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Consulting Articles > MBB Online/Screening Tests > McKinsey Solve Game: Guide, Updates & Free Practice [2024]

McKinsey Solve Game: Guide, Updates & Free Practice [2024]

Updated: November 12, 2024

Table of Content:  

What is mckinsey solve game, how does mckinsey solve game work, what traits is mckinsey assessing in mckinsey solve game.

• Ecosystem Building
• Red Rock Study  
• Ocean Cleanup

The Importance of Practicing McKinsey Solve Game

Check out the only, fully-playable, and FREE McKinsey Solve Test (Problem-Solving Game) Simulation in the entire market!

McKinsey Solve Game, also known as the Problem Solving Game, PSG, Digital Assessment, or informally referred to as the "Imbellus Game," is a gamified assessment developed by Imbellus for McKinsey & Company. ( Click to see all screening tests by McKinsey, BCG and Bain! )

Within McKinsey's hiring process, the Solve Game is screening test, positioned between the application and the case interviews. Its primary objective remains consistent with the traditional Problem-Solving Test: to efficiently identify suitable candidates and streamline the resource-intensive case interview phase. This approach optimizes both time and resources in the recruitment process.

The one and only existing platform to practice three McKinsey Solve simulation games for FREE !

The McKinsey Solve Test, also known as McKinsey Digital Assessment or McKinsey PSG, is subject to a total time of approximately 71 minutes. Candidates are given 2 out of 6 possible mini-games. The assessment evaluates both the final results and the solving process. If a candidate demonstrates skills and tendencies similar to those of a McKinsey consultant, they may receive an invitation for an interview.

The McKinsey Solve Test includes 6 confirmed mini-games: Ecosystem Building , Red Rock Study , Ocean Cleanup , Disaster Management, Disease Management, and Migration Management. It's worth noting that almost all candidates, nearly 100%, will start with the Ecosystem Building Game as their first challenge. In 2024, McKinsey added the third game - Ocean Cleanup. This game is still in beta phase and given after Ecosystem Building and Redrock Study.

Please note that the other three games, namely Disaster Management, Disease Management, and Migration Management, were previously used by McKinsey for beta testing purposes. However, they are no longer included in the McKinsey Solve test in 2023.

The time allocated for tutorials is not factored into the overall time limit. It's advisable for candidates to maximize this tutorial period by attempting to anticipate the mini-game's objectives and crafting a general approach before initiating the mini-game itself. This time can also be utilized for essential preparations, such as having pen, paper, and excel sheet readily available.

To familiarize yourself with the interface and how the McKinsey Solve Game works, sign up for our Free McKinsey PSG Simulation and give it a try!

The McKinsey Solve game assesses five critical cognitive abilities:

• Critical Thinking : Your ability to thoroughly analyze information.
• Decision-Making : Your capacity to take appropriate actions based on your analysis.
• Metacognition : How effectively you implement strategies to achieve the game's objective.
• Situational Awareness : Your capacity to maintain focus on the environment and anticipate future changes.
• Systems Thinking : How well you comprehend the cause-and-effect relationships among the elements within the system.

The positive aspect is that many of the assessed skills are interconnected, meaning that improving one area can also enhance performance in others. This is crucial because it allows you to avoid diving into every tiny detail just to achieve a higher score.

Additionally, while showcasing all abilities is essential for success, some skills carry more weight. Based on the insights from this Imbellus research paper , Critical Thinking, Situational Awareness, and Systems Thinking are the core competencies that top candidates must demonstrate.

Advanced abilities such as Decision-Making and Meta-Cognition, on the other hand, are what separate great candidates from the rest.

What does McKinsey Solve Game include?

Game 1: ecosystem building.

What is Ecosystem Building Game?

This is a 35-minute time limit game. You will be placed randomly into either a mountain or a reef scenario. ( Click here to sign up and play the game for FREE! )

Your goal is to construct an ecosystem comprising 8 species from a selection of 39. There are three primary objectives to accomplish:

• The ecosystem should establish a continuous food chain.
• Ensure a calorie surplus for each predator-prey pair.
• Align the ecosystem with the terrain specifications of your chosen location

What does Ecosystem Building Game include?

1.  Terrain Specifications

Each location within the ecosystem is characterized by seven to eight terrain specifications, which can be selected by pinpointing a location.

In the Mountain scenario, there are 8 terrain specifications: Elevation, Soil pH, Precipitation, Temperature, Wind Speed, Air Pressure, Humidity, and Cloud Height.

In the Reef scenario, you'll find 7 terrain specifications: Depth, Water Current, Water Clarity, Temperature, Salt Content, Dissolved Oxygen, and Wind Speed. 

Each species has its own set of required terrain specifications, typically ranging from two to four. Failure to meet these terrain requirements will result in the species dying out. These requirements often come in ranges.

2. Food Chain Continuity

In the game, you will be given a total of 39 species, categorized into two main types: producers and consumers.

• Producers: These include plants and corals. Their sole source of food is typically sunlight or other natural elements. Consequently, they neither have prey nor consume calories.
• Consumers: This category comprises animals, which can further be classified as Herbivores (plant-eating animals), Carnivores (animal-eating animals), or Omnivores (consumers of both plants and animals). Additionally, some consumers hold the status of "apex animals," indicating that they do not have natural predators and are not consumed by any other species within the ecosystem.

3. Calories Balance

Each species in the ecosystem is characterized by two essential figures: Calories Needed and Calories Provided.

A species can thrive under the following conditions:

• Sufficient Energy for Survival: The species' calorie needed is lower than the total calorie provided by the ecosystem.
• Avoiding Extinction by Predation: The species' calorie provided surpasses the combined calorie consumption by the species that prey upon it.

In essence, for a species to thrive, it must have enough energy to sustain itself and should not be consumed to the point of extinction by its predators.

Main Challenges of Ecosystem Building Game:

• Information Overload: Handling a significant amount of data that requires absorption, calculation, analysis, and prioritization. This encompasses the specifications of 39 species, terrain characteristics of each location, and dietary rules.
• Distracting and Irrelevant Information: Certain details provided may not be relevant and are included to divert your attention or lead you into making assumptions. It's essential to refrain from making any guesses or relying on any prior knowledge related to the environment, ecology, or zoology

How to tackle Ecosystem Building Game:

Here's a breakdown of how to approach the game, following a 3-step process:

Step 1: Location Selection

• Use a spreadsheet or scratch paper to record the terrain specifications and calorie provided data for the mini-game's producers.
• Examine the data to identify which terrain specifications remain consistent across all species and which ones change. Focus only on the changing terrain specifications (usually 2 of them), while the others are intended to divert your attention.
• Calculate the total calories provided for the producers in each group of terrain specifications. The group with the highest calorie provision is likely the easiest to construct the food chain.

Step 2: Food Chain Building

• Examine the data to list consumers with compatible terrain requirements in your spreadsheet.
• Select the apex predator with the lowest calorie requirement as the starting point for the food chain.
• Construct the food chain starting from the apex predators and work your way down, listing the food sources for each top-level predator. Keep progressing in this manner until you reach the lowest tier, which includes corals and plants. Ideally, the food chain should encompass 3-4 levels and encompass 8 species.
• Alternatively, you can build the food chain from the bottom up by examining the "Eaten By" specifications of each species, working your way up to the top predators.
• Throughout the process, prioritize species with high calorie provision and low-calorie requirement. This should increase the chances of a calorie surplus in the food chain and allow room for additional species if the initial chain falls short of the required 8 species.
• If your food chain doesn't reach the necessary 8 species, work from the bottom up to identify gaps of unused surplus calories and fill these gaps with predators or plant-eating animals.

Step 3: Sanity Check

• Ensure that each species in your food chain aligns with the chosen location.
• Verify that the food chain is continuous, meaning that the listed food sources match the descriptions of each species.
• Confirm that each species in the food chain has an adequate calorie supply and isn't consumed to extinction based on the provided eating rules.
• If any of the three checks are not met, make adjustments to the food chain.

Game 2: Red Rock Study

This is a game with a 35-minute time limit, during which you will complete the Red Rock Study, consisting of both Part 1 and Part 2. 

Experience the Redrock Study game firsthand by trying our FREE Simulation today.

The McKinsey Red Rock Study divides the tasks into 2 parts:

Part 1: Study

Part 2: cases.

In Part 1, you'll get one study with a main goal and some data to support it. This part has three steps:

Phase 1: Investigation

• Phase 2: Analysis
• Phase 3: Report

In Part 2, you'll receive 6 short cases that are related to the same topic but not directly connected to the Part 1 Study. Each case will come with two different types of questions:

• Multiple choice questions
• Numerical answer questions

You need to finish both tasks (Part 1 & Part 2) within a total time limit of 35 minutes. Although there are no specific time constraints for each part, it's advisable to allocate more time to the first part and less to the second part.

Now, let's delve into the specifics of Part 1 and Part 2 of the tasks.

Your goal is to read through the case description, recognize the main objective and essential data points, and then gather them in an on-screen Research Journal.

The data and information provided are divided into three sections, with each section containing the necessary information required to complete the study:

• Study Information

How to tackle this phase:

• Understanding the study
• Collecting important data points

1. Understanding the study

Your objective here is to identify case’s objectives.

Every piece of information displayed on the screen is crucial for comprehending and resolving the case. However, some are more critical than others. Significant data points are highlighted and displayed in boxes on the screen, allowing you to click and drag these boxes to focus on them while working within the case.

The data provided comes in two formats:

• Movable data points : These text-based data points consist of case objectives and calculation instructions. They clarify the case's goal, specify the mathematical formulas to be applied, and outline which numbers need to be gathered. Typically, these are detailed sentences or paragraphs that describe the relationships (such as higher, lower, etc.) between the elements within the case.
• Non-Movable data points : These text-based data points encompass background information and test instructions. They are not selectable or movable and are intended solely to provide an overview of the case. They do not need to be collected as their purpose is to offer context.
• Number-based data : These typically consist of movable data points and comprise the majority of the data in the case. They can be found in two locations: within charts, diagrams (such as bar charts, pie charts, tables, etc.), or within the text. It's necessary to gather these numbers into the journal for calculations in the next phase.

2. Collecting important data points

You can drag any movable data point into the Research Journal to collect. In the Research Journal, each collected piece of information will appear as a card, with its own name and description. The data in the Research Journal can then be used in the Calculator or as answers in phase 2.

You have the option to change the labels for all the data yourself. We suggest doing this if the default label doesn't describe the contents well enough. Using the right labels will make your analysis faster because it helps you easily find the important data later on.

After you've collected the data, you can also include your own notes with each piece of information. This can assist you in explaining the information required during the Analysis phase.

Here’s a summary of our recommended approach:

• Determine the objectives of the case.
• Identify the mathematical formulas needed to address these objectives.
• Collect in your Research Journal only the essential data points necessary for the calculations during the Analysis phase.

Phase 2 – Analysis

Your objective here is to use the data points gathered during the Investigation phase to answer three quantitative questions using the provided calculator. These answers will be utilized to complete the report in Phase 3.

The three quantitative questions typically consist of 2 to 3 sub-questions, each with an answer input gap that requires a response from the calculator. To address these questions, you need to input the numerical data points you've collected into an on-screen calculator and then transfer the results to the corresponding gaps.

The calculator features a straightforward interface, resembling a digital calculator found on a phone, and includes basic operators such as multiplication (x), addition (+), subtraction (-), and division (÷).

The calculations required for the questions can be categorized into two types:

• Basic Operations : This category includes addition, subtraction, multiplication, and division. While these operations are fundamental, you may need to use them in combination to perform more complex calculations.
• Percentages/Ratios/Fractions : These calculations are used to address questions related to relationships, such as percentage differences, growth rates, and similar concepts.

We advise you to carry out all calculations using the provided calculator since all your actions are documented in a history log. It's safe to assume that the process you follow in arriving at the answers will also impact the final results.

It's crucial to keep in mind that the answers you obtain from these questions are almost always required in the Report phase. Therefore, it's essential to consistently record your answers in the journal.

• Carefully read the questions to understand what is being asked.
• Drag the relevant data points from your Research Journal into the calculator's input screen to perform the necessary calculations to answer the questions.
• Drag the results and drop them into the empty spaces provided under the questions.
• Ensure that you collect the answers in your Research Journal for use in the Report Phase.

Phase 3 - Report

Your objective here is to finalize the textual and graphical report. You will have the option to choose one of three types of graphs to include in the report, and these graphs will be based on the results calculated in Phase 2.

The Report phase is the final segment of Part 1 Study and comprises two sections: the Written Report and the Visual Report.

• Written Report : This summary report involves completing the text-format report by filling in the blanks with numbers obtained in the preceding phases, as well as using expressions like "higher," "lower," "equal to," and so on. The blanks in this phase are likely to resemble the answer inputs in the Analysis phase.
• Visual Report : This aspect involves data visualization, where you select the appropriate chart type and input the numbers to create a meaningful chart for the report.

Your objective is to answer 6 cases, each featuring a question with instructions, textual information, and data exhibits.

In each of the 6 cases, there is an onscreen tool available to aid you. It's essential to solve the cases in a sequential manner, which means you cannot skip ahead and must address one case before proceeding to the next.

All 6 cases will revolve around the same theme or topic as Part 1 of the study, but they are not interconnected. These cases primarily demand a basic level of quantitative or reasoning skills and do not necessitate advanced mathematical abilities. 

However, it can be challenging to solve all 6 questions within a short time limit, so it's important to use your time wisely.

The question types in Part 2 can be grouped into four primary categories:

• Word problems : These involve mathematical exercises where candidates must read the text and interpret data to find solutions.
• Formulae : These questions are similar to word problems, but candidates only need to identify the formula used for calculation.
• Verbal Reasoning : These are single-select multiple-choice questions that ask candidates to determine which statement is true or false.
• Visualization : These questions require candidates to choose the appropriate chart type to represent the provided data.

Game 3: Ocean Cleanup

What is ocean cleanup game.

Introduced by McKinsey in 2024 as the newest addition to their Problem Solving Game series, the Ocean Cleanup Game—also known as the "McKinsey Ocean Treatment Game"—requires players to manage and restore an ocean ecosystem. Players must strategically select and balance different microbial species to reduce plastic pollution effectively.

The primary objective is to identify the most adaptable microbes that can thrive across varying ocean sections, making "Microbe Management" a critical aspect of the game.

What are the details of this game?

• Time Limit : You have 20 minutes to complete both phases of the game.
• Game Order : The Ocean Cleanup Game follows the Ecosystem Building and Redrock Study games in the assessment series.
• Beta Status : The game is currently in beta, meaning player feedback is crucial and could lead to potential modifications.

How to play the Ocean Cleanup Game?

Step 1: Understanding the Game Structure

The game involves managing two distinct ocean sections, each with unique conditions like temperature, depth, and pollution type. The aim is to select microbes that can adapt well to both environments. Microbial Attributes: Each microbe has five attributes:

• Numerical Attributes (Scale 1-10): This includes aspects like velocity, absorbency, and fluidity.
• Binary Attributes: Traits that are either present or absent, such as heat resistance or acid tolerance.

Ocean Section Attributes vs. Microbes Attributes:

Ocean Section Attributes

At the start of the game, you’ll encounter five attributes that define each ocean section. These attributes remain constant and are divided as follows:

• Fluidity: 2-4
• Velocity: 6-8
• Absorbency: 7-9
• Resistance to Acids: Desirable
• Degradation from Heat: Undesirable

These attributes establish the requirements that the selected microbes must meet for effective performance in each section.

Microbes Attributes

Each microbe has five attributes:

• Fluidity: 5
• Velocity: 7
• Absorbency: 8
• Binary Attributes: Two binary traits that may align (or not) with the desirable or undesirable conditions of the ocean. For example, a microbe could be resistant to acids but vulnerable to heat.

Matching the microbe attributes to the ocean section's attributes is crucial for achieving effective cleanup.

Step 2: Evaluating Microbes

The evaluation process involves two phases:

Phase 1: Selecting Attributes Players select two out of the five attributes (numerical or binary) that will be prioritized across all microbes. This decision guides which microbes are most suitable for each ocean section.

Phase 2: Screening Microbes After the attributes are set, players screen a total of 10 microbes and categorize them:

• Keep for Section 1: Microbes that align well with the first section's characteristics.
• Keep for Section 2: Microbes that fit better with the second section.
• Discard: Microbes that do not meet the required criteria.

Note: The relevance of this step to later stages of the game is currently uncertain and might be omitted in future versions.

Step 3: Selecting 10 Microbes

Players choose 10 microbes from a pool of 20, with the selection based on the microbes' alignment with the attributes of both ocean sections. Each microbe's description must be carefully analyzed to ensure compatibility with the conditions in both environments.

Step 4: Narrowing Down to 3 Microbes

From the selected 10, players further refine their selection to three microbes that best match the preferred range. Here are the key criteria:

• The average values of the selected microbes should fall within the desired range.
• Prioritize microbes with desirable traits while avoiding those with undesirable characteristics.

Main Challenges of the Ocean Cleanup Game

In this game, there are 3 main challenges:

• Randomness: The wide variety of microbes and their attributes can make the game feel unpredictable. To overcome this, focus on identifying patterns that lead to successful outcomes.
• Time Pressure: With a 20-minute time limit, players must manage time well. Practice quick decision-making to avoid getting bogged down.
• Balancing Two Ocean Sections: Managing two ocean sections simultaneously requires a careful approach. Players must continuously cross-check selections to ensure effective performance in both sections.

How to tackle the Ocean Cleanup Game?

Here are some strategies to improve your performance:

• Understand the Game Rules: Familiarize yourself with the requirements and criteria for selecting microbes to make informed choices.
• Aim for Balance: Choose microbes that complement each other, creating a balanced ecosystem. Avoid those that could disrupt the balance.
• Manage Time Efficiently: Make quick, informed decisions to stay on track.
• Leverage Tools: Use tools like Excel Solver to handle complex calculations, helping you optimize microbe selection more effectively and accurately.
• Prioritize Both Sections: Ensure the selected microbes can perform well across both ocean sections, achieving optimal balance.

The McKinsey Solve Game is meant to assess your critical thinking skills. However, if you haven't practiced beforehand, you might not be familiar with these mini-games, including how they work and what you're supposed to do.

According to our survey, many candidates were surprised when they took the test, even if they had read guides and watched game walkthroughs. These mini-games have complicated interfaces and various functions. Most candidates, when they first encounter them, need to spend time just figuring out how they work, then what the goals are, and finally start playing the game. Going through all these steps in a very short time can be nearly impossible.

You might have the exact set of thinking skills McKinsey is looking for and still not do well in the mini-game. This could happen because you might not understand how the game works, struggle with time management, or get confused by some aspects of the game.

That's why practicing with the McKinsey Game is so helpful. With our FREE McKinsey PSG Simulation , you can become familiar with these types of mini-games and improve the thinking skills you need to do well.

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McKinsey Solve Game: Newest Updates, Guide & Free Trial 2024

Check out the only, fully-simulated McKinsey Solve (Problem-Solving Game - PSG) Simulation  in the entire market with the new 2024 Redrock Study Task and its free trial

With that out of the way, let's continue to learn about the test, shall we?

Table of Contents

What is the McKinsey Solve (or Problem-Solving Game)?

Mckinsey solve is a gamified, pre-interview screening test.

McKinsey Solve (formerly called Problem-Solving Game, Digital Assessment, or colloquially the “Imbellus Game”) is a gamified test designed by the assessment company Imbellus for the McKinsey & Company. 

In the McKinsey recruitment process, the Solve Game sits between the resume screening and the case interviews , serving the same purpose as the paper-based tests – ruling out the “unfit” candidates to save time and resources during the expensive case interview phase. 

Solve has entered trial since 2017 (back then it was known as the Digital Assessment) and has been rolling out extensively in 2020. Since then, Solve has replaced the paper-based Problem Solving Test in every McKinsey office.

The test is mandatory for candidates applying in all practices: General, Operations & Implementation, Research & Analytics, Digital, etc.

Note: As this is a gamified test, in this article, the two terms “game” and “test” will be used interchangeably when referring to the McKinsey Solve.

The new gamified test is supposedly crack-proof

Now, why did McKinsey change the test format from a paper-based test to a game? Keith McNulty, McKinsey’s Global Director of People Analytics and Measurement, put it this way:

“Recruiting only knows if candidates got the right answer, not how they approached the question. Plus, there’s a large amount of strategy, preparation, and luck involved in multiple-choice tests, and if you use them in the selection process, it reinforces the status quo—at a time when you are looking to widen the scope of candidates you’re hiring.”

So essentially, McKinsey is trying to create a test/game that is impossible to game (ironic, isn’t it?).

But in fact it can be broken down into bite-size pieces

With field reports from hundreds of real test takers, we have gathered enough insights to break down the McKinsey Solve into bite-size pieces, which are fairly consistent across candidates. Using those insights, we can derive working overall approaches to the game.

In this article, we will cover:

Technical details of the test : time limit, number of tasks and mini-games, assessment criteria

Break-down of each mini-game : description, underlying logic, and recommended strategy

Test-taking tips to maximize your chances

Similar games for practicing the McKinsey Solve Game

It is important to keep in mind that since neither Imbellus nor McKinsey publicizes the exact details of the criteria/mechanisms used in-game, the insights in this article – reported by our correspondents – may not reflect 100% of the in-game elements .

What is the McKinsey Solve like?

The McKinsey Solve Test or Digital Assessment has a time limit of 60-80 minutes . The candidate is asked to solve 2 out of 6 possible mini-games. Both the final results and the process are assessed , and if the candidate is found to possess similar skills and tendencies to a McKinsey consultant, they are offered an interview.

For a more detailed guide on the technical details of the game, please check out the McKinsey PSG Simulation (All-in-one) package.

Time limit is 71 minutes

As of April 2021, the reported time limit for the McKinsey Solve is exactly 70 or 71 minutes , with 35 minutes recommended for the first game (Ecosystem Building), and 35 minutes for the second game (Redrock Study), or 36 minutes (Plant Defense). Time spent on tutorials is not counted towards the limit.

Ever since the start of the game, there have been variations in time limit reports, however, these tend to stay between 60-80 minutes . This variation depends on the length of each mini-game.

Pre-2023, i.e. with Plant Defense mini-game : Actual time allocation depends entirely on the candidate’s decision – however since the first game is much more predictable, we recommend playing this quickly to allow more time for the second game. With a proper approach, the first game should take only 15-20 minutes, with time for a double-check taken into account.

Summer 2023 onward, i.e. with Redrock Study mini-game :  The Ecosystem Building mini-game is now allocated a fixed 35-minutes, and the Redrock Study another 35. That means even if you finish the first game early, there is no additional time for the second game.

Learn more about the Redrock Study game through this free trial !

Candidates should also make the most out of the tutorial time – try to guess the objective of the mini-game, and think of an overall approach before beginning a mini-game. You can also use that time to make necessary preparations, such as pen and paper, or maybe a light snack to keep yourself energized.

Each candidate has to solve 2 out of 6 mini-games 

As of June 2023, 6 mini-games are confirmed for the McKinsey Solve Test: Ecosystem Building, Redrock Study, Plant Defense, Disaster Management, Disease Management, Migration Management . The 2 main mini-games that nearly all candidates will encounter are the Ecosystem Building Game and the Redrock Study Task. 

Our reports indicate that 100% of the McKinsey Solve Test will have Ecosystem Building in the first game slot. For the second game slot, right now, 80-90% of the candidates will have the Redrock Study Task, while 10-20% will have the Plant Defense game (before this, the ratio for the second game was reversed). This means McKinsey is gradually phasing out Plant Defense in favor of the Redrock Study Task.

The first one, Ecosystem Building, is similar to city building games - except with animals instead of buildings - where you have to build an ecosystem with a number of species.

In the Redrock Study Task, your mission is to solve ONE large study using on-screen tools then move on to answering 6-10 smaller cases with a similar topic. Access McKinsey Solve free trial to have an overview of this game!

The other 3 games - Disaster Management, Disease Management, Migration Management - are alternatives that McKinsey previously used for beta testing. They no-longer appear in the McKinsey Solve test in 2023.

Disaster Management involves identifying the natural disaster occurring in an ecosystem and moving the whole system to another location to minimize damage. This mini-game appeared occasionally from 2020 to 2021.

Disease Management is about identifying an infectious disease, figuring out its rules of infection, and predicting its spread within an ecosystem. This mini-game appeared occasionally from 2020 to 2021.

Migration Management is about directing a group of animals from one point to another such that it loses the least amount of resources and animals. This mini-game appeared occasionally from 2021 to 2022.

For the latest insights on the game - Redrock Study, check out the McKinsey Solve free trial or our designed simulation package for this mini-game. 

The next part will be about how candidates are assessed – if that’s not in your interest, you can skip straight to the mini-game and strategy guide using this link.

Every keystroke and mouse movement will be assessed

Each candidate will be assessed using both product scores (i.e. the final results) and process scores (i.e. how they get those results).

Product scores are determined by your level of success in achieving the objectives of the mini-games.

  In the first mini-game, while there is no 100% right answer, some solutions will be better than others. You will be given this information through a report screen. For the second mini-game, the final results are definitive fact-based and data-based answers. There will be right and wrong answers, but McKinsey will not inform you how many correct answers/actions you get.

Mini-game 1: How many species survive? 

Mini-game 2: Did you pick the right data points? Are your calculations and reports correct? Did you choose a suitable graph to display the data?

Process scores, on the other hand, are dictated using data on your patterns during the whole problem-solving process – every keystroke, every click, and every mouse movement will be assessed.

The process and product scores are combined to form a profile of problem-solving skills and capabilities. And while there is no official statement from McKinsey about which candidates they select, it is likely that the more you resemble a high-performing consultant at McKinsey, the higher your chances will be.

Candidates are assessed on five core dimensions

Your problem-solving profile is drawn using the five following dimensions:

Critical thinking : the ability to form a rational judgment from a set of facts

Decision-making : the ability to select the best course of action among options

Meta-cognition : the ability to use strategies to make learning information and solving problems easier (e.g., testing hypothesis, taking notes)

Situational awareness : the ability to determine the relationships between different factors and to project the outcomes of a mini-game

Systems thinking : the ability to understand cause & effect relationships involving several factors and feedback loops (e.g., anticipating several orders of consequences)

The good news is that all the skills assessed are generally not evaluated by themselves, which means training one skill will probably also drive up your assessment scores in others . This is absolutely crucial because you won’t have to go into every nitty-gritty task just to squeeze out some extra score.

Furthermore, while all capabilities must be presented for success, some metrics are considered to be more impactful than others. From this Imbellus research paper , we could deduce that Critical Thinking, Situational Awareness, and Systems Thinking are the fundamental skills that all successful candidates need to possess. 

Meanwhile, Decision-Making and Meta-Cognition skills mastery are the advanced skills that will transform candidates from good to great ones.

The test measures telemetry data to calculate the five dimensions

While it is hard to pinpoint exactly the telemetry data gathered since Imbellus does not fully disclose this information, one way of framing this is by each stage of the problem-solving process itself.

Based on our findings from real candidates, we believe the telemetry could be assorted into the following sets, each directly influencing the key activities during the stages from identifying the problem to delivering the next-step recommendation.

Problem Identification: your systematic thinking pattern

Methodological vs. abstract

Big-picture thinking vs. detail-oriented

Example telemetry: prioritization and focus tendency, clicking and decision pattern

Quantitative analysis & data synthesis: the ability to translate data into insights

Drawing relationship between data

Filter out correlated or irrelevant information

Example telemetry: data focus pattern, time spent on quantitative task

Hypothesis-crafting: bringing insights into actionable hypothesis

Putting emphasis on a certain approach / methodology from insights

Example telemetry: duration of the transition from analysis to decision-making, disrupted status quo period

Decision-making: coherence in actions and thinking

Random selection or well-thought out decisions based on analysis

Decisiveness in carrying out actions with the chosen tactics

Reaction under growing time pressure – panic clicking vs. calm and focus

Example telemetry: factors connecting each selection, time spent deciding between options

Next-step recommendation: learning and reflection

Ability to adjust existing strategy and preference for tried-and-true method in presence of new data set or shifting conditions

Progressive learning and reflection with failures and successes

Example telemetry: number of clicks, scrolling speed, time spent on certain data blocks

Breaking down the test – Redrock Study Task (including free practice test) 

Mini-game overview & description.

The Redrock Study Task began appearing as early as July 2023. Then in March 2023, it received an update which divided the Task into 2 Parts which we will see below.

The first part of the mini-game, also the most important one, consists of ONE large study with a main objective and a set of supporting data . This part is divided into 3 main phases: INVESTIGATION, ANALYSIS, AND REPORT.

Phase 1 - INVESTIGATION : Your task is to skim through the case description, identify the objective and necessary data points, then collect them into an on-screen Research Journal.

Phase 2 - ANALYSIS : Using a provided calculator, you process the data points to answer 3 quantitative questions. These answers will be used to fill in the report in phase 3. Your calculation history will be recorded.

Phase 3 - REPORT : With the results calculated from phase 2, your main job is to complete the textual and graphical report (you have to choose which type of graph to use).

In the second part, you have to answer 10 cases with a similar topic to part one (i.g. If your part 1 case is about clothing sales, the mini cases will also be about clothing sales). Though, our user reports show that the topic is purely cosmetic and does not affect the final assessments. 

As of July 2023, we have only received reports of Single-select Multiple choice questions (that is, choose an answer out of A, B, or C) and Numerical-answer questions . There have been no signs of open-ended questions.

As for the time limit, the whole task is given a total of 35 minutes for both parts. While there are no official time constraints, we recommend spending 25 minutes on the first part , and 10 minutes on the second part to optimize your outcome.

Let’s experience the Redrock Study game through McKinsey Solve free trial !

Breaking down the study in Part 1

In the first part of the Redrock Study Task (we’ll refer to this as the study or case ), the study’s flow is designed to test candidates’ logical and reasoning skills. If you don’t follow the logic carefully, the algorithm may be unable to recognize your thinking process, and view you negatively. Here, we have broken the study down into 4 aspects.

GAME ASPECT 1: UNDERSTANDING THE STUDY

This refers to the first phase of the Redrock Task, which is INVESTIGATION. To truly grasp what you need to do, you must first clearly identify the case's objectives . Then, your next task is to understand all the data points presented within the case, to identify which ones can be used to answer the objective.

In general, all information presented on the screen is needed towards understanding and solving the case. But some are less important than others. Background information and test instructions are usually text-based data that you can’t select or move around. They only serve to give you an overview of the case, like the case’s theme, and don’t need to be collected. 

By contrast, important data points are highlighted and presented in boxes on the screen . You can click and drag these boxes around to work inside the case. Among these movable data points , there are 3 types of crucial information that you need to find:

Case objectives : These are text based data, informing you about the goal that you must solve in the case. It usually sits at the top of the case, right after the instruction. 

Calculation instructions : These are data points telling you which math formula you must use and which numbers to choose. They are often long texts/sentences that describe the relationships (higher/lower/etc.) between subjects in the case.

Numbers : These make up the largest portion of the data points in the case. They usually appear in charts/diagrams (bar chart, pie chart,...), tables, or sometimes in-between texts. You have to collect these numbers into the journal to calculate in the next phase. Only a small percentage of these numbers (10-15%) are actually important to the case.

In general, the rule of thumb is that once you have collected the case’s objectives, you must identify which math formula to use. Only then can you gather suitable numbers that the calculation requires. Also, only a handful of data points are necessary to solve the case, so pick wisely.

You can understand the game aspects better when experiencing the McKinsey Solve Simulation. Click on this link to get a free trial!.

GAME ASPECT 2: COLLECTING DATA POINTS

You can drag any movable data point in any phase of the Redrock test into the Journal to “collect” it. In the Research Journal, each collected “data point” will show up as a card, with its own label and description. Data in the Journal can be used to feed into the Calculator, or into “answer inputs” , (blank spaces under the questions).

Some data comes with appropriate labels for its contents, but some do not . All data labels can be manually changed – we recommend doing so if the default label does not adequately describe the contents. Appropriate labeling will speed up your analysis later, since it allows you to quickly identify the relevant data.

Once collected, each data point can also be highlighted by using the “I” button (presumably for “important”) on the left of its label. Toggling on this button will cover the whole data point in an orange tint. We recommend highlighting information that is needed during the ANALYSIS (or calculation) phase.

Inside the Research Journal, you can move these data points up and to organize them from top to bottom . It’s possible that McKinsey will look at how you organize the data. We’ll give some insights on that later. The specific sorting method is still receiving changes, so we’ll update it as we go.

To familiarize yourself with collecting data points, get access to McKinsey Solve free trial for more practice! 

GAME ASPECT 3: PROCESSING THE DATA POINTS FOR INSIGHTS

During the second phase of the game, you will be provided with 3 quantitative questions that directly relate to the game’s objective. Each one has 2-3 sub-questions with an answer input gap requiring an answer from the calculator. To answer these questions, you have to feed the collected numerical data points into an on-screen calculator, then drag the results into the appropriate gap.

The calculator has a simple interface, similar to a phone’s digital calculator , with basic operators like *,+,-,/. It’s safe to assume that the math involved are usually simple calculations (similar to most candidates' reports). Though they lack the ‘%’ button for percentage calculation.

We recommend that you perform all calculations on the provided calculator, as all your operations are recorded in a history log . So, we assume that how you work towards the answers will also weigh on the final results.

A recommended workflow is to drag the data points from your research journal into the calculator’s input screen to perform the operation. Then you’ll need to drag the result and drop them into the blank space in the question. You should avoid typing the number on your keyboard as it may lead to unfortunate typos.

Here are a few confirmed question types and calculations during phase 2 of part 1:

Basic operations (add/subtract/multiply/divide) : Basic operations don’t often sit alone. They usually have to be involved in complex questions.

Simple percentages and ratios : They require you to calculate simple ratio, percentages and fractions. For example: “What is the percentage of population growth between 2021-2022?” (Provided data: Population number in 2021, Population number in 2022)

Compound percentage questions : They require you to calculate multiple ratios and percentages in a row. For example: “What is the population number at the end of 2023?” (Provided data: Population number at the start of 2022, Population growth rate for 2022, Projected increase in population growth rate for 2023 compared to growth rate for 2022)

One important thing to note, as reported, the results that you get from these questions are almost always needed in the REPORT phase. There’ll be a review screen So ALWAYS collect your answers into the journal.

You can consider using our McKinsey Solve free trial to practice and familiarize with this part. It’s completely free, so try it now!

GAME ASPECT 4: COMPLETING THE CASE REPORT

The Report phase is the last part of the Redrock Study Task. It consists of two parts: Summary and Data Visualization.

Summary involves filling in the blanks of a text-format report, using numbers already given and produced in the previous phases, and expressions such as “higher”, “lower”, “equal to”, etc. The blanks in this phase will likely be somewhat like the answer inputs in the Analysis phase.

Data Visualization involves choosing the correct type of chart and filling in the numbers to produce a meaningful chart for the report. For this step, a difference between the Redrock Study and the old McKinsey PST is the lack of compound chart type. This drastically reduces the difficulty, as you only have to work with simple chart types like bar or pie charts.

Want to experience a simulation of this new game? You can consider purchasing the McKinsey Solve Simulation package from MConsultingPrep. 

We have released the McKinsey Solve free trial, you can click on this link to check it now before purchasing the package.

Mastering the Redrock Study

From what we can see, the Redrock Study Task is more similar to its Problem-Solving Test predecessor than a game. That makes the tips to this task a bit different from the previously-popular Plant Defense game. There’s no instant formula that can guarantee the best chance of survival (maybe this is why Plant Defense got canceled), rather, you must act and think like a McKinsey consultant.

TIP 1: SHOW A TOP-DOWN AND STRUCTURED APPROACH WHILE COLLECTING DATA

A good McKinsey consultant always takes a top-down approach when analyzing a problem, and recruiters often favor candidates with this trait. During the Study, McKinsey can assess this trait when you collect and arrange data.

Always collect the Objectives first . They are the central problems of the case, and represent the highest level of your issue tree. You must always collect them into the Research Journal. If they are too long, you can always note down a summary on a piece of scratch paper.

The next step is to identify the math formula . This type of data governs which calculation formula you need to use, and in turns, which numbers to collect next. We’ll call this the relational data . The objectives will determine the relational data points you need.

Finally, collect the necessary numbers . These are the ones needed for calculating and filling in the final reports . Collect only the ones you need by analyzing the objectives and relational data. Don’t collect all data points erratically , as this showcases that you have no structured thinking.

TIP 2: LABEL AND ORGANIZE DATA

As stated before, once collected into the journal, each data point will have a label and description . Some data points already have good labels, some do not.

It’s possible that McKinsey can recognize good labels , so we suggest always changing the label and description of a data point when necessary. Good label can seem good to an algorithm, and it can also help you analyze them more conveniently. We have a few suggestions as to what constitute a good label:

What is the timeframe? (“Is this data for 2020, or 2021?”)

Which subjects are concerned? (i.e., the things represented by rows and columns in a spreadsheet, or axes on a chart).

Is there anything else I need to keep in mind? (i.e., the footnotes or any auxiliary information that accompanies a chart/table) 

As for arranging data, try to keep it consistent and top-down . “Overview” data points should be placed above the “granular” ones. For example, keep the objectives at the top of your research journal, and below them are relational data points. Numerical data points from the same table should be placed together, and beneath the relational data that refers to them. McKinsey MIGHT take this as a sign that you are a structured person, if not, it will help you solve the case easier.

TIP 3: AVOID GOING BACK AND SHOWING INDECISIVENESS

The game allows you to go back and forth freely between each phase to collect more data points. While this is great for when you make a mistake or need to double check, we don’t recommend doing so.

This behavior signals that the candidate does not understand each section fully and is uncertain about the task. And in phase 1, McKinsey’s instruction clearly states that you should collect all and only relevant data before moving on. It’s possible that moving back and forth can be viewed negatively by the algorithm.

TIP 4: CHOOSE THE CORRECT CHART-TYPE (BAR/LINE/PIE)

We have written an entire guide on how to chart like a McKinsey consultant, so be sure to check it out before attempting this task. But in short, you need to choose the correct type of chart that best describes a certain type of data, in the McKinsey way.

Access to McKinsey Solve free trial , to experience a real simulation and apply these tips to enhance your performance!

Part 2 cases tear down

Since this part of the test has only been introduced recently, we are still in the process of interviewing and synthesizing insights. More information will be updated later as things develop.

TEST FLOW, FORMAT AND DIFFICULTY

There are 6-10 cases in Part 2 , each has a question with directions, text information and data exhibits. Each case also has an onscreen tool to assist you. You must solve the cases sequentially, that means you can’t skip forward and must answer one case before the next.

All 6-10 cases will follow the same theme/topic with the Part 1 study. But from candidate reports, it’s safe to assume that the theme does not play any part in the answer, and each case is self-contained (which means you don’t need numbers of another case to get the answer).

The word count to the 6-10 cases can vary between 100 and 400 words . They only require a fundamental level of quantitative or reasoning skill to solve and don’t require advanced mathematical skills. But most of our candidates struggle to solve them within 10 minutes, so be careful. 

QUESTION TYPES

The questions types that we have seen from candidate reports generally mirror those in part 1. We categorize them into five main types : Word Problems, Formulae, Verbal Reasoning, Critical Reasoning, and Visualization. We also deduced the rate at which these questions appear part 2.

Word problems (50%) are math exercises that require candidates to read the text and exhibit data to solve

Formulae (20-30%) is a similar question type to word problems, but the candidate only needs to identify the formula used for calculation.

Verbal Reasoning (7-8%) and Critical Reasoning (7-8%) are single-select multiple choice questions requiring candidates to choose a “true” or “false” statement among 3-5 options.

Visualization (10%) requires the user to choose the correct type of chart to illustrate the given data.

Part 2 has a near identical format to a traditional Problem-Solving Test (except for the on-screen tool like a calculator similar to Part 1’s). Thus, to save time, we only recommend getting familiar with the interface and mastering fundamental knowledge for a McKinsey consultant (like the issue tree, MECE, etc.) which we covered many times before.

Now, you have a chance to experience these question types and receive their answer guides with no costs. Click on this link for the new McKinsey Solve free trial from MConsultingPrep!

Breaking down the test – Ecosystem Building 

In the Ecosystem Building mini-game, you have to create an ecosystem with 8 species from a list of 39 . There are three key objectives:

(1) The ecosystem must form a continuous food chain

(2) There must be a calorie surplus for every pair of predator and prey (that is, the prey’s production is higher than the predator’s consumption)

(3) The ecosystem must match the terrain specifications of the chosen location.

Here’s a detailed description of data and metrics in the mini-game, and how they relate to the objectives.

Objective 1: Terrain Match

There are two scenarios on which you must build the ecosystem: “the Mountain” and “the Reef”. 

Each location in the Mountain world has the 8 following specifications: Elevation, Temperature, Wind Speed, Humidity, Cloud Height, Soil pH, Precipitation, Air Pressure.

Each location in the Reef has the 7 following specifications: Depth, Water Current, Water Clarity, Temperature, Salt Content, Dissolved Oxygen, Wind Speed.

Terrain specifications have very little correlation.

Each species also has a few required terrain specifications – if these terrain requirements are not met, the species will die out . These requirements are often not exact numbers, but ranges (e.g: Temperature: 20-30 C). 

All 39 species are organized into 3 equal groups using their terrain specs – I call them “layers”. Species of the same layers have exactly the same terrain specs.

Objective 2: Food Chain Continuity

Each species has a few natural predators (Eaten By), and prey (Food Sources) – see below for exceptions.

The species are divided into producers (which are plants and corals, which consume no calories), and consumers. Consumers can be herbivores (plant-eating animal), carnivores (animal-eating animal), or omnivores (eats both plants and animals).

Producers always have the Food Sources as “sunlight” or other natural elements, i.e. they do not have prey. Some consumers are “apex animals”, meaning they do not have natural predators (can be recognized by empty the “Eaten By” specs). These have strategic implications in building the food chain. 

 Objective 3: Energy Balance

Each species has a “calorie needed” and a “calorie provided” figure . A species lives if its calorie needed is less than the sum calorie provided of the species it eats (so it has enough energy to survive) and its calories provided is higher than the sum calorie provided of the species that eat it (so it’s not eaten to extinction).

Two caveats apply here: a species often don't eat all of its prey and is not eaten by all of its predators. There are certain rules for priorities (see the “Feeding Overlap” issue) and more often than not, predators and prey will interact on a one-to-one basis.

In old versions of the game, each species will be placed on a group basis, with the number of individuals in each group ranging from 20 to 60. In these versions, calorie specs are “per individual”, so you have to perform the math to get the true consumption and production figures of the whole species.

New versions discarded this “per individual” feature, presenting the calorie specs for the whole species as one, but there is no guarantee the old feature won’t be re-introduced.

As of game-flow, t he candidate is free to switch between choosing location and species during the mini-game . There is also a time bar on the top of the screen.

Old reports indicate that once you’ve submitted your proposed ecosystem, you would receive a scorecard in the end, showing how it actually plays out. Key measurements might include calories produced and consumed, and the number of species alive.

However, recent reports have indicated that results aren't displayed at the end . In either case, it is safe to assume that the underlying principles remain the same.

Cracking the mini-game

The biggest challenges in the Ecosystem Building mini-game are task prioritization and data processing – most test-takers report that they are overwhelmed by the amount of data given, and do not know how to approach the problem. However, the second problem can be mitigated by reading the rules very carefully, because McKinsey provides specific and detailed instructions in the tutorials.

To overcome both challenges at the same time, first, we need to know the “eating rules” (i.e. how species take turns to eat) and then we can develop a 3-step approach to meet those challenges.

EATING RULES AND FEEDING OVERLAP

In the McKinsey PSG Ecosystem mini-game, species take turns to eat and get eaten, in accordance to very specific and comprehensive rules:

1. The species with the highest Calories Provided in the food chain eats first.

2. It eats the species with the highest Calories Provided among its prey (if the eating species is a producer, you can assume it automatically bypass this step, as well as steps 3-5).

3. The eating species then “consumes” from the eaten species an amount of Calories Provided that is equal to its Calories Needed, which is at the same time substracted an amount equal to the Calories Provided taken from the eaten species.

4. If there are two “top prey” species with the same Calories Provided, the eating species will eat from each of them an amount equal to 1/2 of its Calories Needed.

5. If the Calories Needed hasn’t been reduced to 0 (i.e.: satisfied), even if the eating species has consumed all the Calories Provided of the first prey the eating species will move on to the next prey with the second-highest Calories Provided, and repeat the above steps; the prey that has been exhausted its Calories Provided will be removed permanently from the food chain and considered extinct.

6. After the first species have finished eating, the cycle repeats for the species with the second-highest Calories Provided, then the third-highest, etc. until every species has already eaten. Note: in every step where species are sorted using Calories Provided, it always uses the most recent figure (i.e. the one after consumption by a predator).

7. At the end of this process, all species should have new Calories Provided and Calories Needed, both smaller than the original figures. A species survive when its end-game Calorie Needed is equal to 0, and Calorie Provided is higher than 0.

Let’s take a look at an example – try applying the rules above before reading the explanation, and see if you get it right:

Now, here’s how this food chain is resolved:

The three producers automatically have their Calories Needed satisfied and does not need to eat anything.

The first species to eat is an animal – the Mouse. It eats equally from Grass and Mushroom, which have equal Calories Provided, an amount of 2,000 each. The Mouse’s Calories Needed reduces to 0, while the Calories Provided for Grass and Mushroom reduce to 3,000 each (Grass and Mushroom survive).

The second species to eat is the Squirrel. It should have eaten Grass, but Grass’s new Calories Provided is only 3,000, so the Squirrel picks Nuts instead. Squirrel’s Calories Needed becomes 0, while Nuts’ Calories Needed becomes 500 (Nuts survive).

The third species to eat is the Snake. It eats the Mouse, reducing its own Calories Needed to 0 while taking 2,000 from the 3,000 of the Mouse’s Calories Provided. (Mouse survives)

The fourth species to eat is the Fox. It eats the Squirrel, reducing its own Calories Needed to 0 while taking 2,000 from the 2,500 of the Squirrel’s Calories Provided. (Squirrel survives)

The last species to eat is the Tiger. It eats the Snake first, taking away all of the Snake’s 1,500 Calories Provided, then proceeds to take 500 from the Fox’s 1,200, so that its Calories Needed can be reduced to 0 (Snake becomes extinct, Fox survives)

The Tiger is not eaten by any other animal (Tiger survives).

With these rules in mind, let us go through a 3-step process to building a food chain:

Step 1: Select the location

Use a spreadsheet or scratch paper to list the terrain specs and calories provided of the producers of the mini-game.

Skim through the data to see which terrain specs remain the same across all species, and which ones change. Only changing terrain specs are relevant (there should be 2 of them), the rest are merely “noise” intended to cause information overload.

Calculate the sum calories provided for the producers of each layer. The layer with the highest calories provided is likely to be the easiest to build the chain.

Step 2: Build the food chain

Look through the data to list the consumers with compatible terrain requirements in your spreadsheet.

Pick the apex predator with the lowest calorie needed as the starting point of the food chain.

Build the food chain top-down like an issue tree, by listing the food sources of the top predators. Continue drilling down until you reach the “base” level of corals and plants. Ideally the food chain should contain 3-4 levels, and 8 species.

Alternatively, you can build the food chain in a bottom-up manner, by looking at the “Eaten By” specs of each species, until you reach the top predators. Our reports indicate that in real test conditions, this approach can be just as fast as the top-down one.

During the whole process, try to prioritize species with high calories provided, and low calories needed. This should maximize the chance of calorie surplus in the food chain, and leave room for new additions should the first chain not meet the required 8 species.

If you finish the chain short of the required 8 species, work bottom-up to find gaps (i.e unused surplus calories), and plug in those gaps with predators or plant-eating animals.

The whole process should be done on a spreadsheet/scratch paper to facilitate calculations.

Step 3: Triple-check and adjust

Go back to the beginning of the process and check if every species of your food chain is compatible with the chosen location.

Make sure the food chain is continuous – that is, the food sources listed fit with the description of each species.

Check if each species in the food chain is supplied with enough calories and not eaten into extinction using the given eating rules.

Adjust the food chain if any of the three checks are not met.

Breaking down the test – Plant-Defense

Though McKinsey is gradually phasing out this test, we are still receiving sporadic reports of it being used for candidates (about 10-20% in total) . So for the sake of information sharing, this section will still remain in our article, and will be updated as changes happen.

The second mini-game of the McKinsey Solve Game – Plant-Defense – is a turn-based tower-defense game. The candidate is charged with defending a plant at the center of a grid-based map from invading pests, using obstacles and predators, for as long as possible, until the defenses are overwhelmed and the plant is destroyed.

Here’s a detailed description of the gameplay:

The visual map is divided by a square grid (size from 10×10 to 12×12), with natural obstacles (called Terrain, or Terrain Transformations) scattered across the map.

The game has a recommended time allocation of 12 minutes per stage – which makes 36 minutes in total.

The game is divided into three maps, each with 2 phases – “planning phase” and “fast-forward phase”.

The “planning phase” is divided into 3 waves of 5 turns each. Every 3-5 turns, a new stack of Invaders appears at the border of the map and starts traveling towards the center base – you have to lay out defensive plans to tackle them. The phase lasts until you eliminate all the Invaders / you survive at the end of the 15th turn / your plant is destroyed.

The “fast-forward phase” comes after the 15th turn of the planning phase. All the remaining Invaders from the planning phase will continue attacking. Your defensive scheme remains unchanged, and unchangeable. Invaders will continuously spawn and attack until the base is destroyed.

After you’ve finished the game, the number of turns your plant survived will be used as the basis for the product scores.

Game aspect 1: Resources

At the beginning of each wave, you are allowed to choose and place 5 resources – divided into Defenders (such as Coyote, Snake, Falcon etc. which kill the Invaders) and Terrains (composed of Cliff, Forest, and Rocky, which slow down or block the invaders). Each will be assigned to one turn of the current wave.

After each turn, the Defender/Terrain of that turn will be activated and locked – meaning you cannot change or remove its placement. The rest can be altered to adapt with the circumstances. The only exception is the Cliff, which activates right after its placement. 

Each Defender has a range/territory – once an invader steps into that range/territory, the Defender will damage them, reducing their population. The range varies between each Defender type – but in general the more powerful they are, the smaller their range is.

Each Terrain is effective towards different types of Invaders and in different ways, with some blocking the Invaders while others slowing them down.

Each Terrain and Defender will occupy one square. You cannot place Defender on top of an existing Defender, and if a Terrain is placed on top of an existing Terrain, it will replace the existing Terrain.

Defenders and Terrains form mutually compatible pairs which can exist on one same square. 

 Game aspect 2: Invaders

Invaders will appear from the map borders every 3-5 turns, in stacks of 100-200 population each, and move one step closer to your plant by each turn. The population of the stacks increases gradually.

Each Invader stack is accompanied by a path indicator – a long yellow arrow showing the direction it will take. The invader will always take this path unless blocked by Cliff.

Each Invader is countered by certain types of Terrain/Defender.

As the Plant Defense mini-game of the McKinsey Solve Game is essentially a tower-defense game, the basic tactics of that game genre can be applied – namely inside-out building and kill-zones. However, as the mini-game locks you from changing placement after a number of turns, contingency planning is also necessary.

I’ll elaborate each of those tactics:

INSIDE-OUT, MULTI-LAYERED DEFENSE

In this tactic, you build multiple layers of defenders outwards from the base, assisted by terrain.

Place your resources close to the plant first. As the inner rings of the map are smaller in circumference, and paths usually converge as you advance towards the center, this helps you maximize the coverage of each resource around the plant early on.

In the example below, the “inside-out” approach only takes 8 resources to protect the plant from all directions, while the “outside in” approach takes 24. With this approach, place your most powerful resources closest to the plant, and expand with the less powerful, but longer-range ones.

BIG-PICTURE MINDSET

This isn’t so much of a “tactic”, but a reminder – after 15 turns, you won’t be able to change or place more resources, so try to identify the pattern of the invaders, and quickly adapt your strategy accordingly. It will take a few initial turns to experiment which works best for each type of invader.

Use your resources prudently, create an all-round protection for the plant – lopsided defenses (i.e heavy in one direction, but weak in others) won’t last long – and lasting long is the objective of this mini-game.

Alternative mini-games

In June 2023, we received reports that these alternative mini-games have disappeared completely . When McKinsey decided that these games can’t accurately assess a candidate’s skills , they removed these tests. But in the future, as the McKinsey Solve evolves, there’s a chance they will re-adopt these games or develop new ones based on them. Thus, this section of the article exists only to provide a record, you can skip right to the next part.

Alternative 1: Disaster Management

In the Disaster Management mini-game of the Solve Game, the candidate is required to identify the type of natural disaster that has happened to an ecosystem, using limited given information and relocate that ecosystem to ensure/maximize its survivability.

With the two main objectives in mind, here’s how to deal with them:

Identify the disaster : this is a problem-diagnosis situation – the most effective approach would be to draw an issue tree with each in-game disaster as a branch, skim through data in a bottom-up manner to form a hypothesis, then test that hypothesis by mining all possible data in game (such as wind speed, temperature, etc.)

Relocate the ecosystem : this is a more complicated version of the location-selection step in the Ecosystem-Building mini-game, with the caveat that you will first have to rule out the locations with specs similar to the ongoing disaster. The rest can be done using a spreadsheet listing the terrain requirements of the species.

Like the Ecosystem Building mini-game, you will solve this mini-game only once, unlike the Plant Defense and the next Disease Management mini-games with multiple maps.

Alternative 2: Disease Management

In the Disease Management mini-game of the Solve Game, the candidate is required to identify the infection patterns of a disease within an ecosystem and predict the next individual to be infected.

The game gives you 3-5 factors for the species (increasing as the game progresses), including name, age, weight, and 3 snapshots of the disease spread (Time 1, Time 2, Time 3) to help you solve the problem.

There is one main objective here only: identify the rules of infection (the second is pretty much straight forward after you know the rules) – this is another problem-diagnosis situation. The issue tree for this mini-game should have specific factors as branches. Skim through the 3 snapshots to test each branch – once you’re sure which factor underlies and how it correlates with infection, simply choose the predicted individual.

Alternative 3: Migration Management

The Migration Management mini-game is a turn-based puzzle game. The candidate is required to direct the migration of 50 animals. This group carries a certain amount of resources (such as water, food, etc.), often 4-5 resources, each with an amount of 10-30. Every turn, 5 animals die and 5 of each resource is consumed.

It takes 3-5 turns from start to finish for each stage Migration mini-game, and the candidates must place 15 stages in 37 minutes. The candidate must choose among different routes to drive the animals. In each stage, there are points where candidates can collect 3 additional animals or resources (1-3 for each type), and choose to multiply some of the collected resources (1x, 3x and 6x); the game tells the candidate in advance which resources/animals they will get at each point, but not the amount.

The objective is to help the animals arrive at the destination with minimal animal losses, and with specific amounts of resources.

With all of these limited insights in mind, here’s what I recommend for the strategy:

Nearly every necessary detail is given in advance, so use a scratch paper to draw a table, with the columns being the resources/animals, and the rows being the routes. Quickly calculate the possible ending amount for each resources, assuming you get 2 at every collection point (good mental math will come in handy).

Choose the route with the highest number of animals, and “just enough” resources to meet requirements.

Test-taking tips for the McKinsey Solve 

Besides the usual test-taking tips of “eat, sleep and rest properly before the test”, “tell your friends and family to avoid disturbing”, etc. there are five tips specifically applicable to the McKinsey Solve Game I’ve compiled and derived from the reports of test takers:

Tip 1: Don’t think too much about criteria and telemetry measurements

You can’t know for sure which of your actions they are measuring, so don’t try so much to appear “good” before the software that it hurts your performance. One of our interviewers reported that he refrained from double-checking the species information in the Ecosystem Building mini-game for fear of appearing unsure and unplanned.

My advice is to train for yourself a methodical, analytic approach to every problem, so when you do come in for the test, you will naturally appear as such to the software. Once you’ve achieved that, you can forget about the measurements, and focus completely on problem-solving.

Tip 2: Don’t be erratic with in-game actions

While you don’t want to spend half your brain-power trying to “look good” to the software, do avoid erratic behaviors such as randomly selecting between the info panels, or swinging the mouse cursor around when brainstorming (yes, people do that – my Project Manager does the same thing when we do monthly planning for the website).

This kind of behavior might lead the software into thinking that you have unstable or unreliable qualities (again, we can never know for sure, but it’s best to try). One tip to minimize such “bad judgment” is to take your brainstorming outside of the game window, by using a paper, or a spreadsheet. 

Tip 3: Always strive for a better solution (Ecosystem Building)

Some of the interviewed test-takers seem to be under the wrong impression that “the end results do not matter as much as the process” – however, for the McKinsey Solve, you need good end results too. This is especially true in the Ecosystem Building, where a “right” answer with no species dying can be easily found with the right strategy.

Consulting culture is highly result-oriented, and this game/test has product scores to reflect that. Having a methodical and analytical approach is not enough – it’s no use being as such if you cannot produce good results (or, “exceptional” results, according to MBB work standards).

Tip 4: Showcase fundamental skills for a McKinsey consultant (Redrock Study)

McKinsey is always looking for candidates with the exact skill set for a model consultant: structured, logical, and professional. The McKinsey Solve Test is designed to do just that: to look for the right set of skills (with a lot of tracking and algorithms).

Through all parts of the Redrock Study Task, you must exhibit that you are a model McKinsey prospect. Here are a few things that they will value during the Redrock Study:

Strong mental math skills : A consultant MUST quickly pitch insights and calculations to clients and CEOs (elevator pitch). You’ll have to quickly choose a logical math formula and deliver results (not necessarily accurate). That’s why in all stages of the test involving math and a calculator, always do your calculations step-by-step on screen (if there’s an on-screen tool). 

Structured, top-down thinking : A candidate has to demonstrate that they are  a hypothesis-driven, structured problem solver . In other parts of the interview process (like the case interview), it is shown through a MECE, top-down issue tree. In the Redrock Study Task, you can show off this skill via organizing data points in the Research Journal , which we discussed above.

Choosing the right charts : A McKinsey consultant will chart like a McKinsey consultant . Each type of data must go with a corresponding type of chart. We have included a guide on consulting charts in our product shop. So check it out 

We have also linked to relevant preparation resources below, to help you master these skills more easily. So be sure to check them out. In Particular, we recommend you to use the McKinsey Solve free trial . It’s a free simulation of the real McKinsey Solve game.

Tip 5: Prepare your hardware and Internet properly before the test

While the McKinsey Solve Test does not require powerful hardware, the system requirements are indeed more demanding than the usual recruitment games or tests. A decent computer is highly-advised – the smoother the experience, the more you can focus on problem-solving.

On the other hand, a fast Internet connection is a must – in fact, the faster, the better. You don’t want to be disconnected in the middle of the test – so tell other users on your network to avoid using it at the same time as the test, and go somewhere with a fast and stable connection if it’s not available at your home.

How to practice for the McKinsey Solve Test 

Hypothesis-driven problem-solving approach.

See this article: Issue Tree, MECE

You may have noticed a lot of the solutions for the mini-game involve an “issue tree” – the centerpiece of the hypothesis-driven problem-solving approach that real consultants use in real projects.

This problem-solving approach is a must for every candidate wishing to apply for consulting – so learn and try to master it by applying it into everyday problems and cases you read in business publications. Practicing case interviews also helps with the McKinsey Solve as well.

You can see the above articles for the important concepts of consulting problem-solving.

Mental math and fast reading skills

See this article: Consulting Math, Fast Reading

The McKinsey Solve Test – especially the 3 ecosystem-related mini-games – requires good numerical and verbal aptitude to quickly absorb and analyze the huge amounts of data. Additionally, such skills are also vital to case interviews and real consulting work.

That means a crucial part of practice must include math and reading practice – see the above articles for more details on how to calculate and read 300% faster.

Practice with video games

As many games in the previous PSG have been eliminated, playing video games as part of practice has become less effective. But, we still recommend playing similar games to the Ecosystem Building (mainly) and Plant Defense mini-games.

Test-takers who regularly play video games, especially strategy games, report a significant advantage from their gaming experience. This is likely due to three main factors:

The McKinsey Solve Test’s games are in fact similar in logic and gameplay to a few popular video game genres. The more similar a game is to the McKinsey Solve, the better it is for practice.

Video games with data processing and system management also improve the necessary skills to pass the Solve.

Playing video games helps candidates understand how the interface as well as the objective system of a game works – improving their “game sense”.

I am not a fan of video games – in fact, after leaving McKinsey I founded an entertainment startup with the mission to fight the increasing popularity of video games. Yet now I have to tell you to spend a few hours each week playing them to get into McKinsey.

The question is, which games to play? Here’s a list of the games and game genres my team have found to possess many similarities with the McKinsey Solve Test:

City-building games

SimCity series

Caesar series (Zeus and Poseidon, Caesar III, Emperor ROTK)

Anno series (Anno 1404, Anno 2070, etc.)

Cities Skylines

These are very similar in logic to the Ecosystem Building mini-game – you need to balance the production and consumption of buildings and communities, which usually have specific requirements for their locations.

The difference between these and the PSG is that most games are real-time and continuous, meaning you have the opportunity to watch your city develop and correct the mistakes – in the Solve you need to nail it from the start! With that said, the amount of data you need to process in these games will make the McKinsey Solve a walk in the park; the learning curve is not too high either, making these games good practice grounds.

 Tower defense games

Kingdom Rush series

Plants vs Zombies series

Tower-defense games such as Kingdom Rush are near-perfect practices for the Plant Defense mini-game of the McKinsey PSG. Our basic “kill-zone” tactic in fact comes from these games.

Again, there is a caveat when practicing with games – both Plants vs Zombies and Kingdom Rush allow you to correct your mistakes by having the invaders attack the base multiple times before you lose. Both games also feature fixed and predictable paths of invasion. In the PSG, the path of the invaders changes with your actions, and if they reach your base, you’ll lose immediately.

Grand strategy and 4X games

Civilization series

Europa Universalis series

Crusader Kings series 

Grand strategy and 4X games combine the logic of system-building and tower-defense games (with Civilization being the best example), making them good practice for both games of the Test . They also require players to manage the largest amount of data among popular game genres (sometimes multiple windows with dozens of stats each).

However, they are also the games with the steepest learning curves – so if you are not one for video games, and/or you don’t have much time before the Test, these games are not for you. They are also less similar to the PSG on the surface, compared to the two genres above.

New release: McKinsey Solve free trial

In 2023, we released a new product – Redrock Study Task to feature a new game of McKinsey. The Redrock Simulation can be purchased in Mckinsey Solve Simulation (All-in-one) package.

Recently, we have officially launched the McKinsey Solve free trial . It provides unlimited attempts at a Redrock Study simulation with formats and interfaces similar to the real test. More important: It’s completely free. So, why don’t you give it a trial?

Scoring in the McKinsey PSG/Digital Assessment

The scoring mechanism in the McKinsey Digital Assessment

Related product

McKinsey Solve Simulation (All-in-One)

The one and only existing platform to practice three mini-games of McKinsey Solve in a simulated setting

If you rank above the 75th percentile (i.e. top 25% of candidates), and has a good resume, you are likely to pass the McKinsey Solve Game / PSG.

You can increase your McKinsey Solve scores through: time management, data-scanning, noise-filtering, note-taking, and having a good computer and Internet.

Experienced hires are preferred for expert and implementation roles, while opportunities for freshers are available for positions requiring less expertise"

McKinsey Problem Solving Game (Imbellus): a Complete Practice Guide to Pass the Digital Assessment

Employer? Recruit top talent with ATS - AI Recruitment

There is a lot of secrecy around the McKinsey Problem Solving Game, aka Imbellus.

This gamified assessment is used to filter out a large chunk of the many McKinsey applicants, and it’s supposedly crack-proof.

The internet is packed with blog posts, Reddit discussions, and forum threads about the McKinsey PSG, some even contradicting.

This information overload coupled with the huge importance of the test makes the whole preparation process nerve-wracking.

That’s why this practice guide strives to give you accurate and easy-to-digest information about your upcoming test.

It includes:

• A complete overview of the mini-games
• The best things to keep in mind while playing them
• The most helpful practice options available right now
• Useful tips and tactics to increase your chances of passing it

So, buckle up, and let’s get started.

Find out everything you need about the  McKinsey Problem Solving Game , aka Imbellus, and prepare using actual simulations!

What is the McKinsey Problem Solving Game (PSG)?

The McKinsey Problem Solving Game, also named McKinsey Imbellus, McKinsey Digital Assessment, and Solve Game, is a gamified test that replaces the previous assessment, PST, in the recruiting process. The PSG consists of two mini-games lasting for 70 minutes and evaluates candidates on five key cognitive abilities.

Only candidates who pass this stage are invited to the next hiring step, the case interviews.

What Skills Does the PSG Evaluate?

The PSG evaluates the consulting traits and qualifications of a candidate and then compares them to a real McKinsey consultant. If the test taker appears similar or better than the actual consultant, they'll pass the test.

Five main thinking skills are being assessed :

• Critical Thinking : The ability to solve problems by breaking them down into smaller parts.
• Decision-Making Process : The ability to take in large amounts of information and process it efficiently to make the best possible decision within time constraints.
• Meta Cognition : The ability to monitor your cognitive processes and improve them.
• Situational Awareness : The ability to keep track of several tasks or activities concurrently.
• Systems Thinking : The ability to identify the root causes of problems and possible solutions.

Do All Candidates Get the McKinsey Problem Solving Game?

As of 2024, almost all candidates for nearly all Mckinsey offices receive the McKinsey Solve Game. The PST, on the other hand, is no longer in use.

Get to Know the McKinsey PSG Format Inside Out

The Problem-Solving Game is sent to candidates once they pass the initial resume screening, making it the second hiring step.

McKinsey has created five mini-games, but you'll need to take only two of them. The most common ones are Ecosysystem Building and Redrock Study , and there are four other less common mini-games that only a fraction of the applicants receive (outlined below).

The time limit for the two common mini-games is 70 minutes , and for the others, it may range between 60 to 80 minutes. Each game will also have a tutorial, which is untimed.

Now, let's dive into each of the mini-games so you'll know what to expect on the test.

• Ecosystem Building

The first mini-game you'll need to pass is Ecosystem Building. In this game, you'll be randomly placed in either a mountain ridge or a coral reef scenario.

Your main objective in this mini-game is to build a sustainable ecosystem using exactly eight species from a collection of 39 species.

To achieve this goal successfully, you must strictly follow these guidelines:

• Terrain specs : The chosen location in the ecosystem must provide suitable living conditions for all eight species.
• Calories balance : Each species must be fed with enough calories from food to sustain itself.
• Food chain continuity : Each species must not be eaten into extinction by its predators.

The gaming platform provides specific information to help you meet these guidelines (some are seen in the game's "guidebook"):

Terrain Specs

Each location in the ecosystem has seven to eight terrain specs. You can choose a location using a pinpoint.

Of these seven or eight specs, only four can be displayed at any given time, using a checklist table in the upper-right corner of the screen:

Now, here's what's crucial about these living conditions:

Each species has specific terrain specs that have to be met. If they aren't met, the species won't survive, and you won't achieve the game's main objective.

Luckily, the species' living conditions usually come in ranges, allowing you to be more flexible with the species you choose for your ecosystem.

Additionally, each species has only two to four terrain specs , when Depth/Elevation and Temperature appear for all species:

Knowing that you only need to look at specific terrain specs on the checklist table helps eliminate species or locations that are not suitable for creating a sustainable ecosystem.

Food Chain Continuity

The 39 species are divided into producers and consumers.

Producers are plants and fungi (in the Mountain scenario) and corals and seaweeds (in the Coral Reef scenario). They don't have any calorie needs, so their "calories needed" spec is always zero.

Consumers are animals that eat either plants, other animals, or both. Some consumers are at the top of the food chain and therefore not eaten by any other species.

While creating the food chain, it's important to ensure that no species is eaten to extinction. This can be monitored using the " calorie needed " and the " calorie provided " specs that each species has (shown below).

Calories Balance

Each species has a calorie needed and a calorie provided, as you can see below:

A species lives if its "calories needed" are less than the sum of the calories provided by other species it eats (other consumers or providers).

Furthermore, the species' "calories provided" must be higher than the sum of the calories needed by other species that eat it.

The Main Challenges of the Ecosystem Building Mini-Game

Ecosystem creation is first of all a decision-making game.

You get all the information you need to deliver correct decisions so there's no uncertainty or inaccurate details.

The problem is that you have a vast amount of information to absorb, calculate, analyze, and prioritize . This includes the specs of 39 species, the terrain specs of each location, and eating rules.

Some of the information is irrelevant and is there to distract you or tempt you to make assumptions . In this mini-game, you must not make any assumptions and you don't need to have any environmental, ecological, or zoological knowledge.

So, your ability to make quick and accurate calculations and ignore irrelevant data will have a great impact on your performance.

The preparation course we recommend on this page includes a replica of McKinsey's Ecosystem Building game. It enables you to practice using a like-for-like game experience and learn about every single rule, move, and item in detail. Plus, you’ll master calculation methods and other tactics to ensure the food chain survives in your chosen location.

Redrock Study

The second mini-game you'll most likely encounter is Redrock Study. 

In the game's storyline, your task is to analyze the species inhabiting an island, which includes wolves and elks. The objective of your analysis is to formulate predictions and conduct various calculations , specifically focusing on percentages, by examining data on the evolution of the animal population.

The game has 4 sections:

• Investigation   You will be presented with a written text that includes tables and graphs. Your task is to sort information and gather valuable data points for the following test sections.
• Analysis   You will be presented with 3 or 4 math problems ; each is separated into two parts. You will be given a calculator and a Research Journal to gather information relevant to the questions.
• Report You will be presented with two types of questions - 
• 5 written questions regarding your findings in the analysis section
• 1 visual question in which you will need to choose a graph and use it to show what you found in the analysis.
•  Cases You will be presented with 6 to 10 questions that are unrelated to the analysis you did so far. 

You will have 35 minutes to complete all four sections , with a short, non-timed break before each one. 

Alternative Mini-Games

As of 2024, the Ecosystem Building game is constant, but the second mini-game may vary in rare cases. This means that there's a slight chance you won't get the Plant Defense mini-game, but rather one of the three we show below.

Disaster Management

In the Disaster Management game, you have to identify what type of natural disaster has happened to an animal population in an ecosystem.

Then, based on the data and information given, you need to choose a different location that will ensure the survival of the ecosystem.

The Disaster Management mini-game has only one objective - the sustainability of the ecosystem, similar to the Ecosystem Building mini-game.

Disease Management

In the Disease Management mini-game, you have to identify patterns of a disease within an ecosystem and predict who will be infected next. You can then use the information given about each species to help you solve the problem.

Migration Management

Migration Management is a turn-based puzzle game. The candidate must direct the migration of 50 animals while helping them arrive at their destination with minimal casualties and with a pre-determined amount of resources.

• Plant Defense

Plant Defense is a turn-based mini-game (similar to popular Tower Defense games). Your main objective is to defend a native plant that's located at the center of a 10x10, 10x14, or 12x12 grid from invader species, using defensive resources for as many turns as possible .

This mini-game consists of three maps, and each map is divided into two - the planning phase and the fast-forward phase. McKinsey recommends allocating 12 minutes per map, which makes it 36 minutes in total.

The 36-minute time limit is not fixed though, as it depends on how long it took you to finish the first mini-game, Ecosystem Building.

Many candidates mention that the Plant Defense game is more challenging than the Ecosystem creation. So, keep that in mind while taking the first one and plan your time wisely .

Now, let's take a closer look at the different elements and resources of this mini-game:

Your base is the native plant that you have to defend from invaders at all costs. Once an invader reaches the base, you lose the game.

Note that eventually, everyone loses, and you can't hold your base forever. But the more turns you manage to survive, the better .

There are two types of invaders in the game - Groundhog and Fox. Their movements on the map are the same, and the only difference between them is the terrain type that holds them back (more on terrains below).

Once an invader appears on your map, it will choose the shortest path to reach your base plant. This path will be shown as a yellow arrow .

There are three types of terrains in the game:

• Forest : Slows down the Groundhog for one turn
• Rocky : Slows down the Fox for one turn
• Cliff : Blocks both the Fox and the Groundhog from passing this square

Each terrain holds one grid on the map, and you cannot place terrain on a grid that already has another terrain or a defender on it (more on defenders below).

As opposed to terrains, defenders don't just slow down or block an invader, they eliminate it for good.

There are several defenders you can use in the game: Bobcat, Falcon, Wolf, Python, and Coyote.

Note that you won't see all of the defenders at once.

Each defender has two important specs you must take into account:

Range : Each defender can cover a pre-determined number of grids on the map. For example, a Python can cover only one grid, while a Falcon can cover as many as 13 grids.

Damage : Each defender can cause specific forms of damage to an invader's population. When an invader attacks, you'll be able to see its population number and the damage that your defender can cause him. A Wolf, for example, has a damaging impact of 60, while a Falcon has only 20.

The Main Challenges of the Plant Defense Mini-Game

In this mini-game, you have to make decisions based on limited information and face unexpected events (like new invaders from any direction). Also, you must achieve two simultaneous objectives - survive each of the turns separately and for as long as possible.

This is the complete opposite of the Ecosystem Building game, in which you have all the data in front of you, and you have just one objective.

Two things that can help you overcome these challenges are (1) preparing for the unexpected events that will happen during the game and (2) planning low-risk solutions based on your resources (terrains and defenders).

The prep course that we recommend on this page has the closest simulation possible to the actual Plant Defense game. It has the same gameplay, invaders, and resources, and it's based on the same algorithm that appears in the McKinsey Solve Game. This will enable you to learn the most effective tactics to ensure your base plant survives as many turns as possible.

How to Beat the McKinsey Problem Solving Game?

The proven way to beat the McKinsey PSG is by properly preparing beforehand.

There's no way around it. That’s because the mini-games include an immense amount of information, rules, and patterns you must master . And they require you to use tactics and strategies that are not obvious and take time to plan and execute.

All of that is under great time pressure and the high stakes of possibly failing it and losing an opportunity to work at McKinsey.

Now, there are a few practice options you can use to get a better understanding of the PSG and improve your chances of passing it, with the PSG Interactive Simulation being the most accurate one.

McKinsey Problem Solving Game Practice Options

Psg interactive simulation.

The  PSG Secrets simulation is an interactive platform that includes accurate practice for every part of McKinsey’s PSG. It mirrors what the actual game scenarios look like, what each button does, how the logic of the games works, how it generates the data, and more.

It has a full simulation option (two mini-games, 70 minutes), which includes:

• A full video course in 24 videos and 2h30m of content on Ecosystem, Redrock, and Plant Defense
• 2 excel solvers for the Ecosystem Game
• 10 Redrock test drills specifically for the case section
• 152 page-pdf guide 
• 60-day money-back guarantee.

Tips to Improve Your Performance on the McKinsey Problem Solving Game

Here are several specific tips to help improve your overall performance on the test as well as tips to avoid any disturbances that could hurt your score:

#1 Sharpen Your Mental Math Abilities

The ability to make fast and accurate calculations can help a lot in this Solve Game. That’s because one wrong calculation might ruin your carefully built Ecosystem or cause an invader to reach your Native Plant.

There are several free apps and sites, like the renowned Khan Academy , that can help you improve your math skills quickly.

#2 Learn Fast Reading Skills

Mckinsey’s PSG requires you to absorb and analyze a tremendous amount of information under strict time constraints.

Fast reading skills come in handy in this test and can help reduce the amount of time needed to understand the numerous guidelines of the mini-games.

There are certain apps and browser extensions that allow you to practice this important skill , even on the go.

#3 Focus Only on What Matters

Don't get nervous when you first see the immense amount of data on the mini-games. That’s because a lot of the data is irrelevant, and you’ll be only using some particular parameters .

For example, in the Ecosystem game, you’ll only have to use specific species and terrain specs for your calculations, while ignoring others that are there only for distraction.

In the complete   PSG Simulation Practice , you’ll see how to remove as much as 70% of the irrelevant data and remain just with the information that matters.

#4 Ignore Outside Information

While taking the assessment, especially the Ecosystem game, try to ignore any outside knowledge and information.

For example, if you’ve learned biology or zoology and you see that your food-eating rules don’t seem logical but the numbers are correct, always go with the numbers .

If you start to rely on previous knowledge, you might get confused and mess up your progress in the game.

#5 Learn to Solve Problems Like a Consultant

The PSG measures your consulting traits and compares them to a model McKinsey consultant.

That’s why learning to think and solve problems like a real consultant can help you pass this assessment.

Two main problem-solving skills you should practice are decision-making in fully controlled situations and with limited information.

Both of these skills can be trained using complex strategy games (examples are mentioned above) as well as  practicing with the   full PSG interactive simulation .

#6 Cut Down on Calculation Time Using Microsoft Excel

Mental math is an effective way to make calculations in the mini-games.

But as you’re only human, it’s not error-free. That’s why using a calculation tool, such as Excel formulas, can be a great way to make super fast and accurate calculations.

You can use it to gather all the relevant data, arrange it with columns and formulas (even in advance!), and turn the whole process into a no-brainer.

That said, you’ll need to use another monitor (preferably with a different browser) or another laptop since the assessment’s platform will take over your entire screen.

#7 Prep Your Hardware and Internet Connection

The last thing you want during the assessment is a “blue screen of death.”

It may happen if your hardware is not strong enough, since the McKinsey PSG is pretty demanding in its system requirements.

Any computer that is more than five years old or without an HD screen will likely encounter lags and performance drops.

Also, you must have a fast and stable internet connection. If you get disconnected in the middle of the test, you might need to start all over again or even reschedule for another testing date.

The PSG scores are divided into two types -

• Product score - the final outcome of your performance
• Process score - the efficiency (time and number of clicks) of your performance 

If you get the   PSG Practice Simulation , you’ll have a mock grading system that monitors your results and behavioral patterns.

This will allow you to track your progress while you practice for the test and see which areas demand improvement.

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Why did mckinsey develop the problem-solving game.

McKinsey created the Problem-Solving Game as an unbiased way to identify candidates from around the globe with strong cognitive abilities. The former assessment, Problem Solving Test (PST), was less challenging for candidates who were familiar with standardized tests, such as SAT and GMAT, or used the numerous mock tests found online.

The PSG, on the other hand, is supposedly crack-proof. That's because it takes into account the approach you use to solve the problems and not just the final solution. This seemingly removes any lucky guessing and shortcut techniques that were common on the McKinsey PST.

While on the PST you had just your final score, on the PSG your score is comprised of dozens of scoring criteria apart from your final result , including mouse movement, keystrokes, and clicks.

McKinsey can analyze these factors for every recorded candidate, which allows them to compare candidates more fairly.

What Does Imbellus Mean?

Imbellus is a company that creates immersive simulation-based assessments to assess cognitive processes. To develop a new testing format for the McKinsey recruitment process, they've teamed up with McKinsey consultants and UCLA Cresst psychologists.

In 2020,  Imbellus was purchased by Roblox , an online gaming platform, to help sharpen its recruitment practices.

This was an in-depth prep guide for the McKinsey Problem Solving Game. It gave you an overview of the different mini-games, explained their main challenges, and offered some useful solving tips.

Additionally, you saw the best ways to prepare for the assessment, when the PSG Practice Simulation being the most realistic and accurate one.

McKinsey Recruitment Process

McKinsey's selection process consists of several key phases:

• Application Review: The first step involves submitting your resume, which is briefly examined to filter out unsuitable candidates.
• Online Assessment: Within 7-14 days of applying, you'll likely receive an invitation to participate in the Solve Game, an interactive assessment that evaluates your analytical and decision-making abilities.
• Performance Analysis: McKinsey's team thoroughly examines your Solve Game results with your application materials.
• Case Interview: Candidates who excel in the previous stages advance to face-to-face case interviews. These sessions assess your capacity to tackle real-world business challenges, interpret data, and articulate clear solutions, demonstrating your problem-solving prowess and communication skills.

What is the McKinsey solve game?

The McKinsey Solve Game or Problem Solving Game (PSG) is a gamified evaluation incorporated into McKinsey's hiring process. This assessment features two mini-games that take approximately 70 minutes to complete, measuring candidates across five essential cognitive skills: critical reasoning, decision-making, metacognition, situational awareness, and systems thinking. Test takers who excel in this assessment move forward to the case interview phase. The game replicates real-world challenges, demanding strong strategic problem-solving and decision-making abilities.

How to solve the McKinsey problem-solving game?

To succeed in the McKinsey Solve Game, you should concentrate on enhancing key skills such as critical thinking, pattern recognition, and decision-making. The game assesses test takers' abilities in resource management and environmental evaluation. It's essential to practice organizing your approach, logically analyzing scenarios, and making decisions grounded in data. Additionally, working with simulations that replicate real-world complex systems can be beneficial.

Is the McKinsey solve game hard?

The McKinsey Solve Game can be challenging, requiring critical thinking, decision-making, and adaptability under pressure. The game simulates real-world scenarios that demand strategic problem-solving and analytical skills, which can be difficult for some candidates. However, with adequate preparation, including practicing relevant skills and familiarizing yourself with the game format, you can improve your performance and increase your confidence in tackling the assessment.

How many people pass the McKinsey solve game?

The specific percentage of candidates who pass the McKinsey Solve Game is not publicly disclosed by McKinsey & Company. However, it's known that performing well in the game is critical for advancing to the next stage of the hiring process, the live case interview. The assessment is designed to be challenging, and only those who demonstrate strong problem-solving and analytical skills typically move forward.

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McKinsey Problem Solving Game (Solve)

Welcome to our market-leading interactive McKinsey Solve Game Simulation. You'll have the chance to experience the assessment firsthand with our proprietary software. Our simulation includes three games from the assessment:

• Ecosystem Creation: Create over 6 distinct ecosystems within the Mountain Ridge and Coral Reef scenarios.
• Redrock Study: Practice with 4 full-length scenarios that include both study and cases sections.
• Plant Defense: Protect the native plant across an unlimited number of scenarios.

Explore Our Interactive Demo Today

We invite you to dive into the first few minutes of the Ecosystem Creation: Coral Reef game through our free demo provided below. Experience the tutorial, browse the guidebook, select four monitors, and hover over the ecosystem to pick your starting location.

As you purchase the full game, you can browse the entire set of species and start building your ecosystem. Alternatively, you'll also get access to the other ecosystem creation game, Ecosystem Creation: Mountain Ridge, as well as Redrock Study and Plant Defense games.

Video Guide: Tips and Game Walkthrough

Dive into the McKinsey Solve (Problem Solving Game) with our detailed guide, featuring exclusive in-game footage from Prepmatter's interactive simulation. Our video offers a comprehensive look into the game's structure, covering the Ecosystem Creation (including both Mountain Ridge and Coral Reef scenarios), the Redrock Study, and the Plant Defense game. 

For the Ecosystem Creation game, we break down the general layout and share key eating rules and strategic tips on selecting the optimal location to establish a sustainable ecosystem, supplemented with practical examples. This segment is designed to equip you with the knowledge needed to navigate through the complexities of ecosystem management.

The Redrock Study, a newer addition to the Solve game, receives an in-depth analysis across its Study and Cases sections. We dissect the Investigation, Analysis, and Report phases, providing in-game visuals to demystify the game's setup. Our guide includes best practices for gathering relevant data points, performing accurate calculations, and effectively summarizing findings in both written and graphical forms. Additionally, we delve into the Cases section, where you'll encounter 6 mini-cases, offering insights into what to expect and how to approach each scenario.

Although the Plant Defense game has been largely replaced by the Redrock Study, we haven't overlooked it. Our guide presents the game setup, detailing essential strategies for terrain transformation and the deployment of animals to defend the native plant. Through strategic advice, we aim to help you extend the plant's defense for as long as possible.

Full Game Reveal: Master McKinsey's Problem Solving Game

Dive into the comprehensive gameplay experience with our brand-new video guide! We're excited to unveil the first complete walkthrough of the McKinsey Problem Solving Game in the market, providing an unmatched practical learning opportunity. Follow along as we navigate through the intricacies of Ecosystem Creation, Redrock Study, and Plant Defense.

In the Ecosystem Creation section, discover how to identify the optimal habitat, select a balanced group of species, and employ strategies that pave the way for a thriving ecosystem. Progressing to the Redrock Study, we dissect the study and case phases of the intriguing Nolotiles scenario, exclusively provided through Prepmatter. Concluding with Plant Defense, our guide imparts tactics for positioning animals and barriers to safeguard the native plant.

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