(2024 Update)
Early on in the McKinsey case interview process, candidates will take part in an evaluation called “Solve,” the McKinsey digital assessment game (formerly called the McKinsey Problem Solving Game).
This game evaluates your ability to solve problems efficiently and think critically. It uses algorithms to evaluate your reasoning skills and decision-making process.
In other words, if you simply guess the right answers, the algorithm will know you didn’t follow a systematic-reasoning process.
If you get the answers mostly correct because you used a systematic process, the algorithm would detect that you used a logically disciplined approach. Because of your strong approach, it can tell that you would get the right answer if you had more time to complete more iterations of your process.
What Is the McKinsey Problem Solving Game?
McKinsey’s problem solving game Solve consists of two mini-games that fall into three potential categories. Each of these mini-games requires the candidate to manage an ecological situation.
The three categories that the games may fall into include: 1) Constrained optimization; 2) Data interpretation + critical numerical reasoning; and 3) Cause vs. effect.
Each of these mini-games takes about 30 to 45 minutes to complete.
Time management is a factor in your success, but it’s not everything. Some candidates finish early, while others don’t complete it in time.
The more important thing to focus on is showcasing your critical thinking skills. McKinsey wants to see what you display during that time rather than how quickly you can complete each task.
The metric that McKinsey calculates from the problem solving game is what’s called a “process score.” The process score grades your thought process as you problem-solve.
McKinsey’s Solve calculates this score by tracking your mouse clicks and movements. How often do you have to click back and forth? What do you click on first? What was the actual process through which you reached your conclusion?
These factors make it easier to discern whether you stumbled upon the correct answer through luck or good critical thinking skills.
What Does the McKinsey Problem Solving Game Test For?
McKinsey’s digital assessment game tests for five factors:
- Critical Thinking: How well do you analyze the information?
- Decision-Making: What actions do you take based on your analysis?
- Metacognition: How well do you execute strategies to achieve the objective of the game?
- Situational Awareness: Do you maintain focus on the environment? Can you anticipate future changes?
- Systems Thinking: How well do you understand the cause-and-effect relationships of the items within the system?
How Can You Prepare for the McKinsey Problem Solving Game?
Preparing for McKinsey’s digital assessment game is challenging for two reasons:
- There are multiple versions of the game
- The scenario is randomized for each candidate
These two factors mean that one person’s correct answers for their game will be completely wrong for a different candidate playing a different randomized version of the game.
In other words, even if you had a recording of someone else successfully passing the game, you could not copy their exact strategy and also find success.
How to Win the McKinsey Problem Solving Game
To win McKinsey’s problem solving game Solve, focus on these strategies:
- Remember the skills being tested. Solve is testing for critical thinking skills, decision-making skills, metacognition, situational awareness, and systems thinking. During the game, focus on how you’re performing in those areas and make intentional choices that showcase those skills.
- Understand the instructions and the objective. Take your time to read the instructions and objectives for McKinsey’s Solve to make sure you understand them fully. This portion of the assessment is not timed, and taking an extra minute or two to reread them can help you avoid simple mistakes. If it helps, take notes.
- Watch the time. Don’t spend too much time looking for the perfect answer. Zero in on the relevant information, ignore irrelevant data, and make your best guess when there is limited info. It’s more important to finish the game in time than to spend an excessive amount of time only slightly improving your performance in the first phase of the game.
- Prioritize. McKinsey’s Solve game tests how well you can turn a pile of data into actionable and successful strategies. Remember the big picture and prioritize your goals. This will help you avoid getting caught up in irrelevant details.
Types of McKinsey Problem Solving Game Mini-Games
As previously mentioned, there are three types of mini-games in McKinsey’s digital assessment game:
- Constrained Optimization
- Data Interpretation + Critical Numerical Reasoning
- Cause vs. Effect
These mini-games may seem very different, but they are all designed with one goal in mind: to measure your logical thinking process.
As of this writing, the first type of mini-game — constrained optimization — is the most common and the oldest. The second type of mini-game — data interpretation and critical numerical reasoning — was newly introduced in 2024.
Previously, McKinsey also used a cause vs. effect type of mini-game, but less often. My guess is that the type was being tested with candidates to create a statistical track record in order to see how the scores in cause vs. effect mini-games correlated with the large database of scores collected from the other types of mini-games.
1. Constrained Optimization Mini-Games
Let’s take a closer look at the first type of mini-game in McKinsey’s Solve game: the constrained optimization mini-game. There are a few different versions of this mini-game, including:
- Ecosystem Building: The candidate chooses the best location and species to inhabit that location to create the most sustainable ecosystem. In this version, there are multiple species of creatures with interrelationships. Herbivores eat plants. Omnivores eat herbivores. Carnivores eat omnivores.
- Disaster Identification: An unknown natural disaster is decreasing the population of animals in the ecosystem. Find what the natural disaster is and move the animals to a new location where they can survive. A large amount of data is given to reach the solution, and the candidate must be able to discern the key factors.
Over time, McKinsey will likely introduce new variations of this mini-game.
Strategies to Win
Mini-games like these include systems with linear progressions. This means that the output of one component in an ecosystem is the input of another (e.g., plants “produce” food for herbivores to eat).
Below is a list of strategies and tips to help you succeed in these games. But before I go over them, I want to note that if you simply read these instructions without actually using a practice version of the game, these tips may not make a lot of sense.
To help, I recommend reading the guidelines below twice: once now and a second time after you’ve played a practice version of this type of game. It will make a lot more sense the second time around.
For these and other constrained optimization mini-games, focus on these strategies:
- Note the maximums and minimums. In these games, there are often local minimums and maximums. These are rules on how the metrics used in the game must be greater than or less than some specific threshold.
It’s important that you quickly find out which specific metric (because there will be more than one) is subject to the great-than and less-than rules. These rules create the “constraint” part of the constrained optimization problem to be solved.
- Ignore irrelevant data and focus on solving the optimization problem at hand and navigating the constraints. You will intentionally be given more data than you need to reach your objective. Figure out which information is irrelevant/unnecessary and ignore it. You don’t want to waste time on information that doesn’t help you find the solution.
(By the way, this happens all the time in client engagements! The client hands off a ton of information, and 90% of it is irrelevant to the issue you’re addressing.) - Use a pen and paper to take notes and test out hypotheses. You are allowed to use pen and paper during McKinsey’s Solve. So take advantage of that and take notes of important factors, do some quick math, and test out hypotheses before making changes to the mini-game.
Write down what you think will work and what you’ve tried so far that didn’t work. It will help you avoid simple errors like trying the same strategy over and over again.
Note: Constrained optimization mini-games have an element of trial and error. So you do not want to accidentally test the same hypothesis twice. Each cycle of trial and error consumes valuable time.
Write down every trial you attempt, and when a trial produces an error, make a note that this trial did not work and why. - Don’t start in the middle of the system. Be strategic about how you approach the system. You might start at the bottom with a part of the system that generates output but has no input (e.g., feeder fish in an ecosystem or raw materials in a factory). Then, you can work your way up.
You also might start at the top of the system with a part that accepts input but doesn’t generate output (e.g., an apex predator in a biological ecosystem or finished goods in a factory). Then, you can work your way down from the top.Either of these strategies allows you to move through the creation of the system linearly. A bottom-up approach works. A top-down approach also works. What doesn’t work is starting in the middle and attempting to go both directions at the same time.
You need a baseline to rely on. That’s why it’s important to start at one end and work your way to the other. - Take a step back after a mistake. Don’t completely scratch an attempt and start over if some part doesn’t align with the others before and after it. Instead, go back to the previous step that did align, and try the next option. Take note of the component that didn’t work and review your notes to see what other options you have.