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How to read data in Case Interviews - a comprehensive guide

While preparing for case interviews, there are two ways to read data that you will have to get used to:

  1. To get specific answers, for tests such as the McKinsey Problem Solving Test.
  2. To analyze and communicate business insights, mostly for case interviews.

To improve your proficiency in the first type of questions, you can refer to our tips about the test, and use our mental math tool to exercise your skills. In addition, you should also go through the official McKinsey guidelines and practice tests on their website, as they very closely resemble the actual test that you will face. In this article, we will focus on the second aspect, i.e. on how to analyze and communicate business insights in a case interview.

A three-step guide to read charts and data in a case interview : analyze, contextualize, and interpret

While tests like the McKinsey PST require you to calculate the answer precisely on your own, a case interview situation is quite different.

Typically, you will not be asked math problems in a case interview. However, data in various forms is likely to make an appearance in many different types of cases. For example, when you ask for market, customer or cost information while analyzing the case, the interviewer may provide you with a slide which has a few graphs detailing the various segments of the target market and various numbers (revenue, growth, etc.) pertaining to the segments. You are expected to:

  1. Analyze: Quickly understand the information presented in the exhibit. What is the overall size of the market? How much did our revenue grow in the last two years? Which customer segment saw the maximum fall in revenues?
  2. Contextualize: Put your understanding into the overall context of the case, and also into the specific context of the current discussion. Is the market big enough, given our client’s existing operations? What do we know about the client that could explain the growth in revenue? Is the new competing product, which you just discussed, potentially responsible for slowing down growth in a particular customer segment?
  3. Interpret: Present your business insights, and continue the case in the right direction by asking relevant questions. “So, it looks like the new market is 2x bigger than our current market, and has fragmented competition. I would like to test a hypothesis that this is a good market to enter”, “It looks like our client has seen revenue grow 15% over the last two years. Do we know which of the client’s businesses has been primarily responsible for this increase?” or “It seems that the new product launched by our competitors has cut into our share of wallet for Segment X of customers, reducing our market share by 20%. I would like to analyze why they switched”.

This article will help you understand how to tackle data in a case interview, by providing helpful tips on what to do at each stage with examples from real-life case exhibits.

Analyzing additional data given by the interviewer

There are three forms in which data can be presented to you in a case interview:

  • Only numerical data in a tabular form.
  • Bar or pie charts with markings on the axes to indicate units and reference data points.
  • A mixture of tabular and graphic representation of data.

Making sense of this information requires the same skills you will practice for the problem solving test. Let’s start with an example:

For a boot manufacturer that manufactures two types of boots, you have to plan a strategy for responding to a competitor who has launched a new product. To understand the market, you ask the interviewer how big it is. The exhibit below is presented to you.

Take the following steps to analyze this data:

  1. Keep in mind the question you asked the interviewer (and the overall situation) when he handed you the exhibit. Your primary job is to answer that question! In this case, the question is market size – a Euro figure. So you must assimilate all the customer segmentation, product and pricing data into the total market size.
  2. First read the title of the exhibit, and then study the axes and units of the data (for charts). This will tell you what exactly the data is about. You can see that this exhibit is a mixed chart, with both numerical and graphical representation of data. The first part shows buying propensity (in other words, the percentage of population that bought boots last year). Quickly note that the data is presented as a percentage for different customer segments, and you also have the average price paid and total population for each segment.
  3. Figure out the most efficient approach to get to the final result, and work the math. If all the required data is not present, ask for it.

In this case,

  1. Total revenue from all customers would equal market size.
  2. Total revenue = Number of boots bought * Price per pair
  3. Number of boots bought = Number of buyers * Boots bought per buyer
  4. Number of buyers = (Total Population) * (% of population buying boots)

Now, do we have all information required to calculate the revenue? No! We need to know the number of boots bought per capita by each consumer segment. Quickly explain your approach to the interviewer and ask for the required information. Suppose he asks you to assume that every customer bought just one pair of boots in the last year. Now you have all the information needed to calculate the market size for the total boots category. However, you cannot calculate the size of the Work / Casual boots market separately since you only know the overall average price and not the category average for each. (While these individual prices for the categories can be calculated using simultaneous equations, this is definitely overkill for a case interview, and you should ask the interviewer for this information to calculate the different segments.)

Look at the graph for other interesting trends

Once you’ve achieved the main objective, you should look at the other obvious trends that the data shows. Get a general sense of the situation depicted, and look for trends across categories/years, or major aberrations. Don’t spend more than 5 seconds studying the graph, unless you see something especially striking or confusing. In this case for example, a basic observation is that blue collar workers buy more work boots than casual boots, and since they are paying more than the others on average, it could mean that work boots cost more than casual boots. Another observation is that the work boot category is significantly larger than the casual boot category.

Contextualizing inferences to focus and continue the discussion

Now that you have some numbers and an idea of the general trends shown by the data, you need to put it in the context of the overall case. This means understanding the business implication of the given data. Sometimes at the end of the analysis stage, you may realize that the data given to you has too many implications. Which are the ones most relevant to the case and current discussion? The following steps can help add more context to your analysis. Let us continue with the example of the boot manufacturing company discussed in the last section:

  1. Think of the overall objective, and make a quick hypothesis about the key metrics required to assess the situation. To understand the competitive dynamics of the market, we would need to figure out the key product categories in the market and our company’s positioning with customers.
  2. Understand what the data says about the metrics you have just identified. We understand that the boot market is divided into two segments based on type of usage, and the customers are segmented by profession.
  3. Try to think of reasons that might have caused the data to look like it does, and open new lines of enquiry. This helps the interviewer understand that you can not only do math, but can also draw business inferences from the exhibit – which is a key skill a consultant should possess. In this case, it stands to reason that more blue collar workers will buy work boots, since their professions require more manual effort and thus their shoes will wear out earlier. Also, what is our client’s overall standing in the two boot segments? – that’s the natural next question.

Interpreting and communicating your insights to the interviewer

The final and most important step is synthesizing all the analysis and business reasoning that you just completed, and presenting it to the interviewer in an effective manner. The following aspects should be taken care of while communicating the inferences to the interviewer:

  1. Hypothesis: Consulting is hypothesis-driven, and you should try to lead with a preliminary conclusion, backed by information you currently have, and can be verified by asking for more information. Ask for the further information needed to verify your hypothesis.
  2. Data: If you have been presented with numbers and have made mathematical calculations, it makes sense to present the most important results to the interviewer, to show your comfort with numbers.
  3. Clarity & Logic: The communication needs to be precise and logical. You have to explain the logic behind your conclusions. In general, it often helps to think aloud, so that the interviewer can follow your line of reasoning.

In this case, the situation can be possibly summed up as follows:

The total market size is €X Mn. My hypothesis is that the work boots category is the most important for us here, since it is €Y Mn larger than the casual boots segment. Do we have any information about our market share, and that of our competitors, in each of these segments?

Summary: Practice is the key to draw more insights from charts and data in case interviews!

While these are some things to keep in mind when faced with data in interviews, remember that practice is the best way to improve your data-reading skills. So keep practicing with peers and experts, in real life or over our platform.

Do you have any questions or remarks about this article? Let us know in the comments!

5 Kommentar(e)
19. August 2018 22:26 -

18. Mai 2018 03:55 -

My first approach was to use total population for each segment and multiply by the percentage of boot consumers, and then multiply those numbers by average price paid:

Blue Collar: 80% * 11m = 8.8m consumers * $140 = 1.232b

White Collar: 60% * 12m = 7.2m consumers * $130 = 936m

Student: 70% * 7m = 4.9m * $110 = 539m

I'm sure there is a better, easier way to do this - I'm curious what it is? Is this even data we need?

27. Juli 2017 14:40 -

As far as I understand the graph, the percentages per bar indicate how large the part of that group is that bought these particular type of shoes. I.e., 60% of 11M blue collar workers bought work shoes, while only 20% of 11M bought casual boots.

If you calculate the total pairs of boots sold per type:

  • Work boots: 60% of 11M (=6.6M) + 25% of 12M (=3M) + 15% of 7M (=1,05M) = 10.65M works boots sold last year
  • Casual boots: 20% of 11M (=2.2M) + 35% of 12M (=4.2M) + 55% of 7M (=3.85M) = 10.25M casual boots sold last year

This difference of 0.4M boots is not really significant, but this is my take on the problem.

13. Juni 2017 21:04 -

Another observation is that the work boot category is significantly larger than the casual boot categy

This one I do not get either. Could anyone explain please, how do we conclude this?


13. Juni 2017 20:51 -

In this case for example, a basic observation is that blue collar workers buy more work boots than casual boots

Is this statement actually true?

From the graph we see that blue collar workers buy more work-boots and less casual-boots compared to other customers segments, but this does not necessarily mean that number of work-boots bought by blue collar workers is more than number of casual-boots they buy.

Unless we know, either size of each boot segment, or how often what fraction of each customer segment buys each kind of boot we cannot say that blue collar workers buy more work-boots than casual boots.

E.g.: If work-boot segment is 1M (number of boots) and casual-boot segment is 10M (number of boots), then we have that blue collar workers buy 0.65M work-boots and 2M casual-boots.

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