case study for business statistics

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

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 ”:

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:

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:

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:

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.

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:

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 http://pluto.huji.ac.il/~msby/StatThink/Datasets/discriminate.csv . ↩

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 http://pluto.huji.ac.il/~msby/StatThink/Datasets/job.csv . ↩

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)) ”. ↩

7 Favorite Business Case Studies to Teach—and Why

Explore more.

• Case Teaching
• Course Materials

FEATURED CASE STUDIES

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)

“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.

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

“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

“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

“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

“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

“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

“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.

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.

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.

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.

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.

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.

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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Practical data analysis : case studies in business statistics

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15+ Professional Case Study Examples [Design Tips + Templates]

By Alice Corner , Jan 12, 2023

Have you ever bought something — within the last 10 years or so — without reading its reviews or without a recommendation or prior experience of using it?

If the answer is no — or at least, rarely — you get my point.

Positive reviews matter for selling to regular customers, and for B2B or SaaS businesses, detailed case studies are important too.

Wondering how to craft a compelling case study ? No worries—I’ve got you covered with 15 marketing case study templates , helpful tips, and examples to ensure your case study converts effectively.

Click to jump ahead:

• What is a Case Study?

Business Case Study Examples

Simple case study examples.

• Marketing Case Study Examples

Sales Case Study Examples

• Case Study FAQs

What is a case study?

A case study is an in-depth, detailed analysis of a specific real-world situation. For example, a case study can be about an individual, group, event, organization, or phenomenon. The purpose of a case study is to understand its complexities and gain insights into a particular instance or situation.

In the context of a business, however, case studies take customer success stories and explore how they use your product to help them achieve their business goals.

As well as being valuable marketing tools , case studies are a good way to evaluate your product as it allows you to objectively examine how others are using it.

It’s also a good way to interview your customers about why they work with you.

Related: What is a Case Study? [+6 Types of Case Studies]

Marketing Case Study Template

A marketing case study showcases how your product or services helped potential clients achieve their business goals. You can also create case studies of internal, successful marketing projects. A marketing case study typically includes:

• Company background and history
• The challenge
• How you helped
• Specific actions taken
• Visuals or Data
• Client testimonials

Here’s an example of a marketing case study template:

Whether you’re a B2B or B2C company, business case studies can be a powerful resource to help with your sales, marketing, and even internal departmental awareness.

Business and business management case studies should encompass strategic insights alongside anecdotal and qualitative findings, like in the business case study examples below.

Conduct a B2B case study by researching the company holistically

When it comes to writing a case study, make sure you approach the company holistically and analyze everything from their social media to their sales.

Think about every avenue your product or service has been of use to your case study company, and ask them about the impact this has had on their wider company goals.

In business case study examples like the one above, we can see that the company has been thought about holistically simply by the use of icons.

By combining social media icons with icons that show in-person communication we know that this is a well-researched and thorough case study.

This case study report example could also be used within an annual or end-of-year report.

Highlight the key takeaway from your marketing case study

To create a compelling case study, identify the key takeaways from your research. Use catchy language to sum up this information in a sentence, and present this sentence at the top of your page.

This is “at a glance” information and it allows people to gain a top-level understanding of the content immediately. 

You can use a large, bold, contrasting font to help this information stand out from the page and provide interest.

Learn  how to choose fonts  effectively with our Venngage guide and once you’ve done that.

Upload your fonts and  brand colors  to Venngage using the  My Brand Kit  tool and see them automatically applied to your designs.

The heading is the ideal place to put the most impactful information, as this is the first thing that people will read.

In this example, the stat of “Increase[d] lead quality by 90%” is used as the header. It makes customers want to read more to find out how exactly lead quality was increased by such a massive amount.

If you’re conducting an in-person interview, you could highlight a direct quote or insight provided by your interview subject.

Pick out a catchy sentence or phrase, or the key piece of information your interview subject provided and use that as a way to draw a potential customer in.

Use charts to visualize data in your business case studies

Charts are an excellent way to visualize data and to bring statistics and information to life. Charts make information easier to understand and to illustrate trends or patterns.

Making charts is even easier with Venngage.

In this consulting case study example, we can see that a chart has been used to demonstrate the difference in lead value within the Lead Elves case study.

Adding a chart here helps break up the information and add visual value to the case study. 

Using charts in your case study can also be useful if you’re creating a project management case study.

You could use a Gantt chart or a project timeline to show how you have managed the project successfully.

Use direct quotes to build trust in your marketing case study

To add an extra layer of authenticity you can include a direct quote from your customer within your case study.

According to research from Nielsen , 92% of people will trust a recommendation from a peer and 70% trust recommendations even if they’re from somebody they don’t know.

So if you have a customer or client who can’t stop singing your praises, make sure you get a direct quote from them and include it in your case study.

You can either lift part of the conversation or interview, or you can specifically request a quote. Make sure to ask for permission before using the quote.

This design uses a bright contrasting speech bubble to show that it includes a direct quote, and helps the quote stand out from the rest of the text.

This will help draw the customer’s attention directly to the quote, in turn influencing them to use your product or service.

Less is often more, and this is especially true when it comes to creating designs. Whilst you want to create a professional-looking, well-written and design case study – there’s no need to overcomplicate things.

These simple case study examples show that smart clean designs and informative content can be an effective way to showcase your successes.

Use colors and fonts to create a professional-looking case study

Business case studies shouldn’t be boring. In fact, they should be beautifully and professionally designed.

This means the normal rules of design apply. Use fonts, colors, and icons to create an interesting and visually appealing case study.

In this case study example, we can see how multiple fonts have been used to help differentiate between the headers and content, as well as complementary colors and eye-catching icons.

Marketing case study examples

Marketing case studies are incredibly useful for showing your marketing successes. Every successful marketing campaign relies on influencing a consumer’s behavior, and a great case study can be a great way to spotlight your biggest wins.

In the marketing case study examples below, a variety of designs and techniques to create impactful and effective case studies.

Show off impressive results with a bold marketing case study

Case studies are meant to show off your successes, so make sure you feature your positive results prominently. Using bold and bright colors as well as contrasting shapes, large bold fonts, and simple icons is a great way to highlight your wins.

In well-written case study examples like the one below, the big wins are highlighted on the second page with a bright orange color and are highlighted in circles.

Making the important data stand out is especially important when attracting a prospective customer with marketing case studies.

Use a simple but clear layout in your case study

Using a simple layout in your case study can be incredibly effective, like in the example of a case study below.

Keeping a clean white background, and using slim lines to help separate the sections is an easy way to format your case study.

Making the information clear helps draw attention to the important results, and it helps improve the  accessibility of the design .

Business case study examples like this would sit nicely within a larger report, with a consistent layout throughout.

Use visuals and icons to create an engaging and branded business case study

Nobody wants to read pages and pages of text — and that’s why Venngage wants to help you communicate your ideas visually.

Using icons, graphics, photos, or patterns helps create a much more engaging design. 

With this Blue Cap case study icons, colors, and impactful pattern designs have been used to create an engaging design that catches your eye.

Use a monochromatic color palette to create a professional and clean case study

Let your research shine by using a monochromatic and minimalistic color palette.

By sticking to one color, and leaving lots of blank space you can ensure your design doesn’t distract a potential customer from your case study content.

In this case study on Polygon Media, the design is simple and professional, and the layout allows the prospective customer to follow the flow of information.

The gradient effect on the left-hand column helps break up the white background and adds an interesting visual effect.

Did you know you can generate an accessible color palette with Venngage? Try our free accessible color palette generator today and create a case study that delivers and looks pleasant to the eye:

Add long term goals in your case study

When creating a case study it’s a great idea to look at both the short term and the long term goals of the company to gain the best understanding possible of the insights they provide.

Short-term goals will be what the company or person hopes to achieve in the next few months, and long-term goals are what the company hopes to achieve in the next few years.

Check out this modern pattern design example of a case study below:

In this case study example, the short and long-term goals are clearly distinguished by light blue boxes and placed side by side so that they are easy to compare.

Use a strong introductory paragraph to outline the overall strategy and goals before outlining the specific short-term and long-term goals to help with clarity.

This strategy can also be handy when creating a consulting case study.

Use data to make concrete points about your sales and successes

When conducting any sort of research stats, facts, and figures are like gold dust (aka, really valuable).

Being able to quantify your findings is important to help understand the information fully. Saying sales increased 10% is much more effective than saying sales increased.

While sales dashboards generally tend it make it all about the numbers and charts, in sales case study examples, like this one, the key data and findings can be presented with icons. This contributes to the potential customer’s better understanding of the report.

They can clearly comprehend the information and it shows that the case study has been well researched.

Use emotive, persuasive, or action based language in your marketing case study

Create a compelling case study by using emotive, persuasive and action-based language when customizing your case study template.

In this well-written case study example, we can see that phrases such as “Results that Speak Volumes” and “Drive Sales” have been used.

Using persuasive language like you would in a blog post. It helps inspire potential customers to take action now.

Keep your potential customers in mind when creating a customer case study for marketing

82% of marketers use case studies in their marketing  because it’s such an effective tool to help quickly gain customers’ trust and to showcase the potential of your product.

Why are case studies such an important tool in content marketing?

By writing a case study you’re telling potential customers that they can trust you because you’re showing them that other people do.

Not only that, but if you have a SaaS product, business case studies are a great way to show how other people are effectively using your product in their company.

In this case study, Network is demonstrating how their product has been used by Vortex Co. with great success; instantly showing other potential customers that their tool works and is worth using.

Related: 10+ Case Study Infographic Templates That Convert

Case studies are particularly effective as a sales technique.

A sales case study is like an extended customer testimonial, not only sharing opinions of your product – but showcasing the results you helped your customer achieve.

Make impactful statistics pop in your sales case study

Writing a case study doesn’t mean using text as the only medium for sharing results.

You should use icons to highlight areas of your research that are particularly interesting or relevant, like in this example of a case study:

Icons are a great way to help summarize information quickly and can act as visual cues to help draw the customer’s attention to certain areas of the page.

In some of the business case study examples above, icons are used to represent the impressive areas of growth and are presented in a way that grabs your attention.

Use high contrast shapes and colors to draw attention to key information in your sales case study

Help the key information stand out within your case study by using high contrast shapes and colors.

Use a complementary or contrasting color, or use a shape such as a rectangle or a circle for maximum impact.

This design has used dark blue rectangles to help separate the information and make it easier to read.

Coupled with icons and strong statistics, this information stands out on the page and is easily digestible and retainable for a potential customer.

Case Study Examples Summary

Once you have created your case study, it’s best practice to update your examples on a regular basis to include up-to-date statistics, data, and information.

You should update your business case study examples often if you are sharing them on your website .

It’s also important that your case study sits within your brand guidelines – find out how Venngage’s My Brand Kit tool can help you create consistently branded case study templates.

Case studies are important marketing tools – but they shouldn’t be the only tool in your toolbox. Content marketing is also a valuable way to earn consumer trust.

Case Study FAQ

Why should you write a case study.

Case studies are an effective marketing technique to engage potential customers and help build trust.

By producing case studies featuring your current clients or customers, you are showcasing how your tool or product can be used. You’re also showing that other people endorse your product.

In addition to being a good way to gather positive testimonials from existing customers , business case studies are good educational resources and can be shared amongst your company or team, and used as a reference for future projects.

How should you write a case study?

To create a great case study, you should think strategically. The first step, before starting your case study research, is to think about what you aim to learn or what you aim to prove.

You might be aiming to learn how a company makes sales or develops a new product. If this is the case, base your questions around this.

You can learn more about writing a case study  from our extensive guide.

Related: How to Present a Case Study like a Pro (With Examples)

Some good questions you could ask would be:

• Why do you use our tool or service?
• How often do you use our tool or service?
• What does the process of using our product look like to you?
• If our product didn’t exist, what would you be doing instead?
• What is the number one benefit you’ve found from using our tool?

You might also enjoy:

• 12 Essential Consulting Templates For Marketing, Planning and Branding
• Best Marketing Strategies for Consultants and Freelancers in 2019 [Study + Infographic]

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A Step-By-Step Introduction to Statistics for Business

• Richard N. Landers - University of Minnesota
• Description

A clear and concise introduction to statistics for business and management students, demonstrating how important statistics are in the business decision-making process and covering everything from conducting a survey and collecting data, to summarizing statistical data, and presenting findings.

Each chapter features a real-world business situation and accompanying dataset, the reader is then encouraged to identify the correct statistical concept in the chapter and solve the problem outlined. Offering students a chance to use the newly learned theory in a practical way.

New to the second edition:

• A “Review of Essential Mathematics” prologue, featuring tests and further links to help students refresh their knowledge of the core mathematical concepts used to calculate basic statistics.
• Updated screenshots on using IBM SPSS and Excel.
• A “Statistics in the Real World” feature included at the end of each chapter, demonstrating how statistics are applied in real-world business settings and research, accompanied by reflective questions.
• Updated case studies, examples and diagrams, illustrating key points and helping to reinforce learning.

The book is accompanied by free online resources including step-by-step video tutorials on how to use Excel and IBM SPSS, datasets and worked solutions, an Instructors’ Manual, Testbank, and PowerPoint presentation slides for lecturers. Essential reading for business students wanting to know how to use statistics in a business setting.

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

For assistance with your order: Please email us at [email protected] or connect with your SAGE representative.

SAGE 2455 Teller Road Thousand Oaks, CA 91320 www.sagepub.com

Supplements

Student resources:

• Data Skill Challenges
• Glossary Flashcards
• ‘Stats in the Real World’ Weblinks
• ‘Test Yourself’ Answers
• Tutorial Videos

Lecturer resources:

• Instructor’s Manual
• PowerPoints slides
• Lecturer Testbank
• Test Questions and
• Figures and Tables

Richard N. Landers has written an excellent text. Put simply,  A Step-by-Step Guide to Statistics for Business  needs to be core reading for all students undertaking statistics as part of a Business or Management programme. Landers writes in an accessible and easy-to-follow style, which leads the reader from basic numeracy to inferential statistics.

This book is good for students who want to study statistics at the beginner's level. The only thing is when i saw the name of the book called step-by-step, i thought this book would be structured across the whole process of empirical quantitative research. It turns out that it doesn't refer to step-by-step for research design but step-by-step for using SPSS to run tests. It could be me with limited English language understanding.

IT'S A VERY GOOD BOOK FOR THE STUDENTS ON INTRODUCTORY LEVEL.

The course will be suspended to be re-introduced due to COVID. Nevertheless, it was well written and explained about the statistical concept and suggested as a reference.

I am using it for the first time, so we have only gotten to chapter 3. So far a FABULOUS book with excellent support resources!

A useful introduction for non-specialists in maths/stats. Very clear 'step by step': the title says it..!

• A “Review of Essential Mathematics” prologue, featuring tests and further links to help students refresh their knowledge of the core mathematical concepts used to calculate basic statistics.
• Updated screenshots on using IBM® SPSS and Excel.

Preview this book

Sample materials & chapters.

Chapter 4: Probability Distributions

5 Statistics Case Studies That Will Blow Your Mind

You will learn the transformative impact of statistical science in unfolding real-world narratives from global economics to public health victories.

Introduction

The untrained eye may see only cold, lifeless digits in the intricate dance of numbers and patterns that constitute data analysis and statistics. Yet, for those who know how to listen, these numbers whisper stories about our world, our behaviors, and the delicate interplay of systems and relationships that shape our reality. Artfully unfolded through meticulous statistical analysis, these narratives can reveal startling truths and unseen correlations that challenge our understanding and broaden our horizons. Here are five case studies demonstrating the profound power of statistics to decode reality’s vast and complex tapestry.

• 2008 Financial Crisis : Regression analysis showed Lehman Brothers’ collapse rippled globally, causing a credit crunch and recession.
• Eradication of Guinea Worm Disease : Geospatial and logistic regression helped reduce cases from 3.5 million to 54 by 2019.
• Amazon’s Personalized Marketing : Machine learning algorithms predict customer preferences, drive sales, and set industry benchmarks for personalized shopping.
• American Bald Eagle Recovery : Statistical models and the DDT ban led to the recovery of the species, once on the brink of extinction.
• Twitter and Political Polarization : MIT’s sentiment analysis of tweets revealed echo chambers, influencing political discourse and highlighting the need for algorithm transparency.

1. The Butterfly Effect in Global Markets: The 2008 Financial Crisis

The 2008 financial crisis is a prime real-world example of the Butterfly Effect in global markets. What started as a crisis in the housing market in the United States quickly escalated into a full-blown international banking crisis with the collapse of the investment bank Lehman Brothers on September 15, 2008.

Understanding the Ripples

A team of economists employed regression analysis to understand the impact of the Lehman Brothers collapse. The statistical models revealed how this event affected financial institutions worldwide, causing a credit crunch and a widespread economic downturn.

The Data Weaves a Story

Further analysis using time-series forecasting methods painted a detailed picture of the crisis’s spread. For instance, these models were used to predict how the initial shockwave would impact housing markets globally, consumer spending, and unemployment rates. These forecasts proved incredibly accurate, showcasing not only the domino effect of the crisis but also the predictive power of well-crafted statistical models.

Implications for Future Predictions

This real-life event became a case study of the importance of understanding the deep connections within the global financial system. Banks, policymakers, and investors now use the predictive models developed from the 2008 crisis to stress-test economic systems against similar shocks. It has led to a greater appreciation of risk management and the implementation of stricter financial regulations to safeguard against future crises.

By interpreting the unfolding of the 2008 crisis through the lens of statistical science, we can appreciate the profound effect that one event in a highly interconnected system can have. The lessons learned continue to resonate, influencing financial policies and the global economic forecasting and stability approach.

2. Statistical Fortitude in Public Health: The Eradication of Dracunculiasis (Guinea Worm Disease)

In a world teeming with infectious diseases, the story of dracunculiasis, commonly known as Guinea Worm Disease, is a testament to public health tenacity and the judicious application of statistical analysis in disease eradication efforts.

Tracing the Path of the Parasite

The campaign against dracunculiasis, led by The Carter Center and supported by a consortium of international partners, utilized epidemiological data to trace and interrupt the life cycle of the Guinea worm — the statistical approach underpinning this public health victory involved meticulously collecting data on disease incidence and transmission patterns.

The Tally of Triumph

By employing geospatial statistics and logistic regression models, health workers pinpointed endemic villages and formulated strategies that targeted the disease’s transmission vectors. These statistical tools were instrumental in monitoring the progress of eradication efforts and allocating resources to areas most in need.

The Countdown to Zero

The eradication campaign’s success was measured by the continuous decline in cases, from an estimated 3.5 million in the mid-1980s to just 54 reported cases in 2019. This dramatic decrease has been documented through rigorous data collection and statistical validation, ensuring that each reported case was accounted for and dealt with accordingly.

Legacy of a Worm

The nearing eradication of Guinea Worm Disease, with no vaccine or curative treatment, is a feat that underscores the power of preventive public health strategies informed by statistical analysis. It serves as a blueprint for tackling other infectious diseases. It is a real-world example of how statistics can aid in making the invisible enemy of disease a known and conquerable foe.

The narrative of Guinea Worm eradication is not just a tale of statistical victory but also one of human resilience and commitment to public health. It is a story that will continue to inspire as the world edges closer to declaring dracunculiasis the second human disease, after smallpox, to be eradicated.

3. Unraveling the DNA of Consumer Behavior: A Case Study of Amazon’s Personalized Marketing

The advent of big data analytics has revolutionized marketing strategies by providing deep insights into consumer behavior. Amazon, a global leader in e-commerce, is at the forefront of leveraging statistical analysis to offer its customers a highly personalized shopping experience.

The Predictive Power of Purchase Patterns

Amazon collects vast user data, including browsing histories, purchase patterns, and product searches. Amazon analyzes this data by employing machine learning algorithms to predict individual customer preferences and future buying behavior. This predictive power is exemplified by Amazon’s recommendation engine, which suggests products to users with uncanny accuracy, often leading to increased sales and customer satisfaction.

Beyond the Purchase: Sentiment Analysis

Amazon extends its data analysis beyond purchases by analyzing customer reviews and feedback sentiment. This analysis gives Amazon a nuanced understanding of customer sentiments towards products and services. Amazon can quickly address issues, improve product offerings, and enhance customer service by mining text for customer sentiment.

Crafting Tomorrow’s Trends Today

Amazon’s data analytics insights are not limited to personalizing the shopping experience. They are also used to anticipate and set future trends. Amazon has mastered the art of using consumer data to meet existing demands and influence and create new consumer needs. By analyzing emerging patterns, Amazon stocks products ahead of demand spikes and develops new products that align with predicted consumer trends.

Amazon’s success in utilizing statistical analysis for marketing is a testament to the power of big data in shaping the future of consumer engagement. The company’s ability to personalize the shopping experience and anticipate consumer trends has set a benchmark in the industry, illustrating the transformative impact of statistics on marketing strategies.

4. The Revival of the American Bald Eagle: A Triumph of Environmental Policy and Statistics

In the annals of environmental success stories, the recovery of the American Bald Eagle (Haliaeetus leucocephalus) from extinction stands out as a sterling example of how rigorous science, public policy, and statistics can combine to safeguard wildlife. This case study offers a narrative that encapsulates the meticulous application of data analysis in wildlife conservation, revealing a more profound truth about the interdependence of species and the human spirit’s capacity for stewardship.

The Descent Towards Silence

By the mid-20th century, the American Bald Eagle, a symbol of freedom and strength, faced decimation. Pesticides like DDT, habitat loss, and illegal shooting had dramatically reduced their numbers. The alarming descent prompted an urgent call to action bolstered by the rigorous collection and analysis of ecological data.

The Statistical Lifeline

Biostatisticians and ecologists began a comprehensive monitoring program, recording eagle population numbers, nesting sites, and chick survival rates. Advanced statistical models, including logistic regression and population viability analysis (PVA), were employed to assess the eagles’ extinction risk under various scenarios and to evaluate the effectiveness of different conservation strategies.

The Ban on DDT – A Calculated Decision

A pivotal moment in the Bald Eagle’s story was the ban on DDT in 1972, a decision grounded in the statistical analysis of the pesticide’s impacts on eagle reproduction. Studies demonstrated a strong correlation between DDT and thinning eggshells, leading to reduced hatching rates. Based on this analysis, the ban’s implementation marked the turning point for the eagle’s fate.

A Soaring Recovery

Post-ban, rigorous monitoring continued, and the data collected painted a story of resilience and recovery. The statistical evidence was undeniable: eagle populations were rebounding. As of the early 21st century, the Bald Eagle had made a miraculous comeback, removed from the Endangered Species List in 2007.

The Legacy of a Species

The American Bald Eagle’s resurgence is more than a conservation narrative; it’s a testament to the harmony between humanity’s analytical prowess and its capacity for environmental guardianship. It shows how statistics can forecast doom and herald a new dawn for conservation. This case study epitomizes the beautiful interplay between human action, informed by truth and statistical insight, resulting in a tangible good: the return of a majestic species from the shadow of extinction.

5. The Algorithmic Mirrors of Social Media – The Case of Twitter and Political Polarization

Social media platforms, particularly Twitter, have become critical arenas for public discourse, shaping societal norms and reflecting public sentiment. This case study examines the real-world application of statistical models and algorithms to understand Twitter’s role in political polarization.

Twitter’s Data-Driven Sentiment Reflection

The aim was to analyze Twitter data to evaluate public sentiment regarding political events and understand the platform’s contribution to societal polarization.

Using natural language processing (NLP) and sentiment analysis, researchers from the Massachusetts Institute of Technology (MIT) analyzed over 10 million tweets from the period surrounding the 2020 U.S. Presidential Election. The tweets were filtered using politically relevant hashtags and keywords.

Deciphering the Digital Pulse

A sentiment index was created, categorizing tweets into positive, negative, or neutral sentiments concerning the candidates. This ‘Twitter Political Sentiment Index’ provided a temporal view of public mood swings about key campaign events and debates.

The Echo Chambers of the Internet

Network analysis revealed distinct user clusters along ideological lines, illustrating the presence of echo chambers. The study examined retweet networks and highlighted how information circulated within politically homogeneous groups, reinforcing existing beliefs.

The study showed limited user exposure to opposing political views on Twitter, increasing polarization. It also correlated significant shifts in the sentiment index with real-life events, such as policy announcements and election results.

Shaping the Future of Public Discourse

The study, published in Science, emphasizes the need for transparency in social media algorithms to mitigate echo chambers’ effects. The insights gained are being used to inform policymakers and educators about the dynamics of online discourse and to encourage the design of algorithms that promote a more balanced and open digital exchange of ideas.

The findings from MIT’s Twitter data analysis underscore the platform’s power as a real-time barometer of public sentiment and its role in shaping political discourse. The case study offers a roadmap for leveraging big data to foster a healthier democratic process in the digital age.

Drawing together these varied case studies, it becomes clear that statistics and data analysis are far from mere computation tools. They are, in fact, the instruments through which we can uncover deeper truths about our world. They can illuminate the unseen, predict the future, and help us shape it towards the common good. These narratives exemplify the pursuit of true knowledge, promoting good actions, and appreciating a beautiful world.

As we engage with the data of our daily lives, we continually decode the complexities of existence. From the markets to the microorganisms, consumer behavior to conservation efforts, and the physical to the digital world, statistics is the language in which the tales of our times are written. It is the language that reveals the integrity of systems, the harmony of nature, and the pulse of humanity. Through this science’s meticulous and ethical application, we uphold the values of truth, goodness, and beauty — ideals that remain ever-present in the quest for understanding and improving the world we share.

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Frequently Asked Questions

Q1: What is the significance of the 2008 Financial Crisis in statistics?  The 2008 Financial Crisis is significant in statistics for demonstrating the Butterfly Effect in global markets, where regression analysis revealed the interconnected impact of Lehman Brothers’ collapse on the global economy.

Q2: How did statistics contribute to the eradication of Guinea Worm Disease?  Through geospatial and logistic regression, statistics played a crucial role in tracking and reducing the spread of Guinea Worm Disease, contributing to the decline from 3.5 million cases to just 54 by 2019.

Q3: What role does machine learning play in Amazon’s marketing?  Machine learning algorithms at Amazon analyze vast amounts of consumer data to predict customer preferences and personalize the shopping experience, driving sales and setting industry benchmarks.

Q4: How were statistics instrumental in the recovery of the American Bald Eagle?  Statistical models helped assess the risk of extinction and the impact of DDT on eagle reproduction, leading to conservation strategies that aided in the eagle’s significant recovery.

Q5: What is sentiment analysis, and how was it used in studying Twitter?  Sentiment analysis uses natural language processing to categorize the tone of text content. MIT used it to evaluate political sentiment on Twitter and study the platform’s role in political polarization.

Q6: How did statistical models predict the global effects of the 2008 crisis?  Statistical models, including time-series forecasting, predicted how the crisis would affect housing markets, consumer spending, and unemployment, demonstrating the predictive power of statistics.

Q7: Why is the eradication of Guinea Worm Disease significant beyond public health?  The near eradication, without a vaccine or cure, illustrates the power of preventive strategies and statistical analysis in public health, serving as a blueprint for combating other diseases.

Q8: In what way did statistics aid in the decision to ban DDT?  Statistical analysis linked DDT to thinning eagle eggshells and poor hatching rates, leading to the ban crucial for the Bald Eagle’s recovery.

Q9: How does Amazon’s use of data analytics influence consumer behavior?  By analyzing consumer data, Amazon anticipates and sets trends, meets demands, and influences new consumer needs, shaping the future of consumer engagement.

Q10: What implications does the Twitter political polarization study have?  The study calls for transparency in social media algorithms to reduce echo chambers. It suggests using statistical insights to foster a balanced, open digital exchange in democratic processes.

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Bowling green state university | bowling green, oh.

Prior to implementing Connect and SmartBook / LearnSmart , Professor Kyle Moninger was assigning homework problems from a textbook and using other problems he had collected over the years. Students were required to complete the problems and turn in printed copies of their solutions for Moninger and two Graduate Assistants (GAs) to grade by hand. "The way I had been running the course was outdated, and I wasn’t using any technology. I was spending way too much time doing administrative work (prepping assignments, grading, communicating instructions, etc.) and not enough time actually teaching," he says.

With Connect , Moninger now has the time to focus on teaching again and Connect helps him find effective ways to teach his students the material. "Connect isn’t just about having auto-graded assignments (although that’s a huge time-saver!)," he says. "Connect allows me to reduce the amount of time spent during lecture on conceptual material (since the students are exposed to more of that through SmartBook / LearnSmart ) and gives me more time to devote to calculations and techniques, which is the content students struggle the most with."

Because Moninger requires his students to complete the SmartBook / LearnSmart assignment prior to class, lectures are no longer "a foreign, quiet, dead zone." He says, "I can look into the classroom and see students `connecting the dots’ with what I’m presenting and what they read the day before. They are more confident to participate when I ask questions because they have already answered many of the questions in the SmartBook / LearnSmart assignments."

After implementing Connect , 47% of Moninger’s students earned A’s and B’s in his classes— a 6% increase in student success . Exam scores increased an average of 8% overall, and 24% more students passed the course .

Implementation

The course grade is determined by the following: 15% Connect Homework 10% Connect SmartBook / LearnSmart 10% Quizzes (administered within Connect ) 20% Exam 1 (administered within Connect ) 20% Exam 2 (administered within Connect ) 25% Final Exam (administered within Connect )

In the average week, students have three assignments:

• SmartBook / LearnSmart over one chapter and due before lecture
• Homework assignments with approximately 10 problems over a corresponding chapter covered in SmartBook / LearnSmart
• Timed quiz (5 to 8 questions) at the end of the week covering the prior week’s SmartBook / LearnSmart and homework

Because students are exposed to the material prior to class, Moninger’s lecture format changed. " SmartBook / LearnSmart takes a lot of pressure off the lectures, so my lectures can be less intimidating to students and focus on calculations and techniques. It’s much easier to teach students content when they are slightly familiar with the concept," Moninger says.

Moninger considers SmartBook a student’s first exposure to a topic. He assigns the SmartBook / LearnSmart assignment and makes it due right before the class on which he lectures about that topic. "Otherwise," he says, "students wouldn’t actually read the textbook."

He uses Connect as the platform in which all other assignments are given. He says, "I use the deep integration with our campus LMS, so students don’t actually feel like they’re using Connect , but rather are accessing everything through the LMS course page."

His Connect homework assignments are designed to practice the calculation concepts he teaches in class, and he allows students to access the various learning aids within the assignment. His Connect quizzes are designed to test the concepts and calculations completed in the homework. All quizzes are timed, and most quizzes use question pools to maintain academic integrity. His Connect exams are in a style similar to the quizzes but are longer and have more problems to solve.

Moninger appreciates that Connect allows the instructor to easily adjust assignment settings, grade-syncs to Canvas seamlessly, and allows him to organize the LMS course page "perfectly" so that students don’t have to familiarize themselves with another online system. He says, "The ‘Ask my Teacher’ feature in homework also eliminates cumbersome emailing back and forth about homework questions with students."

Moninger uses the Connect Insight Dashboard to check his assignments. "There should be a positive linear relationship between the time spent on the assignment and the score. Otherwise, the assignment may be too easy or too hard," he says.

Results Achieved

Moninger’s goals were to see an increase in student participation and performance in his classes and to reduce the number of hours he spent with administrative tasks so he can focus more lecture time on calculations and techniques where students are challenged the most.

"Overall, grades have increased," Moninger says. "Without Connect , 41% of students received A’s and B’s. With Connect , that number increased to 47%! Connect and SmartBook / LearnSmart have drastically helped struggling students bring their grade up to a passing grade" (Figure 1).

Student pass rates have increased from 63% without Connect to 87% with Connect , a 24% improvement (Figure 2). Moninger says, "The pass rate has been one of the best improvements we’ve seen with Connect !"

Moninger attributes the students’ success rate to the various resources available in Connect and SmartBook / LearnSmart (such as practice quizzes, Insights, and automated grading that provides timely feedback on homework assignments) for students who are struggling.

Retention rates improved by 2% from 94% without Connect to 96% with Connect (Figure 3).

Without Connect , students scored an average of 73%, 55%, and 60% on Exams 1, 2, and 3. With Connect , students earned either the same average score or improved their scores by as much as 13% on average (Figure 4).

When averaged together, exam scores increased by 8% overall (Figure 5).

"This is a very interesting study between the classes with Connect versus without Connect because the Exams used between the classes were almost identical," Moninger says. "For Exam 1, students’ scores do not change much. However, looking at the results of Exam 2 and the Final Exam for the classes with Connect , there is substantial improvements in students’ scores and an increase to the average score."

Prior to Connect , two GAs graded student coursework exclusively, for ten hours a week each week. "That’s 20 hours, plus the time I spent helping the GAs and making adjustments, which was another maybe 2 to 3 hours each week," Moninger calculates (Figure 6) . "Now I no longer need GAs to help with grading because I do almost none myself!" With Connect , Moninger says he spends approximately two hours a week at most making corrections or adjustments to students’ grades.

His department appreciates the savings. Because most of the assignments are now automatically graded, 20 hours of Graduate Assistant time is available for other areas of the department, and Connect has allowed the department to offer this course online, which is a huge milestone for the department and the college.

Moninger is pleased that students have had a positive experience using Connect . "Students feel Connect is easy to use, provides several resources for them, and is a good value. Whereas, many traditional classrooms require a hard copy textbook that is barely used, Connect is priced well and is used extensively throughout the course."

Before Moninger implemented Connect , he faced a "quiet, dead zone" in lectures and spent too much time with administrative tasks like collecting problems for homework and grading. After implementing Connect and requiring that SmartBook / LearnSmart assignments be completed prior to the lecture in class, students participate more in class and lectures can focus on calculations and techniques. Students’ grades, retention rates, and pass rates have all improved.

Kyle Moninger instructs the Quantitative Business Curriculum at Bowling Green State University in Bowling Green, Ohio. He teaches and plans undergraduate courses in statistics and business calculus, serves on the Quantitative Business Curriculum committee, and manages graduate assistants and supplemental instructors. During the summer of 2016, he was a visiting instructor at Tianjin Polytechnic University in Tianjin, China, and has been a data scientist at Owens Corning in Toledo, Ohio, where he designed and implemented a corporate training program on business intelligence and analytics.

Connect is more than just online homework or an e-book. It’s a digital learning system that provides both students and instructors with necessary resources to be successful.

Reviewing topics for SmartBook / LearnSmart assignments due before classes is great because it makes the lectures easier to understand and less intimidating.

I love using Connect ! I have used MyLab, WebAssign, and Mindtap, and Connect is the best and most user-friendly.

Digital Product in Use: McGraw-Hill Connect ® Business Statistics LMS Integration: Canvas Course Name: STAT 2110 – Elementary Statistical Methods I Course Type: Face-to-Fast Lecture Credit Hours: Three Program in Use: Business Statistics in Practice, Using Data, Modeling, and Analytics by Bowerman / O’Connell / Murphree, 8e Instructor Name: Kyle Moninger Enrollment: 80 students per section; 2 sections; 400/year (university total) Case Study Terms: Spring 2015 (without Connect ); Spring 2016 (with Connect )

Instructor’s implementation goals:

• Encourage students to read the chapter prior to class
• Improve students’ grades
• Save time on administrative tasks like giving quizzes and grading

Issues for instructor before using Connect :

• Students were not reading assigned chapter material
• Students were not engaged in the course material in a meaningful way
• Lectures were a "quiet, dead zone"
• Instructor needed a better way to manage quizzing and grading tasks

Benefits to instructor after using Connect :

• More students read the course content prior to attending class
• Participation in class discussion is no longer a "quiet, dead zone"
• More students earn A’s and B’s in the course
• Students get more value out of course materials used throughout semester
• Reduced instructor hours spent on quizzing and grading tasks
• Fewer department resource hours spent on grading
• Integrates into Canvas so students and instructors have a seamless experience
• Easy to create assignments by selecting questions online through Connect sort by various filters, and schedule an auto-graded assignment

Course Description:

This course teaches: elementary probability, random variables, probability distributions, sampling, descriptive statistics, sampling distributions, and estimation.

Institution Profile:

Founded in 1910 as a teacher-training institution, Bowling Green State University is a public research university that offers over 200 undergraduate programs in addition to masters and doctoral degrees through eight academic colleges. Over 17,000 students attend BGSU and can participate in over 300 student organizations.

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The peer-reviewed journal serves as a forum for writers of data analysis case studies from any environment where statistical analysis is used. As such the journal both fosters a better communication between these different environments and helps improve statistical training overall.

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Understanding the Quantitative Skill Base on Introductory Statistics: A Case Study from Business Statistics

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Basic mathematical skills are critical to a student’s ability to successfully undertake an introductory statistics course. Yet in business education this vitally important area of mathematics and statistics education is under-researched. The question therefore arises as to what level of mathematical skill a typical business studies student will possess as they enter the tertiary environment, and whether there are any common deficiencies that we can identify with a view to tackling the problem. This paper will focus on a study designed to measure the level of mathematical ability of first year business students. The results provide timely insight into a growing problem faced by many tertiary educators in this field.

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Garfield, J. (2003). Assessing statistical reasoning. Statistics Education Research Journal , 2 , 22–38.

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Wilson, T., & MacGillivray, H. (2005). Numeracy counts in the statistical reasoning equation. In 55th Session of the International Statistical Institute , Sydney, Australia.

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Wilson, T. M., & MacGillivray, H. L. (2007). Counting on the basics: Mathematical skills amongst tertiary entrants. International Journal of Mathematical Education in Science and Technology , 38 (1), 19–41.

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School of Economics and Finance, Queensland University of Technology, Gardens Point Campus, Brisbane, QLD, 4001, Australia

Joanne Elizabeth Fuller

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Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia

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Fuller, J.E. (2014). Understanding the Quantitative Skill Base on Introductory Statistics: A Case Study from Business Statistics. In: MacGillivray, H., Phillips, B., Martin, M. (eds) Topics from Australian Conferences on Teaching Statistics. Springer Proceedings in Mathematics & Statistics, vol 81. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0603-1_12

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• Content Marketing

35 Content Marketing Statistics You Should Know

Stay informed with the latest content marketing statistics. Discover how optimized content can elevate your digital marketing efforts.

Content continues to sit atop the list of priorities in most marketing strategies, and there is plenty of evidence to support the reasoning.

Simply put, content marketing is crucial to any digital marketing strategy, whether running a small local business or a large multinational corporation.

After all, content in its many and evolving forms is indisputably the very lifeblood upon which the web and social media are based.

Modern SEO has effectively become optimized content marketing for all intents and purposes.

This is when Google demands and rewards businesses that create content demonstrating experience, expertise, authoritativeness, and trustworthiness (E-E-A-T) for their customers – content that answers all of the questions consumers may have about their services, products, or business in general.

Content marketing involves creating and sharing helpful, relevant, entertaining, and consistent content in various text, image, video, and audio-based formats to the plethora of traditional and online channels available to modern marketers.

The primary focus should be on attracting and retaining a clearly defined audience, with the ultimate goal of driving profitable customer action.

Different types of content can and should be created for each stage of a customer’s journey .

Some content, like blogs or how-to videos, are informative or educational. Meanwhile, other content, like promotional campaign landing pages , gets to the point of enticing prospective customers to buy.

But with so much content being produced and shared every day, it’s important to stay updated on the latest trends and best practices in content marketing to keep pace and understand what strategies may be most effective.

Never has this been more true than in 2024, when we’re in the midst of a content revolution led by generative AI , which some feel represents both an opportunity and a threat to marketers.

To help you keep up, here are 35 content marketing statistics I think you should know:

Content Marketing Usage

How many businesses are leveraging content marketing, and how are they planning to find success?

• According to the Content Marketing Institute (CMI), 73% of B2B marketers, and 70% of B2C marketers use content marketing as part of their overall marketing strategy.
• 97% of marketers surveyed by Semrush achieved success with their content marketing in 2023.
• A B2B Content Marketing Study conducted by CMI found that 40% of B2B marketers have a documented content marketing strategy; 33% have a strategy, but it’s not documented, and 27% have no strategy.
• Half of the surveyed marketers by CMI said they outsource at least one content marketing activity.

Content Marketing Strategy

What strategies are content marketers using or finding to be most effective?

• 83% of marketers believe it’s more effective to create higher quality content less often. (Source: Hubspot)
• In a 2022 Statista Research Study of marketers worldwide, 62% of respondents emphasized the importance of being “always on” for their customers, while 23% viewed content-led communications as the most effective method for personalized targeting efforts.
• With the increased focus on AI-generated search engine results, 31% of B2B marketers say they are sharpening their focus on user intent/answering questions, 27% are creating more thought leadership content, and 22% are creating more conversational content. (Source: CMI)

Types Of Content

Content marketing was synonymous with posting blogs, but the web and content have evolved into audio, video, interactive, and meta formats.

Here are a few stats on how the various types of content are trending and performing.

• Short-form video content, like TikTok and Instagram Reel, is the No. 1 content marketing format, offering the highest return on investment (ROI).
• 43% of marketers reported that original graphics (like infographics and illustrations) were the most effective type of visual content. (Source: Venngage)
• 72% of B2C marketers expected their organization to invest in video marketing in 2022. (Source: Content Marketing Institute – CMI)
• The State of Content Marketing: 2023 Global Report by Semrush reveals that articles containing at least one video tend to attract 70% more organic traffic than those without.
• Interactive content generates 52.6% more engagement compared to static content. On average, buyers spend 8.5 minutes viewing static content items and 13 minutes on interactive content items. (Source: Mediafly)

Content Creation

Creating helpful, unique, engaging content can be one of a marketer’s greatest challenges. However, innovative marketers are looking at generative AI as a tool to help ideate, create, edit, and analyze content quicker and more cost-effectively.

Here are some stats around content creation and just how quickly AI is changing the game.

• Generative AI reached over 100 million users just two months after ChatGPT’s launch. (Source: Search Engine Journal)
• A recent Ahrefs poll found that almost 80% of respondents had already adopted AI tools in their content marketing strategies.
• Marketers who are using AI said it helps most with brainstorming new topics ( 51%) , researching headlines and keywords (45%), and writing drafts (45%). (Source: CMI)
• Further, marketers polled by Hubspot said they save 2.5 hours per day using AI for content.

Content Distribution

It is not simply enough to create and publish content.

For a content strategy to be successful, it must include distributing content via the channels frequented by a business’s target audience.

• Facebook is still the dominant social channel for content distribution, but video-centric channels like YouTube, TikTok, and Instagram are growing the fastest .  (Source: Hubspot)
• B2B marketers reported to CMI that LinkedIn was the most common and top-performing organic social media distribution channel at 84% by a healthy margin. All other channels came in under 30%.
• 80% of B2B marketers who use paid distribution use paid social media advertising. (Source: CMI)

Content Consumption

Once content reaches an audience, it’s important to understand how an audience consumes the content or takes action as a result.

• A 2023 Content Preferences Study by Demand Gen reveals that 62% of B2B buyers prefer practical content like case studies to inform their purchasing decisions, citing “a need for valid sources.”
• The same study also found that buyers tend to rely heavily on content when researching potential business solutions, with 46% reporting that they increased the amount of content they consumed during this time.
• In a recent post, blogger Ryan Robinson reports the average reader spends 37 seconds reading a blog.
• DemandGen’s survey participants also said they rely most on demos ( 62% ) and user reviews (55%) to gain valuable insights into how a solution will meet their needs.

Content Marketing Performance

One of the primary reasons content marketing has taken off is its ability to be measured, optimized, and tied to a return on investment.

• B2C marketers reported to CMI that the top three goals content marketing helps them to achieve are creating brand awareness, building trust, and educating their target audience.
• 87% of B2B marketers surveyed use content marketing successfully to generate leads.
• 56% of marketers who leverage blogging say it’s an effective tactic, and 10% say it generates the greatest return on investment (ROI).
• 94% of marketers said personalization boosts sales.

Content Marketing Budgets

Budget changes and the willingness to invest in specific marketing strategies are good indicators of how popular and effective these strategies are at a macro level.

The following stats certainly seem to indicate marketers have bought into the value of content.

• 61% of B2C marketers said their 2022 content marketing budget would exceed their 2021 budget.
• 22% of B2B marketers said they spent 50% or more of their total marketing budget on content marketing. Furthermore, 43% saw their content marketing budgets grow from 2020 to 2021, and 66% expected them to grow again in 2022.

Content Challenges

All forms of marketing come with challenges related to time, resources, expertise, and competition.

Recognizing and addressing these challenges head-on with well-thought-out strategies is the best way to overcome them and realize success.

• Top 3 content challenges included “attracting quality leads with content” ( 45% ), “creating more content faster” (38%), and “generating content ideas” (35%). (Source: Semrush’s The State of Content Marketing: 2023 Global Report)
• 44% of marketers polled for CMI’s 2022 B2B report highlighted the challenge of creating the right content for multi-level roles as their top concern. This replaced internal communication as the top challenge from the previous year.
• Changes to SEO/search algorithms ( 64% ), changes to social media algorithms (53%), and data management/analytics (48%) are also among the top concerns for B2C marketers.
• 47% of people are seeking downtime from internet-enabled devices due to digital fatigue.
• While generative AI has noted benefits, it also presents challenges for some marketers who fear it may replace them. In Hubspot’s study, 23% said they felt we should avoid using generative AI.
• Another challenge with AI is how quickly it has come onto the scene without giving organizations time to provide training or to create policies and procedures for its appropriate and legal use. According to CMI, when asked if their organizations have guidelines for using generative AI tools, 31% of marketers said yes, 61% said no, and 8% were unsure.

Time To Get Started

As you can clearly see and perhaps have already realized, content marketing can be a highly effective and cost-efficient way to generate leads, build brand awareness, and drive sales. Content, in its many formats, powers virtually all online interactions.

Generative AI is effectively helping to solve some of the time and resource challenges by acting as a turbo-powered marketing assistant, while also raising a few procedural concerns.

However, the demand for content remains strong.

Those willing to put in the work of building a documented content strategy and executing it – by producing, optimizing, distributing, and monitoring high-value, relevant, customer-centric content, with the help of AI or not – can reap significant business rewards.

More resources:

• 6 Ways To Humanize Your Content In The AI Era
• Interactive Content: 10 Types To Engage Your Audience
• B2B Lead Generation: Create Content That Converts

Featured Image: Deemak Daksina/Shutterstock 

Jeff has been helping organizations manage, measure and optimize their Web presences for over 20 years. He has deep knowledge ...

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