s books provide transformative insights, guiding readers to navigate the dynamic realms of user experience, agile methodologies, and personal career strategies.

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One response to “The Hypothesis Prioritization Canvas”

This is really helpful (as always) – thank you.

I wonder if there would be any merit in adding a line to the end of you hypothesis template along the lines of.. “because [of this evidence and scientific theory]

This grounds the hypothesis in existing evidence and established social scientific theory. It might also help avoid the potential pitfall that I’ve seem some business fall in to i.e. assuming that clients are rational actors driven by clear interests, when it might be more helpful to think of them as complex emotional people driven by instincts.

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18+ Free Decision Tree Templates (Excel / Word / PDF / PPT)

Home / Business Templates / 18+ Free Decision Tree Templates (Excel / Word / PDF / PPT)

A decision tree template helps you in making the right decision. Sometimes making decisions on certain matters become complicated. Also, when there is more than one solution available then reaching a good decision can be difficult. Here decision tree is the best way of achieving this scared end. However, you can also use a decision tree template to achieve the right outcome.

Table of Contents

• 1 What is a decision tree?
• 2.1 Root Node:
• 2.2 Branch node:
• 2.3 Leaf node:
• 3 Creating a decision tree template:
• 4 Creating a decision tree in MS Word:
• 5.1 Use basic shapes in lines:
• 5.2 Use SmartArt Graphics:
• 6 Why should you use a decision tree template?
• 7.1 Start by explaining your question or problem area
• 7.2 With a central node of your decision tree, start and create logic branches
• 7.3 On your decision tree, include context for every decision point
• 7.4 Organize in other decision-making frameworks
• 7.5 For more feedback, share it with stakeholders
• 7.6 As a living reference, publish and maintain the decision tree
• 8 Benefits of creating a decision tree:
• 9 Conclusion:
• 10 Faqs (Frequently Asked Questions)

What is a decision tree?

A decision tree is a flowchart used to make all possible decisions and the outcomes of those decisions. Every branch shows a choice that’s available while making a decision. It is driven by cause and effect. When an outcome leads to another course of action, you can extend a branch, and then extend that branch, and so on.

Basic elements of a decision tree:

The decision tree has three basic elements;

It is the very first node that indicates the ultimate objective or decision that you need to be made. It is present on the top of the whole structure. Moreover, from this node, all other elements come from. You have to state the root node carefully because it will affect the type of direction that the other elements of the tree will take.

Branch node:

Just like the branches of tree stems originate from the roots, in the same way, the branch node comes from the root node of a decision tree. As its name implies, this node indicates the certain actions that you have to take to come up with a solution. You can use different arrows to indicate these branches.

As branches of trees have leaves, this node represents the possible results for each action you take. However, there are two types of leaf nodes to use;

• To represent unknown outcomes, use circle nodes.
• To represent that there is a need to make more decisions, use square nodes.
• You may also like Policy Brief Template .

Creating a decision tree template:

You can download decision tree templates from bestcollections.org but if you want to make one yourself then here are some steps for your guidance;

• At first, you have to define a question.
• Next, insert the branches of the tree. You can also add or terminate some branches if needed.
• After inserting branches, add the leaves of the tree.
• When you have made it, check it thoroughly. You should also check the 15+ Best Family Tree Template .

Creating a decision tree in MS Word:

You can easily make a decision tree in MS Word. Let us discuss how you can create it;

Firstly, open a new file in MS Word.

After opening a new file, click on the Insert Tab, then Illustration, and then SmartArt Graphics.

From the hierarchy template that is according to your needs, select your desired SmartArt Graphic.

Next to the SmartArt Graphic, enter your decision in the editing box.

Until you have finished your template, adding your thorough information and elements.

Creating a decision tree in MS Excel:

You can create a decision tree in MS Excel in two different ways;

Use basic shapes in lines:

• At first, open a new file in MS Excel and enter your data into the spreadsheet
• Go to the Insert Tab and from the Text Section select the text box. You can easily draw a text box by using your cursor.
• After that, there comes the most important part of your tree i.e. entering the main question in the text box.
• The next step is to draw the outcomes or answers to your main question. It’s up to you how many answers or outcomes you can draw for your main decision.
• If you want to draw a line, go to the Insert Tab, from the Illustrations select the shapes. Now, you can create a straight line. You should attach an oval or box for your first outcome after inserting a straight line. Similarly, keep adding the ovals or boxes for other outcomes.
• Until you have completed your decision tree, continue using the connectors to draw outcomes.

Use SmartArt Graphics:

• Also, the first step of this method is to open a new file in MS Excel.
• Go to the Insert Tab and from the Illustrations section select the SmartArt Graphics.
• After that, select the horizontal hierarchy from the Hierarchy tab. When you click ‘OK’ then the Horizontal Hierarchy Graphic will open.
• Now it’s time to start editing your tree. Inside the decision box, click the placeholder and enter your content. You can also edit your content by using the editor box next to the SmartArt Graphic. You may also see 20+ Free Obituary Templates & Samples .

Why should you use a decision tree template?

Here are some reasons for using a decision tree template;

• You can easily understand the decisions by using a decision tree template. It helps you to simplify complex problems. Also, it lets the people know how you came to a conclusion.
• With the help of decision trees, without investing actual resources, you can visualize outcomes and play through scenarios.
• Decision trees do not require adequate time like algorithms. This is because they need less coding, analysis, or even dummy variables.

To clarify thinking, how to use a decision tree?

Here are some tips for using a decision tree to clarify thinking;

Start by explaining your question or problem area

You can use a decision tree template for multiple problem areas. First, write down several problem statements on sticky notes and then group them and arrange them in an organized way. Invite various stakeholders to vote on the most relevant decision that has to be analyzed first.

With a central node of your decision tree, start and create logic branches

For every available decision, specify the options. To do so, add simple lines that branch out to display the possible outcomes. When you extend these outcomes to a tree-like structure, they assist you in assessing the outcomes of each decision and better weighing your options.

On your decision tree, include context for every decision point

To every node, attach reference notes, external data, and much more in order to make more accurate assumptions. This way, you can also make more predictable outcomes.

Organize in other decision-making frameworks

For designing a more complete course of action, extend the output of your decision tree. Use RICE frameworks to evaluate the impact of a decision and use a priority grid to prioritize tasks. Make action plans all on the same canvas.

For more feedback, share it with stakeholders

To get feedback for your decision, invite your management/clients/agency or consultants securely. Detailed discussions are important to make sure you arrive at the best decision together.

As a living reference, publish and maintain the decision tree

To clarify thinking and improve strategic thinking, make your decision tree easily accessible to the entire organization.

Benefits of creating a decision tree:

The benefits of creating a decision tree are as follows;

• These trees are non-linear and provide more flexibility for planning, exploring, and making predictions for possible results to decisions either they happened or not.
• It visually displays cause-and-effect relationships. Furthermore, it gives you simpler and effective communication of complex processes. They are easy to understand even to those who never made them before because they are straightforward.
• You can also consult others during making important decisions but rely too much on other’s opinions may put your final decision at risk. While the decision trees allow you to have a balanced look at the decision-making process. In addition, they enable you to calculate both rewards and risks. In short, they pay attention to data and probability instead of biases and emotions.
• They provide you a predictive framework through which you can ultimately identify the course of action that provides you the highest possibility of succeeding. Hence, it prevents your decisions against unacceptable outcomes and risks.
• With the help of a decision tree, you can easily analyze the problem in a way that the traditional approach can’t match. This wholesome analysis provides you more effective and easier interpretation.
• This decision tree is also helpful for the problems that require a quantifiable framework. It is an effective way for tackling it.

Conclusion:

In conclusion, a decision tree is a tree-like model of the decisions to be made. It consists of three basic parts the first one is the root node that represents the decisions to be made, the second one is the branch node that indicates the possible actions, and the third or last one is the leaf nodes that display the possible outcomes. Moreover, you can also use decision tree templates as they provide you raw frameworks.

Faqs (Frequently Asked Questions)

An effective decision tree makes it easier to visualize and evaluate the process of decision-making. You can see the possible consequences of each decision by laying out the problems clearly.

Anyone looking to solve problems or evaluate options can use decision trees. When you have two or more options then use the decision tree template. It helps you to visualize the results and plan for scenarios better.

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