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The mckinsey approach to problem solving.

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

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

You can click any section to go directly there:

Overview of the McKinsey Approach to Problem Solving

Problem solving process.

  • Problem Definition & Problem Statement Worksheet

Stakeholder Analysis Worksheet

Hypothesis trees, issue trees, analyses and workplan, synthesize findings, craft recommendations, distinctiveness practices, harness the power of collaboration, sources and additional reading, download the umbrex toolkit on the mckinsey approach to problem solving.

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

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

There are four fundamental disciplines of the McKinsey method:

1. Problem definition

A thorough understanding and crisp definition of the problem.

2. The problem-solving process

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

3. Distinctiveness practices

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

4. Collaboratio n

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

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

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

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

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

Problem Definition & Problem Statement Worksheet

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

Problem Statement Worksheet

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

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

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

  • Action-oriented

Example situation – A family on Friday evening

Scenario: A mother, a father, and their two teenage children have all arrived home on a Friday at 6 p.m. The family has not prepared dinner for Friday evening. The daughter has lacrosse practice on Saturday and an essay to write for English class due on Monday. The son has theatre rehearsal on both Saturday and Sunday and will need one parent to drive him to the high school both days, though he can get a ride home with a friend. The family dog, a poodle, must be taken to the groomer on Saturday morning. The mother will need to spend time this weekend working on assignments for her finance class she is taking as part of her Executive MBA. The father plans to go on a 100-mile bike ride, which he can do either Saturday or Sunday. The family has two cars, but one is at the body shop. They are trying to save money to pay for an addition to their house.

What is the problem definition?

A statement of facts does not focus the problem solving:

It is 6 p.m. The family has not made plans for dinner, and they are hungry.

A question guides the team towards a solution:

1. What should the family do for dinner on Friday night?

2. Should the family cook dinner or order delivery?

3. What should the family cook for dinner?

4. What should the family cook for dinner that will not require spending more than $40 on groceries?

5. To cook dinner, what do they need to pick up from the supermarket?

6. How can the family prepare dinner within the next hour using ingredients they already have in the house?

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

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

Using the Stakeholder Analysis Worksheet allows you to comprehensively identify:

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

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

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

Example: Alpha Manufacturing, Inc.

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

The hypotheses might be:

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

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

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

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

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

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

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

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

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

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

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

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

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

The issues might be:

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

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

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

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

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

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

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

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

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

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

Pyramid Structure for Grouping Arguments

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

Three Horizons of Engagement Planning

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

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

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

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

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

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

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

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

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

Expand: Construct multiple perspectives

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

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

Link: Identify relationships

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

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

Distill: Find the essence

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

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

Lead: Stay ahead/step back

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

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

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

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

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

The key here is to think open, not closed. Opening up to varied sources of data and perspectives furthers our mission to develop truly innovative and distinctive solutions for our clients.

  • McKinsey Staff Paper 66 — not published by McKinsey but possibly found through an internet search
  • The McKinsey Way , 1999, by Ethan M. Rasiel

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McKinsey Solve

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  • How it works
  • Skills tested
  • How to prepare
  • A guide to the McKinsey Problem Solving Game

MCC is here to help

McKinsey’s Solve assessment has been making candidates sweat ever since it was initially trialled at the firm’s London office back in 2017 - and things have gotten even more difficult since a new version launched in Spring 2023, adding the Redrock case study.

More recently, in Summer 2023, we have seen a new iteration of that Redrock case, as we continue to interview test takers to keep you updated. This replaces the case study about optimising wolf pack populations across Redrock Island with one about boosting the overall plant biodiversity on the same island.

Since its initial roll-out, the Solve assessment has definitely been the most idiosyncratic, but also the most advanced, of the screening tests used by the MBB firms.

It can be hard to understand how an ecology-themed video game can tell McKinsey whether you’ll make a good management consultant, let alone know how to prepare yourself to do well in that game. When you consider that McKinsey are potentially cutting 70%+ of the applicant pool based on this single test, you can hardly blame applicants for being worried.

Matters are definitely not helped by the dearth of reliable, up-to-date information about what could very well be - with a top-tier consulting job on the line - the most important test you will take over your entire career. This was already true with the version of Solve that had been around for a few years, let alone the new iterations.

What information is available online is then often contradictory. For a long time, there was huge disagreement as to whether it is actually possible to meaningfully prepare for the Solve assessment - before you’ve even considered how to go about that preparation. There is also a lot of confusion and inaccuracy around the new Redrock case - largely as it is such a recent addition, and individual test takers tend to misremember details.

Luckily, we at MCC have been interviewing test takers both before and after the Redrock case rollout and have been following up to see which strategies and approaches actually work to push individuals through to interview.

Here, we’ll explain that it is indeed possible to prepare effectively for both versions of Solve and give you some ideas for how you can get started. Understanding how the Solve assessment works, what it tests you for and how is critical for all but the most hurried preparations.

This article makes for a great introduction to the Solve assessment. However, if you are going to be facing this aptitude test yourself and want full information and advice for preparation, then you should ideally get our full PDF guide:

Master the Solve Assessment

What is the mckinsey solve assessment.

In simple terms, the McKinsey Solve assessment is a set of ecology-themed video games. In these games, you must do things like build food chains, protect endangered species, manage predator and prey populations, boost biodiversity and potentially diagnose diseases within animal populations or identify natural disasters.

Usually, you will be given around 70 minutes to complete two separate games, spending about the same amount of time on each.

Until recently, these games had uniformly been Ecosystem Building and Plant Defence. However, since Spring 2023, McKinsey has been rolling out a new version across certain geographies. This replaces the Plant Defence game with the new Redrock case study. Some other games have also been run as tests.

We’ll run through a little more on all these games below to give you an idea of what you’ll be up against for both versions and possible new iterations.

An important aspect that we'll cover in more detail here is that the Solve games don't only score you on your answers (your "product score"), but also on the method you use to arrive at them (your "process score") - considerably impacting optimal strategy.

In the past, candidates had to show up to a McKinsey office and take what was then the Digital Assessment or PSG on a company computer. However, candidates are now able to take the re-branded Solve assessment at home on their own computers.

Test takers are allowed to leverage any assistance they like (you aren’t spied on through your webcam as you would be with some other online tests), and it is common to have a calculator or even another computer there to make use of.

Certainly, we strongly advise every candidate to have at least a pen, paper and calculator on their desk when they take the Solve assessment.

Common Question: Is the Solve assessment the same thing as the PSG?

In short, yes - “Solve” is just the newer name for the McKinsey Problem Solving Game.

We want to clear up any potential confusion right at the beginning. You will hear this same screening test called a few different things in different places. The Solve moniker itself is a relatively recent re-branding by McKinsey. Previously, the same test was known as either the Problem Solving Game (usually abbreviated to PSG) or the Digital Assessment. You will also often see that same test referred to as the Imbellus test or game, after the firm that created the first version.

You will still see all these names used across various sites and forums - and even within some older articles and blog posts here on MyConsultingCoach. McKinsey has also been a little inconsistent on what they call their own assessment internally. Candidates can often become confused when trying to do their research, but you can rest assured that all these names refer to the same screening test - though, of course, folk might be referring to either the legacy or Redrock versions.

How and why does McKinsey use the Solve assessment?

It’s useful to understand where the Solve assessment fits into McKinsey’s overall selection process and why they have felt the need to include it.

Let’s dive right in…

How is the Solve Assessment used by McKinsey?

McKinsey's own account of how the Solve assessment is used in selection can be seen in the following video:

Whilst some offices initially stuck with the old PST, the legacy Solve assessment was soon rolled out globally and given universally to candidates for roles at pretty well every level of the hierarchy. Certainly, if you are a recent grad from a Bachelor’s, MBA, PhD or similar, or a standard experienced hired, you can expect to be asked to complete the Solve assessment.

Likewise, the new Redrock case study versions seem to be in the process of being rolled out globally - though at this point it seems you might be given either (especially as McKinsey has been having significant technical problems with this new online case study) and so should be ready for both.

At present, it seems that only those applying for very senior positions, or perhaps those with particularly strong referrals and/or connections, are allowed to skip the test. Even this will be office-dependent.

As noted above, one of the advantages of the Solve assessment is that it can be given to all of McKinsey’s hires. Thus, you can expect to be run into the same games whether you are applying as a generalist consultant or to a specialist consulting role - with McKinsey Digital , for example.

The takeaway here is that, if you are applying to McKinsey for any kind of consulting role, you should be fully prepared to sit the Solve Assessment!

Where does the Solve assessment fit into the recruitment process?

You can expect to receive an invitation to take the Solve assessment shortly after submitting your resume.

It seems that an initial screen of resumes is made, but that most individuals who apply are invited to take the Solve assessment.

Any initial screen is not used to make a significant cut of the candidate pool, but likely serves mostly to weed out fraudulent applications from fake individuals (such as those wishing to access the Solve assessment more than once so they can practice...) and perhaps to eliminate a few individuals who are clearly far from having the required academic or professional background, or have made a total mess of their resumes.

Your email invitation will generally give you either one or two weeks to complete the test, though our clients have seen some variation here - with one individual being given as little as three days.

Certainly, you should plan to be ready to sit the Solve assessment within one week of submitting your resume!

Once you have completed the test, McKinsey explain on their site that they look at both your test scores and resume (in more detail this time) to determine who will be invited to live case interviews. This will only be around 30% of the candidates who applied - possibly even fewer.

One thing to note here is that you shouldn’t expect a good resume to make up for bad test scores and vice versa. We have spoken to excellent candidates whose academic and professional achievements were not enough to make up for poor Solve performance. Similarly, we don’t know of anyone invited to interview who hadn’t put together an excellent resume.

Blunty, you need great Solve scores and a great resume to be advanced to interview.

Your first port of call to craft the best possible resume and land your invitation to interview is our excellent free consulting resume guide .

Why does this test exist?

Screenshot of an island from the McKinsey Solve assessment

As with Bain, BCG and other major management consulting firms, McKinsey receives far far more applications for each position than they can ever hope to interview. Compounding this issue is that case interviews are expensive and inconvenient for firms like McKinsey to conduct. Having a consultant spend a day interviewing just a few candidates means disrupting a whole engagement and potentially having to fly that consultant back to their home office from wherever their current project was located. This problem is even worse for second-round interviews given by partners.

Thus, McKinsey need to cut down their applicant pool as far as possible, so as to shrink the number of case interviews they need to give without losing the candidates they actually want to hire. Of course, they want to accomplish this as cheaply and conveniently as possible.

The Problem Solving Test (invariably shortened to PST) had been used by McKinsey for many years. However, it had a number of problems that were becoming more pronounced over time, and it was fundamentally in need of replacement. Some of these were deficiencies with the test itself, though many were more concerned with how the test fitted with the changing nature of the consulting industry.

The Solve assessment was originally developed and iterated by the specialist firm Imbellus ( now owned by gaming giant Roblox ) to replace the long-standing PST in this screening role and offers solutions to those problems with its predecessor.

We could easily write a whole article on what McKinsey aimed to gain from the change, but the following few points cover most of the main ideas:

  • New Challenges: Previously, candidates were largely coming out of MBAs or similar business-focussed backgrounds and the PST’s quickfire business questions were thus perfectly sufficient to select for non-technical generalist consulting roles. However, as consulting projects increasingly call for a greater diversity and depth of expertise, McKinsey cannot assume the most useful talent – especially for technical roles – is going to come with pre-existing business expertise. A non-business aptitude test was therefore required.
  • Fairness and the Modern Context: The covid pandemic necessitated at-home aptitude testing. However, even aside from this, online testing dramatically reduces the amount of travel required of candidates. This allows McKinsey to cast a wider net, providing more opportunities to those living away from hub cities, whilst also hugely reducing the carbon footprint associated with the McKinsey selection process.
  • Gaming the System: More pragmatically, the Solve assessment is a much harder test to “game” than was the PST, where highly effective prep resources were available and readily allowed a bad candidate with good preparation to do better than a good candidate. The fact that game parameters change for every individual test taker further cuts down the risk of candidates benefitting from shared information. The recent move towards the Redrock version then also helps McKinsey stay ahead of those developing prep resources for the legacy Solve assessment.
  • Cost Cutting: A major advantage of scrapping the old pen-and-paper PST is that the formidable task of thinning down McKinsey’s applicant pool can be largely automated. No test rooms and invigilation staff need to be organised and no human effort is required to devise, transport, catalogue and mark papers.

Impress your interviewer

Group of blue fish in a coral reef

There has been a bit of variation in the games included in the Solve assessment/PSG over the years and what specific form those games take. Imbellus and McKinsey had experimented with whole new configurations as well as making smaller, iterative tweaks over time. That being said, the new 2023 Redrock case studies (seemingly added by McKinsey themselves without Imbellus) are by far the largest change to Solve since that assessment's genesis back in 2017.

Given that innovation seems to continue (especially with the lengthy feedback forms some candidates are being asked to fill in after sitting the newest iteration), there is always the chance you might be the first to receive something new.

However, our surveys of, and interviews with, those taking the Solve assessment - both before and after recent changes - mean we can give you a good idea of what to expect if you are presented with either the legacy or one of the Redrock versions of Solve.

We provide much more detailed explanation of each of the games in our Solve Assessment PDF Guide - including guidance on optimal scenarios to maximise your performance. Here, though, we can give a quick overview of each scenario:

Ecosystem Building

Screenshot showing the species data from the ecosystem building game

In this scenario, you are asked to assemble a self-sustaining ecosystem in either an aquatic, alpine or jungle environment (though do not be surprised if environments are added, as this should be relatively easy to do without changing the underlying mechanics).

The game requires you to select a location for your ecosystem. Several different options are given, all with different prevailing conditions. You then have to select a number of different plant and animal species to populate a functioning food chain within that location.

In previous versions of the game, you would have had to fit as many different species as possible into a functioning food chain. However, newer iterations of the Solve assessment require a fixed number of eight or, more recently, seven species to be selected.

Species selection isn’t a free-for-all. You must ensure that all the species you select are compatible with one another - that the predator species you select are able to eat the prey you have selected for them etc. All the species must also be able to survive in the conditions prevailing at the location you have selected.

So far, this sounds pretty easy. However, the complexity arises from the strict rules around the manner and order in which the different species eat one another. We run through these in detail in our guide, with tips for getting your food chain right. However, the upshot is that you are going to have to spend some significant time checking your initial food chain - and then likely iterating it and replacing one or more species when it turns out that the food chain does not adhere to the eating rules.

Once you have decided on your food chain, you simply submit it and are moved on to the next game. In the past, test takers were apparently shown whether their solution was correct or not, but this is no longer the case.

Test takers generally report that this game is the easier of the two, whether it is paired with the Plant Defence game in the legacy Solve or the Redrock case study in the new version. Candidates will not usually struggle to assemble a functioning ecosystem and do not find themselves under enormous time pressure. Thus, we can assume that process scores will be the main differentiator between individuals for this component of the Solve assessment.

For ideas on how to optimise your process score for this game, you can see our PDF Solve guide .

Plant Defence

Screenshot showing the plant defence game in progress

As mentioned, this game has been replaced with the Redrock case study in the new newer version of the Solve assessment, rolled out from Spring 2023 and further iterated in Summer 2023. However, you might still be asked to sit the legacy version, with this game, when applying to certain offices - so you should be ready for it!

This scenario tasks you with protecting an endangered plant species from invasive species trying to destroy it.

The game set-up is much like a traditional board game, with play taking place over a square area of terrain divided into a grid of the order of 10x10 squares.

Your plant is located in a square near the middle of the grid and groups of invaders - shown as rats, foxes or similar - enter from the edges of the grid before making a beeline towards your plant.

Your job then is to eliminate the invaders before they get to your plant. You do this by placing defences along their path. These can be terrain features, such as mountains or forests, that either force the invaders to slow down their advance or change their path to move around an obstacle. To actually destroy the invaders though, you use animal defenders, like snakes or eagles, that are able to deplete the groups of invaders as they pass by their area of influence.

Complication here comes from a few features of the game. In particular:

  • You are restricted in terms of both the numbers of different kinds of defenders you can use and where you are allowed to place them. Thus, you might only have a couple of mountains to place and only be allowed to place these in squares adjacent to existing mountains.
  • The main complication is the fact that gameplay is not dynamic but rather proceeds in quite a restricted turnwise manner. By this, we mean that you cannot place or move around your defences continuously as the invaders advance inwards. Rather, turns alternate between you and invaders and you are expected to plan your use of defences in blocks of five turns at once, with only minimal allowance for you to make changes on the fly as the game develops.

The plant defence game is split into three mini-games. Each mini-game is further split into three blocks of five turns. On the final turn, the game does not stop, but continues to run, with the invaders in effect taking more and more turns whilst you are not able to place any more defences or change anything about your set-up.

More and more groups of invaders pour in, and your plant will eventually be destroyed. The test with this “endgame” is simply how many turns your defences can stand up to the surge of invaders before they are overwhelmed.

As opposed to the Ecosystem Building scenario, there are stark differences in immediate candidate performance - and thus product score - in this game. Some test takers’ defences will barely make it to the end of the standard 15 turns, whilst others will survive 50+ turns of endgame before they are overwhelmed.

In this context, as opposed to the Ecosystem Building game typically preceding it, it seems likely that product score will be the primary differentiator between candidates.

We have a full discussion of strategies to optimise your defence placement - and thus boost your product score - in our Solve guide .

Redrock Case Study

Pack of wolves running through snow, illustrating the wolf packs central to the Redrock case study

This is the replacement for the Plant Defence game in the newest iteration of Solve.

One important point to note is that, where the Solve assessment contains this case study, you have a strict, separate time limit of 35 minutes for each half of the assessment. You cannot finish one game early and use the extra time in the other, as you could in the legacy Solve assessment.

McKinsey has had significant issues with this case study, with test takers noting several major problems. In particular:

  • Glitches/crashes - Whilst the newest, Summer 2023 version seems to have done a lot to address this issue, many test takers have had the Redrock case crash on them. Usually, this is just momentary and the assessment returns to where it was in a second or two. If this happens to you, try to just keep calm and carry on. However, there are reports online of some candidates having the whole Solve assessment crash and being locked out as a result. If this happens, contact HR.
  • Poor interface - Even where there are no explicit glitches, users note that several aspects of the interface are difficult to use and/or finicky, and that they generally seem poorly designed compared to the older Ecosystem Building game preceding it. For example, test takers have noted that navigation is difficult or unclear and the drag and drop feature for data points is temperamental - all of this costing precious time.
  • Confusing language - Related to the above is that the English used is often rather convoluted and sometimes poorly phrased. This can be challenging even for native English speakers but is even worse for those sitting Solve in their second language. It can make the initial instructions difficult to understand - compounding the previous interface problem. It can also make questions difficult, requiring a few readings to comprehend.
  • Insufficient time - Clearly, McKinsey intended for Redrock to be time pressured. Whilst the newest, Summer 2023 iteration of the Redrock case seems slightly more forgiving in this regard, time is still so scarce that many candidates don't get through all the questions. This is plainly sub-optimal for McKinsey - as well as being stressful and disheartening for candidates. We would expect further changes to be made to address this issue in future.

McKinsey are clearly aware of these issues, as even those sitting the new version of Redrock have been asked to complete substantial feedback surveys. Do note, then, that this raises the likelihood of further changes to the Redrock case study in the near term - meaning you should always be ready to tackle something new.

For the time being, though, we can take you through the fundamentals of the current version of the Redrock case study. For more detail, see our freshly updated PDF Guide .

The Scenario

Whilst changes to the details are likely in future, the current Redrock case study is set on the Island of Redrock. This island is a nature reserve with populations of various species, including wolves, elk and several varieties of plant.

In the original Redrock case, it is explained that the island's wolves are split into four packs, associated with four geographical locales. These packs predate the elk and depend upon them for food, such that there is a dynamic relationship between the population numbers of both species. Your job is to ensure ecological balance by optimising the numbers of wolves in the four packs, such that both wolves and elk can sustainably coexist.

In the newer iteration of the case, first observed in Summer 2023, you are asked to assess which, if any, of three possible strategies can successfully boost the island's plant biodiversity by a certain specified percentage. Plants here are segmented into grasses, trees and shrubs.

The Questions

The Redrock case study's questions were initially split into three sections, but a fourth was added later. These sections break down as follows:

  • Investigation - Here, you have access to the full description of the case, with all the data on the various animal populations. Your task is to efficiently extract all the most salient data points and drag-and-drop them to your "Research Journal" workspace area. This is important, as you subsequently lose access to all the information you don't save at this stage.
  • Analysis - You must answer three numerical questions using information you saved in the Investigation section. This can include you dragging and dropping values to and from an in-game calculator.
  • Report - Formerly the final section, you must complete a pre-written report on the wolf populations or plant biodiversity levels, including calculating numerical values to fill in gaps and using an in-game interface to make a chart to illustrate your findings. You will leverage information saved in the Investigation section, as well as answers calculated in the Analysis section.
  • Case Questions - This section adds a further ten individual case questions. These are wolf-themed, so are thematically similar to the original Redrock case, but are slightly incongruous with the newer, plant-themed version of Redrock. In both instances, though, these questions are entirely separable from the main case preceding them, not relying on any information from the previous sections. The ten questions are highly quantitative and extremely time pressured. Few test takers finish them before being timed out.

This is a very brief summary - more detail is available in our PDF Guide .

Other Games - Disease and Disaster Identification

Screenshot of a wolf and beaver in a forest habitat from the Solve assessment

There have been accounts of some test takers being given a third game as part of their Solve assessment. At time of writing, these third games have always been clearly introduced as non-scored beta tests for Imbellus to try out potential new additions to the assessment. However, the fact that these have been tested means that there is presumably a good chance we’ll see them as scored additions in future.

Notably, these alternative scenarios are generally variations on a fairly consistent theme and tend to share a good deal of the character of the Ecosystem Building game. Usually, candidates will be given a whole slew of information on how an animal population has changed over time. They will then have to wade through that information to figure out either which kind of natural disaster or which disease has been damaging that population - the commonality with the Ecosystem Building game being in the challenge of dealing with large volumes of information and figuring out which small fraction of it is actually relevant.

Join thousands of other candidates cracking cases like pros

What does the solve assessment test for.

Chart from Imbellus showing how they test for different related cognitive traits

Whilst information on the Solve assessment can be hard to come by, Imbellus and McKinsey have at least been explicit on what traits the test was designed to look for. These are:

Diagram showing the five cognitive traits examined by the Solve Assessment

  • Critical Thinking : making judgements based on the objective analysis of information
  • Decision Making : choosing the best course of action, especially under time pressure or with incomplete information
  • Metacognition : deploying appropriate strategies to tackle problems efficiently
  • Situational Awareness : the ability to interpret and subsequently predict an environment
  • Systems Thinking : understanding the complex causal relationships between the elements of a system

Equally important to understanding the raw facts of the particular skillset being sought out, though, is understanding the very idiosyncratic ways in which the Solve assessment tests for these traits.

Let's dive deeper:

Process Scores

Perhaps the key difference between the Solve assessment and any other test you’ve taken before is Imbellus’s innovation around “process scores”.

To explain, when you work through each of the games, the software examines the solutions you generate to the various problems you are faced with. How well you do here is measured by your “product score”.

However, scoring does not end there. Rather, Solve's software also constantly monitors and assesses the method you used to arrive at that solution. The quality of the method you used is then captured in your “process score”.

To make things more concrete here, if you are playing the Ecosystem Building game, you will not only be judged on whether the ecosystem you put together is self-sustaining. You will also be judged on the way you have worked in figuring out that ecosystem - presumably, on how efficient and organised you were. The program tracks all your mouse clicks and other actions and will thus be able to capture things like how you navigate around the various groups of species, how you place the different options you select, whether you change your mind before you submit the solution and so on.

You can find more detail on these advanced aspects of the Solve assessment and the innovative work behind it in the presentation by Imbellus founder Rebecca Kantar in the first section of the following video:

Compared to other tests, this is far more like the level of assessment you face from an essay-based exam, where the full progression of your argument towards a conclusion is marked - or a maths exam, where you are scored on your working as well as the final answer (with, of course, the major advantage that there is no highly qualified person required to mark papers).

Clearly, the upshot of all this is that you will want to be very careful how you approach the Solve assessment. You should generally try to think before you act and to show yourself in a very rational, rigorous, ordered light.

We have some advice to help look after your process scores in our PDF Guide to the McKinsey Solve Assessment .

A Different Test for Every Candidate

Another remarkable and seriously innovative aspect of the Solve assessment is that no two candidates receive exactly the same test.

Imbellus automatically varies the parameters of their games to be different for each individual test taker, so that each will be given a meaningfully different game to everyone else’s.

Within a game, this might mean a different terrain setting, having a different number of species or different types of species to work with or more or fewer restrictions on which species will eat which others.

Consequently, even if your buddy takes the assessment for the same level role at the same office just the day before you do, whatever specific strategy they used in their games might very well not work for you.

This is an intentional feature designed to prevent test takers from sharing information with one another and thus advantaging some over others. At the extreme, this feature would also be a robust obstacle to any kind of serious cheating.

To manage to give every candidate a different test and still be able to generate a reliable ranking of those candidates across a fundamental skillset, without that test being very lengthy, is a considerable achievement from Imbellus. At high level, this would seem to be approximately equivalent to reliably extracting a faint signal from a very noisy background on the first attempt almost every time.

(Note that we are yet to confirm to what extent and how this also happens with the new Redrock case studies, but it seems to be set up to allow for easy changes to be made to the numerical values describing the case, so we assume there will be similar, widespread of variation.)

Preparation for the McKinsey Solve assessment

Understanding what the Solve assessment tests for immediately begs the question as to whether it is possible to usefully prepare and, if so, what that preparation should look like.

Is it Really Possible to Prepare for the McKinsey Solve Assessment?

Clown fish swimming in a coral reef

In short, yes you can - and you should!

As noted previously, there has been a lot of disagreement over whether it is really possible to prep for the Solve assessment in a way that actually makes a difference.

Especially for the legacy version, there has been a widespread idea that the Solve assessment functions as something like an IQ test, so that preparation beyond very basic familiarisation to ensure you don’t panic on test day will not do anything to reliably boost your scores (nobody is going to build up to scoring an IQ of 200 just by doing practice tests, for example).

This rationale says that the best you can do is familiarise yourself with what you are up against to calm your nerves and avoid misunderstanding instructions on test day. However, this school of thought says there will be minimal benefit from practice and/or skill building.

The utility of preparation has become a clearer with the addition of the Redrock case study to the new version of Solve. Its heavily quantitative nature, strong time pressure and structure closely resembling a traditional business case make for a clearer route to improvement.

However, as we explain in more detail in our PDF guide to the Solve assessment, the idea that any aspect of either version of Solve can't be prepared for has been based on some fundamental misunderstandings about what kind of cognitive traits are being tested. Briefly put, the five key skills the Solve assessment explicitly examines are what are known as higher-order thinking skills.

Crucially, these are abilities that can be meaningfully built over time.

McKinsey and Imbellus have generally advised that you shouldn’t prepare. However, this is not the same as saying that there is no benefit in doing so. McKinsey benefits from ensuring as even a playing field as possible. To have the Solve test rank candidates based purely on their pre-existing ability, they would ideally wish for a completely unprepared population.

How to prep

Two stingrays and a shark swimming in blue water, lit from above

We discuss how to prep for the Solve assessment in full detail in our PDF guide . Here, though, we can give you a few initial pointers to get you started. In particular, there are some great ways to simulate different games as well as build up the skills the Solve assessment tests for.

Playing video games is great prep for the legacy Solve assessment in particular, but remains highly relevant to the new Redrock version.

Contrary to what McKinsey and Imbellus have said - and pretty unfortunately for those of us with other hobbies - test takers have consistently said that they reckoned the Problem Solving Game, and now the Solve assessment, favours those with strong video gaming experience.

If you listened when your parents told you video games were a waste of time and really don’t have any experience, then putting in some hours on pretty much anything will be useful. However, the closer the games you play are to the Solve scenarios, the better. We give some great recommendations on specific games and what to look for more generally in our Solve guide - including one free-to-play game that our clients have found hugely useful as prep for the plant defence game!

PST-Style Questions

The inclusion of the Redrock case studies in the new version of Solve really represents a return to something like a modernised PST. Along with the similar new BCG Casey assessment, this seems to be the direction of travel for consulting recruitment in general.

Luckily, this means that you can leverage the wealth of existing PST-style resources to your advantage in preparation.

Our PST article - which links to some free PST questions and our full PST prep resources - is a great place to start. However, better than old-fashioned PDF question sets are the digital PST-style questions embedded in our Case Academy course . Conducted online with a strict timer running, these are a much closer approximation of the Solve assessment itself. These questions are indeed a subset of our Case Academy course, but are also available separately in our Course Exercises package .

Quick Mathematics With a Calculator and/or Excel

Again, specifically for the Redrock assessment, you will be expected to solve math problems very quickly. The conceptual level of mathematics required is not particularly high, but you need to know what you are doing and get through it fast using a calculator nand/or Excel, if you are already comfortable with that program.

Our article on consulting math is a great place to start to understand what is expected of you throughout the recruiting process, with our consulting math package (a subset of our Case Academy course) providing more in-depth lessons and practice material.

Learn to Solve Case Studies

With the Redrock case studies clearly being ecology-themed analogues to standard business case studies, it's pretty obvious that getting good at case studies will be useful.

However, the Solve assessment as a whole is developed and calibrated to be predictive of case interview performance, so you can expect that improving your case solving ability will indirectly bring up your performance across the board.

Of course, this overlaps with your prep for McKinsey's case interviews. For more on how to get started there, see the final section of this article.

Learning About Optimal Strategies for the Games

The first thing to do is to familiarise yourself with the common game scenarios from the Solve assessment and how you can best approach them to help boost your chances of success.

Now, one thing to understand is that, since the parameters for the games change for each test taker, there might not be a single definitive optimal strategy for every single possible iteration of a particular game. As such, you shouldn’t rely on just memorising one approach and hoping it matches up to what you get on test day.

Instead, it is far better to understand why a strategy is sensible in some circumstances and when it might be better to do something else instead if the version of the game you personally receive necessitates a different approach.

In this article, we have given you a useful overview of the games currently included in the Solve assessment. However, a full discussion with suggested strategies is provided in our comprehensive Solve guide .

With the limited space available here, this is only a very brief sketch of a subset of the ways you can prep.

As noted, what will help with all of these and more is reading the extensive prep guidance in our full PDF guide to the Solve assessment...

The MCC Solve Assessment Guide

Preparing for the Solve assessment doesn’t have to be a matter of stumbling around on your own. This article is a good introduction. From here, though our new, updated PDF guide to the McKinsey Solve assessment is your first stop to optimise your Solve preparation.

This guide is based on our own survey work and interviews with real test takers, as well as iterative follow-ups on how the advice in previous editions worked out in reality.

Does it make sense to invest in a guide?

Short answer: yes. If you just think about the financials, a job at McKinsey is worth millions in the long run. If you factor in experience, personal growth and exit opportunities, the investment is a no-brainer.

How our guide can help you ace the test

Don't expect some magic tricks to game the system (because you can't), but rather an in-depth analysis of key areas crucial to boost your scores. This helps you to:

As noted, the guide is based on interviews with real recent test takers and covers the current games in detail. Being familiar with the game rules, mechanics and potential strategies in advance will massively reduce the amount of new information you have to assimilate from scratch on test day, allowing you to focus on the actual problems at hand.

Despite the innovative environment, the Solve assessment tests candidates for the same skills evaluated in case interviews, albeit on a more abstract level. Our guide breaks these skills down and provides a clear route to develop them. You also benefit from the cumulative experience of our clients, as we have followed up to see which prep methods and game strategies were genuinely helpful.

A clear plan of how to prepare is instrumental for success. Our guide includes a detailed, flexible preparation strategy, leveraging a whole host of diverse prep activities to help you practice and build your skills as effectively as possible. Importantly, our guide helps you prioritise the most effective aspects of preparation to optimise for whatever timeframe you have to work in.

Overall, the MyConsultingCoach Solve guide is designed to be no-nonsense and straight to the point. It tells you what you need to know up front and - for those of you in a hurry - crucial sections are clearly marked to read first to help you prep ASAP.

For those of you starting early with more time to spare, there is also a fully detailed, more nuanced discussion of what the test is looking for and how you can design a more long-term prep to build up the skills you need - and how this can fit into your wider case interview prep.

Importantly, there is no fluff to bulk out the page count. The market is awash with guides at huge page counts, stuffed full of irrelevant material to boost overall document length. By contrast, we realise your time is better spent actually preparing than ploughing through a novel.

If this sounds right for you, you can purchase our PDF Solve guide here:

McKinsey Solve Assessment Guide

  • Full guide to both the legacy version of the Solve assessment and the newer Redrock Case Study versions
  • In-depth description of the different games and strategies to beat them
  • Preparation strategies for the short, medium and long-term prep
  • No fluff - straight to the point, with specific tips for those without much time
  • Straight to your inbox
  • 30 days money-back guarantee, no questions asked. Simply email us and we will refund the full amount.

The Next Step - Case Interviews

Male interviewer with laptop administering a case study to a female interviewee

So, you pour in the hours to generate an amazing resume and cover letter. You prepare diligently for the Solve assessment, going through our PDF guide and implementing all the suggestions. On test day, you sit down and ace Solve. The result is an invitation to a live McKinsey case interview.

Now the real work begins…

Arduous as application writing and Solve prep might have seemed, preparing for McKinsey case interviews will easily be an order of magnitude more difficult.

Remember that McKinsey tells candidates not to prepare for Solve - but McKinsey explicitly expects applicants to have rigorously prepared for case interviews .

The volume of specific business knowledge and case-solving principles, as well as the sheer complexity of the cases you will be given, mean that there is no way around knuckling down, learning what you need to know and practicing on repeat.

If you want to get through your interviews and actually land that McKinsey offer, you are going to need to take things seriously, put in the time and learn how to properly solve case studies.

Unfortunately, the framework-based approach taught by many older resources is unlikely to cut it for you. These tend to falter when applied to difficult, idiosyncratic cases - precisely the kind of case you can expect from McKinsey!

The method MCC teaches is based specifically on the way McKinsey train incoming consultants. We throw out generic frameworks altogether and show you how to solve cases like a real management consultant on a real engagement.

You can start reading about the MCC method for case cracking here . To step your learning up a notch, you can move on to our Case Academy course .

To put things into practice in some mock interviews with real McKinsey consultants, take a look at our coaching packages .

And, if all this (rightfully) seems pretty daunting and you’d like to have an experienced consultant guide you through your whole prep from start to finish, you can apply for our comprehensive mentoring programme here .

Looking for an all-inclusive, peace of mind program?

Our comprehensive packages.

Get our Solve guide for free if you purchase any of the following packages. Just email us with your order number and we will send the guide straight to your inbox.

Access to our Case Academy and to coaching will help you prepare for Solve and for the following rounds!

The MCC bundle

  • All Case Interview Course Videos
  • All Case Interview Course Exercises
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Bridge to Consulting

  • 5 one-hour sessions with ex-MBB (McKinsey/Bain/BCG) coach of your choice
  • Session personalisation (skill level and preparation stage)
  • Choice of interview format (Fit, Case or Both)
  • AI-powered performance benchmarking, skill-gap assessment and actionable feedback through your Dashboard
  • Full Access to Case Academy (Course, Exercises, Self-Assessments, Fit and Math)
  • McKinsey Digital Assessment Guide
  • All our PST material

Case Interview Course

  • 16+ hours of lectures  covering  all aspects of the case interview
  • Introduction to the consulting interview
  • Case Interview foundations section 
  • Problem Driven Approach
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  • Problem driven structure in action
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Product Frameworks: Problem-solving framework from McKinsey

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"a problem well put is half solved" - john dewey.

mckinsey research problem solving

McKinsey’s Problem-solving process

McKinsey’s benchmark is the problem-solving process as practiced by McKinsey. At the most abstract level, McKinsey develops solutions to clients’ strategic problems and, possibly, aids in the implementation of those solutions

mckinsey research problem solving

Business Need

You can’t have problem solving without a problem or, more broadly, a need on the part of the client. In business, those needs come in several forms: competitive, organizational, financial, and operational.
Once your organization has identified the problem, it can begin to seek a solution, whether on its own or with the help of McKinsey (or any other outside agent). McKinsey’s fact-based, hypothesis-driven problemsolving process begins with framing the problem: defining the boundaries of the problem and breaking it down into its component elements to allow the problem-solving team to come up with an initial hypothesis as to the solution. The next step is designing the analysis, determining the analyses that must be done to prove the hypothesis, followed by gathering the data needed for the analyses. Finally comes interpreting the results of those analyses to see whether they prove or disprove the hypothesis and to develop a course of action for the client.
You may have found a solution, but it has no value until it has been communicated to and accepted by the client. For that to happen, you must structure your presentation so that it communicates your ideas clearly and concisely and generates buy-in for your solution for each individual audience to which you present.
The success of the problem-solving process requires good management at several levels. The problemsolving team must be properly assembled, motivated, and developed. The client must be kept informed, involved, and inspired by both the problem-solving process and the solution. The individual team members (that’s you) must strike a balance between life and career that allows them to meet the expectations of the client and the team while not “burning out.”


Your organization may have accepted your solution, but it must still implement it. This requires the dedication of sufficient resources within the organization, the timely reaction of the organization to any stumbling blocks that may arise during implementation, the focus of the organization on completion of the tasks necessary for full implementation. In addition, the organization must institute a process of iteration that leads to continual improvement. That process requires reassessing implementation and rededicating the organization to make additional changes identified during reassessment.
At the nexus of solution and implementation comes leadership. Those at the helm of your organization must conceive a strategic vision for the organization. They must also provide inspiration for those in the organization who will do the hands-on work of implementation. Finally, they must make the right judgments regarding delegation of authority in overseeing implementation throughout the organization.

There is one other piece of the model: the tension between intuition and data. Problem solving doesn’t take place in a vacuum. Even McKinsey has only so many resources to throw at a problem and a limited time in which to solve it. While we are advocates for McKinsey-style fact-based problem solving, we recognize that it’s practically impossible to have all the relevant facts before reaching a decision. Therefore, most executives make business decisions based partly on facts and partly on intuition—gut instinct tempered by experience. We will discuss the pros and cons of each element later in the book. For now, we will simply say that we think a sound decision requires a balance of both.

8 Steps to Problem-Solving from McKinsey

Solve at the first meeting with a hypothesis

Intuition is as important as facts

Do your research but don’t reinvent the wheel

Tell the story behind the data

Start with the conclusion

Hit singles

Respect your time

mckinsey research problem solving

#1 Solve at the first meeting with a hypothesis

The McKinsey problem-solving process begins with the use of structured frameworks to generate fact-based hypotheses followed by data gathering and analysis to prove or disprove the hypotheses.

Gut feeling at this stage is extremely important because we don’t have many facts yet. However, structure strengthens your thinking and ensures that your ideas will stand up. Typically, the problem-solving process would involve defining the boundaries of the problem and then breaking it down into its component elements.

The concept of MECE (pronounced “mee-see” and an acronym for Mutually Exclusive, Collectively Exhaustive), is a basic tenet of the McKinsey thought process. Being MECE in the context of problem-solving means separating your problem into distinct, non-overlapping issues while making sure that no issues relevant to your problem have been overlooked. This allows to simplify the problem and plan the work because in most cases, a complex problem can be reduced to a group of smaller, simpler problems that can be solved individually.

The most common tool McKinsey people use to break problems apart is the  logic tree .

Having reduced the problem to its essential components, you are ready to embark on the next step which is framing it: forming a hypothesis as to its likely solution. By already knowing where your solution is, you eliminate a lot of paths that lead to dead ends.

Using an initial hypothesis to guide your research and analysis will increase both the efficiency and effectiveness of your decision-making because it provides you and your team with a problem-solving roadmap that will lead you to ask the right questions and perform the correct analysis to get to your answer. A good hypothesis will also save you time by pointing out potential blind alleys much more quickly and allowing you to get back to the main issues if you do go down the wrong path.

#2 Intuition is as important as facts

Since you should form your hypothesis at the start of the problem-solving process, you have to rely less on facts (you won’t have done most of your fact gathering yet) and more on instinct or intuition. Take what you know about the problem at hand, combine it with your gut feelings on the issue, and think about what the most likely answers are.

Executives make major strategic decisions based as much on gut instinct as on fact-based analysis. Intuition and data complement each other. You need at least some of each to have a solid basis for your decisions. The key to striking the balance is quality over quantity.

#3 Do your research but don’t reinvent the wheel

When you form an initial hypothesis, you are “solving the problem at the first meeting.” Unfortunately, although you may think you have the answer, you have to prove it through fact-based analysis.

Your next step is to figure out which analyses you have to perform and which questions you have to ask in order to prove or disprove your hypothesis.

When your time and resources are limited, you don’t have the luxury of being able to examine every single factor in detail. Instead, when planning your analyses, figure out which factors most affect the problem and focus on those. Drill down to the core of the problem instead of picking apart each and every piece. In most situations, achieving a scientific level of exactitude for your management decisions is counterproductive.

That’s why also as one of your first steps in designing your analysis, you should figure out what not to do.

As your next step, you should decide which analyses are quick wins — easy to complete and likely to make a major contribution to proving or refuting the initial hypothesis (80/20 rule).

When doing your research, you don’t want to get as much information as possible, you want to get the most important information as quickly as possible.

With a plan of action for what to research, make sure you don’t reinvent the wheel as you start gathering your data. Whatever problem you’re facing, chances are that someone somewhere has worked on something similar. So your next step here is to look through all possible internal documents and then look externally.

#4 Tell the story behind the data

Once you have your analysis finished, you need to interpret it because numbers or data don’t say anything. You have to figure out the story behind it and the message that you want to communicate.

At this stage, first comes the process of understanding the data: piecing together (in your own mind or within your team) the story the data is telling you and the steps you should take based on that story. The second comes assembling your findings into an externally directed end product: a key message that includes a course of action for your organization, ream, or client.

Your interpretation of the data leads to a story, that is, what you think the data means. You select those portions of the story that you believe your audience needs to know in order to understand your conclusion, along with the supporting evidence, and you put them together into your end product as in your presentation.

To succeed here you need to see through your client’s, executive’s or audience’s lenses and speak their language.

The key to successful presentations and getting buy-in (in order for your audience to accept your recommendations) is prewiring.

The reason behind this is because to get the buy-in you need to bridge the information and trust gaps between you and your audience. The information gap exists because you know more about your findings than your audience does. Depending on the relationship between you and your audience, the trust gap (if it exists) could take any of several forms. Your audience may think that you are too inexperienced to comment on their business, or they may mistrust you because you are an outsider, are overeducated, or not educated enough.

In its essence, prewiring means taking your audience through your findings before you give your presentation. This allows for people to trust you, ask questions you may not have thought about to avoid surprises, and then during the presentation say yes and support you among others who may be more skeptical.
Prewire everything. A good business presentation should contain no shocking revelations for the audience. Walk the relevant decision-makers in your organization through your findings before you gather them together for a dog and pony show. At a minimum, you should send out your recommendations via email to request comments from key decision-makers before the presentation if you can’t meet with those people face-to-face.

The earlier you can start the prewiring process, the better. By identifying and getting input from the relevant players early on, you allow them to put their own mark on your solution, which will make them more comfortable with it and give them a stake in the outcome.

#6 Start with the conclusion

When you begin your presentation in front of your desired audience, make sure you start with the conclusion.

Having your conclusions or recommendations upfront is sometimes known as inductive reasoning. Simply put, inductive reasoning takes the form, “We believe X because of reasons A, B, and C.” This contrasts with deductive reasoning, which can run along the lines of, “A is true, B is true, and C is true; therefore, we believe X.” Even in this simplest and most abstract example, it is obvious that inductive reasoning gets to the point a lot more quickly, takes less time to read, and packs a lot more punch. As an additional advantage, starting with your conclusions allows you to control how far you go into detail in your presentation.

You need to explain this clearly within just 30 seconds. Almost like an elevator pitch. If you can pass this “elevator test,” then you understand what you’re doing well enough to sell your solution.

A successful presentation bridges the gap between you — the presenter — and your audience. It lets them know what you know.

It also keeps it simple for them which is why it’s important to stick to a key rule if you are using a deck: one message per slide or chart. No more. The more complex a chart/slide becomes, the less effective it is at conveying information. The meaning should be immediately obvious to the reader, so use whatever tools you need to bring it out.

If you broke out your initial hypothesis into a MECE set of issues and sub-issues (and suitably modified them according to the results of your analysis), then you have a ready-made outline for your presentation that will support your conclusion.

Remember that you have two ears and only one mouth. It’s not just what you say, it’s how you say it. Overcommunication is better than under-communication which is why prewiring as mentioned above is so important.

Finally, if you are proposing a certain solution in which you will be involved in the execution, make sure you don’t overpromise because you’re bound to under-deliver. Instead, balance the demands for the solution with your capabilities and those of your team. If more work is necessary, you can always start a second project once the first is done.

#7 Hit singles

When you begin executing on the solution, aim to hit singles.

This is a metaphor from baseball. You can’t do everything, so don’t try. Just do what you’re supposed to do, and get it right. It’s impossible to do everything yourself all the time. If you do manage that feat once, you raise unrealistic expectations from those around you. Then, when you fail to meet those expectations, you’ll have difficulty regaining your credibility.

Getting on base consistently is much better than trying to hit a home run and striking out nine times out of ten.

Do few things well rather than a ton with mediocre execution or results. Stick to targeted focus rather than perfection and drilling into every little piece.

Quality over quantity. And when there’s a lot of work to be done, delegate around your limitations. Know them for what they are and respect them.

#8 Respect your time

Don’t forget that work is like a gas: it expands to fill the time available.

As in the previous point, share the load by delegating and also perform sanity checks on the way to allow you to take a step back and look at the big picture.

You will also have to get others to respect your time. The better you are at your job or the higher up you go in your organization, the more everyone wants a piece of you. There’s an old saying, “Stress is the feeling you get when your gut says, ‘No,’ and your mouth says, ‘Yes, I’d be glad to.’” You have to train your mouth to say, “No.”

Once you make a commitment — “I won’t work on weekends” or “I’ll cook dinner three nights a week” — stick to it, barring life-and-death emergencies. If you seem to be having life-and-death emergencies every week (and you’re not dealing with matters of real-life and death, as in a trauma ward), take a hard look at your priorities.

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When Your Go-To Problem-Solving Approach Fails

  • Cheryl Strauss Einhorn

mckinsey research problem solving

Eight steps to help you assess what’s not working — and why.

We make decisions all day, every day. The way we make decisions depends largely on context and our own unique problem-solving style. But, sometimes a tough workplace situation turns our usual problem-solving style on its head. Situationality is the culmination of many factors including location, life stage, decision ownership, and team dynamics. To make effective choices in the workplace, we often need to put our well-worn decision-making habits to the side and carefully ponder all aspects of the situation at hand.

Have you ever noticed that when you go home to your parents’ house, no matter what age you are, you make decisions differently than when you’re at work or out with a group of friends? For many of us, this is a familiar and sometimes frustrating experience — for example, allowing our parent to serve us more food than we want to eat. We feel like adults in our day-to-day lives, but when we step into our childhood homes we revert.

mckinsey research problem solving

  • Cheryl Strauss Einhorn is the founder and CEO of Decisive, a decision sciences company using her AREA Method decision-making system for individuals, companies, and nonprofits looking to solve complex problems. Decisive offers digital tools and in-person training, workshops, coaching and consulting. Cheryl is a long-time educator teaching at Columbia Business School and Cornell and has won several journalism awards for her investigative news stories. She’s authored two books on complex problem solving, Problem Solved for personal and professional decisions, and Investing In Financial Research about business, financial, and investment decisions. Her new book, Problem Solver, is about the psychology of personal decision-making and Problem Solver Profiles. For more information please watch Cheryl’s TED talk and visit .

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What is the McKinsey Game and How to Solve It (2023)

In this in-depth article, we delve into the intricacies of the McKinsey Game, shedding light on what it is and providing valuable insights on how to nail this step in the consulting recruitment process.

Posted September 6, 2023

mckinsey research problem solving

The McKinsey Game, or Solve as it is commonly referred to in 2023, is a problem solving approach employed by the consulting firm, McKinsey & Company . In this article, we unravel the details of the McKinsey Game, delving into its core principles and uncovering the strategies that can lead aspiring consultants to nail this step in the recruiting process. Whether you aspire to join the ranks of McKinsey or simply seek to sharpen your problem-solving acumen, understanding the intricacies of the McKinsey Game will prove invaluable in your quest for success in the world of consulting.

What Exactly is the McKinsey Game/Solve and Why Does McKinsey Use It?

Originally founded by Imbellus in 2017 and then reinvigorated in Spring 2023 to include the Redrock Case Study, the McKinsey Game aims to depart from traditional candidate screening assessments. Specifically, the platform uses ecology-themed video games that involve tasks such as constructing food chains, safeguarding endangered species, managing predator and prey populations, and diagnosing animal diseases or identifying natural disasters . Typically, you will have approximately 60-70 minutes to complete two separate games; but, it is up to you to manage your time well enough to be able to complete all games within the allotted period.

Now, you are likely wondering why a prestigious consulting firm would use an ecology-based video game as part of its recruiting process. The firm uses the McKinsey Game with the goal of removing inherent advantages, leveling the playing field, and learning how you think ( McKinsey ). The McKinsey Game’s scientific foundation and game-based assessments provide McKinsey with objective data about a candidate’s cognitive and emotional traits, helping them identify individuals who align with their specific job requirements and company culture. Ultimately, this allows McKinsey to make more informed and fair hiring decisions, leading to a more diverse and talented workforce.

What Does the McKinsey Game/Solve Look Like and What Are Good Results?

Achieving strong results in the McKinsey Game goes beyond simply excelling at the exercises themselves. In other words, it’s not about getting to the solution, but about how you reach the solution. McKinsey uses both a product and process score to assess a candidate’s performance. Specifically, the product score is an evaluation of your success in each of the tasks, whereas the process score is an evaluation of the quality of process you enacted. For the process score, McKinsey is looking for your ability to demonstrate these 5 core skills:

  • Critical thinking
  • Systems thinking
  • Decision making
  • Metacognition
  • Situational awareness

Take a look at the complete list of exercises below, paying particular attention to the key skills being tested in each task; being able to effectively showcase these skills along with the 5 core key skills discussed above is what will get you a good result.

**IMPORTANT NOTE: You will not see all of these activities on Game Day; you are only guaranteed to see Ecosystem Building and Redrock Case Study. However, it is important to be prepared to see any of the above pop up on your screen. Typically, candidates are only required to play 2-3 of these games ( Ecosystem Building , Redrock Case Study, and occasionally EITHER Disaster Management, Disease Management, or Migration Management) . Currently, the latter three are serving as Beta tests of sorts for McKinsey, so candidates may see a variation of game combinations.

Ecosystem Building

  • Description: Here, you are tasked with building a stable ecosystem in aquatic, alpine, or jungle environments. After choosing a setting, you need to carefully pick various plant and animal species to create a functional food chain within that setting. Importantly, recent iterations now require selecting a fixed number of eight species. Compatibility is crucial as the chosen species must be able to coexist with predators being able to consume their chosen prey, and all species must be capable of surviving the prevailing conditions of the selected location.
  • Strategy: Establish a structured process (i.e., build your food chain either from the top down or the bottom up). Then, opt for producers (plants/fungi/coral) that offer high caloric value and are consumed by multiple animal species; when feasible, select small animals/herbivores with low caloric needs but high caloric value provided. Be sure to come prepared with a piece of scratch paper nearby or a separate device to perform basic calculations on.
  • Data analysis
  • Basic calculations

Redrock Case Study

  • Investigation: Your goal is to efficiently extract the most significant data points from the full case description on various animal populations and drag-and-drop them into your “Research Journal” workspace since any information not saved at this stage will no longer be accessible in later sections.
  • Analysis: You must answer three numerical questions using information you saved in the Investigation section.
  • Report: Conduct a pre-written report on the wolf populations, including quantitative data
  • Case Questions: Answer 10 questions regarding how you arrived at your solution
  • Strategy: Due to the diverse variants and constant evolution of the McKinsey Solve Game, the specific challenges you encounter can vary significantly. Be attentive to the instructions provided and carefully monitor your time based on them. Following the game, you will encounter a set of 10 multiple-choice questions that draw from the data you received during gameplay and your calculations. These questions may cover topics such as selecting appropriate graphic formats for specific information visualization or determining the percentage change in the wolf population under hypothetical scenarios. It is essential to manage your time wisely to allocate sufficient attention to answering the questions effectively (this is arguably the most important section of the task as it demonstrates your thought process and reasoning skills).

Disaster Management

  • Description: Your task is to identify the natural disaster that has struck the ecosystem, such as a tsunami or volcanic eruption, and provide assistance to the animals in surviving the catastrophe.
  • Strategy: Pay attention to the environmental data, react quickly, and thoroughly understand the characteristics of each animal and which site would be the best fit for them.
  • Adaptability
  • Reaction time

Disease Management

  • Description: Your task is to identify the disease affecting the animals, and propose an appropriate course of treatment.
  • Strategy: Similar to the Disaster Management game, the key strategies are to pay close attention to data, react quickly, and thoroughly understand the diseases that are impacting each animal.
  • Pattern identification

Migration Management

  • Description: Efficiently migrate a group of animals from one location to another, utilizing minimal resources while ensuring the maximum number of animals survive the journey.
  • Strategy: As you explore different routes, make sure to monitor your resources and the number of surviving animals. Employ quick mathematical calculations to assist you in making informed decisions along the way.

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Strategies to Prepare for the McKinsey Game/Solve

  • Sharpen Analytical Skills: Work on enhancing your data analysis and quantitative reasoning abilities. The McKinsey Game often requires candidates to interpret data and make data-driven decisions.
  • Practice Regularly: Consistent practice is key to mastering the McKinsey Game. Utilize sample games or mock assessments to familiarize yourself with the types of tasks and challenges you might encounter. Regular practice will enhance your performance and boost your confidence.
  • Analyze Your Performance: After completing practice assessments, review your results and identify areas of improvement. Focus on understanding patterns and tendencies in your responses to align with your authentic traits.
  • Time Management: The McKinsey Game assessment is timed. Practice working under time constraints to develop efficiency and accuracy in your responses.
  • Embrace a Growth Mindset: Approach the assessments with a growth-oriented mindset. View challenges as opportunities for learning and improvement, rather than fearing failures
  • Understand Ecological Concepts: This step is optional; however, it can be helpful to familiarize yourself with ecological concepts, food chains, predator-prey relationships, and environmental factors before the day of your assessment.
  • Get a coach: Leland has countless coaches who have gone through the McKinsey recruiting process themselves and understand the pymetrics test like the back of their hand. Check out some of our top coaches below, and sign up today to receive personalized strategies, practice tests, industry insights, and so much more!

FAQ: Common Questions and Answers

  • A: No. The results from the McKinsey Problem Solving Game will be taken into consideration with the rest of your application and any other assessment results.
  • A: Yes, if you have a disability, health condition or specific learning difficulty, please speak to your recruiter and we can accommodate you accordingly.
  • A: All technical issues should be directed from you (the candidate) to [email protected]. You can email the support team directly, or use the live chat function. The support team will be able to run diagnostics on your link and help solve any issues directly with you, including the graphics not working or tech checks failing. Please contact your recruiter if you have nontechnical questions. Who should I contact if I experience technical issues? Please note: if you experience technical issues during the McKinsey Problem Solving Game and do not contact support, we cannot make any exceptions to allow for a reset.
  • A: No, the support team will work with you to solve the issue you are experiencing and offer you extra time or reset your link depending on the issue. You will need to contact the support team at the time your technical issue arises.
  • A: If you have not yet started the McKinsey Problem Solving Game, you can click into your link and schedule a new time slot.
  • A: After clicking on your link, you will be taken through a tech check before starting the McKinsey Problem Solving. This will inform you on whether your device meets the minimum specification requirements. Sound is not necessary and a mouse is optional.
  • A: The McKinsey Problem Solving is only available on a PC or Mac.
  • A: Yes, the McKinsey Problem Solving Game can be taken in English, Spanish (Iberia or Latin America), Portuguese (Iberia or Latin America), or Japanese. Please select the language you feel most comfortable completing the McKinsey Problem Solving Game in.
  • A: The McKinsey Problem Solving Game uses advanced AI methods to ensure that there is ample variation in each scenario for each candidate. It is designed so that no two individuals have the same parameters and combinations of data. You may also be asked at random to take an additional task in person and/or to explain your logic used. Your recruiter will inform you if this is the case.

Looking to break into McKinsey and other top consulting firms? Check out these expert resources dedicated to helping you nail the consulting recruiting process:

  • A Comprehensive Guide to McKinsey & Co., Boston Consulting Group, and Bain & Co.
  • A Day in the Life of a McKinsey Management Consultant
  • Top 3 Tactics to Ace Your Case Interview
  • Five Tips to Break Into Management Consulting
  • How to Network for Management Consulting
  • How to Master BCG Pymetrics: A Comprehensive Guide

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“We can not solve our problems with the same level of thinking that created them”

– Albert Einstein

The problem solving toolkit is the foundation of the success of every top-tier strategy consulting firm. While they hire the best and brightest, they all have extensive training regiments on core problem solving, which can be used in just about every project on just about every type of problem businesses face. Below represents the McKinsey problem solving toolkit which I learned from my 5+ years as a McKinsey consultant .

Problem solving is the ability to break down problems, intimately understand them, and develop highly effective and efficient solutions to them. Sounds simple enough, yet the fundamentals necessitate a high degree of conceptual, quantitative, and analytical thinking. In this section, I’ll focus on the fundamental concepts and tools that are important to the conceptual aspect of problem solving, and then in the next section, I’ll go over the quantitative and analytical tools.

Much of this content may seem a bit esoteric. I like to compare it to learning philosophy, where much of the material may be esoteric, but the concepts and tools are incredibly powerful and essential to learning the fundamentals of logic. Many “first principle” tools and concepts are critical to the strong logic necessary for problem solving. So, while you may think, “Ah this doesn’t seem important” or “That is so simple,” I urge you to be patient with the content and reflect on your problem solving skills and how these tools and concepts can help your strategic thinking and decision making .

In this section, I’ll cover the top problem solving tools and concepts, including:

1. Prioritization Prioritization is one of the core foundational skills in strategy, leadership , and life. It seems like such a simple concept, but the challenge is always figuring out the dimensions you are prioritizing against and then understanding the relative value of options against those dimensions.

2. Problem Statement The first step to all good problem solving is defining the problem clearly and elegantly. The better the problem statement , the better the problem solving.

3. Hypotheses There is a reason why McKinsey problem solving is all about creating hypotheses and then proving or disproving those hypotheses. Learning to be hypothesis-driven is a skill that will serve you well throughout your career.

4. Disaggregation If you want to be a master problem solver, master disaggregating problems into their discrete, mutually exclusive , and collectively exhaustive components. The best problem solvers can disaggregate and frame problems quickly and elegantly.

5. Hypothesis Trees McKinsey teams spend a considerable amount of time as a team on building out hypothesis trees, which helps organize and prioritize the hypotheses they will spend most of the project proving or disproving.

6. Deductive and Inductive Logic Understanding when and how to use deductive versus inductive logic will elevate your problem solving. Most people use induction way too much in problem solving, and when they build up their deductive logic skill set, they dramatically improve their problem solving.

7. The Power of Questions We all can and should ask many more questions than we do. The right questions can cut through problems like a sharp knife through butter. Learn to ask the right questions at the right time to elevate your problem-solving skills.

8. Active Listening We all can and should become better listeners. Truly focusing on processing what someone is saying and building on their insights is how strong problem-solving progresses to elegant solutions.

9. Root Cause Analysis We often develop and execute sub-optimal solutions because we solve the symptoms of a problem rather than the root. Getting to the root cause of a problem usually takes that extra effort. Improving your root cause analysis will pay off in the many years of problem-solving ahead of you.

10. Brainstorming Collaborating with strong minds and perspectives is how you push ideas to improve. Strong brainstorming is both an art and a science. Learn how to facilitate the conversational momentum needed for solid problem solving and ideation.

11. Voice of the Customer There is no more powerful voice than the internal or external customer. They will often tell you the answer to seemingly complex problems. You just need to ask the right questions of them.

Just like all of the sections on this site, you can pick and choose a topic, or if you want a nice overview of a section read the topics in order.

















McKinsey Solve Game: Newest Updates & Guide (2023)

Check out the only, fully-playable McKinsey Solve Test (Problem-Solving Game) Simulation in the entire market, with the new 2023 Redrock Study Task.

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

What is 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 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 had 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.

McKinsey Solve Simulation (All-in-One)

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

Thumbnail of McKinsey Solve Simulation (All-in-One)

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.

mckinsey research problem solving

Figure 1: Overview of McKinsey Solve / McKinsey PSG

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.

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 10 smaller cases with a similar topic.

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 section below 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.

Median Construct Percentile through McKinsey Recruiting Pipeline

Figure 2: Median Construct Percentile through McKinsey Recruiting Pipeline (Source: Imbellus)

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

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.

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.

mckinsey research problem solving

Figure 3: Data points in the study

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.

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.

mckinsey research problem solving

Figure 4: The research journal, which is always present on the left of your screen

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 s o ALWAYS collect your answers into the journal.

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.

mckinsey research problem solving

Figure 5: Screenshots of questions for the report phase

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 recruiter 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.

mckinsey research problem solving

Figure 6: Study's objectives

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.

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.


There are 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 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 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. 


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%) are 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.

Watch more: McKinsey PSG Explained

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) T here 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.

Figure 7: "the Moutain" and "the Reef"

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, the 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.

Description of Ecosystem Building game interface

Figure 8: Description of Ecosystem Building game interface


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:

Example of McKinsey Solve - Ecosystem Building's food chain

Figure 9: Example of a food chain in Ecosystem Building minigame

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).

Solution of a food chain in Ecosystem Building minigame

Figure 10: Solution of a food chain in Ecosystem Building minigame

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

*June 2023 Update: 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 on 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.

Screenshot of Plant Defense minigame

Figure 11: Screenshot of Plant Defense minigame

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) are 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 Invader appears at the border of the map and starts travelling towards the center base – you have lay out defensive plans to tackle them. The phase lasts until you eliminated 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 (comprised 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 vary 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 increase 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.

Description of Plant Defense minigame's interface

Figure 12: Description of Plant Defense minigame's interface

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 will elaborate each of those tactics:


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.

Visualization of Inside-out, multi-layered defense tactic

Figure 13: Visualization of the Inside-out, multi-layered defense tactic


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 have 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 straightforward 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.

Screenshot of Disease Management minigame with description

Figure 14: Screenshot of Disease Management minigame

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.

Watch this video below for a detailed, visualized explanation of all frequently encountered McKinsey Solve games:

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 a 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.

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 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 on 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 – require 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

*June 2023 update: 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.

Screenshot from Cities Skylines

Figure 15: Screenshot from Cities Skylines

 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.

Screenshot from Kingdom Rush

Figure 16: Screenshot from Kingdom Rush

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 game 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.

Screenshot from Civilization VI

Figure 17: Screenshot from Civilization VI

New release: Redrock Expansion (early access), an update of McKinsey PSG simulation

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

As the official game is still in Beta, we are constantly updating the product. The simulation is now providing a 90% accurate reconstruction of the Part 1 case. Part 2 will come later in a future update.

Scoring in the McKinsey PSG/Digital Assessment

The scoring mechanism in the McKinsey Digital Assessment

Related product

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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.

  • June 28, 2023

Unveiling the McKinsey Solve Game: Red Rock Study (latest update 2023)

In the realm of job assessments and recruitment, McKinsey & Company, a renowned global management consulting firm, has introduced an innovative evaluation method to gauge candidates’ aptitude and problem-solving abilities. The Redrock Study, a captivating game within the McKinsey Solve framework, is specifically designed to test these skills. This article delves into the intricacies of the Redrock Study, offering insights into its purpose, structure, and expert strategies to help you conquer this unique challenge.

Table of Contents

What is the redrock study.

The Redrock Study stands as the newest addition to McKinsey Problem Solving Game (PSG) lineup. This assessment serves as a distinctive tool to evaluate candidates’ problem-solving capabilities within a simulated research context. Diverging from previous assessments, which were rooted in consulting and business frameworks, the Redrock Study removes the requirement for specific business knowledge. This shift allows for an unbiased evaluation of candidates, focusing solely on their problem-solving skills, regardless of their domain expertise.

As of March 2023, Redrock Study has undergone updates and can now be completed within 35 minutes. This time is divided into four distinct sections, with the final section featuring a mini-case comprising ten quantitative reasoning questions. The inclusion of these case questions has heightened the difficulty level, as candidates must manage their time effectively to tackle both the study segment and the case questions within the given timeframe.


The Investigation stage initiates the assessment, providing candidates with a comprehensive case description. The primary objective is to identify and extract the most relevant data points from a vast sea of information. It is crucial to save these vital pieces of information in the Research Journal, as access to the original data is limited in subsequent stages. To navigate this section effectively, consider the following steps:

  • Step 1: Focus on the Objective: Begin by understanding the main question of the Redrock Study, as it will guide you throughout the assessment.
  • Step 2 : Collect Key Features: Identify and gather information related to key features associated with the Objective. These features may include units, figures, timeframes, and other relevant data points.
  • Step 3: Select Numerical Data: Choose numerical data that effectively demonstrates the collected information. When dragging and dropping the data into the Research Journal, ensure each item is appropriately labeled to avoid confusion in later stages.

The Analysis stage presents candidates with three numerical questions. These questions require the application of basic mathematical concepts such as percentages, weighted calculations, compound percentages, and probability. The game provides an on-screen calculation tool to process the data collected. To excel in this section, follow these steps:

  • Utilize the numerical data saved in the Research Journal during the Investigation stage to process the questions presented in the Analysis stage. Ensure your answers align with the given question and maintain a high level of precision.
  • Store the calculated results in the Research Journal , as they will be vital for completing the final stage of the study.

The Report stage serves as the culmination of the Redrock Study, where candidates utilize the results obtained from the Analysis stage to create reports in both textual and graphical formats. This stage emphasizes the ability to interpret data effectively and demonstrate relationships between different data points.

  • Documental Report:  In the Documental Report, you’ll utilize the calculated data and other elements to fill in the blanks of a textual report. If you have a thorough understanding of your results, this part should be relatively straightforward.
  • Graphical Report:  For the Graphical Report, you’ll be tasked with selecting an appropriate chart type to visualize the report’s findings. Redrock Study offers three chart types: bar charts, line graphs, and pie charts. To excel in this section, it’s essential to understand the function and purpose of each chart type and choose accordingly.

Case Questions

The Case Questions section is the newest addition to the Redrock Study. It features ten individual case questions that test candidates’ quantitative reasoning skills. These questions require analyzing information from various charts (such as pie, bar, and line graphs) and text paragraphs. Quick and accurate data interpretation, along with strong quantitative and analytical skills , are key to success in this section.

With the updated version of Redrock Study, candidates now have a total of 35 minutes to complete both the study segment and the ten quantitative reasoning questions. Time management is critical, and it is recommended to allocate equal time to the Investigation-Analysis-Report part and the Case Questions for optimal performance.

Mastering the Redrock Study: Key Takeaways

To summarize, achieving success in the Redrock Study requires a strategic approach and honed problem-solving skills. Here are the key takeaways to guide you on your journey:

Embrace the Researcher's Mindset

Approach each stage with a researcher’s mindset, focusing on the objective and collecting relevant information. By adopting this perspective, you’ll be better equipped to handle the challenges presented in Redrock Study

Efficient Data Collection and Processing

During the Investigation stage, aim to collect key data points efficiently. Be selective and discerning in choosing numerical data for your Research Journal. Renaming the items to include essential features will help prevent confusion during subsequent stages.

Master Numerical Reasoning

The Analysis stage tests your numerical reasoning skills. Pay attention to the types of calculations involved, such as percentages, compound percentages, weighted calculations, and probability. Utilize the on-screen calculation tool effectively and record your answers in the Research Journal for future use.

Clear and Concise Reporting

In the Report stage, both the Documental and Graphical Reports require clarity and precision. Ensure you have a solid grasp of the results obtained from the Analysis stage. Utilize the calculated data to complete the textual report accurately. When creating the graphical report, select the most suitable chart type to effectively visualize the data.

Tackle the Case Questions Strategically

The Case Questions section demands strong quantitative and analytical skills. Quickly analyze and interpret information from charts and text paragraphs to set up calculations and arrive at correct answers. Efficient time management is crucial, given the limited time frame. Aim to allocate equal time to the Investigation-Analysis-Report part and the Case Questions.

In conclusion, the Redrock Study is a challenging and engaging assessment within the McKinsey Solve game. By understanding the different stages and applying the recommended strategies, you’ll be well-prepared to tackle this unique problem-solving experience. Embrace the researcher’s mindset, collect and process data efficiently, master numerical reasoning, create clear and concise reports, and tackle the case questions strategically. With these skills in your arsenal, you’ll be ready to conquer the Redrock Study and showcase your problem-solving prowess. Good luck on your journey towards success!

To further support McKinsey candidates, we have developed a PSG Redrock Simulation that closely resembles the real Solve game, with a 95% similarity rate. This simulation is designed to prepare you for the actual test, mirroring the format, interface, and logic of the McKinsey PSG. With realistic simulations and comprehensive guidebooks, our package enhances your chances of success in the Redrock minigame within the McKinsey Solve game.

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McKinsey Solve Game: Red Rock Study

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McKinsey Solve’s Redrock Study game is a unique addition to the Problem Solving Game lineup that evaluates candidates’ skills in data collection, processing, and interpretation, case math, and numerical and verbal reasoning. The game is designed to test candidates’ abilities in analyzing and solving problems in a simulated setting.

This article offers a comprehensive overview of the Redrock Study game, outlining its primary objectives and providing valuable insights to help candidates achieve success.

An introduction to the Redrock Study game

As the latest offering in McKinsey Solve’s Problem Solving Game collection, the Redrock Study game is a research-focused assessment that measures candidates’ aptitude for dissecting and resolving issues within a simulated setting. This game serves as a modernized counterpart to the former Problem Solving Test (PST), which was replaced by the original Solve games, including the ecosystem creation and plant defense games. The reintroduction of a PST-inspired game highlights McKinsey’s evolving approach to candidate evaluation.

Distinct from the original PST, the Redrock Study game situates challenges within a research context rather than a consulting or business framework. As a result, candidates do not require prior business knowledge to excel in this assessment. Instead, the Redrock Study game emphasizes candidates’ problem-solving capabilities independent of their domain expertise. This should allow the evaluation of candidates without any biases.

McKinsey Solve (Imbellus) Game Guide

McKinsey Solve Game Guide

Elevate your Solve Game score with the original game guide, an 11-part video course, an Excel Solver tool, and Red Rock practice tests. Trusted by more than 8,000 customers from 70+ countries since November 2019.

Exploring the tasks within the Redrock Study game

The Redrock Study game assesses candidates’ problem-solving abilities by requiring them to conduct comprehensive research on a specified study with distinct criteria. Within the original context of targeted scenarios, players must navigate a three-phase process: the investigation phase, the analysis phase, and the report phase.

The Investigation Stage

During the initial investigation phase, candidates receive research objectives and an article containing preexisting data. The primary goal of this stage is to collect pertinent information for the Research Journal.

Players should use this phase to scrutinize data related to their objectives, as this information is crucial to their success in the game. With various distractions and misleading data present, candidates must develop the ability to bypass irrelevant information. Key elements should be identified, however, do not miss the minor details such as units and timeframes. Both types of data (important big-picture points and details) should be identified and recorded in the Research Journal. Emphasis should be placed on any particular outliers that you might find in the data (e.g., one of the animals in the study is very different from the others based on certain characteristics).

The Analysis Stage

In the subsequent analysis phase, candidates encounter three numerical questions, each accompanied by sub-questions. Utilizing an on-screen calculator, players must analyze the data gathered during the investigation phase to answer these queries. It is essential to record the most relevant information and document it in the Research Journal. Ultimately, these findings will be used to complete the report in the final stage of the Redrock Study game.

The Report Stage

The concluding report phase tasks candidates with crafting a summary and illustrating the data on an appropriate graph. This stage aims to showcase the relationships between the various data points, making strong data interpretation skills highly advantageous. The report can be presented in either a textual or graphical format.

Composing a textual report is straightforward, provided the player has thoroughly assessed all data points in the previous stages. For the graphical report, a solid understanding of popular chart types (e.g., line charts, bar charts, pie charts) and their functions is essential for success.

In the past, players had 25 minutes to finish these tasks.

The Case Questions

At the beginning of March 2023, McKinsey added a new component to its Red Rock Study assessment – a mini case that combines 10 quantitative reasoning questions related to the context from the study segment of the game. This addition has made the assessment even more challenging, as the time limit for both the study part and the 10 case questions at the end is only 25 minutes.

While the previous version of the simulation allowed 25 minutes for the study segment alone, candidates now have to complete both the study and the ten quantitative reasoning questions in the same amount of time. This means that candidates need to be able to think quickly and make efficient use of their time to complete the task within the given time frame. This has been a major concern for many test takers who have found it difficult to complete the assessment within the given time frame. Everyone I was interviewing over the last two weeks was struggling with the time limit.

Update: McKinsey reacted quickly, and you now have 35 minutes for both components of the Red Rock Study game (which is still challenging).

The 10 quantitative reasoning questions in the mini case require test takers to analyze and take information from charts such as pie, bar, and line graphs, as well as paragraphs of text and set up calculations to arrive at the correct answers. This requires strong quantitative and analytical skills and the ability to interpret data quickly and accurately.

The addition of the mini case to the McKinsey Red Rock simulation takes the assessment beyond just a game-based approach and makes it more aligned with the type of assessments used by other consulting firms. The addition of the mini-case marks a departure from the game-based assessment approach previously employed by McKinsey.

The game, in its current state, has experienced a myriad of technical issues that have negatively impacted players’ experience. Several of our customers have reported encountering bugs and even multiple crashes, forcing them to restart the game and seek assistance from the Imbellus support team. Moreover, the recent update appears to have exacerbated these problems, even in terms of the Ecosystem UX, which has become increasingly unstable. This has not only hindered players’ performance in the game but has also raised concerns about the outcome.

Prepare for the new version

To help test takers prepare for this new addition, we have updated our McKinsey Solve Game Guide McKinsey Red Rock to incorporate these changes. This guide provides tips and strategies on how to approach quantitative reasoning questions and interpret charts quickly and accurately.

Overall, the addition of the mini case to the McKinsey Red Rock simulation is a change that better reflects the assessment practices of other consulting firms. While it does make the assessment more challenging, it also provides an opportunity for test takers to demonstrate a wider range of skills and abilities. With the right preparation, test takers can successfully navigate this new addition and excel in the assessment.

Addressing the primary challenges of the Redrock Study game

With a strict time constraint of 35 minutes, the Redrock Study game diverges from other Problem Solving Game minigames in terms of tasks and time management. To optimize their performance, candidates must remain cognizant of time allocation while carefully and efficiently strategizing their approach. Utilizing the provided data, players must derive accurate and significant conclusions. Following the analysis phase, it is crucial to review one’s work to confirm that all aspects of the question have been addressed and a comprehensive analysis has been conducted.

With the new addition of the Case Study segment, allocate your time appropriately between the 2 segments.

It is recommended to split that time equally between the Investigation-Analysis-Report part and the Mini Case Questions.

The roll-out of the Red Rock Study game

The Red Rock Study game has been rolled out in late summer 2022, and McKinsey was in a testing stage for the remainder of the year with a limited number of candidates actually receiving this game. Since the end of February and the beginning of March 2023, we are seeing increasing reports from our clients, and it seems that the Red Rock Study game has now replaced the Plant Defense game for many.

Over the last couple of months, all of the candidates we talked to had the Ecosystem game and the Redrock Study game (September 2023).

Tips to approach this Red Rock study game

Practice time management.

As mentioned earlier, time management is critical to succeeding in the Red Rock Study game. The 35-minute time limit is short, so you need to make the most of every minute. If the Red Rock study game is part of the assessment, the time for the Ecosystem Creation game is fixed at 35 minutes. In the usual setup with the Ecosystem Creation and the Plant Defense games, candidates can freely choose how to allocate the 60 minutes between both games.

Some of our candidates mentioned that they got caught out by a misunderstanding of the timer at the top as they thought they have time for the case study, and after the timer runs out, they can answer the questions. However, the timer is both for the case study and the mini-case questions afterward.

Maintain focus on the objectives

Given the central role of objectives in the Redrock Study game, it is essential to remain mindful of them throughout the process. When examining information during the investigation phase, concentrate solely on data pertinent to the objectives. Avoid devoting time to unrelated data that will not contribute to answering the questions in the analysis phase. Discern the key signals amid the noise.

Remain vigilant

It is crucial to perform a meticulous analysis during the Redrock Study game. Verify your work and re-examine your calculations to guarantee the accuracy of the information and responses provided. Ensure that all components of the question have been addressed and all germane data has been incorporated into your report. Avoid falling into traps (e.g., by neglecting units and, as a result, comparing apples and oranges).

Practice your data interpretation and quantitative reasoning skills

Data interpretation and quantitative reasoning are critical skills in the Redrock Study game. You need to be able to understand and interpret the data to provide meaningful insights in your report. Make sure you’re familiar with different types of charts and graphs so that you can choose the best one to represent your findings.

For more information on data and chart interpretation, check out our article, as well as our data interpretation drills.

  • How to interpret data and charts in case interviews and aptitude tests
  • Consulting interview data and chart drills

Our Solve Game Guide includes 3 Red Rock full-length Quantitative Reasoning Questions:

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Keep up with the news

As the Redrock Study game is currently being modified, potential additional changes and updates may arise in the future. Stay on top of the news to become aware of any modifications to prepare and adapt your strategy accordingly adequately. Feel free to reach out to us to ask for the latest updates to the McKinsey Solve Game.

In conclusion, the Redrock Study game is a new addition to the McKinsey Solve Game lineup that assesses candidates’ information processing, data gathering, case math, and interpretation of visual data skills. To pass the game, candidates need to be mindful of their time management, stay focused on the objectives, be thorough in their analysis, and practice their data interpretation skills. While the game is still in the testing phase, candidates should stay up to date on any changes or updates. With these tips in mind, you should be well-prepared to tackle the Redrock Study game and succeed in your McKinsey PSG application.

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Florian spent 5 years with McKinsey as a senior consultant. He is an experienced consulting interviewer and problem-solving coach, having interviewed 100s of candidates in real and mock interviews. He started with the goal to make top-tier consulting firms more accessible for top talent, using tailored and up-to-date know-how about their recruiting. He ranks as the most successful consulting case and fit interview coach, generating more than 450 offers with MBB, tier-2 firms, Big 4 consulting divisions, in-house consultancies, and boutique firms through direct coaching of his clients over the last 3 years. His books “The 1%: Conquer Your Consulting Case Interview” and “Consulting Career Secrets” are available via Amazon.

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  • Excellent written and verbal communication
  • Superior problem-solving, analytical, and quantitative skills 
  • Professional attitude and service orientation
  • Ability to work independently as well as in a team
  • Willing to work in a highly demanding and result-oriented team environment
  • Entrepreneurial and enjoys the challenges and rewards of research work in a dynamic and changing environment
  • Willing to travel (typically within the region) as required from time to time
  • Financial Services
  • Strategy & Corporate Finance

FOR U.S. APPLICANTS: McKinsey & Company is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by applicable law.

FOR NON-U.S. APPLICANTS: McKinsey & Company is an Equal Opportunity employer. For additional details regarding our global EEO policy and diversity initiatives, please visit our McKinsey Careers and Diversity & Inclusion sites.

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