McKinsey Solve

  • Fundamentals
  • 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
  • All Fit Interview Course Videos
  • All case interview self-assessment modules
  • Available on all devices
  • Premium support for questions
  • Lifetime access

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
  • Building blocks 
  • Efficiency tools
  • Problem driven structure in action
  • Roadmap for preparation planning

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McKinsey Problem Solving Test: Strategies & Practice Questions

Table of contents.

As a consultant, you need to have strong logical thinking, data interpretation, analytical reasoning, and mental math skills.

So it’s not surprising that McKinsey tests for these skills during their recruitment process.

In fact, they are tested in the very first step of the recruitment process: the McKinsey Problem Solving Test.

What is the McKinsey PST?

The McKinsey Problem-Solving Test (PST) is a data interpretation and analytical reasoning test that candidates take before being offered a first-round case interview .

McKinsey use the test to weed out applications. It is considered to be one of the most difficult recruitment tests because it tests a broad range of skills in a tight time constraint.

The McKinsey PST is formulated to assess whether you have the critical skills to function effectively as an analyst/consultant. It’s not about testing your memory or business knowledge but is directed towards your mathematical acumen and logic skills.

Broadly speaking, the McKinsey PST tests the candidate’s ability to:

  • Accurately identify key data within graphs, tables, and text documents
  • Interpret that data and make quick calculations
  • Select the most appropriate answer based on those calculations

Does every candidate take the McKinsey PST?

Almost everyone does, especially if they’re applying for a role directly out of undergrad (i.e. Business Analyst) or post-MBA (i.e. Associate).

There are some exceptions, such as experienced hires. But it’s safe to assume that you’ll likely be required to take the PST. If you’re unsure, reach out to your McKinsey HR contact.

What’s the pass rate?

Nobody is certain about the exact cut-off score for the McKinsey PST.

However, successful recruits have estimated that the pass rate is about 70%. This implies that for the 26-question test, you need to nail at least 19 of them to be successful.

On average, about 30-35% of candidates achieve a pass rate. And there is no need to worry about outperforming other candidates. The scores are not graded on a curve, so as long as you perform above the pass threshold, you should be called for an interview.

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What is the format of the test?

The McKinsey PST is based on 3 different business case studies and is an hour long. There are a total of 26 multiple choice questions divided over the 3 cases; each case represents a different organization facing a specific business challenge, such as a profitability problem or a new market entry decision.

Electronic tools, such as calculators and smartphones are not allowed. All calculations are to be undertaken mentally or on a blank space on the test sheets. Use of scratch paper is also prohibited.

Types of questions in the McKinsey PST

McKinsey PST questions can be divided into three categories: math word problems, data interpretation questions, and reading comprehension probes.

Math word problems (with examples)

These are questions that represent a mathematical problem in the form of text.

You need to identify and analyze the information provided and manipulate it mathematically to find the correct answer. While you don’t need to be a rocket scientist who smashes out complicated calculations, you still have to hone your mental math capacities to reach the right answer in the minimum time possible.

An example math word problem from McKinsey’s Practice Test A :

If Marcadia had driven higher purchasing from the new customers in Exhibit 3 so that the one year value of customers is in Quantiles 1 thru 4 were each to increase to the next highest quintile, how much greater would Marcadia’s total one year customer value have been? $250,000 $650,000 $2.5 million $6.5 million

Data interpretation questions (with examples)

These questions consist of graphs, tables, exhibits, and text that provides information about example business cases. You have to decipher and analyze the data provided to pick out the correct answers.

The best way to address these is by eliminating the entirely wrong answers and then working through the partially correct and definitively correct ones. These can be tricky and require a sound foundation in critical thinking and analytical skills.

An example data interpretation problem from McKinsey’s Practice Test B :

Based on the data presented in Table 1 and Exhibit 1, which of the following statements is true? The rate of increase in shripe price from May to October is the same as the rate of decrease in profit margin in these three months Freddie’s  made 5% less profit in August than it did in May Freddie’s a greater profit in August than it did in May Restaurant prices for shrimp dishes were 10% higher in October than in May

Reading comprehension questions (with examples)

The third type of question is reading comprehension problems that test your ability to draw the correct conclusion from a text extract. These questions do not require any mathematical calculations.

An example reading comprehension problem from McKinsey’s Practice Test C :

Which of the following questions best summarizes the CEO’s concerns? Does stamp cancellation take up too much unnecessary time in the processing of manual mail? Would the gain in productivity from stopping stamp cancellation in manual mail be worth more than the lost revenue from fraudulent re-use of stamps? Would the amount of time saved from stopping manual stamp cancellation result in a significant decrease in the time spent processing manual mail? Does it make sense to stop the cancllation of stamps on manual mail given that the majority of mail now goes through machines?

Sample tests and practice questions

You can find sample tests on the McKinsey website:

  • McKinsey PST Practice Test A
  • McKinsey PST Practice Test B
  • McKinsey PST Practice Test C

How to prepare for the McKinsey PST?

Here’s our recommended approach for taking the PST:

  • Review the official McKinsey PST sample tests and fa miliarize yourself with the question formats and common mistakes.
  • Polish up your test-taking skills (e.g. mental math skills, reading speed, and data selection).
  • Practice a few questions and time yourself, to identify your strengths and weaknesses. Revisit step 2 to address your weaknesses.
  • Practice at least one mock test replicating the test conditions.
  • Evaluate and revisit your performance and your errors.
  • Take the mock test again and keep repeating until you are confident about getting a score that is equal to or above 90%.

Tips for passing

  • Do not leave any questions blank. There is no negative marking for wrong answers
  • Use your time smartly. For each answer, you have on average about 2 minutes. If the question is taking longer than that, skip it and come back later.
  • Practice, practice, and practice more. The computations on the PST are not very complex; just basic arithmetic operations. But there are a lot of them and you are required to perform all of them at lightning speed without much time to double-check.
  • Brush up your estimation skills. Even if you don’t have the time to calculate precise values, you can still use approximation methods to differentiate the right choice from the wrong ones.
  • For each PST multiple-choice question, you can easily eliminate choices that are very far away from the right answers and focus on the remaining two or three options.

And some advice from candidates who have taken the PST:

mckinsey problem solving test

Break into the McKinsey Problem Solving Game. Launch Your Consulting Career Today!

Your ultimate resource for mastering the mckinsey problem solving game. prepare, excel, and unlock endless consulting opportunities..

Welcome to, the premier platform dedicated to providing detailed and up-to-date information on the McKinsey Problem Solving Game. Our mission is to support aspiring consultants in their journey to excel in the game and secure their dream careers in the consulting industry. With a team of experienced professionals, we curate valuable resources, including test breakdowns, recruitment updates, free practice materials, and a thriving consulting community. Join us as we empower you with the knowledge and tools you need to conquer the McKinsey Problem Solving Game and pave your way to success in the world of consulting.

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Avoid These Common Mistakes in the McKinsey Problem-Solving Game (PSG)

Avoid These Common Mistakes in the McKinsey Problem-Solving Game (PSG) Facebook Reddit Twitter LinkedIn WhatsApp Table of Contents Brief overview

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Mastering the McKinsey Ecosystem Building Game: Expert Tips for Success

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Understanding the McKinsey Problem Solving Game (PSG) Scoring (Latest update 2023)

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McKinsey Problem Solving Game: Full Practice Guide 2023

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

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Mastering the McKinsey Problem Solving Game in 2024

The McKinsey Problem Solving Game is a gamified test to screen candidates.

Your college’s reputation and GPA or GMAT score are not enough anymore.

Now, you must pass the McKinsey Problem Solving Game to get interviews .

And like everything else at McKinsey: it’s very selective.

Thus, in this guide, you’ll learn:

  • What the McKinsey Problem Solving Game is
  • Which skills are assessed during the test
  • What are your chances to pass
  • What technical information you must know (duration, constraints, etc.)
  • How to tackle the game’s various scenarios
  • How to prepare for the McKinsey PSG
  • And lots more.

Update July 2023 : you’ll also find an in-depth analysis of the newest Redrock scenario in this guide.

If you want to practice the McKinsey Solve beforehand to ensure no surprises on test day, check out PSG Secrets’  McKinsey Solve simulation . These exercises simulate the actual exercises you’ll work through on test day.

Let’s dive in right now!

Table of Contents

The McKinsey Problem Solving Game in a nutshell

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Understanding the mckinsey problem solving game.

Imagine yourself in a beautiful, serene forest populated by many kinds of wildlife. As you take in the flora and fauna, you learn about an urgent matter demanding your attention: the animals quickly succumb to an unknown illness. It’s up to you to figure out what to do – and then act quickly to protect what you can.

Are you familiar with this paragraph?

This is the description of one of the McKinsey Problem Solving Game scenarios .

It’s a gamified test to assess candidates’ problem solving skills

The McKinsey Problem Solving Game, also known as the McKinsey Digital Assessment, is designed to evaluate your cognitive ability and problem-solving skills in a fun and engaging way .

But, unlike traditional testing methods, this innovative digital assessment uses the Imbellus software to assess the quality of solutions generated through mini-games, such as the Ecosystem Building Game and the Redrock Study.

McKinsey Problem Solving Game

But how does it work, and how can you prepare for this unique recruitment process?

Let’s dive deeper into the game’s purpose and key components.

The Purpose of the Game is to screen candidates

The McKinsey Problem Solving Game is used to screen candidates and is part of the recruiting process.

McKinsey PSG - recruitment process

Interviewing candidates is expensive.

Thus, the McKinsey Problem Solving Game evaluates if candidates possess the characteristics to become successful consultants before interviewing those candidates .

To do so, the game assesses five key cognitive abilities:

  • Critical thinking,
  • Data decision making,
  • Meta-cognition,
  • Situational awareness,
  • Systems thinking.

McKinsey PSG - skills assessed

And McKinsey will use the test taker’s score for these five criteria to predict the candidate’s likelihood of thriving at McKinsey .

This innovative approach to candidate evaluation goes beyond traditional testing methods.

It utilizes real-time data such as mouse movement, keystrokes, and clicks to assess a candidate’s thinking process.

In other words:

The game’s scoring system considers both the quality of the solutions generated and the efficiency and organization of the approach .

Note: The Solve assessment was developed and iterated by Imbellus ( now owned by gaming giant Roblox ) to replace the McKinsey PST

Note: McKinsey Solve, McKinsey Imbellus, McKinsey Game, Imbellus Test, McKinsey Digital Assessment, and McKinsey Problem Solving Game are all synonyms.

Related article : check this article to learn about the McKinsey recruitment process .

Test takers are asked to play 2 out of 6 mini-games

The McKinsey Problem Solving Game is divided into several mini-games, each designed to assess different aspects of your problem-solving skills.

The game features scenarios, time restrictions, and a scoring system, making it similar to a video game.

The different PSG scenarios

As mentioned in the above picture, since March 2023, the two scenarios you’ll most likely encounter are the Ecosystem Game and Redrock Study.

In the Ecosystem Building Mini Game ?

The sustainability of the constructed ecosystem and the efficiency and organization of the approach taken are evaluated.

Secondly, the Redrock Study is an updated version of the original PST assessment, focusing on chart reading, percentage calculations, and data interpretation .

To succeed in this mini-game, candidates must be able to analyze data and make informed decisions based on the available information.

The 2 parts of the McKinsey Problem Solving Game

There is no right or wrong answer

Like case interviews, there is no right or wrong answer.

Like other top consulting firms, McKinsey is more keen to evaluate candidates’ thinking process.

In other words, your decision making process is as important as your answer .

Finally, check this video – from McKinsey’s website – explaining what to expect in the Problem Solving Game.

To see what these games actually look and feel like, you can practice these games through PSG Secrets’  McKinsey Solve simulation .

Tackling the Ecosystem Building Scenario

The Ecosystem Building scenario is a core McKinsey Problem Solving Game component, requiring candidates to construct a balanced marine or terrestrial ecosystem.

As stated at the beginning of the game, the goal is threefold:

  • Select 8 species (from a list of 39 species) that must survive as an ecosystem
  • Choose a location for the ecosystem
  • Submit your ecosystem

McKinsey PSG - Ecosystem building - main goals

To build this sustainable ecosystem, you must do the following:

  • Terrain specifications: the location of the ecosystem must meet the living conditions for the 8 species you’ll select
  • Calories balance: Each species must be fed with enough calories from food to sustain itself.
  • Food chain management: each species must not be eaten into extinction by its predators.

The next three sections will examine the challenges and strategies for success in the Ecosystem Building scenario .

Terrain Specifications

Understanding terrain specifications is crucial for success in the Ecosystem Building scenario, as it directly impacts species selection.

The terrain specifications refer to the environmental conditions of a given location, including temperature, humidity, and air pressure.

These specifications directly influence the species that can thrive in that location .

The McKinsey Problem Solving Game features Mountain, Reef, and Desert terrains, and each species has required terrain specifications, typically expressed as ranges (e.g., Temperature: 20-30 C).

McKinsey PSG - Ecosystem building - terrain specification and living conditions

To construct a sustainable ecosystem, candidates must carefully consider the terrain specifications and select species that can thrive in the designated location.

Each species has specific terrain specs that have to be met.

If they aren’t met, the species won’t survive, and you won’t achieve the game’s objective .

Food Chain Management

Creating a balanced food chain is another critical aspect of the Ecosystem Building scenario.

In the game, the food chain consists of two types of species: producers and consumers.

Consumers can be classified as herbivores, carnivores, or omnivores, and each species has a few natural predators (Eaten By) and prey (Food Sources).

To ensure the sustainability of the ecosystem, it is vital to monitor the “calorie needed” and “calorie provided” specs of each species, ensuring that no species is eaten to extinction .

McKinsey PSG - Ecosystem building - jaguar

Calories balance

To make the ecosystem sustainable, your food chain must respect 3 rules.

To begin with, the species with the highest “calories provided” eats first. And it eats its “food source” with the highest calories provided.

Secondly, when a “food source” is eaten, its “calories provided” decrease permanently by an amount equal to the eating species’ “calories needed.”

Next, the species with the highest current “calories provided” eats.

To win the game, all the species must have their “calories needed” fully provided and “calories provided” above zero .

McKinsey PSG - Ecosystem building - eating rules

An example of a working food chain:

McKinsey PSG - Ecosystem building - food chain working

And an example of a food chain not working:

McKinsey PSG - Ecosystem building - food chain not working


A sample game

Now that you know the Ecosystem Management game, you can watch the following video.

In this video?

A candidate filmed his screen while taking the McKinsey Solve Game:

Mastering the Redrock Study

The RedRock Study is the latest McKinsey Problem Solving Game addition to test your decision making process.

The game’s plot is that you are sent to an island to analyze the species.

To solve this mini-game, you must go through 3 phases: investigation, analysis, and report .

Each of these phases, mimicking a consulting project, will be detailed in the next sections.

Also, after the 3 phases, you’ll have to answer 10 case questions.

Besides, like the first game (Ecosystem Management), you have 35 minutes to complete the RedRock study.

The investigation phase

In this first phase, you must read a text.

And then collect the information you might need later.

The main challenge is identifying the relevant data.

Because most of the information shown is irrelevant.

Thus, be careful not to waste too much time.

And once you’ve collected all the information, you can move to the analysis phase.

The analysis phase

Here, you’ll be asked to answer three numerical questions.

And don’t worry: a virtual calculator is embedded in the game.

But, you need to use the information gathered in phase 1 to answer these questions correctly.

And very important: write down your answers because you’ll need them in the next phase.

The report phase

Finally, you’ll have to write a summary of your analysis and present your data in charts.

The report phase has two main components:

A written part where you’ll be asked to answer 5 questions based on your analysis,

And a visual part where you’ll be asked to choose a type of graph to show the results of your analysis.

You’ll move to the case questions after completing the report phase.

The case questions

In this final phase, you must answer up to 10 case questions.

And these case questions are similar to those in the McKinsey Problem Solving Test (PST).

McKinsey PST sample question

Thus, I recommend practicing with the old McKinsey PST practice tests (see below).

Because the goal will be to sharpen your numerical and chart interpretation skills.

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Acing the Plant Defense Mini Game

Preliminary note: since March 2023, McKinsey has no longer used the Plant Defense Game.

However, I encourage you to study the following tactics if McKinsey uses this game again .

What is the Plant Defense Game?

The Plant Defense scenario is another McKinsey Problem Solving Game core component.

In this scenario, candidates must defend a base (a square on the map), represented by a native plant, from various invading species, such as rats, foxes, and other predators.

McKinsey Problem Solving Game - Plant Defense Scenario

In the Plant Defense scenario, candidates must strategically place defenders and manage resources effectively to protect the base from invading species .

Defenders, such as terrain features like mountains or forests, or animal defenders like snakes or eagles, have a specified range of coverage and inflict damage on invaders that enter it.

Terrain features can also obstruct or slow down various types of invaders.

How to Win the Plant Defense Game?

You must protect the particular land as long as possible.

To do so, you must predict when and how the invaders will attack to protect the land.

Now, here are my expert tips to win the Plant Defense game.

Step 1: Understand barriers and defenders

First, select the resources you’ll use to protect the plant.

And those resources can be defenders (snakes, eagles, coyotes, etc.) or natural barriers (forests, rocks, cliffs, etc.).

Plus, each animal and barrier has specific characteristics.

Animals can kill the invaders (or damage them), while barriers can slow them down.

And each defender is effective on a certain number of squares around them.

For instance:

Eagles cover more squares but inflict less damage.

Snakes cover only one square but inflict more damage.

McKinsey Problem Solving Game - Plant Defense Scenario - step 1

Step 2: Define a strategy to place resources

At the beginning of the game, you must place five barriers or defenders on the map.

Expert tip : place defenders with large areas of influence close to the plant to defend.

And use barriers to create bottlenecks to make defenders even more effective.

McKinsey Problem Solving Game - Plant Defense Scenario - step 2

Step 3: Understand and analyze the invaders

The invaders appear at the edge of the map.

And their numbers increase during the game.

Check the invaders’ characteristics to identify the best barriers or defenders to use.

McKinsey Problem Solving Game - Plant Defense Scenario - step 3

Step 4: Adjust the strategy

Your defense strategy can be adjusted after each invader’s move.

Remember: try to create circular defenses around the plant.

Note: all of the above screenshots are from our partner .

Now, let’s discuss the other mini games that McKinsey used. 

McKinsey Problem Solving Game - Plant Defense Scenario - step 4

Alternative Mini-Games and Their Challenges

All the candidates who recently passed the game (as of July 2023) had to deal with the Ecosystem building game and Redrock scenarios.

However, consulting is an ever-changing industry.

And so does the McKinsey Problem Solving Game!

Hence, maybe you’ll have one of the following scenarios.

Or a new one that has never been given so far.

Bottom line: be prepared for anything.

Ok, let’s discuss the alternative mini-games previous candidates had.

Disaster Management Game

The natural Disaster Management mini-game involves identifying the type of natural disaster in an ecosystem and relocating the animals from this ecosystem to maximize their survival.

Disease Management

The Disease Management mini-game requires candidates to accurately identify the disease that has infiltrated the ecosystem and implement the necessary measures to contain it.

To succeed in this mini-game, candidates must discern the disease pattern within the ecosystem and anticipate who will be exposed next.

Finally, candidates must select a treatment based on the characteristics of the disease, the animal population, and the treatment options.

Migration Management

The Migration Management mini-game involves identifying the migration patterns of the animals in the ecosystem and implementing necessary steps to ensure their safety.

This requires a deep understanding of the factors that influence animal migration.

Preparing for the McKinsey Problem Solving Game

Mckinsey solve simulation platforms.

Several simulation platforms help candidates practice the mini-games like the original McKinsey Game.

By utilizing these simulation platforms, candidates can familiarize themselves with the game’s mechanics, challenges, and time constraints.

I recommend using the simulations at .

Francesco Rieppi, a former BCG consultant, has designed this platform and offers an incredible money-back guarantee if you don’t pass the Game.

PSG secrets

Note for full transparency : this is an affiliate link. Hence, I’ll get a commission if you purchase Francesco’s product (but without additional costs for you).

Developing Critical Thinking for Success

This is the most important skill to develop to secure a McKinsey offer. 

McKinsey - Importance of Critical Thinking to get an offer

Source: McKinsey and Imbellus teams .

So, what does “critical thinking” mean?

According to Wikipedia :

Critical thinking is the analysis of available facts, evidence, observations, and arguments to form a judgment by applying rational, skeptical, and unbiased analyses and evaluations.

Thus, candidates must be able to quickly assimilate and analyze large quantities of data, identify patterns and trends, and make well-informed decisions based on available information.

How can you develop your critical thinking?

In the next sections, we will discuss strategies to improve your critical thinking and how these strategies can be applied to tackle the game’s various scenarios .

Practice analyzing data with McKinsey PST questions

First, practice with the good old McKinsey PST questions

McKinsey Problem Solving Test

The McKinsey PST practice tests look like this.

First, you have a text to give you some context.

McKinsey PST - example 1

Secondly, you can also find exhibits to provide more information.

McKinsey PST - example 2

And finally, you have a list of questions.

McKinsey PST - example 3

And to answer those questions, you must use the information and data provided at the beginning.

Practice analyzing data with GMAT questions

Besides the McKinsey PST, you can also use GMAT questions to develop your skills.

But not any GMAT question.

The Quantitative Reasoning and Integrated Reasoning questions are great drills to prepare for the screening tests and case interviews used by top consulting firms like McKinsey.

Quantitative Reasoning

There are two questions in the Quantitative Reasoning Section: Problem-Solving: Measures your ability to use logic and analytical reasoning to solve quantitative problems.

Data Sufficiency: Measures your ability to analyze a quantitative problem, recognize the relevant data, and determine when enough data exists to solve the problem.

Both types of questions require some knowledge of arithmetic, elementary algebra, and commonly known concepts of geometry.

Rest assured that the difficulty of the questions stems from the logic and analytical skills required, not the underlying math skills.

Sample Problem-Solving Question

If u > t, r > q, s > t, and t > r, which of the following must be true?

u > s s > q u > r (A) I only

(B) II only

(C) III only

(D) I and II

(E) II and III

Answer: (E)

Sample Data Sufficiency Question

If a real estate agent received a commission of 6 percent of the selling price of a certain house, what was the house’s selling price?

(1) The selling price minus the real estate agent’s commission was $84,600.

(2) The selling price was 250 percent of the original purchase price of $36,000.

(A) Statement (1) ALONE is sufficient, but statement (2) alone is not sufficient.

(B) Statement (2) ALONE is sufficient, but statement (1) alone is not sufficient.

(C) BOTH statements TOGETHER are sufficient, but NEITHER statement ALONE is sufficient.

(D) EACH statement ALONE is sufficient.

(E) Statements (1) and (2) TOGETHER are NOT sufficient.

Answer: (D)

Integrated Reasoning

There are four types of questions in the Integrated Reasoning Section:

Multi-Source Reasoning: Measures your ability to examine data from multiple sources text passages, tables, graphics, or some combination of the three—and to analyze each source of data to answer multiple questions carefully.

Table Analysis: Measures your ability to sort and analyze a data table, like a spreadsheet, to determine what information is relevant or meets certain conditions.

Graphics Interpretation: Measures your ability to interpret the information presented in a graph or other graphical image (scatter plot, x/y graph, bar chart, pie chart, or statistical curve distribution) to discern relationships and make inferences.

and Two-Part Analysis: Measures your ability to solve complex problems. They could be quantitative, verbal, or some combination of both. The format is intentionally versatile to cover a wide range of content. Your ability to evaluate trade-offs, solve simultaneous equations, and discern relationships between two entities is measured.

The questions involve quantitative and verbal reasoning, separately or in combination.

Sample GMAT Integrated Reasoning question:

Sample GMAT Integrated Reasoning Question


And here is the best part:

You can easily find online many GMAT simulation platforms with free trial periods.

Hence, you’ll be able to practice with time constraints.

And better feel the pressure of the clock ticking in a test 😅

Mental calculations

Most questions on the Redrock involve math, particularly percentages.

Therefore, it is wise to practice calculations involving percentages before the game.

What’s 80% of 8,200?

How much sales (today: $82m) should increase to reach $140m?

Like the GMAT, these questions are not too difficult.

But the limited time (and the pressure) makes it challenging.

Hypothesis Formation

Forming hypotheses based on available information is another essential McKinsey Problem Solving Game skill.

Hypothesis formation is the process of devising a predictive statement or tentative explanation about a phenomenon or problem based on limited evidence or prior knowledge.

To form hypotheses effectively, it is necessary to consider the context of the problem, analyze the available data, and evaluate the potential implications of the hypothesis .

Additionally, it is important to consider the potential for bias in the data and contemplate alternative hypotheses.

As new data is presented during the game, candidates must be able to adjust their hypotheses accordingly.

This may include revising the hypothesis, discarding it, or forming a new hypothesis.

Being flexible and adaptable in the face of new information is crucial for success in the McKinsey Problem Solving Game, as it allows candidates to respond effectively to changing circumstances and make the best decisions based on the most up-to-date information.

Gaming for Success

Some applicants said the McKinsey Digital Assessment could be overwhelming if you aren’t accustomed to playing computer games.

The learning curve for the PSG is shortened for players who play frequently, particularly those who enjoy strategy games.

They are quicker to figure out what to do next and when to do it because they are usually more familiar with game environments.

However, some video game genres will be more helpful in preparing for the McKinsey Problem Solving Game.

The gameplay in the following video games is somewhat reminiscent of that in the McKinsey PSG :

  • Zoo Tycoon – similar to Ecosystem creation
  • Planet Zoo – similar to Ecosystem creation
  • Kingdom Rush – similar to Plant Defense
  • Roller Coaster Tycoon – similar to Ecosystem creation
  • Planet Coaster – similar to Ecosystem creation
  • Plants VS Zombies – similar to Plant Defense

As you can see, there is a lot of mini games you can use to practice.

Besides, if you know any, you can play a traditional board game, especially strategy games.

Zoo Tycoon

Frequently asked questions (and final tips)

How hard is it to pass the mckinsey problem solving game.

The McKinsey Problem Solving Game can be challenging due to its difficult passing rate.

Estimates suggest that only 20-30% of test candidates are successful.

Does everyone get invited to the McKinsey problem solving game?

As of 2023, all the candidates who passed the resume screening were asked to take the Problem Solving Game.

And the game is used by all McKinsey divisions (Digital and Operations) in their hiring processes.

Will I receive a score for my performance after the McKinsey Online Assessment?

Usually, you don’t receive a score but a confirmation of whether you passed the game.

What is the purpose of the McKinsey Problem Solving Game?

The McKinsey Problem Solving Game tests a candidate’s ability to think critically and solve complex problems.

And this game is an important part of the McKinsey recruitment process.

It happens before PEI and case interviews.

So McKinsey can identify the best possible candidates for their consulting roles.

What are the differences between the McKinsey PSG and McKinsey PST?

The McKinsey PST (Problem Solving Test) was a traditional standardized admission test.

These traditional assessments focus on content mastery, processing speed, and memory. These factors ignore the increasing need to develop and measure capabilities required by the 21st-century workforce. These tests ignore the cognitive process that users engage in during that task.

Source: Imbellus and McKinsey teams

Thus, unlike the PST, the game doesn’t require business knowledge.

Besides, the McKinsey Problem Solving Game score is based on the final results and the steps to reach the results.

Imbellus assessments focus on evaluating how people think instead of what they know. Through the scenarios in our simulation-based environments, we observe details of users’ cognitive processes, not just their end choices.

How long is the McKinsey Problem Solving Game?

The total time is between 60 and 75 minutes.

And time management is a critical success factor to pass this test.

What is the usual response time from HR?

Normally candidates receive an answer within two weeks after they’ve completed the test.

Will I have to take the test from home?

Yes. McKinsey will send you a link to take the test.

Also, they will provide you with another link to check if the technical characteristics of your laptop are enough to play the McKinsey Problem Solving Game.

Can I pause the game once it has started?

No. You must go through all the mini games at one time once you have started.

Any last advice?

Before you start the Game, make sure you are in a silent room (mute your phone) and check your internet connection.

The best way to practice is to simulate the actual games themselves. PSG Secrets offers a realistic simulation of the McKinsey Solve  that you can play through.

Besides, prepare pens, papers, and the templates provided in the psgsecrets platform.

I hope you enjoyed this guide about the McKinsey digital assessment.

And that you feel more confident of winning the McKinsey Problem Solving Game.

The best way to practice this is by playing through a realistic simulation of the McKinsey Solve .

Now, I’d like to hear from you:

Which scenario is the most challenging, in your opinion?

Will you play more video games to prepare for this test?

Let me know by leaving a quick comment below right now.

Related articles :

How to answer the question “Why consulting” and “Why McKinsey? Why BCG? Why Bain & Company?” .

Also: read this article to write a compelling and personalized cover letter.

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McKinsey Solve Game (2024): How to Prepare and Ace the Imbellus

the image is the cover for an article on the mckinsey solve game

Last Updated on January 26, 2024

The McKinsey Solve Game, previously known as the McKinsey Problem Solving Game, Digital Assessment, or informally as ‘the Imbellus’, serves as a pivotal tool for the renowned consulting firm in evaluating prospective candidates. This assessment is utilized in tandem with the infamous case interviews and personal experience interviews (PEI) . In this comprehensive guide, we delve into the nuances of McKinsey’s innovative assessment game.

It’s important to mention that we consistently update this article with the latest and most relevant information. Initially published in May 2019, our coverage was a pioneering global insight into the Solve Game, even during its beta testing phase. Our most recent update to this article was in mid-January 2024, ensuring you have the most current insights at your fingertips.

History the McKinsey Imbellus Solve Game

Developed in collaboration with Imbellus and psychologists from UCLA Cresst, the McKinsey Solve Game invites candidates to engage in a series of stimulating scenarios. The game challenges players to create sustainable ecosystems within diverse environments such as reefs, mountain ridges, or jungles. Additionally, participants assume the role of a researcher, analyzing animal populations. In an earlier version of the game, your focus was on protecting plant species from invaders in a tower-defense-like game. This immersive experience is a precursor to embarking on a career with McKinsey.

When this game-based assessment was introduced by the world’s leading consulting firms four years ago, it created a significant buzz in the consulting industry for two primary reasons. Firstly, the Solve Game marked a departure from traditional recruitment methods by incorporating an actual computer game. This represented a shift from the conventional Problem Solving Test (PST), a pen-and-paper test designed to gauge candidates’ abilities to solve business problems under time constraints. Secondly, McKinsey’s stance that the game’s nature makes it impervious to specific preparation strategies initially left applicants feeling uncertain about how to best approach the assessment. This was a notable change for applicants accustomed to preparing for weeks or sometimes even months to tackle their case interviews.

Quick reality check…

However, it soon became evident that McKinsey’s claim was more of a strategic marketing move. Our interviews with some of the first candidates who participated in the initial Imbellus Test in London in November 2019 revealed insightful feedback. This was the first instance where the Solve Game was employed as a formal part of the recruitment process beyond its beta testing phase. These early test-takers made it clear that with a better understanding of the game’s format and the skills it assessed, they could have performed more effectively. Several candidates had even prepared for the PST, not anticipating any changes in the assessment approach. They were informed about the switch to the Solve Game merely a week in advance.

Leveraging this feedback and using their dissatisfaction as a starting point, we collaborated with experts in the field and continued to gather insights from test-takers across various countries. This collective input allowed us to develop effective preparation strategies and gameplay techniques to play the games successfully.

What we found is that – in contrast to McKinsey’s initial messaging – it’s indeed possible to prepare effectively for this assessment. Adopting the right strategies for each game segment can quickly enhance relevant skills, as evidenced by our candidates’ significant performance improvements compared to their peers.

This article aims to provide a comprehensive guide to mastering the Solve Game. We’ll cover five key areas:

  • Understanding McKinsey’s motivation for transitioning from the traditional Problem Solving Test to a gamified assessment, and what this means for you.
  • Introducing and examining all six games included in the assessment, along with variations reported by test-takers.
  • Clarifying the actual skills assessed, extending beyond the official communications.
  • Detailing preparation methods, exercises, and tools to elevate your performance.
  • Offering insights into effective test-taking strategies to maximize results.

For those seeking thorough preparation, we offer a detailed preparation package including (with instant access):

  • 146-page guidebook
  • Excel Solver tool for the Ecosystem Game
  • 14 videos that dissect every aspect of the games, including game-winning strategies.
  • Complimentary 14-page primer for McKinsey case and PEI interviews was the pioneer in providing detailed analysis of this new assessment type, backed by authentic firsthand information. This has allowed us to continuously refine our insights based on feedback from our extensive customer base. The program has gone through 19 iterations, last updated in December 2023, and incorporates feedback from over 500 test-takers and several game designers.

We launched our original program at the end of November 2019 and have since been updating it regularly to maintain its relevance and accuracy, distinguishing ourselves from others who have merely replicated our content. To date, more than 8500 applicants  from over 70 nations have used the guide to prepare for their Imbellus. 

McKinsey Solve (Imbellus) Game Guide

Our McKinsey Solve Game Preparation Package

Elevate your Solve Game score with the original game guide, a 14-part video course, an Excel Solver tool, and Red Rock practice tests. Trusted by more than 8,500 customers from 70+ countries since November 2019.

Introduction of the McKinsey Solve Game

“Imagine yourself in a beautiful, serene forest populated by many kinds of wildlife. As you take in the flora and fauna, you learn about an urgent matter demanding your attention: the animals are quickly succumbing to an unknown illness. It’s up to you to figure out what to do—and then act quickly to protect what you can.” McKinsey & Company

Sounds exciting? Well,…you be the judge.

As a consultant with McKinsey or any other top-tier consulting firm, you often find yourself in situations where you must save the day. On an abstract level, the game simulates exactly this reality. While your consulting career mostly relates to strategy engagements with Fortune 500 companies, McKinsey chooses the environmental scenarios deliberately. More on that in a second.

Traditionally, the McKinsey way of hiring candidates was through the following funnel:

  • Screening: Your consulting resume and cover letter are screened based on a number of filters
  • Problem Solving Test: A 60-minute pen-and-paper test, covering 26 business-related questions
  • Consulting Interview Round 1 : 2 to 3 business case and personal experience interviews
  • Consulting Interview Round 2: another 1 to 3 interviews depending on the region (Rounds 1 and 2 can be on the same day in some offices)

With the introduction of the Problem Solving Game (PSG), the Problem Solving Test (PST) was on its way out.

So, why would McKinsey replace a time-tested screening tool, which has evaluated hundreds of thousands of applicants, with a computer game? The reasons are threefold, reflecting McKinsey’s typical approach:

The answer is quite simple and – as ever so often in the McKinsey world – threefold:

  • To  attract new talent  and new types of consultants.
  • To have an assessment  tool that is agnostic  (in theory)  of people’s backgrounds .
  • To have a lower-cost program (in the long run) to  assess a greater amount of candidates .

The Firm is employing the Solve Game to take into account the changes that every consulting firm faces: Changes in its client base, new types of problems the clients face, and its evolution through organic growth and acquisitions. New problems of clients require a new type of consulting workforce. The typical McKinsey career has changed. Hence, McKinsey is investing heavily in the recruitment of new types of talent, including data scientists, implementation practitioners, IT experts, product and digital designers, as well as software developers in addition to their generalist consulting roles. A digital test is only logical when hiring digital natives.

Above, we teased the environmental abstraction of the game tasks. What is that all about? McKinsey stresses that to perform well in the different games, no prior knowledge and preparation is needed or beneficial (contrary to the PST). The natural context should be easily accessible for every possible candidate, regardless of their background. The PST was geared more towards business majors and quant-heavy degrees, evaluating candidates with a simple pen-and-paper test. With the Solve Game, McKinsey has created a much more complex assessment tool to avoid any biases related to a candidate’s culture, experience, or background. Why this is a fallacy and just introduces new types of biases, a bit further down on this page…

Lastly, McKinsey is receiving several hundred thousand applications every year. Can you imagine going through all of them and dedicating proper resources to every single one of them? No? Right, because neither can McKinsey. High-level screening algorithms decide what consulting cover letter and resume gets screened by a human and even then, many candidates are quickly sorted out. As a result, many potentially talented individuals do not make the cut. The Solve Game attacks this issue from two ends.

Administering the Imbellus Game to one additional candidate comes with almost zero additional cost for the Firm. The assessment can be taken from home (in most cases) and does not block many recruitment resources from the local office. It is part of a streamlined and automated process ( sounds exactly like what a top-tier management consulting firm would do, eh? ). For the PST, on the other hand, candidates had to go to the office to take the test, blocking many resources in the process. Second, with a negligible marginal cost for one additional test-taker, more people can be evaluated and potentially deemed ‘worthy’ of moving on to the interview rounds, even if their resume lacked some important metric that was relevant to the old screening algorithm.

To hit those three points, McKinsey hired Imbellus (which has since been acquired by Roblox ) to develop the different games of the Solve Game, a company that claims to reinvent how we measure human potential. A bold claim.

Does the Solve Game live up to this claim and fill its new role as a screening device for applicants?

If you want to learn more about McKinsey’s rationale for the Solve Game, Fortune spoke with Katy George, McKinsey & Company’s chief people officer, regarding the impact of prevailing labor market trends on the consulting firm’s talent strategy.

The Firm wanted to change its talent recruitment strategy to align with current labor market trends. Shifting its focus from prestigious educational backgrounds to the potential and diverse skill sets of candidates, McKinsey now recruits from a broader range of educational institutions, increasing its outreach from 700 to about 1,500 schools, with plans to expand to 5,000. This approach supports the “paper ceiling” movement, valuing talent over formal qualifications.

To support this move, McKinsey developed the video game ‘Solve’ to attract a wider pool of applicants, including tech talent. This evaluation has reached over 150,000 candidates in the first two years of the game’s introduction, highlighting the game’s role in identifying talent with varied backgrounds, particularly in technology.

The Role of the McKinsey Solve Game

As a candidate, the Solve Game immerses you in several digital, scenario-based assessments, designed to understand and measure how you approach and solve problems, basically putting you in situations that McKinsey consultants face every day. This approach diverts significantly from other well-known testing formats such as the PST or the BCG Online Case , which test problem-solving skills in a business context.

A Digital Case Interview with Twists and Turns

The Imbellus replaces the McKinsey Problem Solving Test (which has been discontinued in several offices such as Germany and Austria years ago due to the bias it introduced –   business majors usually got much higher scores).

While the PST is useful when gathering information about a candidate’s problem-solving skills, it introduces a bias toward candidates who are familiar with business problems. Since it favors business major backgrounds, it is not in line with McKinsey looking to expand its hiring base. Also, the PST does not allow for understanding how the candidates arrived at a solution. The Imbellus Assessment allows McKinsey to get both a product score, evaluating how good your solution is, and a process score, providing insights into your problem-solving prowess and approach.

By changing this part of the recruiting process and introducing an abstracted digital assessment, McKinsey hopes to gauge applicants’ cognitive abilities in a bias-free environment while at the same time collecting way more data points on them.

The Format of McKinsey Solve Game

The Imbellus Solve Game has evolved to a format where candidates engage in two out of six available mini-games within a 70-minute timeframe. This represents a change from previous versions, which allotted up to 81 minutes for game play. According to our data and surveys, every candidate since March 2023 has participated in a version of the Ecosystem Creation game and the Red Rock Study game. Notably, since the end of February 2023, the Plant Defense game, previously a consistent element of the assessment, has not been featured.

This setup emphasizes effective time management, as candidates must ensure completion of both games within the allocated time., 35 minutes for each.

In the following sections, we will provide an in-depth analysis of each game, outlining various strategies and techniques to efficiently manage time and maximize performance.

The Scoring of the Solve Game

The essence of the Imbellus test aligns closely with the conventional approach of consulting cases and interviews. It demands the identification of a problem, gathering and analyzing data, making informed decisions under time constraints and with incomplete information, and then crafting actionable recommendations. Essentially, the test is designed to assess problem-solving skills, but it does so in an online format, leveraging sophisticated algorithms.

Data on the test’s efficacy indicates that a candidate’s performance in the Imbellus problem-solving simulation is a reliable predictor of their likelihood to receive an offer following the case interviews. This predictive accuracy is reportedly superior to that of the traditional Problem Solving Test (PST). Further details and specific data on these outcomes will be discussed in subsequent sections.

the image depicts the correlation of success in the Imbellus McKinsey Problem Solving Game with the success in the case interviews

The McKinsey Solve Game is tailored to evaluate candidates’ skills in scenarios that mimic real-life situations, going beyond what can be inferred from a consulting cover letter or resume. It scrutinizes candidates’ problem-solving approaches, their creativity in tackling tasks, and their overall thought processes. Specifically, the game is designed to assess:

  • Problem identification : The ability to accurately discern the core problem that needs resolution.
  • Analysis of information : Skill in sourcing and scrutinizing information from diverse channels.
  • Strategic solution development : Competence in formulating and methodically testing hypotheses to solve the problem.
  • Conclusion and decision making : Aptitude for drawing appropriate conclusions and making informed decisions.
  • Adaptability : Agility in responding to evolving situations or changing parameters.
  • Quantitative reasoning: With the introduction of the Red Rock game, McKinsey now also evaluates how effectively candidates can comprehend, process, and apply quantitative data in problem-solving scenarios.

To effectively measure these attributes, McKinsey and Imbellus use a dual scoring system:

  • Product Score:  This evaluates the quality of the outcome achieved. Did you complete the game objectives, like creating a sustainable ecosystem, providing the correct outcomes for your analyses, or protecting the plant?
  • Process Score:  This score reflects the method and strategy used to achieve the outcome. It tracks every interaction, including over 100 different variables during gameplay. Factors like apparent nervousness or the execution of a logical plan are considered.

The implications of this sophisticated scoring system for new candidates are multifaceted:

For candidates, the McKinsey Solve Game’s scoring system has significant implications. It means that the assessment isn’t just about reaching the correct outcome, but also about how you get there. This dual focus on both product and process offers a more holistic evaluation of a candidate’s abilities.

  • Holistic assessment : Candidates are evaluated on their results and the strategies they employ. This approach rewards not only correct outcomes but also thoughtful, strategic processes.
  • Behavior under pressure : The game assesses how candidates perform under pressure, including decision-making speed, adaptability, and handling incomplete information.
  • Broader accessibility : Since the game is less reliant on specific business knowledge and more on general problem-solving skills, it potentially opens the door for candidates from diverse academic and professional backgrounds.
  • Increased stress : The knowledge that every action is being recorded and analyzed might increase stress levels for some candidates, possibly affecting their performance.

You might think that with such an assessment and with a focus on process, it’s harder for candidates to ‘game’ the system by preparing for specific outcomes. Yet, what we found out over time is that the range of potential outcomes for the Ecosystem game and the types of questions for the Red Rock Study game is very narrow. We have developed strategies and step-by-step approaches to navigate this challenge very well.

Overall, the McKinsey Solve Game represents a shift in how candidates are assessed, placing equal importance on the journey and the destination. For candidates, this means preparing for the game requires a focus on developing the right approach and the ability to remain calm and effective under pressure.

Current Roll-out and Scope of the McKinsey Solve Game

It’s all fun and games until your score actually determines your future McKinsey career.

A frequently asked question from candidates is about the necessity of participating in the Solve Game during their application process. The straightforward answer is that in almost all cases, yes, it’s required.

Initially, the game underwent testing with 5,000 candidates across 20 countries between May 2018 and October 2019, alongside the PST. This phase wasn’t about evaluating candidates; rather, it focused on gathering data, beta testing, and fine-tuning the games. Additionally, McKinsey’s active consultants were invited to play in trial runs, contributing further to the data collection.

As of now, McKinsey has globally implemented the Solve Game for a vast majority of applicant types, aiming to evaluate a larger pool of individuals with more refined metrics. Our internal data indicates that the game has been adopted in virtually every country with a McKinsey office. The comprehensive global deployment was finalized during the 2020 recruiting season, with many key markets initiating the rollout from January to June of that year.

Since 2022, the use of the Solve Game has extended beyond office applications. Candidates are also required to complete the game as a prerequisite for certain recruiting events, such as the McKinsey Women’s Leadership Summit.

In terms of the roles it applies to, the game is obligatory for all practice areas, including Generalist Consulting, Operations and Implementation, Research & Analytics, Digital, among others. The only exception, as of now, appears to be Orphoz , a McKinsey subsidiary specializing in transformations, which has not yet incorporated the Imbellus games.

Additionally, it’s noteworthy that senior and professional hires are often exempt from this requirement.

Timing of the Imbellus in the McKinsey Recruiting Process

Upon successful screening of your consulting cover letter and resume, you’ll be sent an email with a link to the Imbellus assessment. You have the flexibility to choose when to take the test, provided it’s within 7 calendar days of receiving the link, for most candidates.

However, in some offices and regions, you might be notified earlier (up to a month in advance) about your deadline for the test. In certain cases, you might even be required to visit the office for the test, which could coincide with your case interviews.

It’s advisable to begin preparing for the Imbellus as early as possible to develop and refine the skills evaluated in the assessment.

Post-Game Process: Waiting for Results

If you take the test remotely, the notification period to learn if you’ve passed and can proceed to the interview stage typically ranges from 1 to 14 days, though this can vary based on the office and the volume of candidates. The longest wait reported by one of our candidates was two months, an outlier, with the average wait time usually under a week. Some offices in Asia recruit continuously but only finalize decisions on Solve Game results on specific dates, potentially extending wait times. If you need a quicker response due to another job offer, contacting HR can often expedite the process.

If the Imbellus is taken in conjunction with the first round of interviews, such as in Germany, your game performance will be evaluated alongside your interview results. Different offices place varying levels of emphasis on the assessment’s outcome. For some, it’s an additional factor in the initial interview round, while for others, it acts as a crucial gateway to the interviews. Some offices may also weigh the Solve Game results in conjunction with your application and documents, where a strong resume or referral could potentially compensate for an average game performance.

Requirements to Pass the McKinsey Solve Game

After completing the McKinsey Solve Game, you can gauge your performance even before the official notification.

How to assess your performance?

  • Ecosystem Game : The key is to know whether the ecosystem you created will survive. A quick completion time can be a positive indicator. Creating a sustainable ecosystem in less than 25 minutes generally suggests a good chance of success. Tools like our Excel Solver in combination with the right strategy can assist in predicting ecosystem survival, enabling you to craft a viable solution in under 20 minutes.
  • Red Rock Game : While there’s no explicit benchmark for what constitutes a passing score, drawing parallels from the previous Problem Solving Test’s approximate 70% cutoff, a similar threshold might apply.
  • Plant Defense Game : A strong performance typically involves surviving at least 15 turns per round, with higher numbers like 25 or 30 being ideal. We delve into the implications of these benchmarks in more detail later.

The pass rate for the Solve Game is expected to be similar to or slightly lower than that of the PST. Unofficial pass rates circulating for the Solve Game suggest that only around 20% of candidates successfully pass. With thorough preparation and a clear strategy, this success rate can be increased to over 80%.

McKinsey has conducted extensive beta testing with a large pool of applicants and internal staff to fine-tune the Imbellus assessment. Over time, as more candidates become familiar with the test and preparation efforts intensify, we see a trend of score inflation due to better prepared candidates. In response, Imbellus frequently updates and introduces new games to maintain a level of unpredictability and mitigate the effects of overpreparation.

There is a reason why our current preparation package is already version 19 in just 4 years.

The Skills Assessed by the McKinsey Solve Game

The McKinsey Solve Game, while not requiring specific business knowledge like the traditional pen-and-paper assessments, focuses on evaluating similar cognitive abilities and problem-solving skills in a gamified context. To excel in the game, candidates need to:

  • Understand the skills tested : Gain a deep understanding of what each game assesses.
  • Learn effective preparation methods : Master the right techniques and strategies to win.

The 8 Core Skills Assessed by Imbellus Games

The games aim to create a comprehensive profile of your skills across various domains. Every keystroke and mouse movement is captured and analyzed to evaluate your performance, which is reflected in both a product score and a process score. The assessment goes beyond just the outcomes; it also focuses on the cognitive dynamics behind your decisions, your adaptability to changing scenarios, and your approach to error correction.

To score well, it’s crucial to optimize both scores and understand the diverse factors influencing the outcomes in each game scenario.

The key skills assessed, which are not officially communicated by either McKinsey or Imbellus, include:

  • Critical thinking : Ability to form logical judgments from a set of facts both in the qualitative and quantitative realm.
  • Decision making : Skill in choosing the most effective course of action from multiple options.
  • Meta-cognition : Using strategies to simplify learning and problem-solving (e.g., hypothesis testing, note-taking).
  • Situational awareness : Understanding the interrelationships between various factors and predicting scenario outcomes.
  • Systems thinking : Grasping cause-and-effect relationships involving multiple factors and feedback loops, including foreseeing multiple layers of consequences.
  • Cognition : The capacity to memorize, process, store, and integrate new information with existing knowledge for later retrieval.
  • Adaptability : Flexibility in altering actions and strategies to accommodate new situations or changing conditions.
  • Creativity : Inventiveness in developing unique solutions, approaches, and ideas for various problems.

The image describes all cognitive skills that the Imbellus McKinsey Problem Solving Game assesses

The McKinsey Solve Game employs advanced data science techniques to meticulously track and analyze each candidate’s actions, offering a comprehensive assessment of their abilities. This digital format provides a wealth of insights into candidates’ skills, leveraging the vast amount of data collected and calibrated from thousands of applicants over time.

This digital assessment method enables McKinsey to observe candidates’ thought processes in a manner akin to traditional consulting interviews but with greater efficiency and depth. It’s a sophisticated approach that goes beyond just the outcomes, focusing on understanding how candidates think, analyze, and solve problems in real-time scenarios.

Demonstrating Key Skills in the McKinsey Solve Game

Maximizing your performance in the McKinsey Solve Game involves showcasing a range of skills through your actions and decision-making processes within the game. Here’s how you can demonstrate these essential skills:

  • Critical thinking : Exhibit your ability to sift through large datasets, discard irrelevant information, analyze crucial data, and synthesize your findings to devise optimal solutions. This should be done systematically and methodically both for qualitative decisions and quantitative problems.
  • Decision making : The game analyzes your decision-making process by tracking the time spent in each game menu and section, and how you form recommendations based on this data.
  • Metacognition : While not directly trackable, your choice of paths and tools in navigating the game can reveal your metacognitive strategies – how you process and approach the games.
  • Situational awareness : Demonstrate your understanding of the game’s elements, objectives, available options, and time constraints.
  • Systems thinking : Show your ability to recognize interdependencies within the game’s parameters, such as aligning the food chain characteristics with the appropriate location in the ecosystem game.
  • Adaptability : Particularly important in games like the plant defense game, where you need to adjust to changing scenarios and strategies.
  • Cognition : Utilize your skills in memorizing, storing, integrating, and retrieving information as needed throughout the game.
  • Creativity : McKinsey values innovative approaches. Display your ability to deviate from conventional methods and find unique solutions to the challenges.

To optimize both your product and process scores, it’s also crucial to have a clear understanding of the various games included in the assessment and their specific requirements. This knowledge allows you to tailor your strategies and approaches effectively to each unique scenario, thereby enhancing your overall performance.

Combine your Solve Game preparation with our McKinsey Interview Academy.

the image is the cover of the ready for mcKinsey Case Interview Consulting video academy

The Current Games of the McKinsey Solve Game

The McKinsey Solve Game typically allocates a total of 70 minutes for completion, dividing this time equally with 35 minutes dedicated to each of the two games. This standard timing, however, has not always been the case. In the past, the duration varied among candidates, ranging from 60 to 81 minutes, depending on the specific requirements of the tasks at hand.

An integral part of the McKinsey Solve Game experience is the inclusion of untimed tutorial sessions before each game. These tutorials are invaluable for candidates, as they provide a detailed introduction to the games, explaining their mechanics and objectives. The length of these tutorials is flexible, allowing candidates to take as much time as needed to fully grasp the concepts and strategies required for the games. This flexibility is particularly beneficial for ensuring a comprehensive understanding of the game’s intricacies.

Once the actual timed games commence, it’s important for candidates to be aware that these cannot be paused. This aspect of the game adds a layer of complexity, emphasizing the importance of effective time management. Candidates need to be well-prepared and focused from the start, as the non-pausable nature of the games demands continuous engagement and strategic thinking throughout the allotted time. This dynamic is crucial in testing candidates’ ability to efficiently navigate and solve problems under time constraints.

Focus on Environmental Topics with Many Evolutions

The McKinsey Problem Solving Game, known for its focus on environmental themes, has seen a series of evolutions and variations since its introduction. Originally, the game included two distinct scenarios: Ecosystem Creation and Plant Defense. However, feedback starting from August 2020 suggested changes in these scenarios. Some candidates encountered a variation of the Ecosystem Creation along with the Disease identification game, instead of the usual Plant Defense game. It’s important to note that in these cases, the Disease Identification was not used for scoring but rather for future calibration of the Imbellus test.

In 2021, a new scenario focused on Migration Planning was introduced to the game. Yet, by the end of 2022, indications emerged that this scenario had been discontinued. McKinsey’s approach to introducing new games and variations appears strategic and careful. Over the last three years, six different games have been featured in the assessment. However, consistent with McKinsey’s methodology in their consulting interviews, the introduction of new scenarios or variations of existing games is primarily for beta testing and calibration. These new elements are not immediately used to evaluate candidates but to ensure consistency in results and skill assessments over time.

As of March 2023, the two scenarios that candidates face are the Ecosystem Creation and the Red Rock Study Game. Every candidate since then has encountered these games, making them the current standard in the Problem Solving Game.

Watch this space as we always update the article and our preparation package as soon as the next evolution is launched by McKinsey.

Let’s take a deeper look at the different games.

Ecosystem Creation

the image shows a screenshot from the mckinsey ecosystem creation game

The Ecosystem game, often referred to as the Ecosystem Building or Ecosystem Creation game, has been a cornerstone of the McKinsey Problem Solving Game. It is the only game that is still part of the Solve Game lineup since the very beginning, albeit with a couple of minor variations.

In this game, you are placed on an island (either in the reef, the jungle, or on a mountain ridge) and tasked with establishing a sustainable ecosystem in a chosen location. The primary objectives are twofold:

  • Create a sustainable chain : You need to select 8 species out of 39 that together form a sustainable ecosystem.
  • Find a suitable location : Determine the best location for this ecosystem on a map.

These tasks must be completed within a 35-minute timeframe.

The game begins with a tutorial that is untimed, providing an opportunity to understand the game mechanics.

At the core, the game is an optimization problem. You will be confronted with an overload of different data points (similar to the McKinsey Problem Solving Test, yet not business-related). You match the location to the species as well as the species with each other based on many different characteristics such as calorie need or provision and environmental requirements such as temperature, sun exposure, etc. All requirements need to be fulfilled at the same time to create and sustainable ecosystem and to successfully pass this game.

There are 2 parts:

First, you need to pick 8 species, either animal or plant, to inhabit the mountain, reef, or jungle location. Selecting a suitable, heterogeneous sample for the food chain relationship out of the numerous species is crucial. You need to account for the interaction effects between the species (e.g., coral, aquatic animals, algae, etc. in the reef) and several individual characteristics such as the required environment, place in the food chain, how many calories they need to survive, or how much energy they need, how many calories or energy they provide when consumed, etc.

Second, you need to decide on the location of the ecosystem to create good living conditions for several species. You need to consider several characteristics of the location such as altitude, cloud height, ph-level of the soil, wind speeds, precipitation, etc. for the mountain ridge or depth, temperature, salinity, etc. for the coral reef.

The catch in this game is that you are presented with information overload and need to show proper systems thinking. The food chain must not collapse, and the ecosystem must sustain itself. You will know if you have provided a good answer before submitting it since you can test your hypotheses to see if the ecosystem could actually sustain itself.

In the summer of 2020, McKinsey started to introduce new boundary conditions to make the game more challenging. For instance, you not only need to create the food chain with several levels and match it with a location but also adhere to certain new rules related to the hierarchy of the food chain. This twist adds another dimension you need to consider when drafting your solution.

There are several ways how to approach this scenario, which we worked on with our candidates and created an Excel sheet that helps you solve the eco-system puzzle. Below is a high-level approach you can use when going into the game.

What you need to know when approaching the species selection

  • Selecting 8 species : From a set of 39 animals, you must choose 8. These species include 9 producers (like corals and algae) and 30 animals (such as sharks, tuna, etc.). Producers consume natural resources and do not require calories, while animals consume other organisms and require calories for survival.
  • Environmental conditions : Species are divided into three environmental ranges, each with specific environmental characteristics like depth and temperature. For instance, depth may be categorized into ranges such as 11-15m, 16-21m, and 22-27m.
  • Distribution of species : In each environmental range, you’ll find 3 producers and 10 animals. Your final ecosystem should consist of species all from the same range.

Having this key insight about the food chain mechanics in the McKinsey Ecosystem Game can be a significant advantage. As this information isn’t explicitly communicated by McKinsey, most candidates would typically need to deduce these details during the game, consuming valuable time within the 35-minute limit. However, being aware of this beforehand allows you to approach the game with a more informed strategy.

  • Start with producers : Knowing the calorie dynamics, you can begin by selecting a set of producers that not only share the same location characteristics but also provide the right amount of calories for enough animals. This understanding narrows down your options significantly, reducing the initial choice of 39 animals to a more manageable 10.
  • Focus on the right producers : Identifying the correct set of producers is crucial, as they form the foundation of your food chain. Choosing the right producers simplifies the subsequent steps in creating a sustainable ecosystem.

In our McKinsey Solve Game Guide , we delve deeper into these strategies, offering a step-by-step approach to solve the Ecosystem Creation game efficiently – in less than 20 minutes. Our guide is designed to streamline your process, ensuring you can focus on building a viable ecosystem without getting bogged down by the multitude of options.

We also provide an Excel Solver tool as part of the guide. This tool is immensely helpful in assessing the sustainability of your ecosystem. It aids in determining whether the food chain you’ve created can sustain itself, saving you the trial-and-error time during the game. Additionally, the Excel Solver can suggest instantly which set of producers is most likely to support a survivable food chain, further enhancing your ability to make quick and effective decisions in the game.

The game intricately simulates a natural food chain, requiring you to strategically link species as either food sources or predators to create a sustainable ecosystem. Here’s a breakdown of how this works and how you can effectively create a sustainable chain:

1. Species interactions:

Each species in the game has relationships with others – as either a predator or a food source. For instance, a Blue Jay might be preyed upon by a Shark, while it feeds on Yellow Fish.

2. Caloric dynamics:

Every species is assigned specific caloric values: calories provided and calories needed.These caloric values are crucial in determining which species from the available 13 you should select to form your final ecosystem of 8. The moment you select your 3 producers, you are only left with choosing 5 animals out of 10. A much easier task than before.

3. Eating rules and algorithm to test your sustainability:

The game outlines essential rules about the feeding mechanism. The key rules include:

  • The species with the highest ‘calories provided’ value eats first.
  • It consumes the species offering the highest caloric value as a food source. In case of ties, it splits its consumption 50/50.
  • Consumption reduces the ‘calories provided’ of the prey by the amount ‘calories needed’ by the predator. A species needs a non-zero ‘calories provided’ to survive, and all its ‘calories needed’ should be zero after feeding.
  • After the first species feeds, the next one with the highest ‘calories provided’ follows suit, and the process repeats.

4. Ensuring chain sustainability:

It’s crucial to ensure each animal receives adequate calories from its food source and that no species depletes its ‘calories provided’ to zero. If a species either doesn’t receive enough calories or depletes its own, the chain becomes unsustainable, leading to failure in the game.

To quickly and efficiently establish a sustainable chain, you must:

  • Carefully analyze caloric values : Assess the ‘calories provided’ and ‘calories needed’ for each species to determine the feeding order and the sustainability of the chain.
  • Ensure continuity : Verify that every animal in your chain is connected and that there’s continuity in the food chain.
  • Balance the ecosystem : Maintain a balance where no species runs out of calories while ensuring each one’s dietary needs are met.

By following these steps and paying close attention to the caloric requirements and relationships between species as well as the eating rules algorithm about who eats first, second, third, etc., you can successfully create a sustainable food chain within way less than the allotted time in the McKinsey Ecosystem Game.

Once you have successfully identified the 8 species for your ecosystem, the next critical step is to choose an appropriate location for this ecosystem on the island.

What you need to know when approaching the location selection

How to Approach the Location Selection:

  • Navigating the map : The game presents you with a map where you can use your cursor to explore different potential locations for your ecosystem.
  • Analyzing location conditions : Each location on the map comes with seven different environmental conditions. However, not all of these conditions are relevant to your task. Your focus should be on the variables that you identified as important in the previous step while choosing your species, usually just 2 to 4 variables.
  • Identifying relevant variables : Recall the parameters you noted earlier for each species. These are the variables you need to match in the location selection process.
  • Utilizing the interface for matching : As you hover your cursor over different locations on the map, you can refer to the top-right menu on your screen. This menu displays the environmental variables at the current cursor position. You need to check if they are all within the required range for your selected species. If you approach this effectively, you can do this in less than 1 minute.

By methodically checking these variables and finding a location that aligns with the environmental requirements of your 8 species, you can complete this task efficiently. Proper selection of species in the first task significantly simplifies this process, allowing you to quickly identify a suitable location without getting distracted by irrelevant data.

This streamlined approach helps in ensuring that your ecosystem is not only sustainable in terms of species interdependence but also well-suited to the chosen location’s environmental conditions.

Red Rock Study Simulation

the image is a cover for the mckinsey redrock game

The Red Rock Study game has become a staple in the McKinsey Solve Game lineup, replacing the Plant Defense game for all candidates since March 2023. This game marks a shift towards a more conventional analysis and problem-solving context, reminiscent of the approach used in older BCG Online Cases. Despite the game casting you in the role of a researcher, the tasks closely resemble those undertaken by a typical consultant. The game is designed to assess abilities like information processing, data collection, mathematical calculations (involving growth rates, averages, percentages), and the interpretation of exhibits.

The game is divided into two main sections, the Study Section and the Case Section, each with distinct tasks and objectives, and you have a total of 35 minutes to navigate through them.

The Study Section

The Study Section consists of a three-step process:

Investigation Stage : Here, you are presented with an objective for your research, accompanied by data in various formats such as text, tables, and charts. Your primary task in this stage is to identify and gather insightful, relevant data, which you then record in your on-screen research journal.

  • Objective and data collection : You start by receiving text, graphs, and tables, along with a specific objective for your research. Your task is to sift through this information.
  • Selective data gathering : Key information can be dragged and dropped into your Research Journal, located on the right-hand side of the screen. It’s important to discern which data is relevant and avoid unnecessary information.
  • Preparation for analysis : Once you’ve collected all the relevant information, you proceed to the Analysis phase.

Analysis Stage : This stage involves answering three mathematical questions related to your research objective. You have access to an on-screen calculator for computations, but the main challenge lies in developing the correct approach and filtering the right data. There’s flexibility to move back and forth between the Investigation and Analysis stages, allowing you to retrieve any additional information you might need.

  • Mathematical questions : This phase presents 3 to 5 math questions, usually pertaining to different groups of animals.
  • Using tools : An embedded calculator is provided for calculations. The tricky part is setting up the right calculations and equations. You’ll also refer back to the data collected in your Research Journal to answer these questions.
  • Drag-and-drop feature: Also here, you need to drag and drop information to create your calculations and move your answers around.

Report Stage : The final stage requires you to synthesize your findings by filling the blanks of a report and present them effectively. This latter involves summarizing your research and choosing an appropriate chart to visually represent your supporting data.

  • Combination of Written and Visual Tasks : This phase includes a written section and a visual representation task.
  • Written part : Answer questions based on your findings from the Analysis phase by filling the blanks of a report text.
  • Visual part : Select and create a graph to effectively represent your analysis results.

After completing the Report, you transition to the final section of the Red Rock Study game, known as the Cases Phase.

The Case Section

In March 2023, McKinsey introduced a significant update to the Red Rock Study assessment, adding a new mini case component. This new section includes 6 to 10 quantitative reasoning questions, each associated with the context of the study segment, yet distinct in terms of data and information.

The introduction of the mini case has notably increased the assessment’s complexity. Candidates now face the dual challenge of completing both the study part and the case questions within a consolidated time frame of 35 minutes. This is a marked change from the previous format, where the time limit was solely allocated to the study segment. The recommended approach is to divide the time equally between the two parts, emphasizing efficient time management.

The quantitative reasoning questions in the mini case require strong quantitative and analytical skills. Candidates must swiftly interpret information presented in various charts and textual sources, perform calculations accurately, and derive correct answers. The added time pressure necessitates not only quick thinking but also precision in analysis and calculations.

Given these heightened demands, thorough preparation and practice become even more crucial. Familiarizing oneself with quick data interpretation and ways to set up calculation under time constraints is highly advantageous. Such preparation mirrors the real-world demands of consulting, where professionals are often required to process complex information rapidly and make informed decisions under pressure.

We cover this game and 6 practice tests in more detail in our McKinsey Solve Game Guide .

the image shows mckinsey red rock practice tests

Each phase of the Red Rock game is designed to mimic real-world consulting tasks, testing your ability to process information, perform quantitative analysis, and present findings coherently. The game challenges you to filter through data, apply mathematical concepts, and communicate results clearly, skills that are essential in a consulting environment. By understanding the structure and requirements of each phase, you can better prepare and strategically navigate through this component of the McKinsey Solve Game.

The Red Rock game, with its business-like analysis and structured approach to problem-solving, tests a range of skills that are directly applicable to the world of consulting. It challenges candidates to not only understand and interpret data but also to apply it effectively in a simulated research context. The game’s emphasis on analytical thinking, data interpretation, and effective communication of findings mirrors the skills required for a successful career in consulting.

The integration of the Red Rock Study game into the McKinsey Solve Game lineup signifies a notable shift in McKinsey’s approach to candidate assessment. This change not only diverges from McKinsey’s previous game-based assessment strategies but also aligns more closely with the types of evaluations commonly used by other consulting firms.

In that sense, it is much more a problem-solving test rather than a game.

The demand for information for this game was so big, that we dedicated a full-length article to it here.

Creating a Strategy for the Red Rock

Developing an effective strategy for the Red Rock Study game is essential to successfully navigate its complexities. We have crafted a four-step approach to optimize your performance in the game:

1. Understanding the Objective (Investigation Stage)

  • Interpretation is key : Begin by carefully reading and interpreting the objective of the game. Understanding what is expected of you is crucial in setting the right direction for your investigation.
  • Clarity of goals : Ensure you have a clear grasp of what the game is asking you to accomplish. This understanding will guide your decisions and actions throughout the different stages of the game.

2. Identifying Relevant Data (Investigation Stage)

  • Data selection : Amidst the plethora of information provided, focus on identifying and prioritizing data that is directly relevant to the game’s objective.
  • Efficient data gathering : Aim to distinguish between essential information and potential distractors. Collecting the right data in your Research Journal will streamline your analysis process.

3. Conducting the Analysis (Analysis Stage)

  • Strategic analysis : Set up and execute your analysis and calculations. This step involves applying the data you’ve gathered to solve the problems posed in the game.
  • Accuracy in calculations : Use the provided tools, such as the on-screen calculator, efficiently to ensure your calculations are accurate and relevant to the task at hand.

4. Visualizing the Findings (Report Stage)

  • Effective presentation : Once your analysis is complete, the next step is to report your findings and visualize your data effectively in the Report Stage.
  • Choosing the right format : Select a graph or chart that best represents your findings, making sure it aligns with the narrative of your analysis.

By following these steps, you can create a focused approach to the Red Rock Study game. This strategy helps in navigating the game’s challenges methodically, ensuring that each stage is tackled with precision and clarity. Preparation, practice, and a clear understanding of each stage’s requirements are key to mastering this McKinsey assessment.

For the Red Rock Case Section (and the Study section actually as well), developing a strong proficiency in quantitative reasoning is crucial. This part of the assessment requires you to not only understand and analyze numerical data but also to set up and solve equations swiftly and effectively.

Enhancing Quantitative Reasoning Skills for the Red Rock

  • Practice with quantitative questions : Regularly engage with various types of quantitative reasoning questions. This practice will help you become familiar with different question formats and data interpretation challenges.
  • Efficient equation setup : Focus on setting up equations quickly. This skill is crucial for solving the mathematical problems presented in the game efficiently. Read up on percentages, growth rates, averages – the 3 most common operations found in the game,
  • Speed and accuracy : Balance speed with accuracy. It’s essential to work through questions rapidly, but not at the expense of making careless errors. If you get stuck on one question for too long, move on!
  • Utilize tools effectively : Make the most of the on-screen calculator provided in the game. Familiarize yourself with its functionality to enhance your efficiency during the test.
  • Analytical thinking : Develop your ability to think analytically, particularly in interpreting charts, graphs, and tables, and in drawing conclusions from complex sets of data.
  • Mock tests and timed practice : Engage in timed practice sessions. These simulate the pressure of the actual test and help improve your time management skills.

By honing these skills, you can approach the Red Rock Case Section with greater confidence, speed, and accuracy.

The skills that are needed in this game are much closer to an actual case interview and we would recommend that you also take a look at our articles on

  • Case Interview Math
  • Case Interview Exhibit Interpretation

Be aware that the game is still relatively new and we have seen many iterative changes to new games in the past. As a result, be prepared to encounter minor variations or adaptations when you face the Red Rock simulation.

The Former Games of the McKinsey Solve Game

If you are pressed for time, you can skip this section. If anything changes in the Solve Game lineup, we will adjust this article and our preparation package accordingly.

Plant Defense

The image shows the McKinsey Plant Defense game which is part of the Solve Game

In this scenario, which was active until March 2023, you need to defend a plant species from invaders using several tools at your disposal in a static, round-based tower defense-style game. The tools consist of barriers that slow down invaders and predators that damage and eradicate them.

In this game, which, for the majority of candidates, is a bit more challenging than the first, you need to defend a plant at the center of a map from an invasive species for as long as possible. This scenario is broken down into 3 rounds. Each round lasts between 8 to 12 minutes, presenting a slight variation of the game with increasing complexity and an increase in the map size. For each round, invaders spawn in several turns per map.

Each round is divided into two parts.

In the first part, you can actively manage your defense strategy in order to react to new invaders that spawn every 3 to 5 turns. You can manage 15 turns by initially placing your defense units on the map, adjusting their positioning after every turn, and selecting new defense units every 5 turns.

Your goal is to have the plants survive each of these increasingly difficult turns. You can slow the invaders down so that they do not arrive at your plant within the number of turns or eliminate them fully before they would do so.

In the second part, the endgame, you are no longer able to change your strategy and the placement of your defense units. The game fast-forwards until your plant is defeated. Depending on the quality of your last placement strategy it might take the invaders many turns to kill the plant, ideally more than 30.

Your goal is to optimize for the plant to survive as many turns as possible. Your product score is the direct result of the turns survived, while your process score focuses on how well you adjust to changing behaviors of attackers and how much you can learn and adapt over the course of the turns and over the course of the 3 rounds.

In order to do this, you need to choose certain animals that eat the invasive species and natural barriers/ terrain to slow them down and block them, in a static and turn-based environment, contrary to most other tower defense games that are dynamic.

You are presented with information about what each tool such as animals or geographical/terrain barriers can do, e.g., how many invasive species an animal can kill in a given time or how much a forest can slow the invaders down. These animals have different stats in terms of their reach/sphere of influence (shown as squares) as well as the damage that they are able to inflict on the invaders.

For instance, there could be a dog and an eagle as animals. The eagle has a large radius and inflicts less damage whereas the dog has high damage but a smaller range of effectiveness (e.g., one square only). Some animals have a large radius and high damage (usually during the last game). The damage inflicted might also differ depending on the type of invader. The barriers are elements such as mountains, rocks, and forests. Mountains block invaders and make them change their pathway toward the plant (ideally make the pathway longer). Rocks, and forests slow invaders down (different effectiveness for different invaders)

The invaders will start attacking the plants once they reach it in the middle and the game ends.

While initially, you will be able to kill the invaders, they will show up in greater numbers in each consecutive wave and it is possible that you will be defeated. This is not, per se, a bad thing since it will die eventually in the fast-forward mode of the game. Keep the plant alive for as long as possible.

The aim is to defend the plant in the center for as long as possible, hence, to kill all invaders before they reach the plant. It is very important to make use of both defending animals and barriers to unlock their synergistic effects and keep the invaders as long as possible in the sphere of influence of the animals.

Use the untimed tutorial to think about the most effective combinations and layouts of the tools before starting the game. Prepare using video games in the tower defense niche to train yourself for this scenario. The key in this game is to show adaptability by being able to learn quickly, and improve your strategies and reactions with each turn and with each game.

Creating a strategy

Let’s again break down your approach into several steps.

  • Familiarize yourself with the map
  • Create your initial strategy
  • Focus on new invaders first
  • Secure the plant from future attacks
  • Adjust your strategy as the game evolves

We discuss each step, variation, and successful start-to-finish strategies in full in our McKinsey Solve Game Guide , which has been co-created with the help of tower defense game designers, who developed games for iOS and Android.

Disease Identification

The image is a screenshot of the Imbellus McKinsey disease identification game

It seems that McKinsey reintroduced a game briefly that was already present in the beta testing stages of the PSG, with a slight variation. It replaced the tower defense game for roughly 5% of the candidates over the course of late 2020 and early 2021. By June 2021, it appears that the game never really made it out of the testing stage and we have not heard about any reappearance in 2022. Nonetheless, let’s look into them since we cannot guarantee that they won’t come back in one form or another.

As a player, you are tasked with  identifying which animals on the map will be infected by a given disease . The nature of the disease is not important. What is important is to identify patterns of the disease and ultimately identify which animals would be infected in the next turn.

The game has many animals on the map. There are also three time periods, which they call Time 1, Time 2, and Time 3. In Time 1, a small subset of animals is already infected. When you click on Time 2, that same map will show which additional animals got infected. Your goal is to identify which animals will get infected in Time 3. The approach to this game is relatively simple:

  • Figure out what the key variables are that could give a hint about the disease progression.
  • Create an array of different filters and look at them through different points in time to see the changes in the animal population.
  • Move to time 3 and select the next animals that will be affected by the disease based on your tested hypotheses from step 2 (e.g., if you know that all animals above 6 years are affected by the disease and in time 3 there are 20 new animals that are above 6 years of age, select them)

Contrary to the old version which was used in beta tests before the game was actually launched, you do not need to provide a remedy or a treatment plan.

Disaster Identification

Another game has not made a new appearance since 2021. In this game, candidates had to figure out the nature of a natural disaster impacting an animal population and then place the animals on another area of the map so that the most number of animals survive. The mechanics are similar to the ecosystem game.

In this game, you can display three things, a map, species, and a list of events. You can tackle the game in 4 steps:

  • Identify what event has happened in an area (a natural disaster such as a tornado or a flood) by combining information from an event description with variables on the screen.
  • Identify dominant ranges to move the animals to an area that is best suited for their survival.
  • Select the location by clicking on it and check for the relevant ranges you identified before. Prioritize characteristics that allow for the greatest number of animals to survive.
  • Sanity check your selection in a similar manner as for the ecosystem game.

Migration Planning

the image depicts the mckinsey imbellus migration management game

A new game was briefly tested in 2022. We call it the Migration Planning game.

Your task is to plan the migration of 30 to 50 animals from a starting position to an endpoint on a map by selecting the best route out of several alternatives.

You have to solve up to 15 different scenarios within 35 to 40 minutes. Each scenario consists of 3 to 5 turns that have you decide on the next step of your route. In turn 1 you select the first step on your route, in turn, 2, the second leg, and so on until you reach the desired endpoint.

You start with a given number of animals and a specific set of resources (consumables such as food or water). With each turn of the game, a predetermined number of animals will die, and resources will be reduced by a specific amount, depending on your selected route. Alternatively, you can also select intermediate points on your route that will replenish and multiply existing resources as well as collect additional animals along the way.

The objective of the game is two-fold: First, you need to ensure that the highest number of animals survive until you reach the destination. Second, you need to arrive at the endpoint with some of the resources preserved as well. As said before, there are up to 15 different scenarios with 3 to 5 turns each, which leads to 45 to 75 unique decisions you must make along the way.

Organize the migration of 30 to 50 animals from one spot to the next by managing resources and animals from start to finish in 3 to 5 turns. Select the most optimal route to preserve resources and animals along the way and pass 15 rounds in total.

Map the routes on a piece of paper or in an Excel sheet.

  • Write down each available route
  • Calculate the outcome variables for resources and animals for every route
  • Select the route where most animals survive and resource requirements are met

We provide you with a specific table and approach that you can use to create your strategy for each route in our McKinsey Solve Game Guide .

Preparing for the McKinsey Solve Game

Addressing the critical question: Is it beneficial to prepare for the Imbellus test, despite official advisories suggesting otherwise? The answer is a resounding yes.

Why preparation is crucial:

  • Significant impact on outcomes : Our data indicates that preparation can dramatically increase your chances of success, from a 20% to an 80% success rate. This is even more pronounced than with the old PST, as the games in the Imbellus are more predictable than a traditional pen-and-paper test.
  • Consequences of failure : Failing the Imbellus test results in a 2-year ban from reapplying to McKinsey (1 year for internships). Post-ban, you must demonstrate substantial improvements in your consulting cover letter and resume .
  • Learnable skills : While McKinsey suggests that the games can’t be prepared for, Imbellus emphasizes that their games assess higher-order thinking skills, which are typically acquired through education, training, and experience.
  • Gaming experience matters : Familiarity with computer games and digital environments can provide an advantage in a video game-based assessment. This introduces a different kind of bias in candidate evaluation, which can be mitigated by employing effective strategies.

Understanding the games and their objectives is key to effective preparation. Knowing what each game assesses, and the skills it targets, allows you to focus your preparation on enhancing those specific abilities. Additionally, familiarizing yourself with the gaming environment and practicing similar types of games can improve your comfort level and performance during the assessment.

While McKinsey advises candidates that preparation for the Imbellus game is neither necessary nor feasible, our extensive feedback collection from over 500 candidates we talked to suggests otherwise. In fact, thorough preparation can significantly enhance performance in the game’s various scenarios.

To aid candidates, we have meticulously analyzed the test, consulted with game design experts, and applied science-backed methods to develop a comprehensive guide detailing the game’s mechanics. Here are some overarching strategies to lay the groundwork for your preparation:

Imbellus Game Practice

Train the key skills that are being assessed  by Imbellus. Playing logic games, mobile games, and tower defense games with similar themes can be beneficial to train these areas specifically. While these games will differ somewhat in their user interface, objectives, and mechanics they still train your skills, make you think about potential strategies, and just get you in the habit of interacting with a gamified environment. If you have sufficient time before taking the Imbellus, try out some of the games below to practice the Imbellus gameplay.

Games for the Ecosystem and Migration Planning

  • Plague Inc. – if you have limited time, focus on this game
  • Cities: Skylines

Games for the Plant Defense

  • Tower Duel – if you have limited time, focus on this game
  • Kingdom Rush
  • Plants vs. Zombies

Preparation for the Red Rock

Focusing on quantitative reasoning tests is an excellent way to prepare for the McKinsey Solve Game, particularly for the Red Rock Study section. Here are some effective ways to enhance your quantitative reasoning skills:

  • GMAT Quantitative Reasoning : The quantitative reasoning sections of the GMAT are a great resource to start with. They offer a wide range of problems that can improve your ability to analyze data, perform calculations, and make logical deductions under time constraints.
  • Red Rock Practice Tests : We have developed specialized practice tests specifically designed to mirror the challenges you will face in the Red Rock Study game. These tests are tailored to give you a realistic experience of what to expect during the actual game.
  • Additional Quantitative Reasoning Resources : For those seeking more extensive practice in quantitative reasoning, we provide a comprehensive question bank in our Bain SOVA Guide . This bank contains hundreds of questions similar to those you might encounter in the game.
  • Case Math Mastery Package : This package is another valuable resource that focuses on developing your case math skills. It is particularly useful for candidates who want to strengthen their ability to handle numerical data and complex calculations efficiently.

By incorporating these resources into your preparation plan, you can significantly improve your quantitative reasoning abilities. This will not only aid you in the Red Rock Study game but also enhance your overall problem-solving skills, which are crucial for a successful case interview performance.

General Preparation Advice

Enhancing your performance in the McKinsey Imbellus Game involves more than just playing similar games. It requires a multifaceted approach that focuses on developing key skills and strategies.

Approach each decision methodically and develop a plan for tackling each decision. A step-by-step decision-making process helps in making more deliberate and thoughtful choices, increasing the likelihood of selecting the most effective solution.

  • Identify the decision : Clearly define what you need to decide. Understand the primary goal and any additional objectives.
  • Gather information : Collect relevant information needed for the decision. Identify the best sources and methods for acquiring this data.
  • Identify alternatives : As you gather information, recognize various possible courses of action.
  • Weigh the evidence : Consider the potential outcomes of each alternative based on the information you have.
  • Choose among alternatives : Select the option that seems best after weighing all the evidence.
  • Review your decision : Reflect on the decision’s outcome and assess if it addressed your initial goal.

Learn to take proper notes and document your observations about each scenario’s mechanics. Using tools like Excel templates can help structure your thoughts and find solutions more efficiently.

Develop skills in structuring, analyzing, and synthesizing complex issues. Combine logical thinking with creativity to formulate effective recommendations.

Adopt a hypothesis-driven mindset. Start each game with one or more hypotheses, then test and refine them as you progress. This approach helps in focusing your analysis and quickly deriving recommendations.

Visualize processes and relationships. Practice creating quick sketches to visualize situations, processes, and relationships. This skill is particularly useful in unfamiliar scenarios and helps in breaking down complex issues.

Practice estimations and setting up equations. Engage in exercises that improve your quick math skills. These are essential in all games, from calculating calorie budgets in the ecosystem game to determining damage points and optimal routes in others. Become familiar again with basic equations, ratios, growth rates, and averages.

Test-taking Tips and Advice

To excel in the McKinsey Imbellus Game and enhance your test performance, consider the following tips and advice. These guidelines are designed to help you navigate the unique challenges of the game effectively:

Avoid replicating solutions : Each test taker encounters unique scenarios and numbers in the Imbellus game. The games are set in ecological contexts, making them accessible to all backgrounds, but with thousands of possible variations, no two experiences are identical. Focus on your strategy and process rather than trying to replicate specific results.

Make decisions with incomplete information and practice 80/20 decision making : Often, you won’t have time to reach the perfect answer in the ecosystem game. Aim for a good answer that demonstrates a sound problem-solving strategy and fulfills the objectives. Avoid getting lost in excessive details and consider writing down various outcomes to test your ideas.

Read instructions thoroughly and understand the tasks : With the increasing variety in game scenarios, it’s crucial to read and comprehend all instructions. A missed detail can make your approach invalid. Ensure clarity on your objectives before proceeding.

Ensure a stable test environment andcheck your setup : If taking the test from home, ensure a reliable internet connection and a fully charged computer. Some candidates have reported high CPU usage; consider using a more powerful system if needed. Remember, you can always contact the 24/7 Imbellus service center for any issues during the test.

Monitor time closely and manage it well : The complexity and depth of the games can make it easy to lose track of time. Keep a close eye on the time, aiming to allocate the right amount of time for each step of the way (e.g., 15 minutes for the ecosystem species and 2 minutes). The progress bar will help you monitor remaining time. Have pre-determined time goals that you execute if they are met (e.g., only taking 2 minutes to think about a quantitative reasoning question in the Red Rock).

Elevate Your Score with Our Comprehensive Preparation Package

Unlock your potential to ace the Imbellus game with our comprehensive Solve Game preparation package. It comes with

  • a 146-page guidebook
  • an Excel Solver for the Ecosystem Creation
  • a 14-part video series
  • 6 Red Rock full-length practice tests
  • a McKinsey case interview and PEI interview primer

The package gives you the definite edge in your preparation and test-taking, detailing winning strategies for the Ecosystem in less than 20 minutes and ample practice opportunities for the Red Rock Game. Gain immediate access to PDFs, Excel tools, templates, and video content, ensuring you’re up-to-date with McKinsey’s evolving assessment criteria.

Since November 2019, we’ve led with first-hand information, starting with interviews from early test-takers and experts. Our ongoing customer interviews have built a vast database, aiding over 8500 candidates in 70+ countries. We regularly update our guide, offering you the latest insights. On top of that, our team, comprised of ex-McKinsey consultants and interviewers, brings deep insights into McKinsey’s evaluation criteria, surpassing the generic advice found elsewhere.

Six pillars of our strategy:

  • Understanding McKinsey’s criteria : As former McKinsey consultants and interviewers, we grasp what McKinsey seeks in their next-gen consultants.
  • In-depth scenario analysis : Learn the nuances of user interfaces and gameplay mechanics.
  • Skill development : We cover the core skills with actionable advice and practice resources.
  • Effective test strategies and shortcuts : Benefit from proven strategies and tools, derived from successful candidate experiences, meticulously refined over 4 years.
  • Efficient preparation hacks : Accelerate your readiness with our targeted tips and techniques.
  • Low-cost and accessibility: We are a small team and selling directly to consumer without an intermediary. Hence, we are able to offer this product at a much lower price than every competitor.

Additional benefits:

  • Exclusive support : Join our McKinsey applicants’ inner circle for 24-hour support on all consulting interview questions. Get access to the world’s leading McKinsey interview coach, who has helped generate almost 200 McKinsey offers for coaching clients in 3 years.
  • Regular updates : Stay ahead with our constant updates and a free 1-year access guarantee.
  • Free McKinsey interview primer : Get a 14-page primer with essential case and PEI preparation tips.

Our credentials:

  • Extensive reach : Assisted 8500+ students from 70+ countries over the last 4 years.
  • Rich experience : Built on 500+ test-taker interviews, expert inputs, and McKinsey know-how, 100% proprietary information
  • Comprehensive materials : Includes a 146-page guide, automated Excel Solver, 14 concise videos to get you up to speed quickly, and 6 full-length Red Rock practice tests.

Currently, the package leads to a 87% success rate with our clients ( based on customer feedback from Sep – Nov 2023 )

Latest update: December 2023 (includes the new Red Rock Simulation variation and 6 practice tests)

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McKinsey Solve Game Guide (Imbellus) 19th Edition

SALE: $89 / $54


If you have taken the Imbellus game and want to share your experience and Imbellus game tips or have further questions, please let us know in the comment section below!

15 Responses

Hello, thank you for this introduction. I would like to ask about one thing. In the ecosystem… From all 8 species – they have to survive? Or they can be eaten by predators? I understand how to create the food chain, but still…if you create a food chain and the species do not replicate, they will be eaten by predators…

Dear Lenka, All species in the food chain (animals and plants) need to survive. The sum of the calories provided by a species – the sum of calories needed for the predator species should always be positive. Cheers, Florian

hi Florian,

I only have 3 hours before the PSG is due, is it possible or useful to buy the guide given such a short time limit? Thank you

Dear Angelina, 3 hours would be enough to read through the strategy section, watch the videos and familiarize yourself with the Excel. While not ideal and we receommend more time to practice, it would still make sense. Cheers, Florian

[…] using digital badges to recognise learning and, for example, the consultant company McKinsey uses a game during its recruitment process,” adds Nikoletta-Zampeta […]

Hello Florian Daniel or Colleague, I am very pleasantly surprised to see this guide that you have masterfully complied. Having tips from insiders is such a confidence boost! I purchased this pack without hesitation and am hoping to try it out before investing in the comprehensive 6h coaching program. Nonetheless, I wonder if you can email me back by helping me with downloading the actual guide? I encountered a technical issue whereby I completed my payment on my phone, but it became impossible to download it via my laptop. I am very worried as the deadline of the test is approaching so could you please get back to me asap?

Many Thanks Aspiring Consultant

I have just sent you your documents, which also contain access to the video program.

Please let me know if I can assist further.

Kind regards, Florian

Hi, how long would you suggest I prepare for the McKinsey digital assessment test after purchasing the digital assessment guide? 2 weeks? 4 weeks?

Hi Emmanuel,

We have candidates that prepare between 2 days and 1 month. The shorter your preparation time, the more your focus should be on learning the proven strategies we outline in our guide (so that you can implement them properly on the game day) and go through and practice the most effective and important tools we provide you with to quickly raise your skill levels.

Obviously, when you have more time on your hands, you can prepare in a much more relaxed way and go deeper with all our exercises and tools. Generally, I would say that 2 weeks is the sweet spot we have seen with our candidates and it is rare for them to fail after they have gone through all exercises and tools, practiced the preparation tips, and have our game-plan and strategies internalized over this time period.

4 weeks would give you enough time to prepare without a rush, and in parallel to the case interview practice. In any case, should something change in the game between your purchase and the testing date, we will send you a new version of the guide and the videos free of charge!

Let me know if you have any further questions!

All the best for your preparation and your application.

I heard that there are also other games that could be part of the PSG like predicting and preventing an environmental disaster. Are you sure that there are ‘only’ the 2 two games you describe?

Hi Luiz, we talk briefly about these potential other scenarios in our Problem Solving Game Guide. Be aware that they were used during the trial stages in 2018/19 only and none of our more than 700 customers has reported on them pro-actively. From the 80+ customers we interviewed since November 2019, all went solely through the ecosystem game and the tower defense-like game. In the ecosystem game, recent candidates report having done the mountain ridge scenario and not the reef (even though this has no impact on the actual gameplay).

Hi, how do I know if I passed the ecosystem simulation task?

Hi Patricia, on an aggregate level the game looks at both your product score (did you produce a good outcome?) and your process score (did you perform well under stress while working towards the outcome?).

In order to pass the ecosystem simulation, ideally, you reach the threshold McKinsey set for both scores (which is unknown). For the product score, you should be able to test your hypotheses during the game and see if your food chain is actually sustainable and works out. However, for the process score, you can only take a guess. McKinsey and Imbellus record every movement of your mouse, every click, as well as how long you pause, go back and forth in the menus, etc. In short, the more you have worked in a calm and collected manner towards selecting your food chain, the higher the chances to reach a solid process score.

Hi, I have one question, Is McKinsey problem-solving game material included in Mc Kinsey program?

Hi Federico, do you mean the Video Academy or the Interview coaching?

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mckinsey problem solving test

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 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 500 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 3.5 years. His books “The 1%: Conquer Your Consulting Case Interview” and “Consulting Career Secrets” are available via Amazon.

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McKinsey Solve Game: Newest Updates & Guide (2024)

Check out the only, fully-playable McKinsey Solve Test (Problem-Solving Game) Simulation in the entire market, with the new 2024 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 problem solving test

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 problem solving test

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 problem solving test

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 problem solving test

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 problem solving test

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

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

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

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

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

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How to Successfully Pass the McKinsey Problem Solving Test?

If you would like to start a career in strategic consultancy, McKinsey & Company is one of the best options. This company has over 14, 000 employees in offices located throughout the world. is one of the top management consulting firms.

So, getting in can be very competitive. More than 80% of applicants are rejected by McKinsey based on their online aptitude tests. That is why it is important that you do your best at every step of the recruitment process.

In this article, we cover how to successfully pass McKinsey’s problem-solving test, online aptitude tests, the selection process, assessment center, and interviews.

Table of Contents

About McKinsey PST

The McKinsey Problem Solving Test (McKinsey PST) aims to test your logical thinking and problem-solving skills. It consists of a math computation, data interpretation, and critical reasoning test that are used by McKinsey to select the best candidates. The successful candidates will move onto the first round of case interviews.

The PST test is what differentiates the McKinsey recruiting process from its competitors. Another distinctive element is the Personal Experience Interview, or PEI. The McKinsey PST is one of the most challenging tests that you will come across in your life. It tests a wide range of skills and pushes you to work under tight time constraints.

McKinsey uses a PST because to be successful in consulting, there is a specific set of numerical computation and critical thinking skills that are required. It tests skills that you will be using on a daily basis as a consultant.

McKinsey PST Format

The McKinsey Problem Solving Test consists of 26 multiple-choice questions that need to be answered on paper. You have a 60 minute time limit. That gives you about two minutes per question. Most candidates fail because they run out of time.

The 26 questions are divided between three business cases. Each case study assesses how you would perform in the different stages of a consulting project: client interaction, problem solving, analytical work, problem definition, and implementation.

Every case study is based on real issues that consultants will be faced with in the field. This includes issues like profitability, market entry, or operational development.

In certain offices, non-native English speakers are given an extra 10 to 15 minutes to complete the test. You can find out from your McKinsey HR representative if you are allowed this additional time or not. No business knowledge is necessary to take the test. However, being familiar with common business knowledge terms is helpful.

You can use a pencil, a pen, and paper. You are not allowed to use a calculator or any commuting device during the PST test. You need to ensure that you have developed and practised the necessary skillset, especially time-saving skills.

McKinsey PST Questions

In the McKinsey Problem Solving Test, you will see a graph, chart, or table containing numerical data. This diagram will be followed by descriptive text about a company or industry.

The McKinsey PST test consists of 40% math word problems, 30% data interpretation, and 30% reading comprehension. You will be required to answer four to five questions that refer to the diagram. The two most challenging questions are math word problems and data interpretation.

McKinsey Math Word Problems

In math word problems, you will be given data in table X and required to calculate A, B, or C. These calculations may be profit margins. It may be calculating growth rates or the difference in sales from 2021 to 2019 for three different companies.

The point of these math word problems is to provide you with raw data presented in a text paragraph and assess whether you can figure out the math equation required to solve the problem.

Usually, the actual math word problem isn’t difficult to solve. It’s just addition, subtraction, multiplication, division, or growth rate calculations. The real challenge with the math word problems is time. The most common reason for errors in a math word problem is misunderstanding, misreading, or misinterpreting the data presented or the question being asked.

McKinsey Data Interpretation

The other tough PST test question is data interpretation. You will be presented with a chart or table with data and required to determine which variation of the question is correct. Some variations include: definitively correct, could be correct but you can’t be 100% certain, and definitely wrong.

You will need to use math to answer these questions, but they are not as computation-intensive as math word problems. They focus on assessing your logic and critical reasoning skills. You need to be able to examine the data and differentiate between a factual conclusion and a hypothesis suggested.

One common challenge is to quickly find information about different topics.

McKinsey Reading Comprehension

The last part of the McKinsey PST is a reading comprehension test. This section tests your ability to draw a conclusion based on a paragraph of text. This is similar to data interpretation, but it does not involve math. They test your ability to understand a situation based on the information provided. And then select the statement that is the most relevant.

You will encounter three types of answers: false conclusions, true conclusions, and unproven conclusions. With false conclusions, the answer will either be wrong or false. Even if the conclusion seems rational, the outcome may be incorrect.

True conclusions are statements that are logical and draw the correct conclusion. Unproven conclusions are those where the logic may be correct and the conclusion may seem rational. But they cannot be completely supported by the facts presented in the diagram or text.

McKinsey PST Passing Strategies

  • First skim through the questions to understand what is being asked of you, and then read the table or chart with the data. This allows you to see what you should be paying attention to while you examine the data or read the text.
  • Answer ALL questions on the PST test as there are no penalties for incorrect answers. If you are running out of time, it is better to guess an answer than leave it blank.
  • Fill in the answer sheet as you progress. This may be obvious, but under stress, some candidates forget to note down their answers throughout the test.
  • Carefully read through the questions and text descriptions.
  • Provide what the questions are literally asking.
  • If you need to sharpen your math computation skills, ensure you practise your math accuracy and speed. You won’t have enough time to double check all your computations. The more certain you are about your math skills, the more time you will have to answer all the other questions.
  • For the data interpretation questions, be cautious of the multiple choice questions that seem consistent with the data but are not 100% supported by the data. The best way to tackle these questions is to instantly eliminate options that are obviously wrong. Then carefully examine the other options.
  • In the data interpretation questions, you need to ask yourself whether the conclusion is correct under all scenarios. Although the conclusion may be true under the most common scenario, it doesn’t mean it is true under every scenario.
  • Always remember that the conclusion that is true most of the time is not the same as the conclusion that is true every time.
  • Wear a watch to stay track of time. Don’t assume that every testing room has a clock.

How to Prepare for the McKinsey PST

McKinsey and Company

There are three different ways you can prepare for the McKinsey Problem Solving Test. JobTestPrep is a platform that offers resources, professional guides, and practise tests that can help you successfully pass the McKinsey PST.

The first prep tip for the PST is to practise math computations. You need to practise the accuracy and speed of your math. The test is timed, so you need to be skilled in arithmetic. Remember, the more you practice, the better you perform.

Use the selection technique to help you select calculations beforehand that are important to finding the answer.

The anchoring technique is another great time management method. When you are searching for the highest value among several potential answers, first calculate the first answer and then use that value as a benchmark as you work through others.

The second prep tip is to practise interpreting data. Be prepared to answer multi-paragraph stories with more than one diagram. Every chart is followed by four or five questions. You need to polish your ability to interpret data.

Another pro tip is to master quick percentage calculations. Growth rates and percentages are very common in the McKinsey PST test, but they require a lot of time to calculate.

Here are some useful time management tips. Firstly, with positive growth rates, the compound rate is generally underestimated. For example, we estimate 30%, but the real figure is actually 31%. On the other hand, with negative growth rates, this method will overestimate the compound value.

Another point to note is that the bigger the magnitude of the annual growth rate and the bigger the number of years for which it is applied, the less accurate this method becomes. The final prep tip is to take McKinsey PST practise tests. This will help show you what to expect on the actual test. You will find useful practise tests available on JobTestPrep .

McKinsey PST Score

McKinsey does not have an exact cut-off score for its PST. But according to research, the best estimate is that the PST has a cut-off score of 70%. This means that you need to get at least 19 out of the 26 questions correct.

Your success depends solely on your score. It does not depend on how well others perform on the test. If all the candidates in your PST session score above the cut-off score, then they will all go to the next round of interviews.

Most candidates think that scoring 70% will be sufficient to get them into the first round of interviews. However, scoring even higher can increase your chances of being hired by McKinsey.

In the final round, if you performed just as well as another candidate but only one position is available, McKinsey will choose the person who scored higher on the PST. So aim for 90% to set yourself apart from the other candidates.

McKinsey Aptitude Tests

You will need to pass three other aptitude tests, other than the McKinsey Problem Solving Test, to be a successful candidate. These are the McKinsey numerical reasoning test, verbal reasoning test, and the Excel test.

McKinsey Numerical Reasoning Test

The SHL numerical reasoning test aims to examine your arithmetic skills and ability to interpret data from diagrams. It tests your ability to examine an unfamiliar text passage and its data. You will need to solve basic math problems.

The challenge does not lie in the questions asked, but rather in the stress and time pressure. When taking the numerical reasoning test, you will only have a minute to read the question, analyse the data, and do the necessary calculations.

The most effective way to prepare for this numerical test is to take practise tests. These practise test questions will help show you the types of questions you will encounter on the actual test.

McKinsey Verbal Reasoning Test

McKinsey’s verbal reasoning test aims to assess how well you can extract information from an unfamiliar passage of text. And then use this information to determine whether the statements that follow are true, false, or impossible to say.

The verbal reasoning test evaluates your comprehension level.

The McKinsey verbal reasoning test can be more difficult for non-native English speakers. The most effective preparation tip to pass a verbal reasoning test is to take practise tests. These verbal reasoning practise test questions will help you determine how texts are created and questions are asked.

McKinsey Excel Test

Focused young african american businesswoman or student looking at laptop holding book learning, serious black woman working or studying with computer doing research or preparing for exam online

The McKinsey Excel Test is another important test that you will encounter during the examination stage of your interview process.

Regardless of what position or role you hold, you will most likely be working with Excel. That is why a lot of companies have an exam during the assessment stage. In Excel practise tests, you will find graphs and tables that display data. You will be required to answer the questions in a multiple-choice format.

McKinsey Selection Process

The McKinsey interview process can be nerve-wracking with its intensive application process. However, equipped with the right preparation, you are likely to impress McKinsey’s upper management. We have included a breakdown of what you can expect during your selection process at McKinsey.

McKinsey CV Submission

Most McKinsey applicants apply online for vacant positions directly on the McKinsey website. You will find an online application form that you can fill in and attach your CV to. You will find a list of current available positions and their locations.

McKinsey Online Assessment Tests

After completing and submitting your McKinsey online application form, you will be invited to take some of the McKinsey aptitude tests. These include numerical reasoning, verbal reasoning, Excel, and problem-solving tests.

Pre-Employment Telephone Screening

During the telephone conversation, you will be required to answer McKinsey behavioural interview questions. These questions are similar to situational judgement test questions. You will be given a scenario and asked particular questions about what you will do in that situation.

McKinsey Job Interview

The last stage in the McKinsey selection process is the in-person interview. You need to know a little about the company and show interest. The interview questions are best answered using the STAR (situation, task, action, and result) method. This is a concise way of answering and allows you to outshine your competition.

McKinsey Assessment Centre

At the McKinsey Assessment Centre event , you will attend presentations about the company.

You may be asked to participate in presentation exercises that assess your verbal and non-verbal communication skills. You will be assessed on your body language and tone to see how well you can cope with delivering professional presentations. A few tips are to stand straight, make eye contact with the audience, and don’t speak too fast.

At the McKinsey Assessment Centre, you may be invited to join a group exercise. This evaluates your ability to communicate and work together as a team to reach conclusions. You will be assessed in this exercise. So, demonstrate your strengths and share your ideas.

Show that you are open to building on someone else’s input and able to persuade others towards your options. We advise you to remain calm and speak with confidence and clarity.

You will also engage in role-playing games. These are games where you are allocated 20 minutes to work in a pair and analyse information. You and your partner will need to prepare your response.

In the second part, your interviewer will play the role of a client, and you will be the McKinsey representative. You need to demonstrate your teamwork, negotiation skills, and critical thinking.

You may also have to take further assessment tests, participate in group interviews, and have a one-on-one direct interview.

McKinsey Advanced Professional Degree Interviews

Business Lady interviewing a man

The McKinsey Advanced Professional Degree (APD) Interview consists of three interviews. The first two are 45-minute, one-on-one interviews. Both of these interviews will include a personal experience interview (PEI) and a McKinsey case interview to assess your problem-solving skills .

In the personal experience interview, you will be asked questions about the specific role you have experience in and how you contributed to the success of the company. You will also be asked questions that focus on your motivations. These are generally CV-based questions that are competency-based.

In the McKinsey case interview, you will need to answer questions based on a business case study. You need to demonstrate how you interpret the information presented, select key elements, and back your reasoning. In the end, you need to arrive at a conclusion and be able to back your decision.

The final interview will happen in the specific office you have applied to. The format of the interview varies depending on the office, so you will receive detailed information before your assessment day.

During this interview, ask your interviewer about their own experiences within McKinsey and their career path. They want to see how interested you are in McKinsey.

Who Has to Take the McKinsey PST?

Not every candidate is required to take the McKinsey Problem Solving Test. People who have extensive experience and those hired from top-tier business schools generally don’t have to take the test.

However, most people who are applying for an entry-level business analyst role will be required to take and successfully pass the McKinsey PST test to be considered.

If you are uncertain about whether you need to take the PST, you should assume that you will. You can ask the HR team at the office you are applying to for confirmation.

What Is the McKinsey PST Success Rate?

According to surveys, the common PST pass rate is 33%. The majority of candidates fail because they did not work efficiently or effectively manage their time.

One of the main contributors to failure is that candidates run out of time at the end of the test to fill in the PST answer sheet.

The McKinsey PST is a skills test that you should practise for beforehand. It is not an assessment where you can walk in and use your natural intelligence to pass. You need to be aware of the strategies and time-saving techniques before you approach the test.

McKinsey PST vs. GMAT

If you’ve taken a GMAT before, then this point can be helpful. Most candidates who have prepared for and taken the GMAT believe that the PST is the same test with a different name.

They believe the tests are very similar. However, even though the tests share a similar format, they are very different. They both require you to have good math skills. But the PST requires a less academic style of math. In the PST, you need to estimate and prioritise calculations.

You need to treat the PST as an entirely separate test and prepare for it accordingly.

Final Thoughts

If you want to get your dream job at McKinsey, then you need to prepare for the selection process. As we highlighted earlier, there are a few assessment tests and interviews that you will need to pass to be a successful candidate. You can get the practise you need with the resources available on JobTestPrep.

Sarah Duncan

Sarah is an accomplished educator, researcher and author in the field of testing and assessment. She has worked with various educational institutions and organisations to develop innovative evaluation methods and enhance student learning. Sarah has published numerous articles and books on assessment and learning. Her passion for promoting equity and fairness in the education system fuels her commitment to sharing insights and best practices with educators and policymakers around the world.

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Full Guide To the McKinsey SHL Test (Numerical & Verbal) 2024

Are you considering applying to McKinsey? JobTestPrep is ready to assist you in preparing for the crucial online McKinsey numerical and verbal SHL tests, which are essential components of the comprehensive recruitment procedure. Delve deeper to find our McKinsey-style practice tests and how we can improve your chances of securing a position at McKinsey.

In 2024, most McKinsey applicants now face the McKinsey Problem Solving Game (Imbellus). Acquire a thorough understanding of this challenging assessment with the support of our comprehensive preparation guide.

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During the recruitment process, you will come across the McKinsey Numerical and McKinsey Verbal assessments, which are conducted online and managed by SHL. To enhance your likelihood of advancing to the next stage of the hiring procedure, utilize JobTestPrep's comprehensive preparation package specifically designed for McKinsey's online assessments.

What Are the McKinsey SHL Tests?

The SHL numerical reasoning assessment comprises 18 questions that must be finished within a 25-minute timeframe. This assessment involves the presentation of numerical data in either tabular or graphical format, followed by multiple-choice questions pertaining to the data. Responding to these questions typically necessitates performing calculations involving fractions, percentages, ratios, and conversions.

Similarly, the SHL verbal reasoning assessment consists of 30 questions to be completed within 19 minutes. In this assessment, you are presented with a brief text followed by several subsequent questions. Each question presents a statement that requires swift analysis to determine its truth, falsehood, or inability to be determined based on the provided text.

In total, both the numerical and verbal assessments together consume approximately 45 minutes of your time. Be sure to allocate additional time for reading and comprehending the instructions.

Tailored Tests for You

Access customized McKinsey practice exams designed to align with the content of SHL tests. Our McKinsey test preparation program offers a comprehensive learning experience, including full-length practice tests, detailed explanations, score reports, instructional tutorials, and downloadable PDF guides. Begin your practice today to enhance your prospects of excelling in McKinsey's examinations and securing your desired job.

How to Successfully Pass Your McKinsey SHL Tests

The McKinsey SHL assessment score is norm-referenced, meaning it's evaluated in relation to other test-takers, and it follows a typical bell-curve distribution. Only those who achieve scores in the top percentile of this distribution will progress to the next phase of the recruitment process.

It's crucial to thoroughly prepare for this test because a lower assessment score can significantly diminish your chances of securing your desired job. Preparing for McKinsey's SHL aptitude tests is particularly essential as your performance is benchmarked against a group of individuals with similar educational backgrounds. Given that many candidates invest time in test preparation, it's advisable to dedicate as much practice as possible to distinguish yourself from the competition.

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To enhance your likelihood of advancing to the next phase, make use of our SHL numerical and verbal preparation package.

The Recruitment Process

Following the completion of the numerical and verbal reasoning McKinsey exams, you may also be required to undergo the McKinsey Problem Solving Test. The subsequent diagram illustrates the various stages of the recruitment process:

McKinsey's Recruitment Process

McKinsey's Recruitment Process

McKinsey Problem Solving Test (PST)

The McKinsey Problem Solving Test (PST) is a distinctive assessment designed to evaluate your proficiency in resolving business issues through deductive, inductive, and quantitative reasoning. This test consists of 26 questions, and you have a 60-minute time frame to respond to as many of these questions as possible.

Throughout the PST, you will encounter three scenarios rooted in real McKinsey client cases. Information pertaining to each scenario is presented through text, tables, and exhibits, all displayed in shaded sections distributed within the scenario. Your task is to identify the best solution to the problem presented, using only the provided information.

A portion of the questions focuses on assessing your numerical reasoning skills. These questions involve the analysis of tables and graphs, requiring you to interpret the information or perform calculations and then select the correct answer from the given options. Other questions resemble verbal reasoning inquiries and evaluate your ability to comprehend and interpret written information. Lastly, the third type of question gauges your deductive reasoning, specifically your capacity to draw conclusions based on the presented information. You can find more resources for practicing deductive reasoning tests here.

It's important to note that electronic devices, including calculators, are not allowed during the PST. Although scratch paper will not be provided, you can utilize the blank space within the test booklet for calculations and note-taking. After completing the test, the booklets will be discarded and will not impact your PST score.

The McKinsey Problem Solving Test poses a significant challenge for many candidates, as it requires assimilating a substantial amount of information and using it to arrive at accurate conclusions within a time-sensitive setting. To enhance your foundational skills essential for success in the McKinsey Problem Solving Test, you can utilize our PrepPacks™.

McKinsey Personal Experience Interview (PEI)

The McKinsey interview process comprises two key components. The first segment is the personal interview, during which your CV is thoroughly examined. Any aspects of your CV that piqued McKinsey's interest are open for discussion. For instance, if you mentioned your role in overseeing a youth group, you will be expected to provide detailed insights into your responsibilities, the challenges you encountered, and the strategies you employed to overcome them. Likewise, all your qualifications will be scrutinized, and you will be prompted to elucidate your rationale for selecting a specific academic course and how you believe it will benefit your future.

Here are some illustrative McKinsey interview questions:

1. Share an experience when you successfully resolved a conflict. 2. Describe a situation in which you effectively managed a challenging team member. 3. Why do you believe your qualifications make you an ideal fit for a consulting role? 4. Narrate an instance when you demonstrated professionalism in your work.

McKinsey Case Interview

Following the personal experience interview, the next step is the McKinsey case study interview. During this phase, you will be presented with a specific case scenario, akin to those encountered in the McKinsey Problem Solving Test. Your task is to collaboratively tackle the challenges and potential solutions with the interviewer, carefully analyze various possible courses of action, and ultimately arrive at a decision on the most optimal approach. Your performance in this interview will be assessed based on your capacity for creative problem-solving and your lateral thinking abilities. It's important to bear in mind that you will also be evaluated in the context of the company's core values, so aim to incorporate these values into your responses. If you're interested in our assistance with case study preparation and role-play exercises, please explore how we can support your preparation.

Here are some valuable pointers for the case study interview:

1. Take notes of critical information; you can use the paper provided for this purpose. 2. Don't hesitate to seek clarification if anything is unclear, as this demonstrates your commitment to making well-informed decisions rather than rushed ones. 3. Following successful completion of all the application stages, you'll be invited to additional interviews that are less formal in nature. These interviews primarily assess your cultural fit with the company.

Prepare for Success

The McKinsey hiring process can be quite demanding and definitely necessitates thorough preparation. With JobTestPrep's specialized McKinsey test prep materials, helpful tips, and comprehensive guides, you can be confident that you'll excel at each phase. JobTestPrep is dedicated to supporting your success with our meticulously crafted and designed PrepPacks™.

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How to master the seven-step problem-solving process

In this episode of the McKinsey Podcast , Simon London speaks with Charles Conn, CEO of venture-capital firm Oxford Sciences Innovation, and McKinsey senior partner Hugo Sarrazin about the complexities of different problem-solving strategies.

Podcast transcript

Simon London: Hello, and welcome to this episode of the McKinsey Podcast , with me, Simon London. What’s the number-one skill you need to succeed professionally? Salesmanship, perhaps? Or a facility with statistics? Or maybe the ability to communicate crisply and clearly? Many would argue that at the very top of the list comes problem solving: that is, the ability to think through and come up with an optimal course of action to address any complex challenge—in business, in public policy, or indeed in life.

Looked at this way, it’s no surprise that McKinsey takes problem solving very seriously, testing for it during the recruiting process and then honing it, in McKinsey consultants, through immersion in a structured seven-step method. To discuss the art of problem solving, I sat down in California with McKinsey senior partner Hugo Sarrazin and also with Charles Conn. Charles is a former McKinsey partner, entrepreneur, executive, and coauthor of the book Bulletproof Problem Solving: The One Skill That Changes Everything [John Wiley & Sons, 2018].

Charles and Hugo, welcome to the podcast. Thank you for being here.

Hugo Sarrazin: Our pleasure.

Charles Conn: It’s terrific to be here.

Simon London: Problem solving is a really interesting piece of terminology. It could mean so many different things. I have a son who’s a teenage climber. They talk about solving problems. Climbing is problem solving. Charles, when you talk about problem solving, what are you talking about?

Charles Conn: For me, problem solving is the answer to the question “What should I do?” It’s interesting when there’s uncertainty and complexity, and when it’s meaningful because there are consequences. Your son’s climbing is a perfect example. There are consequences, and it’s complicated, and there’s uncertainty—can he make that grab? I think we can apply that same frame almost at any level. You can think about questions like “What town would I like to live in?” or “Should I put solar panels on my roof?”

You might think that’s a funny thing to apply problem solving to, but in my mind it’s not fundamentally different from business problem solving, which answers the question “What should my strategy be?” Or problem solving at the policy level: “How do we combat climate change?” “Should I support the local school bond?” I think these are all part and parcel of the same type of question, “What should I do?”

I’m a big fan of structured problem solving. By following steps, we can more clearly understand what problem it is we’re solving, what are the components of the problem that we’re solving, which components are the most important ones for us to pay attention to, which analytic techniques we should apply to those, and how we can synthesize what we’ve learned back into a compelling story. That’s all it is, at its heart.

I think sometimes when people think about seven steps, they assume that there’s a rigidity to this. That’s not it at all. It’s actually to give you the scope for creativity, which often doesn’t exist when your problem solving is muddled.

Simon London: You were just talking about the seven-step process. That’s what’s written down in the book, but it’s a very McKinsey process as well. Without getting too deep into the weeds, let’s go through the steps, one by one. You were just talking about problem definition as being a particularly important thing to get right first. That’s the first step. Hugo, tell us about that.

Hugo Sarrazin: It is surprising how often people jump past this step and make a bunch of assumptions. The most powerful thing is to step back and ask the basic questions—“What are we trying to solve? What are the constraints that exist? What are the dependencies?” Let’s make those explicit and really push the thinking and defining. At McKinsey, we spend an enormous amount of time in writing that little statement, and the statement, if you’re a logic purist, is great. You debate. “Is it an ‘or’? Is it an ‘and’? What’s the action verb?” Because all these specific words help you get to the heart of what matters.

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Simon London: So this is a concise problem statement.

Hugo Sarrazin: Yeah. It’s not like “Can we grow in Japan?” That’s interesting, but it is “What, specifically, are we trying to uncover in the growth of a product in Japan? Or a segment in Japan? Or a channel in Japan?” When you spend an enormous amount of time, in the first meeting of the different stakeholders, debating this and having different people put forward what they think the problem definition is, you realize that people have completely different views of why they’re here. That, to me, is the most important step.

Charles Conn: I would agree with that. For me, the problem context is critical. When we understand “What are the forces acting upon your decision maker? How quickly is the answer needed? With what precision is the answer needed? Are there areas that are off limits or areas where we would particularly like to find our solution? Is the decision maker open to exploring other areas?” then you not only become more efficient, and move toward what we call the critical path in problem solving, but you also make it so much more likely that you’re not going to waste your time or your decision maker’s time.

How often do especially bright young people run off with half of the idea about what the problem is and start collecting data and start building models—only to discover that they’ve really gone off half-cocked.

Hugo Sarrazin: Yeah.

Charles Conn: And in the wrong direction.

Simon London: OK. So step one—and there is a real art and a structure to it—is define the problem. Step two, Charles?

Charles Conn: My favorite step is step two, which is to use logic trees to disaggregate the problem. Every problem we’re solving has some complexity and some uncertainty in it. The only way that we can really get our team working on the problem is to take the problem apart into logical pieces.

What we find, of course, is that the way to disaggregate the problem often gives you an insight into the answer to the problem quite quickly. I love to do two or three different cuts at it, each one giving a bit of a different insight into what might be going wrong. By doing sensible disaggregations, using logic trees, we can figure out which parts of the problem we should be looking at, and we can assign those different parts to team members.

Simon London: What’s a good example of a logic tree on a sort of ratable problem?

Charles Conn: Maybe the easiest one is the classic profit tree. Almost in every business that I would take a look at, I would start with a profit or return-on-assets tree. In its simplest form, you have the components of revenue, which are price and quantity, and the components of cost, which are cost and quantity. Each of those can be broken out. Cost can be broken into variable cost and fixed cost. The components of price can be broken into what your pricing scheme is. That simple tree often provides insight into what’s going on in a business or what the difference is between that business and the competitors.

If we add the leg, which is “What’s the asset base or investment element?”—so profit divided by assets—then we can ask the question “Is the business using its investments sensibly?” whether that’s in stores or in manufacturing or in transportation assets. I hope we can see just how simple this is, even though we’re describing it in words.

When I went to work with Gordon Moore at the Moore Foundation, the problem that he asked us to look at was “How can we save Pacific salmon?” Now, that sounds like an impossible question, but it was amenable to precisely the same type of disaggregation and allowed us to organize what became a 15-year effort to improve the likelihood of good outcomes for Pacific salmon.

Simon London: Now, is there a danger that your logic tree can be impossibly large? This, I think, brings us onto the third step in the process, which is that you have to prioritize.

Charles Conn: Absolutely. The third step, which we also emphasize, along with good problem definition, is rigorous prioritization—we ask the questions “How important is this lever or this branch of the tree in the overall outcome that we seek to achieve? How much can I move that lever?” Obviously, we try and focus our efforts on ones that have a big impact on the problem and the ones that we have the ability to change. With salmon, ocean conditions turned out to be a big lever, but not one that we could adjust. We focused our attention on fish habitats and fish-harvesting practices, which were big levers that we could affect.

People spend a lot of time arguing about branches that are either not important or that none of us can change. We see it in the public square. When we deal with questions at the policy level—“Should you support the death penalty?” “How do we affect climate change?” “How can we uncover the causes and address homelessness?”—it’s even more important that we’re focusing on levers that are big and movable.

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Simon London: Let’s move swiftly on to step four. You’ve defined your problem, you disaggregate it, you prioritize where you want to analyze—what you want to really look at hard. Then you got to the work plan. Now, what does that mean in practice?

Hugo Sarrazin: Depending on what you’ve prioritized, there are many things you could do. It could be breaking the work among the team members so that people have a clear piece of the work to do. It could be defining the specific analyses that need to get done and executed, and being clear on time lines. There’s always a level-one answer, there’s a level-two answer, there’s a level-three answer. Without being too flippant, I can solve any problem during a good dinner with wine. It won’t have a whole lot of backing.

Simon London: Not going to have a lot of depth to it.

Hugo Sarrazin: No, but it may be useful as a starting point. If the stakes are not that high, that could be OK. If it’s really high stakes, you may need level three and have the whole model validated in three different ways. You need to find a work plan that reflects the level of precision, the time frame you have, and the stakeholders you need to bring along in the exercise.

Charles Conn: I love the way you’ve described that, because, again, some people think of problem solving as a linear thing, but of course what’s critical is that it’s iterative. As you say, you can solve the problem in one day or even one hour.

Charles Conn: We encourage our teams everywhere to do that. We call it the one-day answer or the one-hour answer. In work planning, we’re always iterating. Every time you see a 50-page work plan that stretches out to three months, you know it’s wrong. It will be outmoded very quickly by that learning process that you described. Iterative problem solving is a critical part of this. Sometimes, people think work planning sounds dull, but it isn’t. It’s how we know what’s expected of us and when we need to deliver it and how we’re progressing toward the answer. It’s also the place where we can deal with biases. Bias is a feature of every human decision-making process. If we design our team interactions intelligently, we can avoid the worst sort of biases.

Simon London: Here we’re talking about cognitive biases primarily, right? It’s not that I’m biased against you because of your accent or something. These are the cognitive biases that behavioral sciences have shown we all carry around, things like anchoring, overoptimism—these kinds of things.

Both: Yeah.

Charles Conn: Availability bias is the one that I’m always alert to. You think you’ve seen the problem before, and therefore what’s available is your previous conception of it—and we have to be most careful about that. In any human setting, we also have to be careful about biases that are based on hierarchies, sometimes called sunflower bias. I’m sure, Hugo, with your teams, you make sure that the youngest team members speak first. Not the oldest team members, because it’s easy for people to look at who’s senior and alter their own creative approaches.

Hugo Sarrazin: It’s helpful, at that moment—if someone is asserting a point of view—to ask the question “This was true in what context?” You’re trying to apply something that worked in one context to a different one. That can be deadly if the context has changed, and that’s why organizations struggle to change. You promote all these people because they did something that worked well in the past, and then there’s a disruption in the industry, and they keep doing what got them promoted even though the context has changed.

Simon London: Right. Right.

Hugo Sarrazin: So it’s the same thing in problem solving.

Charles Conn: And it’s why diversity in our teams is so important. It’s one of the best things about the world that we’re in now. We’re likely to have people from different socioeconomic, ethnic, and national backgrounds, each of whom sees problems from a slightly different perspective. It is therefore much more likely that the team will uncover a truly creative and clever approach to problem solving.

Simon London: Let’s move on to step five. You’ve done your work plan. Now you’ve actually got to do the analysis. The thing that strikes me here is that the range of tools that we have at our disposal now, of course, is just huge, particularly with advances in computation, advanced analytics. There’s so many things that you can apply here. Just talk about the analysis stage. How do you pick the right tools?

Charles Conn: For me, the most important thing is that we start with simple heuristics and explanatory statistics before we go off and use the big-gun tools. We need to understand the shape and scope of our problem before we start applying these massive and complex analytical approaches.

Simon London: Would you agree with that?

Hugo Sarrazin: I agree. I think there are so many wonderful heuristics. You need to start there before you go deep into the modeling exercise. There’s an interesting dynamic that’s happening, though. In some cases, for some types of problems, it is even better to set yourself up to maximize your learning. Your problem-solving methodology is test and learn, test and learn, test and learn, and iterate. That is a heuristic in itself, the A/B testing that is used in many parts of the world. So that’s a problem-solving methodology. It’s nothing different. It just uses technology and feedback loops in a fast way. The other one is exploratory data analysis. When you’re dealing with a large-scale problem, and there’s so much data, I can get to the heuristics that Charles was talking about through very clever visualization of data.

You test with your data. You need to set up an environment to do so, but don’t get caught up in neural-network modeling immediately. You’re testing, you’re checking—“Is the data right? Is it sound? Does it make sense?”—before you launch too far.

Simon London: You do hear these ideas—that if you have a big enough data set and enough algorithms, they’re going to find things that you just wouldn’t have spotted, find solutions that maybe you wouldn’t have thought of. Does machine learning sort of revolutionize the problem-solving process? Or are these actually just other tools in the toolbox for structured problem solving?

Charles Conn: It can be revolutionary. There are some areas in which the pattern recognition of large data sets and good algorithms can help us see things that we otherwise couldn’t see. But I do think it’s terribly important we don’t think that this particular technique is a substitute for superb problem solving, starting with good problem definition. Many people use machine learning without understanding algorithms that themselves can have biases built into them. Just as 20 years ago, when we were doing statistical analysis, we knew that we needed good model definition, we still need a good understanding of our algorithms and really good problem definition before we launch off into big data sets and unknown algorithms.

Simon London: Step six. You’ve done your analysis.

Charles Conn: I take six and seven together, and this is the place where young problem solvers often make a mistake. They’ve got their analysis, and they assume that’s the answer, and of course it isn’t the answer. The ability to synthesize the pieces that came out of the analysis and begin to weave those into a story that helps people answer the question “What should I do?” This is back to where we started. If we can’t synthesize, and we can’t tell a story, then our decision maker can’t find the answer to “What should I do?”

Simon London: But, again, these final steps are about motivating people to action, right?

Charles Conn: Yeah.

Simon London: I am slightly torn about the nomenclature of problem solving because it’s on paper, right? Until you motivate people to action, you actually haven’t solved anything.

Charles Conn: I love this question because I think decision-making theory, without a bias to action, is a waste of time. Everything in how I approach this is to help people take action that makes the world better.

Simon London: Hence, these are absolutely critical steps. If you don’t do this well, you’ve just got a bunch of analysis.

Charles Conn: We end up in exactly the same place where we started, which is people speaking across each other, past each other in the public square, rather than actually working together, shoulder to shoulder, to crack these important problems.

Simon London: In the real world, we have a lot of uncertainty—arguably, increasing uncertainty. How do good problem solvers deal with that?

Hugo Sarrazin: At every step of the process. In the problem definition, when you’re defining the context, you need to understand those sources of uncertainty and whether they’re important or not important. It becomes important in the definition of the tree.

You need to think carefully about the branches of the tree that are more certain and less certain as you define them. They don’t have equal weight just because they’ve got equal space on the page. Then, when you’re prioritizing, your prioritization approach may put more emphasis on things that have low probability but huge impact—or, vice versa, may put a lot of priority on things that are very likely and, hopefully, have a reasonable impact. You can introduce that along the way. When you come back to the synthesis, you just need to be nuanced about what you’re understanding, the likelihood.

Often, people lack humility in the way they make their recommendations: “This is the answer.” They’re very precise, and I think we would all be well-served to say, “This is a likely answer under the following sets of conditions” and then make the level of uncertainty clearer, if that is appropriate. It doesn’t mean you’re always in the gray zone; it doesn’t mean you don’t have a point of view. It just means that you can be explicit about the certainty of your answer when you make that recommendation.

Simon London: So it sounds like there is an underlying principle: “Acknowledge and embrace the uncertainty. Don’t pretend that it isn’t there. Be very clear about what the uncertainties are up front, and then build that into every step of the process.”

Hugo Sarrazin: Every step of the process.

Simon London: Yeah. We have just walked through a particular structured methodology for problem solving. But, of course, this is not the only structured methodology for problem solving. One that is also very well-known is design thinking, which comes at things very differently. So, Hugo, I know you have worked with a lot of designers. Just give us a very quick summary. Design thinking—what is it, and how does it relate?

Hugo Sarrazin: It starts with an incredible amount of empathy for the user and uses that to define the problem. It does pause and go out in the wild and spend an enormous amount of time seeing how people interact with objects, seeing the experience they’re getting, seeing the pain points or joy—and uses that to infer and define the problem.

Simon London: Problem definition, but out in the world.

Hugo Sarrazin: With an enormous amount of empathy. There’s a huge emphasis on empathy. Traditional, more classic problem solving is you define the problem based on an understanding of the situation. This one almost presupposes that we don’t know the problem until we go see it. The second thing is you need to come up with multiple scenarios or answers or ideas or concepts, and there’s a lot of divergent thinking initially. That’s slightly different, versus the prioritization, but not for long. Eventually, you need to kind of say, “OK, I’m going to converge again.” Then you go and you bring things back to the customer and get feedback and iterate. Then you rinse and repeat, rinse and repeat. There’s a lot of tactile building, along the way, of prototypes and things like that. It’s very iterative.

Simon London: So, Charles, are these complements or are these alternatives?

Charles Conn: I think they’re entirely complementary, and I think Hugo’s description is perfect. When we do problem definition well in classic problem solving, we are demonstrating the kind of empathy, at the very beginning of our problem, that design thinking asks us to approach. When we ideate—and that’s very similar to the disaggregation, prioritization, and work-planning steps—we do precisely the same thing, and often we use contrasting teams, so that we do have divergent thinking. The best teams allow divergent thinking to bump them off whatever their initial biases in problem solving are. For me, design thinking gives us a constant reminder of creativity, empathy, and the tactile nature of problem solving, but it’s absolutely complementary, not alternative.

Simon London: I think, in a world of cross-functional teams, an interesting question is do people with design-thinking backgrounds really work well together with classical problem solvers? How do you make that chemistry happen?

Hugo Sarrazin: Yeah, it is not easy when people have spent an enormous amount of time seeped in design thinking or user-centric design, whichever word you want to use. If the person who’s applying classic problem-solving methodology is very rigid and mechanical in the way they’re doing it, there could be an enormous amount of tension. If there’s not clarity in the role and not clarity in the process, I think having the two together can be, sometimes, problematic.

The second thing that happens often is that the artifacts the two methodologies try to gravitate toward can be different. Classic problem solving often gravitates toward a model; design thinking migrates toward a prototype. Rather than writing a big deck with all my supporting evidence, they’ll bring an example, a thing, and that feels different. Then you spend your time differently to achieve those two end products, so that’s another source of friction.

Now, I still think it can be an incredibly powerful thing to have the two—if there are the right people with the right mind-set, if there is a team that is explicit about the roles, if we’re clear about the kind of outcomes we are attempting to bring forward. There’s an enormous amount of collaborativeness and respect.

Simon London: But they have to respect each other’s methodology and be prepared to flex, maybe, a little bit, in how this process is going to work.

Hugo Sarrazin: Absolutely.

Simon London: The other area where, it strikes me, there could be a little bit of a different sort of friction is this whole concept of the day-one answer, which is what we were just talking about in classical problem solving. Now, you know that this is probably not going to be your final answer, but that’s how you begin to structure the problem. Whereas I would imagine your design thinkers—no, they’re going off to do their ethnographic research and get out into the field, potentially for a long time, before they come back with at least an initial hypothesis.

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Hugo Sarrazin: That is a great callout, and that’s another difference. Designers typically will like to soak into the situation and avoid converging too quickly. There’s optionality and exploring different options. There’s a strong belief that keeps the solution space wide enough that you can come up with more radical ideas. If there’s a large design team or many designers on the team, and you come on Friday and say, “What’s our week-one answer?” they’re going to struggle. They’re not going to be comfortable, naturally, to give that answer. It doesn’t mean they don’t have an answer; it’s just not where they are in their thinking process.

Simon London: I think we are, sadly, out of time for today. But Charles and Hugo, thank you so much.

Charles Conn: It was a pleasure to be here, Simon.

Hugo Sarrazin: It was a pleasure. Thank you.

Simon London: And thanks, as always, to you, our listeners, for tuning into this episode of the McKinsey Podcast . If you want to learn more about problem solving, you can find the book, Bulletproof Problem Solving: The One Skill That Changes Everything , online or order it through your local bookstore. To learn more about McKinsey, you can of course find us at

Charles Conn is CEO of Oxford Sciences Innovation and an alumnus of McKinsey’s Sydney office. Hugo Sarrazin is a senior partner in the Silicon Valley office, where Simon London, a member of McKinsey Publishing, is also based.

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