Introduction to Statistical Thinking

Chapter 16 case studies, 16.1 student learning objective.

This chapter concludes this book. We start with a short review of the topics that were discussed in the second part of the book, the part that dealt with statistical inference. The main part of the chapter involves the statistical analysis of 2 case studies. The tools that will be used for the analysis are those that were discussed in the book. We close this chapter and this book with some concluding remarks. By the end of this chapter, the student should be able to:

Review the concepts and methods for statistical inference that were presented in the second part of the book.

Apply these methods to requirements of the analysis of real data.

Develop a resolve to learn more statistics.

16.2 A Review

The second part of the book dealt with statistical inference; the science of making general statement on an entire population on the basis of data from a sample. The basis for the statements are theoretical models that produce the sampling distribution. Procedures for making the inference are evaluated based on their properties in the context of this sampling distribution. Procedures with desirable properties are applied to the data. One may attach to the output of this application summaries that describe these theoretical properties.

In particular, we dealt with two forms of making inference. One form was estimation and the other was hypothesis testing. The goal in estimation is to determine the value of a parameter in the population. Point estimates or confidence intervals may be used in order to fulfill this goal. The properties of point estimators may be assessed using the mean square error (MSE) and the properties of the confidence interval may be assessed using the confidence level.

The target in hypotheses testing is to decide between two competing hypothesis. These hypotheses are formulated in terms of population parameters. The decision rule is called a statistical test and is constructed with the aid of a test statistic and a rejection region. The default hypothesis among the two, is rejected if the test statistic falls in the rejection region. The major property a test must possess is a bound on the probability of a Type I error, the probability of erroneously rejecting the null hypothesis. This restriction is called the significance level of the test. A test may also be assessed in terms of it’s statistical power, the probability of rightfully rejecting the null hypothesis.

Estimation and testing were applied in the context of single measurements and for the investigation of the relations between a pair of measurements. For single measurements we considered both numeric variables and factors. For numeric variables one may attempt to conduct inference on the expectation and/or the variance. For factors we considered the estimation of the probability of obtaining a level, or, more generally, the probability of the occurrence of an event.

We introduced statistical models that may be used to describe the relations between variables. One of the variables was designated as the response. The other variable, the explanatory variable, is identified as a variable which may affect the distribution of the response. Specifically, we considered numeric variables and factors that have two levels. If the explanatory variable is a factor with two levels then the analysis reduces to the comparison of two sub-populations, each one associated with a level. If the explanatory variable is numeric then a regression model may be applied, either linear or logistic regression, depending on the type of the response.

The foundations of statistical inference are the assumption that we make in the form of statistical models. These models attempt to reflect reality. However, one is advised to apply healthy skepticism when using the models. First, one should be aware what the assumptions are. Then one should ask oneself how reasonable are these assumption in the context of the specific analysis. Finally, one should check as much as one can the validity of the assumptions in light of the information at hand. It is useful to plot the data and compare the plot to the assumptions of the model.

16.3 Case Studies

Let us apply the methods that were introduced throughout the book to two examples of data analysis. Both examples are taken from the case studies of the Rice Virtual Lab in Statistics can be found in their Case Studies section. The analysis of these case studies may involve any of the tools that were described in the second part of the book (and some from the first part). It may be useful to read again Chapters  9 – 15 before reading the case studies.

16.3.1 Physicians’ Reactions to the Size of a Patient

Overweight and obesity is common in many of the developed contrives. In some cultures, obese individuals face discrimination in employment, education, and relationship contexts. The current research, conducted by Mikki Hebl and Jingping Xu 87 , examines physicians’ attitude toward overweight and obese patients in comparison to their attitude toward patients who are not overweight.

The experiment included a total of 122 primary care physicians affiliated with one of three major hospitals in the Texas Medical Center of Houston. These physicians were sent a packet containing a medical chart similar to the one they view upon seeing a patient. This chart portrayed a patient who was displaying symptoms of a migraine headache but was otherwise healthy. Two variables (the gender and the weight of the patient) were manipulated across six different versions of the medical charts. The weight of the patient, described in terms of Body Mass Index (BMI), was average (BMI = 23), overweight (BMI = 30), or obese (BMI = 36). Physicians were randomly assigned to receive one of the six charts, and were asked to look over the chart carefully and complete two medical forms. The first form asked physicians which of 42 tests they would recommend giving to the patient. The second form asked physicians to indicate how much time they believed they would spend with the patient, and to describe the reactions that they would have toward this patient.

In this presentation, only the question on how much time the physicians believed they would spend with the patient is analyzed. Although three patient weight conditions were used in the study (average, overweight, and obese) only the average and overweight conditions will be analyzed. Therefore, there are two levels of patient weight (average and overweight) and one dependent variable (time spent).

The data for the given collection of responses from 72 primary care physicians is stored in the file “ discriminate.csv ” 88 . We start by reading the content of the file into a data frame by the name “ patient ” and presenting the summary of the variables:

Observe that of the 72 “patients”, 38 are overweight and 33 have an average weight. The time spend with the patient, as predicted by physicians, is distributed between 5 minutes and 1 hour, with a average of 27.82 minutes and a median of 30 minutes.

It is a good practice to have a look at the data before doing the analysis. In this examination on should see that the numbers make sense and one should identify special features of the data. Even in this very simple example we may want to have a look at the histogram of the variable “ time ”:

case study business size statistics

A feature in this plot that catches attention is the fact that there is a high concventration of values in the interval between 25 and 30. Together with the fact that the median is equal to 30, one may suspect that, as a matter of fact, a large numeber of the values are actually equal to 30. Indeed, let us produce a table of the response:

Notice that 30 of the 72 physicians marked “ 30 ” as the time they expect to spend with the patient. This is the middle value in the range, and may just be the default value one marks if one just needs to complete a form and do not really place much importance to the question that was asked.

The goal of the analysis is to examine the relation between overweigh and the Doctor’s response. The explanatory variable is a factor with two levels. The response is numeric. A natural tool to use in order to test this hypothesis is the \(t\) -test, which is implemented with the function “ t.test ”.

First we plot the relation between the response and the explanatory variable and then we apply the test:

case study business size statistics

Nothing seems problematic in the box plot. The two distributions, as they are reflected in the box plots, look fairly symmetric.

When we consider the report that produced by the function “ t.test ” we may observe that the \(p\) -value is equal to 0.005774. This \(p\) -value is computed in testing the null hypothesis that the expectation of the response for both types of patients are equal against the two sided alternative. Since the \(p\) -value is less than 0.05 we do reject the null hypothesis.

The estimated value of the difference between the expectation of the response for a patient with BMI=23 and a patient with BMI=30 is \(31.36364 -24.73684 \approx 6.63\) minutes. The confidence interval is (approximately) equal to \([1.99, 11.27]\) . Hence, it looks as if the physicians expect to spend more time with the average weight patients.

After analyzing the effect of the explanatory variable on the expectation of the response one may want to examine the presence, or lack thereof, of such effect on the variance of the response. Towards that end, one may use the function “ var.test ”:

In this test we do not reject the null hypothesis that the two variances of the response are equal since the \(p\) -value is larger than \(0.05\) . The sample variances are almost equal to each other (their ratio is \(1.044316\) ), with a confidence interval for the ration that essentially ranges between 1/2 and 2.

The production of \(p\) -values and confidence intervals is just one aspect in the analysis of data. Another aspect, which typically is much more time consuming and requires experience and healthy skepticism is the examination of the assumptions that are used in order to produce the \(p\) -values and the confidence intervals. A clear violation of the assumptions may warn the statistician that perhaps the computed nominal quantities do not represent the actual statistical properties of the tools that were applied.

In this case, we have noticed the high concentration of the response at the value “ 30 ”. What is the situation when we split the sample between the two levels of the explanatory variable? Let us apply the function “ table ” once more, this time with the explanatory variable included:

Not surprisingly, there is still high concentration at that level “ 30 ”. But one can see that only 2 of the responses of the “ BMI=30 ” group are above that value in comparison to a much more symmetric distribution of responses for the other group.

The simulations of the significance level of the one-sample \(t\) -test for an Exponential response that were conducted in Question  \[ex:Testing.2\] may cast some doubt on how trustworthy are nominal \(p\) -values of the \(t\) -test when the measurements are skewed. The skewness of the response for the group “ BMI=30 ” is a reason to be worry.

We may consider a different test, which is more robust, in order to validate the significance of our findings. For example, we may turn the response into a factor by setting a level for values larger or equal to “ 30 ” and a different level for values less than “ 30 ”. The relation between the new response and the explanatory variable can be examined with the function “ prop.test ”. We first plot and then test:

case study business size statistics

The mosaic plot presents the relation between the explanatory variable and the new factor. The level “ TRUE ” is associated with a value of the predicted time spent with the patient being 30 minutes or more. The level “ FALSE ” is associated with a prediction of less than 30 minutes.

The computed \(p\) -value is equal to \(0.05409\) , that almost reaches the significance level of 5% 89 . Notice that the probabilities that are being estimated by the function are the probabilities of the level “ FALSE ”. Overall, one may see the outcome of this test as supporting evidence for the conclusion of the \(t\) -test. However, the \(p\) -value provided by the \(t\) -test may over emphasize the evidence in the data for a significant difference in the physician attitude towards overweight patients.

16.3.2 Physical Strength and Job Performance

The next case study involves an attempt to develop a measure of physical ability that is easy and quick to administer, does not risk injury, and is related to how well a person performs the actual job. The current example is based on study by Blakely et al.  90 , published in the journal Personnel Psychology.

There are a number of very important jobs that require, in addition to cognitive skills, a significant amount of strength to be able to perform at a high level. Construction worker, electrician and auto mechanic, all require strength in order to carry out critical components of their job. An interesting applied problem is how to select the best candidates from amongst a group of applicants for physically demanding jobs in a safe and a cost effective way.

The data presented in this case study, and may be used for the development of a method for selection among candidates, were collected from 147 individuals working in physically demanding jobs. Two measures of strength were gathered from each participant. These included grip and arm strength. A piece of equipment known as the Jackson Evaluation System (JES) was used to collect the strength data. The JES can be configured to measure the strength of a number of muscle groups. In this study, grip strength and arm strength were measured. The outcomes of these measurements were summarized in two scores of physical strength called “ grip ” and “ arm ”.

Two separate measures of job performance are presented in this case study. First, the supervisors for each of the participants were asked to rate how well their employee(s) perform on the physical aspects of their jobs. This measure is summarizes in the variable “ ratings ”. Second, simulations of physically demanding work tasks were developed. The summary score of these simulations are given in the variable “ sims ”. Higher values of either measures of performance indicates better performance.

The data for the 4 variables and 147 observations is stored in “ job.csv ” 91 . We start by reading the content of the file into a data frame by the name “ job ”, presenting a summary of the variables, and their histograms:

case study business size statistics

All variables are numeric. Examination of the 4 summaries and histograms does not produce interest findings. All variables are, more or less, symmetric with the distribution of the variable “ ratings ” tending perhaps to be more uniform then the other three.

The main analyses of interest are attempts to relate the two measures of physical strength “ grip ” and “ arm ” with the two measures of job performance, “ ratings ” and “ sims ”. A natural tool to consider in this context is a linear regression analysis that relates a measure of physical strength as an explanatory variable to a measure of job performance as a response.

Scatter Plots and Regression Lines

FIGURE 16.1: Scatter Plots and Regression Lines

Let us consider the variable “ sims ” as a response. The first step is to plot a scatter plot of the response and explanatory variable, for both explanatory variables. To the scatter plot we add the line of regression. In order to add the regression line we fit the regression model with the function “ lm ” and then apply the function “ abline ” to the fitted model. The plot for the relation between the response and the variable “ grip ” is produced by the code:

The plot that is produced by this code is presented on the upper-left panel of Figure  16.1 .

The plot for the relation between the response and the variable “ arm ” is produced by this code:

The plot that is produced by the last code is presented on the upper-right panel of Figure  16.1 .

Both plots show similar characteristics. There is an overall linear trend in the relation between the explanatory variable and the response. The value of the response increases with the increase in the value of the explanatory variable (a positive slope). The regression line seems to follow, more or less, the trend that is demonstrated by the scatter plot.

A more detailed analysis of the regression model is possible by the application of the function “ summary ” to the fitted model. First the case where the explanatory variable is “ grip ”:

Examination of the report reviles a clear statistical significance for the effect of the explanatory variable on the distribution of response. The value of R-squared, the ration of the variance of the response explained by the regression is \(0.4094\) . The square root of this quantity, \(\sqrt{0.4094} \approx 0.64\) , is the proportion of the standard deviation of the response that is explained by the explanatory variable. Hence, about 64% of the variability in the response can be attributed to the measure of the strength of the grip.

For the variable “ arm ” we get:

This variable is also statistically significant. The value of R-squared is \(0.4706\) . The proportion of the standard deviation that is explained by the strength of the are is \(\sqrt{0.4706} \approx 0.69\) , which is slightly higher than the proportion explained by the grip.

Overall, the explanatory variables do a fine job in the reduction of the variability of the response “ sims ” and may be used as substitutes of the response in order to select among candidates. A better prediction of the response based on the values of the explanatory variables can be obtained by combining the information in both variables. The production of such combination is not discussed in this book, though it is similar in principle to the methods of linear regression that are presented in Chapter  14 . The produced score 92 takes the form:

\[\mbox{\texttt{score}} = -5.434 + 0.024\cdot \mbox{\texttt{grip}}+ 0.037\cdot \mbox{\texttt{arm}}\;.\] We use this combined score as an explanatory variable. First we form the score and plot the relation between it and the response:

The scatter plot that includes the regression line can be found at the lower-left panel of Figure  16.1 . Indeed, the linear trend is more pronounced for this scatter plot and the regression line a better description of the relation between the response and the explanatory variable. A summary of the regression model produces the report:

Indeed, the score is highly significant. More important, the R-squared coefficient that is associated with the score is \(0.5422\) , which corresponds to a ratio of the standard deviation that is explained by the model of \(\sqrt{0.5422} \approx 0.74\) . Thus, almost 3/4 of the variability is accounted for by the score, so the score is a reasonable mean of guessing what the results of the simulations will be. This guess is based only on the results of the simple tests of strength that is conducted with the JES device.

Before putting the final seal on the results let us examine the assumptions of the statistical model. First, with respect to the two explanatory variables. Does each of them really measure a different property or do they actually measure the same phenomena? In order to examine this question let us look at the scatter plot that describes the relation between the two explanatory variables. This plot is produced using the code:

It is presented in the lower-right panel of Figure  16.1 . Indeed, one may see that the two measurements of strength are not independent of each other but tend to produce an increasing linear trend. Hence, it should not be surprising that the relation of each of them with the response produces essentially the same goodness of fit. The computed score gives a slightly improved fit, but still, it basically reflects either of the original explanatory variables.

In light of this observation, one may want to consider other measures of strength that represents features of the strength not captures by these two variable. Namely, measures that show less joint trend than the two considered.

Another element that should be examined are the probabilistic assumptions that underly the regression model. We described the regression model only in terms of the functional relation between the explanatory variable and the expectation of the response. In the case of linear regression, for example, this relation was given in terms of a linear equation. However, another part of the model corresponds to the distribution of the measurements about the line of regression. The assumption that led to the computation of the reported \(p\) -values is that this distribution is Normal.

A method that can be used in order to investigate the validity of the Normal assumption is to analyze the residuals from the regression line. Recall that these residuals are computed as the difference between the observed value of the response and its estimated expectation, namely the fitted regression line. The residuals can be computed via the application of the function “ residuals ” to the fitted regression model.

Specifically, let us look at the residuals from the regression line that uses the score that is combined from the grip and arm measurements of strength. One may plot a histogram of the residuals:

case study business size statistics

The produced histogram is represented on the upper panel. The histogram portrays a symmetric distribution that my result from Normally distributed observations. A better method to compare the distribution of the residuals to the Normal distribution is to use the Quantile-Quantile plot . This plot can be found on the lower panel. We do not discuss here the method by which this plot is produced 93 . However, we do say that any deviation of the points from a straight line is indication of violation of the assumption of Normality. In the current case, the points seem to be on a single line, which is consistent with the assumptions of the regression model.

The next task should be an analysis of the relations between the explanatory variables and the other response “ ratings ”. In principle one may use the same steps that were presented for the investigation of the relations between the explanatory variables and the response “ sims ”. But of course, the conclusion may differ. We leave this part of the investigation as an exercise to the students.

16.4 Summary

16.4.1 concluding remarks.

The book included a description of some elements of statistics, element that we thought are simple enough to be explained as part of an introductory course to statistics and are the minimum that is required for any person that is involved in academic activities of any field in which the analysis of data is required. Now, as you finish the book, it is as good time as any to say some words regarding the elements of statistics that are missing from this book.

One element is more of the same. The statistical models that were presented are as simple as a model can get. A typical application will required more complex models. Each of these models may require specific methods for estimation and testing. The characteristics of inference, e.g. significance or confidence levels, rely on assumptions that the models are assumed to possess. The user should be familiar with computational tools that can be used for the analysis of these more complex models. Familiarity with the probabilistic assumptions is required in order to be able to interpret the computer output, to diagnose possible divergence from the assumptions and to assess the severity of the possible effect of such divergence on the validity of the findings.

Statistical tools can be used for tasks other than estimation and hypothesis testing. For example, one may use statistics for prediction. In many applications it is important to assess what the values of future observations may be and in what range of values are they likely to occur. Statistical tools such as regression are natural in this context. However, the required task is not testing or estimation the values of parameters, but the prediction of future values of the response.

A different role of statistics in the design stage. We hinted in that direction when we talked about in Chapter  \[ch:Confidence\] about the selection of a sample size in order to assure a confidence interval with a given accuracy. In most applications, the selection of the sample size emerges in the context of hypothesis testing and the criteria for selection is the minimal power of the test, a minimal probability to detect a true finding. Yet, statistical design is much more than the determination of the sample size. Statistics may have a crucial input in the decision of how to collect the data. With an eye on the requirements for the final analysis, an experienced statistician can make sure that data that is collected is indeed appropriate for that final analysis. Too often is the case where researcher steps into the statistician’s office with data that he or she collected and asks, when it is already too late, for help in the analysis of data that cannot provide a satisfactory answer to the research question the researcher tried to address. It may be said, with some exaggeration, that good statisticians are required for the final analysis only in the case where the initial planning was poor.

Last, but not least, is the theoretical mathematical theory of statistics. We tried to introduce as little as possible of the relevant mathematics in this course. However, if one seriously intends to learn and understand statistics then one must become familiar with the relevant mathematical theory. Clearly, deep knowledge in the mathematical theory of probability is required. But apart from that, there is a rich and rapidly growing body of research that deals with the mathematical aspects of data analysis. One cannot be a good statistician unless one becomes familiar with the important aspects of this theory.

I should have started the book with the famous quotation: “Lies, damned lies, and statistics”. Instead, I am using it to end the book. Statistics can be used and can be misused. Learning statistics can give you the tools to tell the difference between the two. My goal in writing the book is achieved if reading it will mark for you the beginning of the process of learning statistics and not the end of the process.

16.4.2 Discussion in the Forum

In the second part of the book we have learned many subjects. Most of these subjects, especially for those that had no previous exposure to statistics, were unfamiliar. In this forum we would like to ask you to share with us the difficulties that you encountered.

What was the topic that was most difficult for you to grasp? In your opinion, what was the source of the difficulty?

When forming your answer to this question we will appreciate if you could elaborate and give details of what the problem was. Pointing to deficiencies in the learning material and confusing explanations will help us improve the presentation for the future editions of this book.

Hebl, M. and Xu, J. (2001). Weighing the care: Physicians’ reactions to the size of a patient. International Journal of Obesity, 25, 1246-1252. ↩

The file can be found on the internet at . ↩

One may propose splinting the response into two groups, with one group being associated with values of “ time ” strictly larger than 30 minutes and the other with values less or equal to 30. The resulting \(p\) -value from the expression “ prop.test(table(patient$time>30,patient$weight)) ” is \(0.01276\) . However, the number of subjects in one of the cells of the table is equal only to 2, which is problematic in the context of the Normal approximation that is used by this test. ↩

Blakley, B.A., Qui?ones, M.A., Crawford, M.S., and Jago, I.A. (1994). The validity of isometric strength tests. Personnel Psychology, 47, 247-274. ↩

The file can be found on the internet at . ↩

The score is produced by the application of the function “ lm ” to both variables as explanatory variables. The code expression that can be used is “ lm(sims ~ grip + arm, data=job) ”. ↩

Generally speaking, the plot is composed of the empirical percentiles of the residuals, plotted against the theoretical percentiles of the standard Normal distribution. The current plot is produced by the expression “ qqnorm(residuals(sims.score)) ”. ↩

Hertz CEO Kathryn Marinello with CFO Jamere Jackson and other members of the executive team in 2017

Top 40 Most Popular Case Studies of 2021

Two cases about Hertz claimed top spots in 2021's Top 40 Most Popular Case Studies

Two cases on the uses of debt and equity at Hertz claimed top spots in the CRDT’s (Case Research and Development Team) 2021 top 40 review of cases.

Hertz (A) took the top spot. The case details the financial structure of the rental car company through the end of 2019. Hertz (B), which ranked third in CRDT’s list, describes the company’s struggles during the early part of the COVID pandemic and its eventual need to enter Chapter 11 bankruptcy. 

The success of the Hertz cases was unprecedented for the top 40 list. Usually, cases take a number of years to gain popularity, but the Hertz cases claimed top spots in their first year of release. Hertz (A) also became the first ‘cooked’ case to top the annual review, as all of the other winners had been web-based ‘raw’ cases.

Besides introducing students to the complicated financing required to maintain an enormous fleet of cars, the Hertz cases also expanded the diversity of case protagonists. Kathyrn Marinello was the CEO of Hertz during this period and the CFO, Jamere Jackson is black.

Sandwiched between the two Hertz cases, Coffee 2016, a perennial best seller, finished second. “Glory, Glory, Man United!” a case about an English football team’s IPO made a surprise move to number four.  Cases on search fund boards, the future of malls,  Norway’s Sovereign Wealth fund, Prodigy Finance, the Mayo Clinic, and Cadbury rounded out the top ten.

Other year-end data for 2021 showed:

  • Online “raw” case usage remained steady as compared to 2020 with over 35K users from 170 countries and all 50 U.S. states interacting with 196 cases.
  • Fifty four percent of raw case users came from outside the U.S..
  • The Yale School of Management (SOM) case study directory pages received over 160K page views from 177 countries with approximately a third originating in India followed by the U.S. and the Philippines.
  • Twenty-six of the cases in the list are raw cases.
  • A third of the cases feature a woman protagonist.
  • Orders for Yale SOM case studies increased by almost 50% compared to 2020.
  • The top 40 cases were supervised by 19 different Yale SOM faculty members, several supervising multiple cases.

CRDT compiled the Top 40 list by combining data from its case store, Google Analytics, and other measures of interest and adoption.

All of this year’s Top 40 cases are available for purchase from the Yale Management Media store .

And the Top 40 cases studies of 2021 are:

1.   Hertz Global Holdings (A): Uses of Debt and Equity

2.   Coffee 2016

3.   Hertz Global Holdings (B): Uses of Debt and Equity 2020

4.   Glory, Glory Man United!

5.   Search Fund Company Boards: How CEOs Can Build Boards to Help Them Thrive

6.   The Future of Malls: Was Decline Inevitable?

7.   Strategy for Norway's Pension Fund Global

8.   Prodigy Finance

9.   Design at Mayo

10. Cadbury

11. City Hospital Emergency Room

13. Volkswagen

14. Marina Bay Sands

15. Shake Shack IPO

16. Mastercard

17. Netflix

18. Ant Financial

19. AXA: Creating the New CR Metrics

20. IBM Corporate Service Corps

21. Business Leadership in South Africa's 1994 Reforms

22. Alternative Meat Industry

23. Children's Premier

24. Khalil Tawil and Umi (A)

25. Palm Oil 2016

26. Teach For All: Designing a Global Network

27. What's Next? Search Fund Entrepreneurs Reflect on Life After Exit

28. Searching for a Search Fund Structure: A Student Takes a Tour of Various Options

30. Project Sammaan

31. Commonfund ESG

32. Polaroid

33. Connecticut Green Bank 2018: After the Raid

34. FieldFresh Foods

35. The Alibaba Group

36. 360 State Street: Real Options

37. Herman Miller

38. AgBiome

39. Nathan Cummings Foundation

40. Toyota 2010

7 Favorite Business Case Studies to Teach—and Why

Explore more.

  • Case Teaching
  • Course Materials


The Army Crew Team . Emily Michelle David of CEIBS

ATH Technologies . Devin Shanthikumar of Paul Merage School of Business

Fabritek 1992 . Rob Austin of Ivey Business School

Lincoln Electric Co . Karin Schnarr of Wilfrid Laurier University

Pal’s Sudden Service—Scaling an Organizational Model to Drive Growth . Gary Pisano of Harvard Business School

The United States Air Force: ‘Chaos’ in the 99th Reconnaissance Squadron . Francesca Gino of Harvard Business School

Warren E. Buffett, 2015 . Robert F. Bruner of Darden School of Business

To dig into what makes a compelling case study, we asked seven experienced educators who teach with—and many who write—business case studies: “What is your favorite case to teach and why?”

The resulting list of case study favorites ranges in topics from operations management and organizational structure to rebel leaders and whodunnit dramas.

1. The Army Crew Team

Emily Michelle David, Assistant Professor of Management, China Europe International Business School (CEIBS)

case study business size statistics

“I love teaching  The Army Crew Team  case because it beautifully demonstrates how a team can be so much less than the sum of its parts.

I deliver the case to executives in a nearby state-of-the-art rowing facility that features rowing machines, professional coaches, and shiny red eight-person shells.

After going through the case, they hear testimonies from former members of Chinese national crew teams before carrying their own boat to the river for a test race.

The rich learning environment helps to vividly underscore one of the case’s core messages: competition can be a double-edged sword if not properly managed.

case study business size statistics

Executives in Emily Michelle David’s organizational behavior class participate in rowing activities at a nearby facility as part of her case delivery.

Despite working for an elite headhunting firm, the executives in my most recent class were surprised to realize how much they’ve allowed their own team-building responsibilities to lapse. In the MBA pre-course, this case often leads to a rich discussion about common traps that newcomers fall into (for example, trying to do too much, too soon), which helps to poise them to both stand out in the MBA as well as prepare them for the lateral team building they will soon engage in.

Finally, I love that the post-script always gets a good laugh and serves as an early lesson that organizational behavior courses will seldom give you foolproof solutions for specific problems but will, instead, arm you with the ability to think through issues more critically.”

2. ATH Technologies

Devin Shanthikumar, Associate Professor of Accounting, Paul Merage School of Business

case study business size statistics

“As a professor at UC Irvine’s Paul Merage School of Business, and before that at Harvard Business School, I have probably taught over 100 cases. I would like to say that my favorite case is my own,   Compass Box Whisky Company . But as fun as that case is, one case beats it:  ATH Technologies  by Robert Simons and Jennifer Packard.

ATH presents a young entrepreneurial company that is bought by a much larger company. As part of the merger, ATH gets an ‘earn-out’ deal—common among high-tech industries. The company, and the class, must decide what to do to achieve the stretch earn-out goals.

ATH captures a scenario we all want to be in at some point in our careers—being part of a young, exciting, growing organization. And a scenario we all will likely face—having stretch goals that seem almost unreachable.

It forces us, as a class, to really struggle with what to do at each stage.

After we read and discuss the A case, we find out what happens next, and discuss the B case, then the C, then D, and even E. At every stage, we can:

see how our decisions play out,

figure out how to build on our successes, and

address our failures.

The case is exciting, the class discussion is dynamic and energetic, and in the end, we all go home with a memorable ‘ah-ha!’ moment.

I have taught many great cases over my career, but none are quite as fun, memorable, and effective as ATH .”

3. Fabritek 1992

Rob Austin, Professor of Information Systems, Ivey Business School

case study business size statistics

“This might seem like an odd choice, but my favorite case to teach is an old operations case called  Fabritek 1992 .

The latest version of Fabritek 1992 is dated 2009, but it is my understanding that this is a rewrite of a case that is older (probably much older). There is a Fabritek 1969 in the HBP catalog—same basic case, older dates, and numbers. That 1969 version lists no authors, so I suspect the case goes even further back; the 1969 version is, I’m guessing, a rewrite of an even older version.

There are many things I appreciate about the case. Here are a few:

It operates as a learning opportunity at many levels. At first it looks like a not-very-glamorous production job scheduling case. By the end of the case discussion, though, we’re into (operations) strategy and more. It starts out technical, then explodes into much broader relevance. As I tell participants when I’m teaching HBP's Teaching with Cases seminars —where I often use Fabritek as an example—when people first encounter this case, they almost always underestimate it.

It has great characters—especially Arthur Moreno, who looks like a troublemaker, but who, discussion reveals, might just be the smartest guy in the factory. Alums of the Harvard MBA program have told me that they remember Arthur Moreno many years later.

Almost every word in the case is important. It’s only four and a half pages of text and three pages of exhibits. This economy of words and sparsity of style have always seemed like poetry to me. I should note that this super concise, every-word-matters approach is not the ideal we usually aspire to when we write cases. Often, we include extra or superfluous information because part of our teaching objective is to provide practice in separating what matters from what doesn’t in a case. Fabritek takes a different approach, though, which fits it well.

It has a dramatic structure. It unfolds like a detective story, a sort of whodunnit. Something is wrong. There is a quality problem, and we’re not sure who or what is responsible. One person, Arthur Moreno, looks very guilty (probably too obviously guilty), but as we dig into the situation, there are many more possibilities. We spend in-class time analyzing the data (there’s a bit of math, so it covers that base, too) to determine which hypotheses are best supported by the data. And, realistically, the data doesn’t support any of the hypotheses perfectly, just some of them more than others. Also, there’s a plot twist at the end (I won’t reveal it, but here’s a hint: Arthur Moreno isn’t nearly the biggest problem in the final analysis). I have had students tell me the surprising realization at the end of the discussion gives them ‘goosebumps.’

Finally, through the unexpected plot twist, it imparts what I call a ‘wisdom lesson’ to young managers: not to be too sure of themselves and to regard the experiences of others, especially experts out on the factory floor, with great seriousness.”

4. Lincoln Electric Co.

Karin Schnarr, Assistant Professor of Policy, Wilfrid Laurier University

case study business size statistics

“As a strategy professor, my favorite case to teach is the classic 1975 Harvard case  Lincoln Electric Co.  by Norman Berg.

I use it to demonstrate to students the theory linkage between strategy and organizational structure, management processes, and leadership behavior.

This case may be an odd choice for a favorite. It occurs decades before my students were born. It is pages longer than we are told students are now willing to read. It is about manufacturing arc welding equipment in Cleveland, Ohio—a hard sell for a Canadian business classroom.

Yet, I have never come across a case that so perfectly illustrates what I want students to learn about how a company can be designed from an organizational perspective to successfully implement its strategy.

And in a time where so much focus continues to be on how to maximize shareholder value, it is refreshing to be able to discuss a publicly-traded company that is successfully pursuing a strategy that provides a fair value to shareholders while distributing value to employees through a large bonus pool, as well as value to customers by continually lowering prices.

However, to make the case resonate with today’s students, I work to make it relevant to the contemporary business environment. I link the case to multimedia clips about Lincoln Electric’s current manufacturing practices, processes, and leadership practices. My students can then see that a model that has been in place for generations is still viable and highly successful, even in our very different competitive situation.”

5. Pal’s Sudden Service—Scaling an Organizational Model to Drive Growth

Gary Pisano, Professor of Business Administration, Harvard Business School

case study business size statistics

“My favorite case to teach these days is  Pal’s Sudden Service—Scaling an Organizational Model to Drive Growth .

I love teaching this case for three reasons:

1. It demonstrates how a company in a super-tough, highly competitive business can do very well by focusing on creating unique operating capabilities. In theory, Pal’s should have no chance against behemoths like McDonalds or Wendy’s—but it thrives because it has built a unique operating system. It’s a great example of a strategic approach to operations in action.

2. The case shows how a strategic approach to human resource and talent development at all levels really matters. This company competes in an industry not known for engaging its front-line workers. The case shows how engaging these workers can really pay off.

3. Finally, Pal’s is really unusual in its approach to growth. Most companies set growth goals (usually arbitrary ones) and then try to figure out how to ‘backfill’ the human resource and talent management gaps. They trust you can always find someone to do the job. Pal’s tackles the growth problem completely the other way around. They rigorously select and train their future managers. Only when they have a manager ready to take on their own store do they open a new one. They pace their growth off their capacity to develop talent. I find this really fascinating and so do the students I teach this case to.”

6. The United States Air Force: ‘Chaos’ in the 99th Reconnaissance Squadron

Francesca Gino, Professor of Business Administration, Harvard Business School

case study business size statistics

“My favorite case to teach is  The United States Air Force: ‘Chaos’ in the 99th Reconnaissance Squadron .

The case surprises students because it is about a leader, known in the unit by the nickname Chaos , who inspired his squadron to be innovative and to change in a culture that is all about not rocking the boat, and where there is a deep sense that rules should simply be followed.

For years, I studied ‘rebels,’ people who do not accept the status quo; rather, they approach work with curiosity and produce positive change in their organizations. Chaos is a rebel leader who got the level of cultural change right. Many of the leaders I’ve met over the years complain about the ‘corporate culture,’ or at least point to clear weaknesses of it; but then they throw their hands up in the air and forget about changing what they can.

Chaos is different—he didn’t go after the ‘Air Force’ culture. That would be like boiling the ocean.

Instead, he focused on his unit of control and command: The 99th squadron. He focused on enabling that group to do what it needed to do within the confines of the bigger Air Force culture. In the process, he inspired everyone on his team to be the best they can be at work.

The case leaves the classroom buzzing and inspired to take action.”

7. Warren E. Buffett, 2015

Robert F. Bruner, Professor of Business Administration, Darden School of Business

case study business size statistics

“I love teaching   Warren E. Buffett, 2015  because it energizes, exercises, and surprises students.

Buffett looms large in the business firmament and therefore attracts anyone who is eager to learn his secrets for successful investing. This generates the kind of energy that helps to break the ice among students and instructors early in a course and to lay the groundwork for good case discussion practices.

Studying Buffett’s approach to investing helps to introduce and exercise important themes that will resonate throughout a course. The case challenges students to define for themselves what it means to create value. The case discussion can easily be tailored for novices or for more advanced students.

Either way, this is not hero worship: The case affords a critical examination of the financial performance of Buffett’s firm, Berkshire Hathaway, and reveals both triumphs and stumbles. Most importantly, students can critique the purported benefits of Buffett’s conglomeration strategy and the sustainability of his investment record as the size of the firm grows very large.

By the end of the class session, students seem surprised with what they have discovered. They buzz over the paradoxes in Buffett’s philosophy and performance record. And they come away with sober respect for Buffett’s acumen and for the challenges of creating value for investors.

Surely, such sobriety is a meta-message for any mastery of finance.”

More Educator Favorites


Emily Michelle David is an assistant professor of management at China Europe International Business School (CEIBS). Her current research focuses on discovering how to make workplaces more welcoming for people of all backgrounds and personality profiles to maximize performance and avoid employee burnout. David’s work has been published in a number of scholarly journals, and she has worked as an in-house researcher at both NASA and the M.D. Anderson Cancer Center.

case study business size statistics

Devin Shanthikumar  is an associate professor and the accounting area coordinator at UCI Paul Merage School of Business. She teaches undergraduate, MBA, and executive-level courses in managerial accounting. Shanthikumar previously served on the faculty at Harvard Business School, where she taught both financial accounting and managerial accounting for MBAs, and wrote cases that are used in accounting courses across the country.

case study business size statistics

Robert D. Austin is a professor of information systems at Ivey Business School and an affiliated faculty member at Harvard Medical School. He has published widely, authoring nine books, more than 50 cases and notes, three Harvard online products, and two popular massive open online courses (MOOCs) running on the Coursera platform.

case study business size statistics

Karin Schnarr is an assistant professor of policy and the director of the Bachelor of Business Administration (BBA) program at the Lazaridis School of Business & Economics at Wilfrid Laurier University in Waterloo, Ontario, Canada where she teaches strategic management at the undergraduate, graduate, and executive levels. Schnarr has published several award-winning and best-selling cases and regularly presents at international conferences on case writing and scholarship.

case study business size statistics

Gary P. Pisano is the Harry E. Figgie, Jr. Professor of Business Administration and senior associate dean of faculty development at Harvard Business School, where he has been on the faculty since 1988. Pisano is an expert in the fields of technology and operations strategy, the management of innovation, and competitive strategy. His research and consulting experience span a range of industries including aerospace, biotechnology, pharmaceuticals, specialty chemicals, health care, nutrition, computers, software, telecommunications, and semiconductors.

case study business size statistics

Francesca Gino studies how people can have more productive, creative, and fulfilling lives. She is a professor at Harvard Business School and the author, most recently, of  Rebel Talent: Why It Pays to Break the Rules at Work and in Life . Gino regularly gives keynote speeches, delivers corporate training programs, and serves in advisory roles for firms and not-for-profit organizations across the globe.

case study business size statistics

Robert F. Bruner is a university professor at the University of Virginia, distinguished professor of business administration, and dean emeritus of the Darden School of Business. He has also held visiting appointments at Harvard and Columbia universities in the United States, at INSEAD in France, and at IESE in Spain. He is the author, co-author, or editor of more than 20 books on finance, management, and teaching. Currently, he teaches and writes in finance and management.

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How to write a case study — examples, templates, and tools

How to write a case study — examples, templates, and tools marquee

It’s a marketer’s job to communicate the effectiveness of a product or service to potential and current customers to convince them to buy and keep business moving. One of the best methods for doing this is to share success stories that are relatable to prospects and customers based on their pain points, experiences, and overall needs.

That’s where case studies come in. Case studies are an essential part of a content marketing plan. These in-depth stories of customer experiences are some of the most effective at demonstrating the value of a product or service. Yet many marketers don’t use them, whether because of their regimented formats or the process of customer involvement and approval.

A case study is a powerful tool for showcasing your hard work and the success your customer achieved. But writing a great case study can be difficult if you’ve never done it before or if it’s been a while. This guide will show you how to write an effective case study and provide real-world examples and templates that will keep readers engaged and support your business.

In this article, you’ll learn:

What is a case study?

How to write a case study, case study templates, case study examples, case study tools.

A case study is the detailed story of a customer’s experience with a product or service that demonstrates their success and often includes measurable outcomes. Case studies are used in a range of fields and for various reasons, from business to academic research. They’re especially impactful in marketing as brands work to convince and convert consumers with relatable, real-world stories of actual customer experiences.

The best case studies tell the story of a customer’s success, including the steps they took, the results they achieved, and the support they received from a brand along the way. To write a great case study, you need to:

  • Celebrate the customer and make them — not a product or service — the star of the story.
  • Craft the story with specific audiences or target segments in mind so that the story of one customer will be viewed as relatable and actionable for another customer.
  • Write copy that is easy to read and engaging so that readers will gain the insights and messages intended.
  • Follow a standardized format that includes all of the essentials a potential customer would find interesting and useful.
  • Support all of the claims for success made in the story with data in the forms of hard numbers and customer statements.

Case studies are a type of review but more in depth, aiming to show — rather than just tell — the positive experiences that customers have with a brand. Notably, 89% of consumers read reviews before deciding to buy, and 79% view case study content as part of their purchasing process. When it comes to B2B sales, 52% of buyers rank case studies as an important part of their evaluation process.

Telling a brand story through the experience of a tried-and-true customer matters. The story is relatable to potential new customers as they imagine themselves in the shoes of the company or individual featured in the case study. Showcasing previous customers can help new ones see themselves engaging with your brand in the ways that are most meaningful to them.

Besides sharing the perspective of another customer, case studies stand out from other content marketing forms because they are based on evidence. Whether pulling from client testimonials or data-driven results, case studies tend to have more impact on new business because the story contains information that is both objective (data) and subjective (customer experience) — and the brand doesn’t sound too self-promotional.

89% of consumers read reviews before buying, 79% view case studies, and 52% of B2B buyers prioritize case studies in the evaluation process.

Case studies are unique in that there’s a fairly standardized format for telling a customer’s story. But that doesn’t mean there isn’t room for creativity. It’s all about making sure that teams are clear on the goals for the case study — along with strategies for supporting content and channels — and understanding how the story fits within the framework of the company’s overall marketing goals.

Here are the basic steps to writing a good case study.

1. Identify your goal

Start by defining exactly who your case study will be designed to help. Case studies are about specific instances where a company works with a customer to achieve a goal. Identify which customers are likely to have these goals, as well as other needs the story should cover to appeal to them.

The answer is often found in one of the buyer personas that have been constructed as part of your larger marketing strategy. This can include anything from new leads generated by the marketing team to long-term customers that are being pressed for cross-sell opportunities. In all of these cases, demonstrating value through a relatable customer success story can be part of the solution to conversion.

2. Choose your client or subject

Who you highlight matters. Case studies tie brands together that might otherwise not cross paths. A writer will want to ensure that the highlighted customer aligns with their own company’s brand identity and offerings. Look for a customer with positive name recognition who has had great success with a product or service and is willing to be an advocate.

The client should also match up with the identified target audience. Whichever company or individual is selected should be a reflection of other potential customers who can see themselves in similar circumstances, having the same problems and possible solutions.

Some of the most compelling case studies feature customers who:

  • Switch from one product or service to another while naming competitors that missed the mark.
  • Experience measurable results that are relatable to others in a specific industry.
  • Represent well-known brands and recognizable names that are likely to compel action.
  • Advocate for a product or service as a champion and are well-versed in its advantages.

Whoever or whatever customer is selected, marketers must ensure they have the permission of the company involved before getting started. Some brands have strict review and approval procedures for any official marketing or promotional materials that include their name. Acquiring those approvals in advance will prevent any miscommunication or wasted effort if there is an issue with their legal or compliance teams.

3. Conduct research and compile data

Substantiating the claims made in a case study — either by the marketing team or customers themselves — adds validity to the story. To do this, include data and feedback from the client that defines what success looks like. This can be anything from demonstrating return on investment (ROI) to a specific metric the customer was striving to improve. Case studies should prove how an outcome was achieved and show tangible results that indicate to the customer that your solution is the right one.

This step could also include customer interviews. Make sure that the people being interviewed are key stakeholders in the purchase decision or deployment and use of the product or service that is being highlighted. Content writers should work off a set list of questions prepared in advance. It can be helpful to share these with the interviewees beforehand so they have time to consider and craft their responses. One of the best interview tactics to keep in mind is to ask questions where yes and no are not natural answers. This way, your subject will provide more open-ended responses that produce more meaningful content.

4. Choose the right format

There are a number of different ways to format a case study. Depending on what you hope to achieve, one style will be better than another. However, there are some common elements to include, such as:

  • An engaging headline
  • A subject and customer introduction
  • The unique challenge or challenges the customer faced
  • The solution the customer used to solve the problem
  • The results achieved
  • Data and statistics to back up claims of success
  • A strong call to action (CTA) to engage with the vendor

It’s also important to note that while case studies are traditionally written as stories, they don’t have to be in a written format. Some companies choose to get more creative with their case studies and produce multimedia content, depending on their audience and objectives. Case study formats can include traditional print stories, interactive web or social content, data-heavy infographics, professionally shot videos, podcasts, and more.

5. Write your case study

We’ll go into more detail later about how exactly to write a case study, including templates and examples. Generally speaking, though, there are a few things to keep in mind when writing your case study.

  • Be clear and concise. Readers want to get to the point of the story quickly and easily, and they’ll be looking to see themselves reflected in the story right from the start.
  • Provide a big picture. Always make sure to explain who the client is, their goals, and how they achieved success in a short introduction to engage the reader.
  • Construct a clear narrative. Stick to the story from the perspective of the customer and what they needed to solve instead of just listing product features or benefits.
  • Leverage graphics. Incorporating infographics, charts, and sidebars can be a more engaging and eye-catching way to share key statistics and data in readable ways.
  • Offer the right amount of detail. Most case studies are one or two pages with clear sections that a reader can skim to find the information most important to them.
  • Include data to support claims. Show real results — both facts and figures and customer quotes — to demonstrate credibility and prove the solution works.

6. Promote your story

Marketers have a number of options for distribution of a freshly minted case study. Many brands choose to publish case studies on their website and post them on social media. This can help support SEO and organic content strategies while also boosting company credibility and trust as visitors see that other businesses have used the product or service.

Marketers are always looking for quality content they can use for lead generation. Consider offering a case study as gated content behind a form on a landing page or as an offer in an email message. One great way to do this is to summarize the content and tease the full story available for download after the user takes an action.

Sales teams can also leverage case studies, so be sure they are aware that the assets exist once they’re published. Especially when it comes to larger B2B sales, companies often ask for examples of similar customer challenges that have been solved.

Now that you’ve learned a bit about case studies and what they should include, you may be wondering how to start creating great customer story content. Here are a couple of templates you can use to structure your case study.

Template 1 — Challenge-solution-result format

  • Start with an engaging title. This should be fewer than 70 characters long for SEO best practices. One of the best ways to approach the title is to include the customer’s name and a hint at the challenge they overcame in the end.
  • Create an introduction. Lead with an explanation as to who the customer is, the need they had, and the opportunity they found with a specific product or solution. Writers can also suggest the success the customer experienced with the solution they chose.
  • Present the challenge. This should be several paragraphs long and explain the problem the customer faced and the issues they were trying to solve. Details should tie into the company’s products and services naturally. This section needs to be the most relatable to the reader so they can picture themselves in a similar situation.
  • Share the solution. Explain which product or service offered was the ideal fit for the customer and why. Feel free to delve into their experience setting up, purchasing, and onboarding the solution.
  • Explain the results. Demonstrate the impact of the solution they chose by backing up their positive experience with data. Fill in with customer quotes and tangible, measurable results that show the effect of their choice.
  • Ask for action. Include a CTA at the end of the case study that invites readers to reach out for more information, try a demo, or learn more — to nurture them further in the marketing pipeline. What you ask of the reader should tie directly into the goals that were established for the case study in the first place.

Template 2 — Data-driven format

  • Start with an engaging title. Be sure to include a statistic or data point in the first 70 characters. Again, it’s best to include the customer’s name as part of the title.
  • Create an overview. Share the customer’s background and a short version of the challenge they faced. Present the reason a particular product or service was chosen, and feel free to include quotes from the customer about their selection process.
  • Present data point 1. Isolate the first metric that the customer used to define success and explain how the product or solution helped to achieve this goal. Provide data points and quotes to substantiate the claim that success was achieved.
  • Present data point 2. Isolate the second metric that the customer used to define success and explain what the product or solution did to achieve this goal. Provide data points and quotes to substantiate the claim that success was achieved.
  • Present data point 3. Isolate the final metric that the customer used to define success and explain what the product or solution did to achieve this goal. Provide data points and quotes to substantiate the claim that success was achieved.
  • Summarize the results. Reiterate the fact that the customer was able to achieve success thanks to a specific product or service. Include quotes and statements that reflect customer satisfaction and suggest they plan to continue using the solution.
  • Ask for action. Include a CTA at the end of the case study that asks readers to reach out for more information, try a demo, or learn more — to further nurture them in the marketing pipeline. Again, remember that this is where marketers can look to convert their content into action with the customer.

While templates are helpful, seeing a case study in action can also be a great way to learn. Here are some examples of how Adobe customers have experienced success.

Juniper Networks

One example is the Adobe and Juniper Networks case study , which puts the reader in the customer’s shoes. The beginning of the story quickly orients the reader so that they know exactly who the article is about and what they were trying to achieve. Solutions are outlined in a way that shows Adobe Experience Manager is the best choice and a natural fit for the customer. Along the way, quotes from the client are incorporated to help add validity to the statements. The results in the case study are conveyed with clear evidence of scale and volume using tangible data.

A Lenovo case study showing statistics, a pull quote and featured headshot, the headline "The customer is king.," and Adobe product links.

The story of Lenovo’s journey with Adobe is one that spans years of planning, implementation, and rollout. The Lenovo case study does a great job of consolidating all of this into a relatable journey that other enterprise organizations can see themselves taking, despite the project size. This case study also features descriptive headers and compelling visual elements that engage the reader and strengthen the content.

Tata Consulting

When it comes to using data to show customer results, this case study does an excellent job of conveying details and numbers in an easy-to-digest manner. Bullet points at the start break up the content while also helping the reader understand exactly what the case study will be about. Tata Consulting used Adobe to deliver elevated, engaging content experiences for a large telecommunications client of its own — an objective that’s relatable for a lot of companies.

Case studies are a vital tool for any marketing team as they enable you to demonstrate the value of your company’s products and services to others. They help marketers do their job and add credibility to a brand trying to promote its solutions by using the experiences and stories of real customers.

When you’re ready to get started with a case study:

  • Think about a few goals you’d like to accomplish with your content.
  • Make a list of successful clients that would be strong candidates for a case study.
  • Reach out to the client to get their approval and conduct an interview.
  • Gather the data to present an engaging and effective customer story.

Adobe can help

There are several Adobe products that can help you craft compelling case studies. Adobe Experience Platform helps you collect data and deliver great customer experiences across every channel. Once you’ve created your case studies, Experience Platform will help you deliver the right information to the right customer at the right time for maximum impact.

To learn more, watch the Adobe Experience Platform story .

Keep in mind that the best case studies are backed by data. That’s where Adobe Real-Time Customer Data Platform and Adobe Analytics come into play. With Real-Time CDP, you can gather the data you need to build a great case study and target specific customers to deliver the content to the right audience at the perfect moment.

Watch the Real-Time CDP overview video to learn more.

Finally, Adobe Analytics turns real-time data into real-time insights. It helps your business collect and synthesize data from multiple platforms to make more informed decisions and create the best case study possible.

Request a demo to learn more about Adobe Analytics.

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Vector illustration for the article How to Write a Great Case Study for Business

How to Write a Great Case Study for Business

Julian lumpkin.

case study business size statistics

  • December 9, 2021
  • Creating Case Studies

A Case Study for business is extremely powerful. If your company has at least one strong Case Study for your sales and marketing teams to use when engaging prospects, your business will prosper. But what makes for a great Case Study?

The secret: A great Case Study tells a great story.

This post summarizes how to structure your Case Study for business in a way that will tell a compelling story and help turn prospects into clients.

How to Structure a Case Study for Business

There are five parts to any successful Case Study for Business: the Summary, the Overview, the Problem/Situation, the Solution, and the Results. Take a look at the directions for using our Case Study template to learn about each section in greater depth, but for a high-level overview, read on.

The Summary highlights the most crucial aspect of the Case Study: the big result(s) you helped your client achieve. Remember: Most readers will glance at your Case Study for just a few seconds to determine if it’s relevant, so make sure the Summary grabs the reader’s attention. It needs to jump off the page in both formatting and the message itself. A good example is Tech company increases renewals 22 percent by adopting intelligent billing software.

2. Overview

The Overview is part of the next section, which is the Problem or Situation. This opening paragraph or set of bullet points identifies your client and summarizes the client’s industry, size, clientele, revenue, and any other type of identifying information. It also gives the reader context, sets the scene for your Case Study, and paves the way to introducing the problem. A strong sample segue is Company X realized it was missing out on opportunities for sales to connect with prospects.

After the Overview, transition to the Problem or Situation. This is the core scenario or reason that led your client to your solution.

To enhance the story of the Case Study, make sure you lay out the cause and effect of your client’s situation. It isn’t enough to state that the client was, for example, not seeing many sales. Dig deeper: What does a lack of sales mean for the client, its growth, and its future?

Also, your Case Study will resonate more strongly with readers if you use specific statistics, direct quotes, and relevant images.

An example of a Problem is as follows: Company X wasn’t living up to its expectations in sales revenue (60 percent dip YOY) and realized its key issue was an inability to connect with a portion of prospects. Perfectly qualified prospects continued to walk away from Company X’s product offering.

4. Solution

The Solution is where you’ll introduce a little self-promotion as you break down how your company resolved each and every one of your client’s problems.

Supporting evidence is essential at this point. A direct quote about  exactly  how your company eliminated the client’s pain points goes a long way toward a successful case study.

And as you did in the Problem section, incorporate specific metrics and details. These elements not only make your Case Study a great story, they also make it a more compelling sales tool. Prospects who read your Case Study will put themselves in your customers’ shoes and see themselves having a similar win.

The following is a sample Solution:

Once we integrated our product, Company X saw an immediate 20 percent boost on close rates. In less than six months, Company X had a library of more than one hundred Success Stories built up for its sales team to recall.

“SuccessKit solved an age-old problem on the sales floor—making sure the sales reps put their best feet forward each and every time they engage an opportunity,” said Jane Doe, VP of Sales at Company X.

The Results section summarizes some of your value propositions. It’s best to be specific to the client in question and use facts and figures (e.g., Increased revenue by 15 percent in three months ). However, you can be more general as well (e.g., simply Increased revenue ).

Bullet points work very well in this section, as this is where most people will look at first to get an idea of what you’re really offering. Make sure it stands out and is easy to read.

We at SuccessKit can help you tell great stories with your Case Studies for business. Contact us at [email protected] to learn how.

case study business size statistics

Julian has focused his career on B2B sales and sales management, specifically bringing new technologies to market. After years as an elite sales rep, he began leading teams, specifically focused on coaching sales reps on how to be direct, credible, and respected throughout the sales process. Julian conceived of and designed SuccessKit when running an 18 person sales-team at Axial, a b2b startup, as a way to help sales reps have better conversations by utilizing customer success examples and other content more effectively.

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Master Market Sizing Questions in Consulting Interviews

the image is the cover for an article on market sizing in consulting interviews

Last Updated on March 6, 2024

Market sizing stands as a critical analytical tool in the consulting domain, offering insights into the total market demand for a product or service. This process, pivotal for informing business strategy and guiding investment decisions, involves a thorough analysis of industry trends, market dynamics, and economic factors.

By estimating the size and growth potential of a market, consultants can identify lucrative opportunities for new products or services, compare different market segments, and evaluate the competitive landscape. Essential for any organization’s strategic planning, market sizing equips businesses with a clear understanding of market potential.

In the high-stakes environment of consulting case interviews , particularly with prestigious firms like McKinsey & Company , Boston Consulting Group (BCG) , and Bain & Company , market sizing questions emerge as a fundamental challenge for candidates. These questions, a crucial subset of estimation questions, test a candidate’s analytical prowess and strategic thinking.

As you navigate through the rigorous consulting interview process , which typically encompasses three to six interviews, the likelihood of encountering market sizing scenarios is high. Preparing to tackle these questions effectively is key to demonstrating your capability to dissect complex scenarios and deliver insightful quantitative analyses, a skill highly valued by top consulting firms worldwide.

In this article, I want to dive deeper into the subject to talk about market sizing, estimation questions, and everything that helps you become better at it for your top-tier consulting interviews.

What Are Estimation Questions?

Estimation questions in a case interview are questions that test a candidate’s ability to make rough, educated guesses about specific numerical values or quantities. These questions are commonly asked in case study interviews for management consulting, finance, or strategy positions, as they can help investigate a candidate’s problem-solving, analytical, and quantitative skills.

Estimation questions can cover a variety of topics, such as market size, but, also other things such as

  • growth rates (e.g., “ By how much will the job market grow over the next 5 years?” )
  • cost structures (e.g., “How expensive is the production of one aircraft engine?” )
  • resource requirements (e.g., “How many employees does an average burger joint need?” )

The goal of estimation questions is to evaluate a candidate’s ability to think critically, communicate clearly, and make informed decisions based on limited information in the same way as in a market sizing question (just with a broader and/or different context).

The most important subset of these questions for a case interview is market sizing (that’s why they are often called market sizing questions as a blanket term).

What Are Market Sizing Questions?

In short, market sizing questions are an essential part of market analysis and business strategy. They involve determining the size of a particular market or target audience and are often used to assess the potential of a new product or service.

In a case interview, you might have to size a market in an abbreviated task either as a standalone question or as part of a longer case. For instance, in many countries, Bain has one full interview dedicated to market sizing in their first round.

What Are Market Sizing Example Questions?

Before we dive into the mechanics of market sizing, let’s first look at typical questions you might be asked.

Some common examples include:

  • “What is the size of the market for electric vehicles in Europe?”
  • “What is the potential market size for a new smartphone app in the US?”
  • “What is the size of the market for organic food in Asia?”

There is essentially an unlimited number of market sizing questions that you could be asked.

Everything has a market. Every occurrence or quantity can be estimated.

Why Are Consulting Firms Using Market Sizing Questions?

Consulting firms use market sizing questions in their case study assessments because the process and approach to market sizing that candidates take allow the firms to gather a lot of insights into different skills that are also important for the daily life of a consultant.

What are those skills?

  • Problem-Solving : The ability to identify the market sizing problem correctly and develop a structured approach to solving it.
  • Data Collection and Analysis: The ability to gather relevant market data by asking the right question and analyzing it to draw meaningful insights, all while working with imperfect information.
  • Market Knowledge and Common Sense: An understanding of the market, industry trends, and key players is essential for accurate market sizing (in a case interview, you are not expected to have in-depth knowledge about a particular industry, unless you are hired for a specialized role)
  • Quantitative Reasoning : The ability to comfortably create equations, manipulate numbers, and perform mental and pen-and-paper math
  • Communication: The ability to effectively communicate your approach, findings, and insights to the interviewer.
  • Creativity : The ability to think creatively and develop innovative solutions to complex market sizing problems (e.g., by considering different segments of the market than average candidates).
  • Attention to Detail : Market sizing requires precision and attention to detail to ensure accurate and reliable results.
  • Ability to Work Under Pressure : Solving complex market sizing questions puts a lot of stress on you as a candidate as you need to create an outcome in a short amount of time and combine several different skills and actions, all while being watched and evaluated by the interviewer.

Now that it is clear what you need to display, let us look more into the process

How Should You Approach Market Sizing Questions?

The key to solving market sizing problems is to approach them with a strategic and thorough approach. Utilizing multiple market sizing techniques and strategies, as well as validating and verifying findings through cross-referencing and considering market trends, can help you get directionally correct results.

What interviewers are looking for are not accurate results but a coherent logic and approach, well-justified and qualified assumptions, and directionally correct outcomes.

To demonstrate this, I would recommend that you approach market sizing in five steps .

Let’s use a practical example.

How many pizzas are sold in the United States per year? A tasty question

Step 1: Understand the outcome variable

  • Play back the question to the interviewer and any data that you have received until this point.
  • Ask the interviewer to repeat or explain the information you were given to make sure you understand it correctly and clarify what they are expecting you to do. Make sure that you understand what outcome variable you need to calculate and what unit of measurement they are asking for (e.g., number of sales or revenue of sales per day/week/month/quarter/year).

Step 2: Think about your approach

  • Ask the interviewer for some time to gather your thoughts.
  • Draft your approach either by going top-down and thinking about the big picture and the slice of that big picture (pun intended). For instance, you could think about the U.S. population, segment it, and then narrow it down to the pizza consumption within that population. Alternatively, you could go bottom-up looking at the details and then aggregating it up, for instance by considering your own pizza consumption and extending it to the total population.
  • The method you choose depends on your own preferences and the type of question.
  • For a quick approach, think about 3 to 4 key variables that influence the outcome variable and their relationship. In our case, I choose a bottom-up approach.
  • Communicate your approach to the interviewer.

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Total number of pizzas sold per year: pizza per month x number of months in a year x population Pizza eaten per month: 1 (e.g., basing this off your individual pizza consumption; you could argue that there are people who eat more than 1 and some that eat less than that, hence balancing out at around 1 pizza per person) Number of months per year: 12 Population: 331 million (either you know this number and you should if you are from the U.S. or you ask the interviewer for it if you are a not a US citizen) Quick approach to market sizing (you end up in the right ballpark)

This approach gives you a quick indication of where the final number should be, yet it is not very accurate. You could now start thinking of refining it. How?

Step 3: Calculate and refine your analysis

  • Plug in the numbers and calculate your outcome, which is roughly 4 billion pizzas per year .
  • There are several ways to refine your analysis. Some interviewers are happy with a quick and dirty analysis, especially if the question comes up as part of a longer case. Some might want to drill deeper.
  • In the latter case, you could start segmenting the market to get more accurate and refined outcomes. These filters help you carve out irrelevant segments or finetune the consumption of each segment. Out of the 331 million people, not everyone eats 1 pizza per month. For a quick win, you could take out the age groups from 0 to 6 as they are likely not eating any pizza at all. Assuming a normal distribution of age and a life expectancy of 80 years, that would be around 25 million people less. If you want to go further you could argue that people between 6 and 12 and 70 and 80 only eat half a pizza per month on average. You end up with roughly 66 million people less or 33 million pizzas less per month. If you combine both assumptions and refine your initial analysis, you end up with roughly 3.2 billion pizzas per year.

Every number you have come up with in Steps 2 and 3 relies on assumptions. If interviewers do not provide any data, you need to come up with your own assumptions and justify them in front of the interviewer (e.g., “I would assume that babies and young children do not consume any pizza at all” ).

Step 4: Sanity-check your answer

  • Quickly sanity-check your approach and calculations, then think about whether the final number is realistic and in the right ballpark.
  • You could look at the outcome and relate it to another statistic or figure you know (e.g., you might have heard about burger or pasta consumption in the U.S. or pizza consumption in another population and could compare it).
  • You could discuss the number in the context of the prompt (e.g., “the U.S. is big on fast food so 1 pizza per month does not sound like an exaggeration” ).
  • You can quickly go in the other direction, in our case top-down to see where you end up (if time permits).
  • If the interviewer points out any obvious mistakes or flaws, go back and either check your approach and assumptions or the actual calculations.

Step 5: Interpret the result in the context of the case and/or prompt

  • Communicate the outcome and your insights in the context of the case and/or brief (e.g., “3.2 billion pizzas are sold per year in the U.S., which initially sounds like a lot but given my assumptions and the type of market I think that is a. realistic and b. an attractive market for fast food overall” ).
  • Consider what adjustments could be made to your logic to make the number smaller or larger (e.g., “If I wanted to drive this further, I would potentially look more into the differences across segments, such as differences in geographical segments” ).
  • If you are working on a full-length case interview, discuss how you would move forward with this new information.

If you are curious to get the actual number of pizzas eaten per year in the U.S., continue reading. I’ll share this with you later in this article.

case study business size statistics

Quick note: If the interviewer only asks you about market sizing and has allocated 20 minutes or more for the interview, chances are that they want you to go much deeper into your analysis.

What this would mean for you is to create more segments and assumptions for each segment (e.g. split the population into age groups of 10 years and discuss consumption habits for each group individually). You might also want to think about regional differences in pizza consumption (e.g, cities vs. more rural areas, etc.). There are plenty of ways to refine your analysis. Usually, there is no single best way but multiple approaches. What is important is that your approach is logical and coherent.

If that is the case, the approach is the same that I discussed above, just more granular. Pay attention to keeping your approach logically coherent and avoid minor mistakes in your calculations.

Tips and Tricks For Market Sizing Questions

When working on market sizing and estimation questions, you should look for ways to make your life easier.

  • Ask clarifying questions: Make sure you understand the scope and constraints of the question before proceeding with drafting your approach and logic.
  • Choose a simple approach: Work on the 20% of the problem that gives you 80% of the solution. Avoid getting bogged down into details of the logic that might change your final number by 1-2% but take more time than getting the first 80% right.
  • Simplify and round numbers: A common error that I see with many candidates is that they strive for excessive precision, which leads to complicated numbers that make calculations extremely time-consuming and increase the risk of errors along the way. When calculating, use round numbers and approximations to make your calculations easier. For instance, if you look at the population in Germany, use 90 million people instead of 86 million. When thinking about how many people in Germany own a car, think about 60% instead of 63% (also by rounding one number up and one down you balance out both simplifications). We are looking for directionally correct results that can be calculated quickly.
  • Show your reasoning and assumptions: Explain your thought process, sources of information, and the assumptions made. Use your own experience and analogies to justify your assumptions.
  • Be ready to defend your estimation: Be prepared to answer follow-up questions and be able to defend your estimate with supporting data or logical arguments.

What Data Should You Know to Help You With Market Sizing?

In tackling market sizing questions, especially during case interviews, it’s crucial to consider a range of common factors. These elements are instrumental in developing a comprehensive understanding of the market size for a specific product or service.

  • Population Size : Think about the overarching population size or drill down to specific geographical regions to gauge the baseline potential customer base.
  • Geographic Factors : Delve into the particulars of the market’s geography, considering if the scope is regional, national, or international, and aligning this with demographic data.
  • Demographics : Detailing the target market’s age, gender, income levels, and more refines your market size estimate, aligning it more closely with the product or service’s intended audience.
  • Average Household Size : Incorporating the average household size into your analysis can provide insights into potential market penetration and product adoption rates (e.g., items per household instead of items per population).
  • Market Segmentation : Analyze specific market segments to better understand nuanced customer groups, whether segmented by demographic, industry, lifestyle, or other relevant categories.
  • Market Growth Rate : Evaluate both historical and projected growth rates to estimate future market demand and expansion possibilities.
  • Competitive Analysis : Assessing the competitive landscape, including major players and their market share, frames your market size within the context of existing market dynamics.
  • Pricing and Revenue Models : Understanding pricing strategies and the revenue potential per unit sold or service rendered is key to sizing the market value accurately.
  • Regulatory Environment : Factor in any regulatory constraints or opportunities that may affect market access, size, and growth potential.
  • Replacement Rate : Estimating the replacement or repurchase rate of certain products offers additional insight into recurring revenue potential and market sustainability (e.g., how often is a TV replaced?).

Keeping these factors in mind during market sizing exercises can significantly enhance the accuracy and depth of your analysis, providing a solid foundation for strategic decisions and business planning.

You can ask the interviewer for specific data points – such as the population size of a target market. However, it’s equally important to demonstrate your analytical acumen by making informed assumptions about other factors, like the replacement rate of a product.

This balance showcases your strategic thinking, reflects your ability to navigate through ambiguity with logical assumptions, and highlights your interactive engagement skills, making you a standout candidate.

How Can You Practice Market Sizing and Estimation Questions?

Mastering market sizing questions is an entirely learned skill that needs a lot of practice and targeted preparation. How should you go about it?

Familiarize yourself with the key techniques and approaches

Market sizing questions are always very similar. Familiarize yourself with the typical types of problems and the key drivers. Identify the important market drivers, such as the population size, GDP, and other pertinent economic indicators, to properly size different markets. As you practice estimating their impact on market size, try to identify the primary drivers for various markets. On top of that, being able to make informed assumptions or estimations is essential for market sizing. To practice estimating prices for various marketplaces, you can utilize tactics like the “rule of thumb” or “top-down/bottom-up” approaches.

Become comfortable with quant problems in general

Market sizing is just another type of the many different math problems you have to solve during a case interview. As a result, work on your quantitative muscles as this will also improve your performance for market sizing.

How to approach case interview math.

Learn about different industries and economies

Knowing the industry you are looking at or the country you are dealing with can be an advantage as it allows you to perform more informed analyses. The more you are familiar with different industries or countries, their drivers, and characteristics the better you are at identifying the key drivers, the more accurate will be your assumptions, and the more exhaustive will be your analysis.

Case interview industry overview.

Leverage case books and resources

Get exposure to a variety of different market sizing questions. You can review market sizing questions in the majority of case books to get a sense of the overall process and the different types. Whenever you encounter a new problem, you should first try to solve it on your own before checking the proposed solution.

Click here for free case practice materials

Practice with peers

Similar to business case practice, you can also work on market sizing and estimation questions with a case practice partner. First, the other party acts as an interviewer and asks you a question, that you answer in the same way as I described earlier. Once you have finished your analysis and communicated the result, it is time to debrief your process, logic, and presentation with the interviewer. After that, switch roles and repeat the process with a different question. You will act as the interviewer now.

Create your own estimation questions

You can create an unlimited amount of market sizing cases for yourself. Look for actual examples of market sizing questions, then attempt to resolve them on your own. You could, for instance, try to determine the size of the market for a specific product or sector of the economy or calculate the potential profits of a startup.

  • If you see a business while walking outside, ask yourself how much revenue they are making, how many customers they are serving, etc., or calculate these numbers for the total industry in your country, another country, etc.
  • If you see a product, ask yourself how many are sold per year or what the total sales revenue is per year (e.g., a specific type of car).
  • If you see an item, think about how many there are in your city or country (e.g., a window). This is for more abstract market sizing questions.

To compare your answer with the actual number, just google it.

So, How Many Pizzas Are Sold in The U.S.?

In case you were wondering, 3 billion pizzas are sold (and presumably) eaten in the U.S. per year (according to an article by the Washington Post).

If you look closer, that are 350 slices of pizza that are consumed every second, or the equivalent of 100 acres of pizza every day. If you divide that by the number of slices per person per year, each person in the USA receives 46 slices annually.

Does McKinsey Ask Market Sizing Questions?

Rarely. The McKinsey case format is different and the interviews are more structured and standardized to safeguard a certain level of objectivity across interviewers, offices, and countries.

However, it might still very well be that the partner you are interviewing with is interested in testing your market sizing and estimation questions. On top of that, you will benefit from practicing market sizing and estimation questions as it helps you develop structure and rigor for any other quantitative analysis as well.

In sum, to be on the safe side it makes sense to prepare for this type of question if you are interviewing with McKinsey, in the same way as if you are interviewing with other top consulting firms such as BCG, Bain, Kearney, PwC, Deloitte, Oliver Wyman, L.E.K., Roland Berger, Strategy&, Accenture, and EY that ask estimation questions as part of their case interviews and case study assessments more often.

Avoid the Most Common Market Sizing Mistakes

When tackling market sizing or estimation questions, it’s crucial to sidestep common errors that can undermine your solution. Here’s an extended list of mistakes to avoid:

Market Sizing Practice Problems

Here is a list of 20 diverse market sizing practice problems, spanning various products, services, and countries, to help you prepare for consulting case interviews:

  • Estimate the annual market size for electric vehicles in Germany.
  • Calculate the total number of coffee cups sold in cafés across the United States daily.
  • Determine the market size for online education platforms in India.
  • Assess the yearly demand for running shoes in Brazil.
  • Estimate the market value of smart home devices in Japan.
  • Gauge the size of the fast-food industry in China.
  • Calculate the total revenue generated by movie theaters in France over a weekend.
  • Determine the number of bicycles sold annually in the Netherlands.
  • Estimate the annual consumption of bottled water in the Middle East.
  • Assess the market size for pet insurance in the United Kingdom.
  • Calculate the number of smartphones sold in South Korea each year.
  • Determine the market value of eco-friendly packaging materials in the United States.
  • Estimate the total annual spend on wedding services in Italy.
  • Gauge the market size for solar panels in Australia.
  • Calculate the yearly expenditure on public transportation in New York City.
  • Determine the annual sales of prescription eyeglasses in Canada.
  • Estimate the market size for organic food products in Scandinavia.
  • Assess the total number of digital music streaming subscriptions in South Africa.
  • Calculate the market value of luxury watches in Switzerland.
  • Determine the size of the market for home fitness equipment in Russia.

These problems are designed to challenge and improve your market sizing skills, requiring you to consider various factors such as demographic trends, consumer behavior, economic indicators, and specific market dynamics. They cover a wide range of industries and geographical locations to provide a comprehensive practice experience.

Market Sizing: Frequently Asked Questions

  • How do market sizing techniques differ between products and services? Market sizing techniques for products often focus on physical units sold and replacement rates, while services require estimating the number of potential users and frequency of service use. Products may rely more on tangible metrics like sales data, whereas services often consider factors such as subscription rates or hourly usage.
  • Can you provide specific examples of how market sizing has directly influenced a company’s strategic decision? An example includes Netflix’s decision to invest in international expansion after market sizing indicated a high potential for subscriber growth outside the U.S. This insight led to localized content creation and marketing strategies to capture new markets.
  • How do emerging technologies or trends (e.g., AI, blockchain) impact market sizing in their respective industries? Emerging technologies can create entirely new markets or significantly expand existing ones by introducing innovative applications and efficiencies. For instance, AI’s role in healthcare, through predictive analytics and personalized medicine, has drastically increased the market size for technology-driven health solutions.
  • What are the common challenges when working on market sizing questions in a case interview? Common challenges include defining clear boundaries for the market, making reasonable assumptions without real-world data, dealing with ambiguous or incomplete information, performing calculations under time pressure, and effectively communicating the logic behind estimations. Candidates often struggle with maintaining a structured approach while adapting to unexpected turns in the case. Additionally, balancing simplicity with accuracy in estimations to arrive at a credible conclusion without overcomplicating the analysis is a significant challenge.
  • How do you account for cultural differences when sizing markets across different countries or regions? Accounting for cultural differences involves understanding local consumer behaviors, preferences, and purchasing power. This can include adapting products or services to fit cultural norms and values and using local market research to refine market size estimates. Including these considerations in your market sizing approach showcases business sense and judgment.
  • Can you detail a case where a common market sizing mistake led to a significant business error? One notable case is the misestimation of the smartphone market’s growth potential in the late 2000s by companies like Nokia and BlackBerry, who underestimated the impact of smartphones on their existing product lines. This failed to innovate adequately, leading to a significant loss in market share when Apple and Android devices gained popularity. This example should highlight the importance of proper market sizing and the rationale behind using it in consulting case interviews.

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Florian spent 5 years with McKinsey as a senior consultant. He is an experienced consulting interviewer and problem-solving coach, having interviewed 100s of candidates in real and mock interviews. He started 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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Case Study: Camera & Photography Industry Statistics (2024)

In this article, our team collected the most detailed camera and photography industry statistics up to 2023. Discover how COVID has affected sales etc

Learn | Statistics & Facts | By Usnea Lebendig | Last Updated: January 2, 2024

In this article, our team collected the most detailed camera and photography industry statistics up to 2022.

First, we took a look at the global and local sales market as well as shipment values for different types of cameras.

Then, we took into consideration the industry drop amid the Covid-19 pandemic that affected most industries.

Based on the data we collected, there are some interesting patterns in the market to analyze!

Overall Digital Cameras Statistics

  • The digital camera industry had an estimated market value of $8.2 billion in 2021. ( Research and Markets )
  • By 2028, it’s expected that the market will show $12.1 billion growth. ( Report Linker )
  • According to the Camera & Imaging Products Association, in 2021, there were over 8 million units shipped. This reveals an estimated drop of 9% compared to the year 2020. ( CIPA )
  • Worldwide digital camera revenue has reached over $21 billion in 2022. ( Statista )
  • CIPA predicts that the total digital camera shipment will drop by 6.1% near the end of 2022. ( CIPA )
  • By calculating the compound annual growth rate (CAGR), it’s expected that the market will grow annually by 3.43% from 2022 to 2027. ( Statista )
  • The highest digital camera revenue is generated in China, with a total of over $5 billion in 2022. ( Statista )
  • In 2022, there are $2.79 generated per person when it comes to global digital camera revenues in relation to the total population. ( Statista )
  • In 2022, the total Shipment quantity of DSC cameras worldwide has reached more than 738,000 units for August. ( CIPA )
  • In 2023, the digital camera industry is expected to show a growth of 7.6%. ( Statista )

Interchangeable-lens Cameras (ILCs) Statistics

  • By August 2022, Interchangeable Lens Cameras (ILCs) accounted for 70% of the total shipped units. ( CIPA )
  • The total shipped units of ILCs (DSLRs and mirrorless combined) went up by about 0.8% from the end of 2020 to the end of 2021. ( CIPA )
  • DSLRs (Digital Single-Lens Reflex) shipped units are less than mirrorless cameras with a difference of about a million units. ( Statista )
  • There is a gap between DSLR shipped units and mirrorless cameras , which continues to widen. Over the course of one year, DSLR shipped units went from 2.4 to 2.1 million ( Statista ), while mirrorless cameras shipped units went from 2.9 to 3.1 million. ( Statista )
  • In 2021, the worldwide camera industry shipped 132,797 ILC units with Single-Lens Reflex or SLR less than the net amount shipped the year before. ( CIPA )
  • The total DSLR units that were shipped out in 2021 is a little over 2 million. ( Statista )
  • Worldwide, the number of DSLR camera loads has been steadily declining since 2012. ( Statista )

Instant Cameras Statistics

  • It’s expected that the instant film camera market, which held a value of nearly 1 billion dollars in 2019, should grow by 2.5% until 2026. ( Data Intelo )
  • There are over 3.5 million persons between the age of 25 and 54 who own a polaroid camera, according to Simmons’s Study of Media and Markets

Built-in Lens Cameras (Compact Cameras) Statistics

  • Total Shipment for built-in lens cameras went down by 15.8% in 2021, with a total of a bit over 3 million shipped units. ( CIPA )

Camera Lenses Statistics

  • According to the Camera & Imaging Products Association (CIPA), the total number of interchangeable lenses shipped was more than 9.5 million units last year. ( CIPA )
  • By the end of 2022, it’s expected that the number of shipped lens units for cameras with interchangeable lenses will decrease from last year’s value by a minor 0.7%. ( CIPA )
  • The number of shipped ILC lenses jumped by an increase of 5.8% in 2021, which could be an indication that this segment didn’t get affected by the global pandemic. ( CIPA )

Canon Cameras Statistics

  • Canon foresees a net sales increase of 10% by the end of 2022. ( Nikkei )
  • Canon’s imaging division (camera industry) makes up 19% of the company’s net sales, which comes in second place after its printing division. ( Peta Pixel )
  • The imaging division in Canon witnessed a net sales increase of 20.7% last year. ( Peta Pixel )
  • Canon’s sales in 2021 totaled 2.74 million units, which was a drop from the company’s performance in 2020. ( The Digital Picture )

Sony Cameras Statistics

  • Sony held the no.1 spot in the full-frame mirrorless cameras segment in North America for eight consecutive years up to 2021. ( Sony )
  • Sony’s A7IV camera has been the best-selling mirrorless model on the market for the last four years. ( Peta Pixel )

Overall Digital Cameras Market Share Distribution

  • Two years ago, only five companies dominated the market as follows: ( Mirrorless Rumors )
  • Panasonic : 4.4%
  • Fujifilm : 5.6%
  • Nikon : 13.7%
  • Sony : 22.1%
  • Canon : 47.9%
  • Other brands, like Pentax, Leica , Hasselblad, GoPro and OM SYSTEM, shared the leftover 6.3%.

Photography Statistics

  • There are over 12,458 companies currently working in the photography services industry around the U.S. ( Splento )
  • Last year, the photography industry was valued at over $36 billion. ( business wire )
  • By 2023, it’s estimated that the world’s total photo count on iPhones or Android phones will increase to 93% from 89% in 2020. ( Mylio )
  • About 90% of consumers have only taken photos with their smartphones instead of an actual camera. ( Communities Dominate Brands )
  • The coronavirus pandemic caused a more than 23% decrease in the photography industry revenues. ( IBIS World )
  • According to a study, more than 63% of photographers said that business got slower amid the 2019 global pandemic. ( Globe News Wire )
  • 57% of photographers in a survey showed cautious optimism that 2022 would be better for business than 2021. (Zenfolio)

The Takeaway

After summarizing the results of camera and photography industry statistics, we’ve learned so much about the market trends over the past few years leading up to 2022.

It’s obvious that this market is dominated by large camera manufacturing companies, like Canon, Sony, Nikon, FUJIFILM, and Panasonic.

With the ongoing silicone shortage and the residual effects of the global pandemic, it’s still a long road ahead in the digital camera industry.


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Usnea Lebendig is an experienced photographer and reviewer who loves to photograph professionally and in moments where the world’s beauty and/or strangeness leaps in front of the lens.


With profits being squeezed and the quality advancements in Cell Phones, there may be a shift in digital formats. Full frame sensors, mechanical shutters, and mechanical mirrors may just be too expensive and will start to fade away. The market may force a rethink of what a professional / consumer camera is.

Originally, Nikon felt that full frame cameras would be sold only to Pro’s with cropped sensor cameras marketed to enthusiasts and the masses. Fujifilm took this strategy to another level by not even producing a full frame camera. For the short to medium term we may be seeing another transition in the market. Medium Format for Pro’s, and an evolution toward cropped sensor for some Pro’s and everyone else.

A mirrorless / cropped sensor camera can have better profit margins and image quality has progressed to a point where they rival full frame. It would not surprise me, if some new start up would create a new medium format film camera for the well to do.

I think there are still lots of gaps in the market to fill for photographers. Camera companies just need to listen better to them. Example: In my circle of pro camera talk, we talk about designs that get back to simplicity more, better ergos and haptic feedback, eliminate issues better before coming to the market. Stop trying to make a profit as a primary goal, and make a great camera- profit will follow. Now multiply that relatively small circle of talk by other circles around the world. OF course we are all discussing the same topics.

They just aren’t listening. Put some serious camera enthusiasts in charge of product placement, see what happens. Mr. Kobayashi of Cosina/Voigtlander is a prime example of that, and it worked.

One is capture and the other is viewing. Since most of day-to-day viewing has moved to smartphones, the requirement of a high-end sensor for capture, is no more essential. Coupled with the fact that smartphones are now offering truly amazing results with megapixels that easily provides good quality prints. DSLRs will never make a comeback. Mirrorless will grow but only marginally. Smartphones will eventually eat into the mirrorless market. People want convenience. They do not want to lug extra equipment. As far as the small segment of pro photographers are concerned, they will continue to invest in cameras and lenses as they need to provide visually superior quality of images than the amateur photographer. Smartphones are yet to provide such images. Going a bit away from the subject, i see print growing once again. Though the traction is slow, the opportunity is immense. Photo books, wedding albums, calendars, greeting cards and several customised products.

Greeting card market is dying, too easy to just send a text from your smart phone or send an electronic card by text or email. Print will never return in large numbers, too wasteful , bad for the environment, and expensive. 35mm film processing is not going to return in any meaningful numbers except for maybe a few companies like Fuji. Too bad because I really enjoyed spending time developing pictures and will never get rid of my AE-1 and Canon T-90. That said I am shocked at just how good of a picture I can get out of my Apple 11 Pro phone.

I have seen a lot of people return to film photography. Fujifilm instax sold 8.5 million last year and accounts for a large part of Fuji’s profit I guess if these companies would still manufacture a film camera and market it as a clockwork gears vintage item, but new and reliable would sell some more. At the processing lab where i develop film, 4 years ago there was almost nobody in sight, now i need to wait for weeks to get the films. Smartphones will replace digital except projects that require ultra high quality. I allready shoot landscapes with my smartphone, and use prime 50 lenses for street and portrait photography

The bottom line is that sales soared unnaturally from about 40 million (around 2002) to 121 million (around 2010). This was due to the change from film to digital and the ability to view and share images quickly. This was an attractive advancement for all of us. Once the smartphone came along, and the need for Point&Shoot cameras decreased, the sales dropped off sharply headed towards the “pre-digital” sales numbers. This indicates that the majority of cameras sold during that period were PS. Notice how the current sales is near the sales that were seen before digital technology began to take hold. This can be seen as a correction to the unnatural increase in sales in the preceding years (due to technology change). However, SLR sales have declined much slower. Which simply means that the PS cameras are being replaced with smartphones, but SLR sales have not suffered as much. I believe that the slight decline in SLR sales during this same period is due to the current technologies being adequate for many of our needs these days… less need for upgrade. But it looks as tho the SLR sales have seen very little change in last few years. So, in short, the camera market has seen an extraordinary change in the past 2 decades, but seems to be leveling back out to more natural levels now.

Interesting! Thanks for the comment, Andy.

I believe the idea of pro gear prices going up significantly goes against one simple fact… supply and demand.

There are only a handful of industries that I know of that currently would warrant/require a pro photographer… Documentary/War, Legal/Law Enforcement, Modeling/Fashion

There are a few industries that currently would be better off using a pro, but could move (or is moving) to amateurs in the near future… Science/Astro, Sports, Architectural/RealEstate, Aerial, Food/Product/Still, Fineart, Ad/Lifestyle, Other commercial/On-Demand

And there are demands that are increasingly being filled by amateurs using minimal equipment… Stock (Landscape/Cityscape, Street, Abstract, Travel, Wildlife, Macro, Underwater, etc), Events (Wedding, News/Journalism, Concert, etc), People/Pets (Portraits, Candid, Family).

The demand for better technology is decreasing because we are keeping what we currently have. The leap in technology advancement has slowed in recent years or we are satisfied with the

technology we currently own. So without a reason to upgrade, we have no reason to buy the next body or lens.

As amateurs, we also have an over-saturation of images available for viewing on social media. This ability to view others work reduces our craving for creating content ourselves. We are

seeing the best of the best images as we scroll thru our social media feed. This will either prompt us to try to do better with our own imagery or lose interest in the image making process

all together since we may feel inadequate to out-do the ‘competition’. As long as we satisfy our brain’s craving for ‘viewing the ultimate image’ (by viewing other posts online), why go to

the trouble of trying to create it ourselves? The only other reason would be the attention we get from others that tell us what a great job we’ve done. This “attention” from others is also

declining in our on-line world, since there is always a better image for others to “show love to”. So unless you can create images that would top the “the best of the best of the best”, then

the only person loving your images will be yourself. Is this motivation enough to buy the next gadget?

As interest and demand decreases by current gear owners (there is a lot of great used gear out there, and much of it holds its value for now), many of us will sell our gear (on ebay, etc),

which will increase supply and reduce prices that brands can sell their equipment for. This will require brands to reduce overhead (cutting jobs and real estate footprint).

The only thing that will help is better technology and the demand for that technology. There are many parts of the current technology that is adequate (including sensor resolution). However,

I know of one change that brands could make that would catapult their current market share. I’m not going to say what this change is (here), but it’s a goldmine for the first one who

implements it. Why they have not done it yet, is a huge oversight.

Wow, sorry but I disagree with just about everything you said. If you don’t evolve you will just be left behind. Technology will continue to improve, just look at Moore’s law and computers. I have never felt my pictures were as you say “inadequate”. And the demand for new technology is declining and technology improvements slowing? You are clearing uninformed or living in a bubble. Technology innovations are why The photography industry is changing, not people’s apathy to technology change. Companies that don’t evolve will find themselves relegated to the dustbin such as Kodak and Polaroid.

One change? You mean put a phone on their camera, LOL

At some point , what happened to mechanical watches with the introduction of quartz watches will happen in the camera industry. A lot of makers will collapse as they depended on the point and shoot for cash flow. The low end market will be abandoned altogether. A few camera makers will continue to turn out superb cameras for those that can afford them. At a guess that would be Sony with their mirrorless and Canon with their SLRs for those who value tradition. Maybe Hassablad.

I just bought a mirrorless for a trekking adventure. I needed something lighter. I do love it but my heart is still with my DSLR. I think with the way things are going, I need to make sure I have the best glass NOW!!! All those lenses I’m longing for need to be purchased before those prices increase to unobtainable.

Thanks for providing such a great post.

While smartphone photography has become very popular, replacing those boxy point and shoot cameras, they still are limited in what they can do with their fixed lenses. Gimicky digital zooms can only do so much before digital artifacts and soft images become apparent. As one who teaches photography workshops to all ages from 16 to 80 year olds at a community center in Montreal, the DSLR and now mirrorless cameras are still very popular. If anything, smartphones may breath new life into the DSLR/mirrorless cam market, as amateur and passionate photographers aspire for better imagery. You certainly can’t stick a 200-500mm zoom on a smartphone for that sharp closeup of a bird or scenic landscape.

I’ve had many new workshop participants, who started with smartphones, but moved up to interchangeable lens cameras, because they wanted better imagery.

Smartphones will still dominate the market, but I firmly believe there will remain a place for a mix of DSLR and mirrorless cameras. Happy shooting!

Cheers from Montreal. Frederic Hore

Thanks for the long comment as per usual, Frederic :-) Interesting insight from your workshops!

i think photography will be a hoppy for rich people only and professional photographers will raise their prices . Photography will be a rare profession

Even with a cheap camera, you can still have fun with photography, Anwar ;-)

Quite the opposite, camera phones can take great photos even better than DSLR’s ten years ago. Barrier to entry in this great hobby or profession is low now. Besides, it has always been the skill not the equipment that makes a true professional photographer.

Usnea, more of a question than a comment. As mirrorless cameras increase with their lighter lens, will not the price of the higher end SLR lens drop? Yes, it would be cheaper to purchase whatever converter so that your lens investment is protected or just use the older lens, but that defeats some of the reasons to upgrade to mirrorless.

I’m not sure the price of the higher-end SLR lenses will be able to drop. The manufacturer’s are pretty squeezed right now and don’t have a lot of wiggle room.

As far as reasons to change to mirrorless, I’m not sure price is the main factor for most folks (except perhaps for full frames). Size, weight, and performance are pretty key factors as well.

The current DSLR cameras still made by Canon and Nikon will become the most worthless pile of junk in a few years. There will never be a Renaissance for this in-between film and digital abomination as there is for old film cameras now. They’re like buildings that require air conditioners in the windows versus new ones with central air 1

The Sony/Minolta synergy (and money) gave way to the first real end to end digital interchangeable still camera. It took Sony knocking Nikon into the #3 spot to wake up Canon and Nikon to the fact that mirrorless is just an inconvenient way to describe a true digital camera. You can’t shrink glass and a sensor needs physical area to absorb enough light to write a quality image. If print makes a comeback (and in the growing diy economy) the value of better quality images will increase.

Prediction, Sony still cameras will join Sony video as the #1 manufacturer in both categories by 2021.

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