The Definitive Guide to Issue Trees
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
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.
How do I know that?
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.)
And you can make some areas of your tree deeper than others.
But each part must FULLY explain the previous bucket from the previous layer.
To do this you use the MECE principle.
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).
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:
And each full layer is a complete equation of the variable that's being broken down.
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:
to define in this problem is what is "quality"
as a 1st layer and why?
Well, you could very well end your Issue Tree here.
Or you could dig a few more layers deeper. It really depends on the situation.
Click the arrow on the right to see what my next layer would look like.
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.
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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.
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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.
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Issue Tree Examples
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%?
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.
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).
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.
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