case study examples statistics

Skip to secondary menu
Skip to main content
Skip to primary sidebar

Statistics By Jim

Making statistics intuitive

What is a Case Study? Definition & Examples

By Jim Frost Leave a Comment

Case Study Definition

A case study is an in-depth investigation of a single person, group, event, or community. This research method involves intensively analyzing a subject to understand its complexity and context. The richness of a case study comes from its ability to capture detailed, qualitative data that can offer insights into a process or subject matter that other research methods might miss.

A case study involves drawing lots of connections.

A case study strives for a holistic understanding of events or situations by examining all relevant variables. They are ideal for exploring ‘how’ or ‘why’ questions in contexts where the researcher has limited control over events in real-life settings. Unlike narrowly focused experiments, these projects seek a comprehensive understanding of events or situations.

In a case study, researchers gather data through various methods such as participant observation, interviews, tests, record examinations, and writing samples. Unlike statistically-based studies that seek only quantifiable data, a case study attempts to uncover new variables and pose questions for subsequent research.

A case study is particularly beneficial when your research:

Requires a deep, contextual understanding of a specific case.
Needs to explore or generate hypotheses rather than test them.
Focuses on a contemporary phenomenon within a real-life context.

Learn more about Other Types of Experimental Design .

Case Study Examples

Various fields utilize case studies, including the following:

Social sciences : For understanding complex social phenomena.
Business : For analyzing corporate strategies and business decisions.
Healthcare : For detailed patient studies and medical research.
Education : For understanding educational methods and policies.
Law : For in-depth analysis of legal cases.

For example, consider a case study in a business setting where a startup struggles to scale. Researchers might examine the startup’s strategies, market conditions, management decisions, and competition. Interviews with the CEO, employees, and customers, alongside an analysis of financial data, could offer insights into the challenges and potential solutions for the startup. This research could serve as a valuable lesson for other emerging businesses.

See below for other examples.


What impact does urban green space have on mental health in high-density cities?	Assess a green space development in Tokyo and its effects on resident mental health.
How do small businesses adapt to rapid technological changes?	Examine a small business in Silicon Valley adapting to new tech trends.
What strategies are effective in reducing plastic waste in coastal cities?	Study plastic waste management initiatives in Barcelona.
How do educational approaches differ in addressing diverse learning needs?	Investigate a specialized school’s approach to inclusive education in Sweden.
How does community involvement influence the success of public health initiatives?	Evaluate a community-led health program in rural India.
What are the challenges and successes of renewable energy adoption in developing countries?	Assess solar power implementation in a Kenyan village.

Types of Case Studies

Several standard types of case studies exist that vary based on the objectives and specific research needs.

Illustrative Case Study : Descriptive in nature, these studies use one or two instances to depict a situation, helping to familiarize the unfamiliar and establish a common understanding of the topic.

Exploratory Case Study : Conducted as precursors to large-scale investigations, they assist in raising relevant questions, choosing measurement types, and identifying hypotheses to test.

Cumulative Case Study : These studies compile information from various sources over time to enhance generalization without the need for costly, repetitive new studies.

Critical Instance Case Study : Focused on specific sites, they either explore unique situations with limited generalizability or challenge broad assertions, to identify potential cause-and-effect issues.

Pros and Cons

As with any research study, case studies have a set of benefits and drawbacks.

Provides comprehensive and detailed data.
Offers a real-life perspective.
Flexible and can adapt to discoveries during the study.
Enables investigation of scenarios that are hard to assess in laboratory settings.
Facilitates studying rare or unique cases.
Generates hypotheses for future experimental research.
Time-consuming and may require a lot of resources.
Hard to generalize findings to a broader context.
Potential for researcher bias.
Cannot establish causality .
Lacks scientific rigor compared to more controlled research methods .

Crafting a Good Case Study: Methodology

While case studies emphasize specific details over broad theories, they should connect to theoretical frameworks in the field. This approach ensures that these projects contribute to the existing body of knowledge on the subject, rather than standing as an isolated entity.

The following are critical steps in developing a case study:

Define the Research Questions : Clearly outline what you want to explore. Define specific, achievable objectives.
Select the Case : Choose a case that best suits the research questions. Consider using a typical case for general understanding or an atypical subject for unique insights.
Data Collection : Use a variety of data sources, such as interviews, observations, documents, and archival records, to provide multiple perspectives on the issue.
Data Analysis : Identify patterns and themes in the data.
Report Findings : Present the findings in a structured and clear manner.

Analysts typically use thematic analysis to identify patterns and themes within the data and compare different cases.

Qualitative Analysis : Such as coding and thematic analysis for narrative data.
Quantitative Analysis : In cases where numerical data is involved.
Triangulation : Combining multiple methods or data sources to enhance accuracy.

A good case study requires a balanced approach, often using both qualitative and quantitative methods.

The researcher should constantly reflect on their biases and how they might influence the research. Documenting personal reflections can provide transparency.

Avoid over-generalization. One common mistake is to overstate the implications of a case study. Remember that these studies provide an in-depth insights into a specific case and might not be widely applicable.

Don’t ignore contradictory data. All data, even that which contradicts your hypothesis, is valuable. Ignoring it can lead to skewed results.

Finally, in the report, researchers provide comprehensive insight for a case study through “thick description,” which entails a detailed portrayal of the subject, its usage context, the attributes of involved individuals, and the community environment. Thick description extends to interpreting various data, including demographic details, cultural norms, societal values, prevailing attitudes, and underlying motivations. This approach ensures a nuanced and in-depth comprehension of the case in question.

Learn more about Qualitative Research and Qualitative vs. Quantitative Data .

Morland, J. & Feagin, Joe & Orum, Anthony & Sjoberg, Gideon. (1992). A Case for the Case Study . Social Forces. 71(1):240.

Reader Interactions

Comments and questions cancel reply.

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)) ”. ↩

LEARN STATISTICS EASILY

Learn Data Analysis Now!

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.

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.

Inter-Class Correlation: Mastering the Art of Evaluating Rater Agreement

Explore the Inter-Class Correlation to enhance the reliability of your statistical analyses and embrace the beauty of data consistency.

Think Coin Flips Are 50/50? This Study Will Make You Think Again

This groundbreaking study challenges the odds to reveal the hidden bias in coin flips. Coin flips aren’t just chance.

Avoiding the Pitfall of Jumping to Conclusions in Data Science

Avoid jumping to conclusions in data science with these statistical insights and methods. Learn how to ensure accuracy and validity.

Join the Data Revolution: A Layman’s Guide to Statistical Learning

Explore the transformative power of Statistical Learning in our comprehensive guide and become part of the data revolution.

Confounding Variables in Statistics: Strategies for Identifying and Adjusting

Explore how confounding variables in statistics can impact your research and learn effective strategies for identifying and adjusting them.

How Statistics Can Change Your Life: A Guide for Beginners

Discover the transformative power of statistics in daily life. This guide equips you with essential skills for informed decision-making.

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

What is case study research?

Last updated

8 February 2023

Reviewed by

Cathy Heath

Short on time? Get an AI generated summary of this article instead

Suppose a company receives a spike in the number of customer complaints, or medical experts discover an outbreak of illness affecting children but are not quite sure of the reason. In both cases, carrying out a case study could be the best way to get answers.

Organization

Case studies can be carried out across different disciplines, including education, medicine, sociology, and business.

Most case studies employ qualitative methods, but quantitative methods can also be used. Researchers can then describe, compare, evaluate, and identify patterns or cause-and-effect relationships between the various variables under study. They can then use this knowledge to decide what action to take.

Another thing to note is that case studies are generally singular in their focus. This means they narrow focus to a particular area, making them highly subjective. You cannot always generalize the results of a case study and apply them to a larger population. However, they are valuable tools to illustrate a principle or develop a thesis.

Analyze case study research

Dovetail streamlines case study research to help you uncover and share actionable insights

What are the different types of case study designs?

Researchers can choose from a variety of case study designs. The design they choose is dependent on what questions they need to answer, the context of the research environment, how much data they already have, and what resources are available.

Here are the common types of case study design:

Explanatory

An explanatory case study is an initial explanation of the how or why that is behind something. This design is commonly used when studying a real-life phenomenon or event. Once the organization understands the reasons behind a phenomenon, it can then make changes to enhance or eliminate the variables causing it.

Here is an example: How is co-teaching implemented in elementary schools? The title for a case study of this subject could be “Case Study of the Implementation of Co-Teaching in Elementary Schools.”

Descriptive

An illustrative or descriptive case study helps researchers shed light on an unfamiliar object or subject after a period of time. The case study provides an in-depth review of the issue at hand and adds real-world examples in the area the researcher wants the audience to understand.

The researcher makes no inferences or causal statements about the object or subject under review. This type of design is often used to understand cultural shifts.

Here is an example: How did people cope with the 2004 Indian Ocean Tsunami? This case study could be titled "A Case Study of the 2004 Indian Ocean Tsunami and its Effect on the Indonesian Population."

Exploratory

Exploratory research is also called a pilot case study. It is usually the first step within a larger research project, often relying on questionnaires and surveys . Researchers use exploratory research to help narrow down their focus, define parameters, draft a specific research question , and/or identify variables in a larger study. This research design usually covers a wider area than others, and focuses on the ‘what’ and ‘who’ of a topic.

Here is an example: How do nutrition and socialization in early childhood affect learning in children? The title of the exploratory study may be “Case Study of the Effects of Nutrition and Socialization on Learning in Early Childhood.”

An intrinsic case study is specifically designed to look at a unique and special phenomenon. At the start of the study, the researcher defines the phenomenon and the uniqueness that differentiates it from others.

In this case, researchers do not attempt to generalize, compare, or challenge the existing assumptions. Instead, they explore the unique variables to enhance understanding. Here is an example: “Case Study of Volcanic Lightning.”

This design can also be identified as a cumulative case study. It uses information from past studies or observations of groups of people in certain settings as the foundation of the new study. Given that it takes multiple areas into account, it allows for greater generalization than a single case study.

The researchers also get an in-depth look at a particular subject from different viewpoints. Here is an example: “Case Study of how PTSD affected Vietnam and Gulf War Veterans Differently Due to Advances in Military Technology.”

Critical instance

A critical case study incorporates both explanatory and intrinsic study designs. It does not have predetermined purposes beyond an investigation of the said subject. It can be used for a deeper explanation of the cause-and-effect relationship. It can also be used to question a common assumption or myth.

The findings can then be used further to generalize whether they would also apply in a different environment. Here is an example: “What Effect Does Prolonged Use of Social Media Have on the Mind of American Youth?”

Instrumental

Instrumental research attempts to achieve goals beyond understanding the object at hand. Researchers explore a larger subject through different, separate studies and use the findings to understand its relationship to another subject. This type of design also provides insight into an issue or helps refine a theory.

For example, you may want to determine if violent behavior in children predisposes them to crime later in life. The focus is on the relationship between children and violent behavior, and why certain children do become violent. Here is an example: “Violence Breeds Violence: Childhood Exposure and Participation in Adult Crime.”

Evaluation case study design is employed to research the effects of a program, policy, or intervention, and assess its effectiveness and impact on future decision-making.

For example, you might want to see whether children learn times tables quicker through an educational game on their iPad versus a more teacher-led intervention. Here is an example: “An Investigation of the Impact of an iPad Multiplication Game for Primary School Children.”

When do you use case studies?

Case studies are ideal when you want to gain a contextual, concrete, or in-depth understanding of a particular subject. It helps you understand the characteristics, implications, and meanings of the subject.

They are also an excellent choice for those writing a thesis or dissertation, as they help keep the project focused on a particular area when resources or time may be too limited to cover a wider one. You may have to conduct several case studies to explore different aspects of the subject in question and understand the problem.

What are the steps to follow when conducting a case study?

1. Select a case

Once you identify the problem at hand and come up with questions, identify the case you will focus on. The study can provide insights into the subject at hand, challenge existing assumptions, propose a course of action, and/or open up new areas for further research.

2. Create a theoretical framework

While you will be focusing on a specific detail, the case study design you choose should be linked to existing knowledge on the topic. This prevents it from becoming an isolated description and allows for enhancing the existing information.

It may expand the current theory by bringing up new ideas or concepts, challenge established assumptions, or exemplify a theory by exploring how it answers the problem at hand. A theoretical framework starts with a literature review of the sources relevant to the topic in focus. This helps in identifying key concepts to guide analysis and interpretation.

3. Collect the data

Case studies are frequently supplemented with qualitative data such as observations, interviews, and a review of both primary and secondary sources such as official records, news articles, and photographs. There may also be quantitative data —this data assists in understanding the case thoroughly.

4. Analyze your case

The results of the research depend on the research design. Most case studies are structured with chapters or topic headings for easy explanation and presentation. Others may be written as narratives to allow researchers to explore various angles of the topic and analyze its meanings and implications.

In all areas, always give a detailed contextual understanding of the case and connect it to the existing theory and literature before discussing how it fits into your problem area.

What are some case study examples?

What are the best approaches for introducing our product into the Kenyan market?

How does the change in marketing strategy aid in increasing the sales volumes of product Y?

How can teachers enhance student participation in classrooms?

How does poverty affect literacy levels in children?

Case study topics

Case study of product marketing strategies in the Kenyan market

Case study of the effects of a marketing strategy change on product Y sales volumes

Case study of X school teachers that encourage active student participation in the classroom

Case study of the effects of poverty on literacy levels in children

Should you be using a customer insights hub?

Do you want to discover previous research faster?

Do you share your research findings with others?

Do you analyze research data?

Start for free today, add your research, and get to key insights faster

Editor’s picks

Last updated: 18 April 2023

Last updated: 27 February 2023

Last updated: 22 August 2024

Last updated: 5 February 2023

Last updated: 16 August 2024

Last updated: 9 March 2023

Last updated: 30 April 2024

Last updated: 12 December 2023

Last updated: 11 March 2024

Last updated: 4 July 2024

Last updated: 6 March 2024

Last updated: 5 March 2024

Last updated: 13 May 2024

Latest articles

Related topics, .css-je19u9{-webkit-align-items:flex-end;-webkit-box-align:flex-end;-ms-flex-align:flex-end;align-items:flex-end;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-box-flex-wrap:wrap;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;row-gap:0;text-align:center;max-width:671px;}@media (max-width: 1079px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}}@media (max-width: 799px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}} decide what to .css-1kiodld{max-height:56px;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}@media (max-width: 1079px){.css-1kiodld{display:none;}} build next, decide what to build next, log in or sign up.

Get started for free

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

Knowledge Base

Methodology

What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

Case study examples
Research question	Case study
What are the ecological effects of wolf reintroduction?	Case study of wolf reintroduction in Yellowstone National Park
How do populist politicians use narratives about history to gain support?	Case studies of Hungarian prime minister Viktor Orbán and US president Donald Trump
How can teachers implement active learning strategies in mixed-level classrooms?	Case study of a local school that promotes active learning
What are the main advantages and disadvantages of wind farms for rural communities?	Case studies of three rural wind farm development projects in different parts of the country
How are viral marketing strategies changing the relationship between companies and consumers?	Case study of the iPhone X marketing campaign
How do experiences of work in the gig economy differ by gender, race and age?	Case studies of Deliveroo and Uber drivers in London

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

Provide new or unexpected insights into the subject
Challenge or complicate existing assumptions and theories
Propose practical courses of action to resolve a problem
Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

Exemplify a theory by showing how it explains the case under investigation
Expand on a theory by uncovering new concepts and ideas that need to be incorporated
Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Normal distribution
Degrees of freedom
Null hypothesis
Discourse analysis
Control groups
Mixed methods research
Non-probability sampling
Quantitative research
Ecological validity

Research bias

Rosenthal effect
Implicit bias
Cognitive bias
Selection bias
Negativity bias
Status quo bias

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, November 20). What Is a Case Study? | Definition, Examples & Methods. Scribbr. Retrieved August 26, 2024, from https://www.scribbr.com/methodology/case-study/

Is this article helpful?

Shona McCombes

Other students also liked, primary vs. secondary sources | difference & examples, what is a theoretical framework | guide to organizing, what is action research | definition & examples, get unlimited documents corrected.

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

This site uses cookies to improve your experience. By viewing our content, you are accepting the use of cookies. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country we will assume you are from the United States. View our privacy policy and terms of use.

Instructional Design
Authoring Tools
Blended Learning
Virtual Classroom
Learning Strategy
Remote Learning
Gamification

Learning analytics examples: 5 case studies of data insights in action

Learning Pool

MAY 5, 2021

Examples of what real organizations are doing with it in real-life situations make it easier to grasp the scale of this advance and apply the learnings to your own situation. . Learning analytics examples . Case study 1: Measuring behavior change at InterContinental Hotels Group with xAPI. .

Case Study, Scenario, Story: What’s the Difference?

MAY 30, 2017

The term Case Study is often used loosely and interchangeably with the terms scenario and story-based learning. Case Study . Case studies are used to teach how knowledge is to be applied in real-world situations, and the consequences one could face while doing so. This often causes a lot of confusion.

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Mastering Remote Onboarding: Proven Strategies for Seamless New Hire Integration
Beyond the Basics: How to Develop and Retain a Top-Performing Sales Team

MORE WEBINARS

Trending Sources

InSync Training
The Logical Blog by IconLogic
Experiencing eLearning

How Two Businesses Scored Big with Gamification (Case Studies)

JUNE 8, 2016

Case Study 1. Case Study 2. For example , they got leveling icons, that ranged from glass to diamond. The statistics saw a drastic change after deploying gamification. Example of use of Badges – Paradiso LMS. And if the training is game based, then simulation becomes all the more interesting.

Creating Quality Leadership Development Training for New Leaders

Infopro Learning

MARCH 19, 2021

Recent leadership development industry statistics show that leadership training affects individuals, teams they work with, and the organization as a whole. Leadership development statistics have shown that training can lead to a 28% increase in leadership behaviors. Read the case study , Leadership Training for New Leaders.

How Educators Can Leverage Generative AI to Augment Teaching and Learning

JANUARY 11, 2024

Read further for some key insights highlighted in learner case studies and discover how you, as educators, may be able to use GenAI to augment your teaching and learning. Sample prompts mirror learner prompts from research but are not written verbatim. N/B learner names have been changed to protect the identity of learners.

Creating Inclusive Designs: Universal Design Principles Explained

Hurix Digital

JULY 29, 2024

Summary This blog explores Universal Design Principles and guidelines for inclusive designs that enhance satisfaction, expand market reach, and ensure legal compliance, with practical tips and examples . According to statistics , those who use assistive technology devices such as screen readers, Braille displays, etc.

A 5-Step Guide to Create an Employee Onboarding Plan

NOVEMBER 8, 2019

According to the US Bureau of Labor Statistics , 3 million Americans quit their jobs every month (1). For example , a mentor may work with a new employee on the job to learn about customer interaction so they can watch, learn, and practice.

Using ChatGPT to Develop Course Content for E-Learning

SEPTEMBER 20, 2023

This may involve adding practical examples , case studies , or real-life applications to make the content more engaging and relatable. ChatGPT can help by generating quiz questions, discussion prompts, and sample answers for assignments. These interactive components contribute to a dynamic and engaging learning experience.

Recycle for a Greater ROI: How to Future-Proof Your Corporate Training Content

MARCH 8, 2016

To future-proof training content, it should contain broad statistical trends rather than specific annual statistics , be published in interoperable formats, and contain timeless case studies and examples rather than current events and topical stories. Broad Statistics > Specific Data.

Keep Learners Guessing to Increase Engagement and Retention

Learningtogo

OCTOBER 29, 2022

No matter what grade you received in high school math, your brain instinctively uses statistics to learn about the world. Using information from experience, your brain constantly predicts how people, systems, and the natural world will behave, applying a statistical called Bayesian inference. Surprise Them—But Not Too Much.

What is Gamification in Marketing? How to Use Gamification to Boost Your Marketing Campaigns?

OCTOBER 30, 2023

Recent statistics not only validate the effectiveness of this innovative approach but also shed light on its immense growth potential. Core Elements of Gamification in Marketing Examples of Gamification in Marketing How to Effectively Use Gamification in Marketing Campaigns? Table of Contents: What is Gamification in Marketing?

How to introduce your new LMS to the company

NOVEMBER 9, 2021

A sample communications plan (downloadable PDF). Win them over by: Sharing case studies of similar organizations who’ve smashed old KPIs and targets after switching over to a new LMS. Win them over by: Providing post-launch data, statistics , and user feedback. For example , HR, L&D, IT, Internal Comms, and Marketing.

Why statistics are not enough

Clive on Learning

JULY 2, 2013

Presenting people with statistical information and descriptions of scientific studies will only get you so far. Your students may be able to recite a statistic in an exam and even regurgitate the ''official interpretation'', but that doesn''t mean they really believe it, not deep down, at least as far as it applies to them.

Benefits of Educational Explainer Videos for K-12 Learning

FEBRUARY 10, 2022

For example , when you jump on the bandwagon to create educational videos, you will hear terms like educational explainer videos , how-to videos, promo videos, thought leadership videos, case study videos, demo videos, and whatnot. What is an Educational Explainer Video? Videos are Everywhere.

15m microlearning lessons delivered. What we have learned.

SEPTEMBER 21, 2020

Have we learned anything from this statistic ? Here are a few reasons from a recent Chinese case study review of micro-lessons: The “one knowledge point” content + the short lesson time (5-12 minutes) allow learners to stay focused. At the moment, EdApp, for example , can deliver your educational/training content in 103 languages.

Why Fun in Learning is Important

Growth Engineering

MARCH 21, 2017

A study in the journal, College Teaching, found that students could recall a statistics lecture more easily when the lecturer added jokes about relevant topics. In a study for the Journal of Vocational Behaviour, Michael Tews found that employees are more likely to try new things if their work environment is fun.

Quiz Funnel: Examples, Benefits, & Steps to Create One

MAY 9, 2023

In this blog post, we will explore the concept of quiz funnels in more detail, including their benefits and examples . A recent case study of a company that used a quiz funnel is Sephora, one of the top international cosmetic brands. That was all about the various examples of quizzes that you can use in your quiz funnels.

How to Create an Online Course Outline | Complete Guide & Template

Think Orion

MARCH 3, 2023

For example , this Microsoft Word Basics course outline highlights all the essential information that relates to the course outline. For example , as an online course instructor, you can explore a range of books available online to create a comprehensive course outline. Here you can see 10,000 results for SEO Courses.

eBooks and Professional Development for K-12 Teachers

MARCH 14, 2023

Statistics on the Use of eBooks in K-12 Education 1. Case Studies and Success Stories Many leading schools and educational institutions have used KITABOO to deliver high-quality, engaging educational content. In one example , a school district used KITABOO to create and distribute a digital textbook to all of its students.

The Benefits of Personalization in E-Learning

Webanywhere

MAY 13, 2024

Examples include platforms that offer more advanced reading materials as comprehension improves or adjust math problems’ complexity based on the student’s performance. Several case studies highlight the effectiveness of personalized learning in increasing motivation and engagement.

Predictive Analytics VS Traditional Business Forecasting: A Comprehensive Guide

JANUARY 31, 2024

For decades, businesses relied on statistical models and time series analysis to forecast future outcomes. The Evolution of Business Forecasting For decades, businesses relied on statistical models and time series analysis to forecast future outcomes.

Harnessing the Power of Assessment for Data-Driven Curriculum Optimization

JUNE 13, 2024

The National Statistical Office Ministry of Statistics & Programme Implementation reported a 7.6% Authentic Assessments These involve tasks that reflect real-world challenges and standards such include research projects and case studies . Examples include online quizzes and computer-adaptive tests.

Mastering the craft: A comprehensive guide to technical skills training

JANUARY 16, 2024

Isolated from soft skills: Professionals need strong soft skills (for example , communication, teamwork, and critical thinking) to complement their technical expertise. Proficiency in data analysis tools, statistical methods, and data visualization informs decision making. For example , videos, quizzes, and interactive content.

Case study: Lehigh Valley’s investment in talent pipeline attracts people, business

CLO Magazine

OCTOBER 24, 2023

For example , the business and tech climate today is looking to artificial intelligence, which requires training and collaboration between educational institutions in order to best train and employ upcoming talent. For example , the pandemic generated a conversation about remote internships. Topics at the Summit change every year.

Tips for teaching HR Business Partners to be analytics savvy

FEBRUARY 28, 2023

There is no need to teach HR business partners any statistics ; just give them a workshop with some ‘everyday analytics’ case studies so they can see what it’s really all about and then give them a chance to practice the basic skills they need.

Customer training: your ticket to stronger loyalty, retention, and brand advocacy

SEPTEMBER 15, 2021

Customer training examples . Customer training examples : onboarding, support, and more . Companies that offer software, tech, and subscription-based services, as an example , are particularly suited to customer training. What is customer training? Why is customer training important? Benefits of customer training.

How to Tell if Remote Workers Are Working: 4 Ways to Engage Remote Employees Through Microlearning

KnowledgeAnywhere

AUGUST 17, 2023

A new major survey from Microsoft states 87% of employees feel they are as efficient or more productive remotely, whereas 87% of managers disagree with this statistic . Below are a few case studies of how you can use QuickQuiz. So how can we solve the discrepancy between these two groups? What is Microlearning?

5 New, Unusual And Bold Elearning Trends In 2023

Spark Your Interest

DECEMBER 5, 2021

There are many studies and statistics that show the effectiveness and efficacy of online learning. Numerous examples now exist that help support hybrid learning, none better than Cindy Huggett’s “ Facilitators Guide To Immersive, Blended and Hybrid Learning.”. “90% Elearning is here to stay in one form or another.

Intervention learning

Learning with e's

FEBRUARY 19, 2019

Day 1 session S2 From Hype to Reality: AR and VR in Action for example , showcased some great case studies from Sponge , The Royal Mail (James Barton) and Finger Foods Studios (Ryan Peterson). Barton's mention of postal workers being bitten or chased by dogs, at first caused ripples of laughter across the audience.

Rekindle The Passion for Learning with Nine Events of instructions.

Tesseract Learning

AUGUST 11, 2022

The online training module can begin with a story, scenario, thought-provoking question, interesting situation, problem, or intriguing statistics . Corporate eLearning modules can include extra documents for further studies , such as case studies , recorded videos of experts, and so on.

From Stormy Seas to Smooth Sailing: The Influence of Quality Maritime Training

SEPTEMBER 28, 2023

Modern LMS platforms, with interactive courses, exams, and real-life case studies , ensure that the message isn't just delivered but also retained. 25% of seafarers show signs of depression." - Seafarers International Research Centre These statistics are the canary in the coal mine warning us about the dangers ahead.

Top 5 Interactive Math Tool Apps You Should Try!

SEPTEMBER 22, 2023

Rather, this extensive app combines various topics, including geometry, statistics , algebra, and calculus, into one dynamic package. It helps improve learners’ skills in various subjects, including elementary math, geometry, algebra, calculus, trigonometry, statistics , and more. This helps drive innovation in teaching methods.

Learning Food Safety in 2021

AUGUST 1, 2021

eLearning statistics show, that online learning makes information more accessible (students only need a mobile device with internet access) therefore providing employees with a chance of continuous learning. Technological advancements make studying easier and more digestible. Int J Educ Technol High Educ 16, 37 (2019). [2] 2] [link].

The Skills Gap – The Correlation Between Training & the Skills Gap – Part III

Jigsaw Interactive

AUGUST 18, 2022

Case Study . Take international business analytics software and services giant SAS, for example . Consider the following statistics : 74% of workers want to learn new skills. According to a Training Magazine report, U.S. companies spend an average $4.5 billion on training and development programs.

What is the future of sales enablement?

APRIL 14, 2024

For example , companies that sell transactional low-investment items or industries that have a high attrition rate for their sales team. An example of a response to the buying process is the “DSR”—the digital sales room—a term used by Gartner in 2020. The sales team must adapt to provide customized interactions with the buyers.

Ad-hoc Social Learning Environment - How a Blog Drives Learning

Vikas Joshi on Interactive Learning

FEBRUARY 20, 2010

Blog posts provided a guiding theme and then pointed to a web resource such as a YouTube video, or a podcast or a news story article or a case study . For example , a blog post would show a crisis situation, followed by a media interview of the person who was in charge of handling the crisis.

Your organizations learning analytics journey: Your map and compass

JUNE 4, 2021

For example , if you have talented people who are data-savvy but technology poor, you are in a different position from someone with no capabilities in the team at all. . You can view more of our learning analytics examples via our case studies page. Goals are of fundamental importance to the model.

How to Design an Online Course in 2022? Best Practices, Tips & Templates

learnWorlds

APRIL 26, 2022

For example , you might want to consider: Adding more instructor interactions, Shooting a better video, Adding instructions to certain learning units, or. For example , show yourself in videos or other multimedia formats so that students can hear your voice and see your body language. Updating your material. Be available and present.

10 tips on how to create great online sales trainings

Melon Learning

FEBRUARY 5, 2018

We are inundated with so much data, facts, statistics and knowledge that tiredness and boredom occur easily. Simulations for example are an excellent way for new employees to get a hands-on experience and for your existing people to hone in on their techniques and improve their skills. 8 Integrate case studies .

Leveraging Content Gamification to Engage and Retain Customers

JUNE 1, 2023

Statistics reveal that good customer experience can improve profitability by 2% and sales revenue by 7%. One major example of content gamification is in K-12 education trends to keep students motivated. Read multiple case studies to know what worked best for them and why. What is Content Gamification?

8 Unexpected Uses Of Compliance Online Training Infographics

JUNE 1, 2017

For example , they are in the middle of a task and forget which safety gear they need, or how to complete the sales process in accordance with company policy. For example , how the ethics compliance policies lead to a more supportive and positive work environment, enhancing workplace performance and productivity.

E-Learning Service Providers Can Transform Your Business In These 3 Ways

DECEMBER 14, 2022

Statistics like this are very concerning. For example , the same AR/VR simulations can be used by employees to try out newly released product features or experiment with workplace tools. Poll/quiz-based or case - study -centric self-paced training modules can be particularly powerful in these situations.

Generative AI vs. Predictive AI: Unraveling the Future of Artificial Intelligence

Epilogue Systems

Coming Soon: Opus : A Case Study in AI Adoption Opus is a digital adaptation platform designed to address a common challenge faced by medium to large organizations – the adoption of complex applications and software. . Some example of Generative AI Tools Generative AI has seen the emergence of various powerful tools and models.

Stay Connected

Join 84,000+ Insiders by signing up for our newsletter

Learn More about eLearning Learning
Participate in eLearning Learning
Selecting the right LMS
2019 eLearning Learning Summer Reading List
Stay At Home Reading List
Add a Source
Add a Resource
See All
2017 eLearning Learning MVP awards
2018 eLearning Learning MVP Awards
2019 eLearning Learning MVP Awards
2020 eLearning Learning MVP Awards
2021 eLearning Learning MVP Awards
2022 eLearning Learning MVP Awards
Sun. Aug 25
Sat. Aug 24
Fri. Aug 23
Thu. Aug 22
Aug 17 - Aug 23
Performance
More Topics

Input your email to sign up, or if you already have an account, log in here!

Enter your email address to reset your password. a temporary password will be e‑mailed to you., be in the know on.

eLearning Learning

Expert insights. Personalized for you.

We organize all of the trending information in your field so you don't have to. Join 84,000+ users and stay up to date on the latest articles your peers are reading.

Get the good stuff

Subscribe to the following eLearning Learning newsletters:

You must accept the Privacy Policy and Terms & Conditions to proceed.

You know about us, now we want to get to know you!

Check your mail, we've sent an email to . please verify that you have received the email..

We have resent the email to

Let's personalize your content

Use social media to find articles.

We can use your profile and the content you share to understand your interests and provide content that is just for you.

Turn this off at any time. Your social media activity always remains private.

Let's get even more personalized

Choose topics that interest you., so, what do you do.

Are you sure you want to cancel your subscriptions?

Cancel my subscriptions

Don't cancel my subscriptions

Changing Country?

Accept terms & conditions.

It looks like you are changing your country/region of residence. In order to receive our emails, you must expressly agree. You can unsubscribe at any time by clicking the unsubscribe link at the bottom of our emails.

You appear to have previously removed your acceptance of the Terms & Conditions.

We noticed that you changed your country/region of residence; congratulations! In order to make this change, you must accept the Aggregage Terms and Conditions and Privacy Policy. Once you've accepted, then you will be able to choose which emails to receive from each site .

You must choose one option

Please choose which emails to receive from each site .

Update All Sites
Update Each Site

Please verify your previous choices for all sites

Sites have been updated - click Submit All Changes below to save your changes.

We recognize your account from another site in our network , please click 'Send Email' below to continue with verifying your account and setting a password.

You must accept the Privacy Policy and Terms & Conditions to proceed.

This is not me

27 Case Study Examples Every Marketer Should See

Published: July 22, 2024

Putting together a compelling case study is one of the most powerful strategies for showcasing your product and attracting future customers. But it's not easy to create case studies that your audience can’t wait to read.

In this post, I’ll go over the definition of a case study and the best examples to inspire you.

Table of Contents

What is a case study?

Marketing case study examples, digital marketing case study examples.

Free Case Study Templates

Showcase your company's success using these three free case study templates.

Data-Driven Case Study Template
Product-Specific Case Study Template
General Case Study Template

Download Free

All fields are required.

You're all set!

Click this link to access this resource at any time.

A case study is a detailed story of something your company did. It includes a beginning — often discussing a challenge, an explanation of what happened next, and a resolution that explains how the company solved or improved on something.

A case study proves how your product has helped other companies by demonstrating real-life results. Not only that, but marketing case studies with solutions typically contain quotes from the customer.

This means that they’re not just ads where you praise your own product. Rather, other companies are praising your company — and there’s no stronger marketing material than a verbal recommendation or testimonial.

A great case study also has research and stats to back up points made about a project's results.

There are several ways to use case studies in your marketing strategy.

From featuring them on your website to including them in a sales presentation, a case study is a strong, persuasive tool that shows customers why they should work with you — straight from another customer.

Writing one from scratch is hard, though, which is why we’ve created a collection of case study templates for you to get started.

There’s no better way to generate more leads than by writing case studies . However, without case study examples from which to draw inspiration, it can be difficult to write impactful studies that convince visitors to submit a form.

To help you create an attractive and high-converting case study, we've put together a list of some of our favorites. This list includes famous case studies in marketing, technology, and business.

These studies can show you how to frame your company's offers in a way that is useful to your audience. So, look, and let these examples inspire your next brilliant case study design.

These marketing case studies with solutions show the value proposition of each product. They also show how each company benefited in both the short and long term using quantitative data.

In other words, you don’t get just nice statements, like “this company helped us a lot.” You see actual change within the firm through numbers and figures.

You can put your learnings into action with HubSpot's Free Case Study Templates . Available as custom designs and text-based documents, you can upload these templates to your CMS or send them to prospects as you see fit.

digital marketing case study example from Rozum Robotics

digital marketing case study example from carolhwilliams

digital marketing case study example from fantasy

digital marketing case study example from google

digital marketing case study example from herman miller

digital marketing case study example from aws

digital marketing case study example from asana

digital marketing case study example from ampagency

digital marketing case study example from evisort

digital marketing case study example from cloudflight

digital marketing case study example from textel

digital marketing case study example from happeo

digital marketing case study example from ctp boston

digital marketing case study example from genuine

digital marketing case study example from apptio

digital marketing case study example from biobot analytics

18. " Discovering Cost Savings With Efficient Decision Making ," by Gartner

digital marketing case study example from gartner

digital marketing case study example from Redapt

$digital marketing case study example from fractl$

digital marketing case study example from switch

How to Write a Case Study: Bookmarkable Guide & Template

7 Pieces of Content Your Audience Really Wants to See [New Data]

How to Market an Ebook: 21 Ways to Promote Your Content Offers

How to Write a Listicle [+ Examples and Ideas]

What Is a White Paper? [FAQs]

What is an Advertorial? 8 Examples to Help You Write One

How to Create Marketing Offers That Don't Fall Flat

20 Creative Ways To Repurpose Content

16 Important Ways to Use Case Studies in Your Marketing

11 Ways to Make Your Blog Post Interactive

Showcase your company's success using these free case study templates.

Marketing software that helps you drive revenue, save time and resources, and measure and optimize your investments — all on one easy-to-use platform

FOR EMPLOYERS

Top 10 real-world data science case studies.

Aditya Sharma

Aditya is a content writer with 5+ years of experience writing for various industries including Marketing, SaaS, B2B, IT, and Edtech among others. You can find him watching anime or playing games when he’s not writing.

Frequently Asked Questions

Real-world data science case studies differ significantly from academic examples. While academic exercises often feature clean, well-structured data and simplified scenarios, real-world projects tackle messy, diverse data sources with practical constraints and genuine business objectives. These case studies reflect the complexities data scientists face when translating data into actionable insights in the corporate world.

Real-world data science projects come with common challenges. Data quality issues, including missing or inaccurate data, can hinder analysis. Domain expertise gaps may result in misinterpretation of results. Resource constraints might limit project scope or access to necessary tools and talent. Ethical considerations, like privacy and bias, demand careful handling.

Lastly, as data and business needs evolve, data science projects must adapt and stay relevant, posing an ongoing challenge.

Real-world data science case studies play a crucial role in helping companies make informed decisions. By analyzing their own data, businesses gain valuable insights into customer behavior, market trends, and operational efficiencies.

These insights empower data-driven strategies, aiding in more effective resource allocation, product development, and marketing efforts. Ultimately, case studies bridge the gap between data science and business decision-making, enhancing a company's ability to thrive in a competitive landscape.

Key takeaways from these case studies for organizations include the importance of cultivating a data-driven culture that values evidence-based decision-making. Investing in robust data infrastructure is essential to support data initiatives. Collaborating closely between data scientists and domain experts ensures that insights align with business goals.

Finally, continuous monitoring and refinement of data solutions are critical for maintaining relevance and effectiveness in a dynamic business environment. Embracing these principles can lead to tangible benefits and sustainable success in real-world data science endeavors.

Data science is a powerful driver of innovation and problem-solving across diverse industries. By harnessing data, organizations can uncover hidden patterns, automate repetitive tasks, optimize operations, and make informed decisions.

In healthcare, for example, data-driven diagnostics and treatment plans improve patient outcomes. In finance, predictive analytics enhances risk management. In transportation, route optimization reduces costs and emissions. Data science empowers industries to innovate and solve complex challenges in ways that were previously unimaginable.

Hire remote developers

Tell us the skills you need and we'll find the best developer for you in days, not weeks.

Design for Business
Most Recent
Presentations
Infographics
Data Visualizations
Forms and Surveys
Video & Animation
Case Studies
Digital Marketing
Design Inspiration
Visual Thinking
Product Updates
Visme Webinars
Artificial Intelligence

15 Real-Life Case Study Examples & Best Practices

Written by: Oghale Olori

Case studies are more than just success stories.

They are powerful tools that demonstrate the practical value of your product or service. Case studies help attract attention to your products, build trust with potential customers and ultimately drive sales.

It’s no wonder that 73% of successful content marketers utilize case studies as part of their content strategy. Plus, buyers spend 54% of their time reviewing case studies before they make a buying decision.

To ensure you’re making the most of your case studies, we’ve put together 15 real-life case study examples to inspire you. These examples span a variety of industries and formats. We’ve also included best practices, design tips and templates to inspire you.

Let’s dive in!

What is a case study, 15 real-life case study examples, sales case study examples, saas case study examples, product case study examples, marketing case study examples, business case study examples, case study faqs.

A case study is a compelling narrative that showcases how your product or service has positively impacted a real business or individual.
Case studies delve into your customer's challenges, how your solution addressed them and the quantifiable results they achieved.
Your case study should have an attention-grabbing headline, great visuals and a relevant call to action. Other key elements include an introduction, problems and result section.
Visme provides easy-to-use tools, professionally designed templates and features for creating attractive and engaging case studies.

A case study is a real-life scenario where your company helped a person or business solve their unique challenges. It provides a detailed analysis of the positive outcomes achieved as a result of implementing your solution.

Case studies are an effective way to showcase the value of your product or service to potential customers without overt selling. By sharing how your company transformed a business, you can attract customers seeking similar solutions and results.

Case studies are not only about your company's capabilities; they are primarily about the benefits customers and clients have experienced from using your product.

Every great case study is made up of key elements. They are;

Attention-grabbing headline: Write a compelling headline that grabs attention and tells your reader what the case study is about. For example, "How a CRM System Helped a B2B Company Increase Revenue by 225%.
Introduction/Executive Summary: Include a brief overview of your case study, including your customer’s problem, the solution they implemented and the results they achieved.
Problem/Challenge: Case studies with solutions offer a powerful way to connect with potential customers. In this section, explain how your product or service specifically addressed your customer's challenges.
Solution: Explain how your product or service specifically addressed your customer's challenges.
Results/Achievements : Give a detailed account of the positive impact of your product. Quantify the benefits achieved using metrics such as increased sales, improved efficiency, reduced costs or enhanced customer satisfaction.
Graphics/Visuals: Include professional designs, high-quality photos and videos to make your case study more engaging and visually appealing.
Quotes/Testimonials: Incorporate written or video quotes from your clients to boost your credibility.
Relevant CTA: Insert a call to action (CTA) that encourages the reader to take action. For example, visiting your website or contacting you for more information. Your CTA can be a link to a landing page, a contact form or your social media handle and should be related to the product or service you highlighted in your case study.

Now that you understand what a case study is, let’s look at real-life case study examples. Among these, you'll find some simple case study examples that break down complex ideas into easily understandable solutions.

In this section, we’ll explore SaaS, marketing, sales, product and business case study examples with solutions. Take note of how these companies structured their case studies and included the key elements.

We’ve also included professionally designed case study templates to inspire you.

1. Georgia Tech Athletics Increase Season Ticket Sales by 80%

Georgia Tech Athletics, with its 8,000 football season ticket holders, sought for a way to increase efficiency and customer engagement.

Their initial sales process involved making multiple outbound phone calls per day with no real targeting or guidelines. Georgia Tech believed that targeting communications will enable them to reach more people in real time.

Salesloft improved Georgia Tech’s sales process with an inbound structure. This enabled sales reps to connect with their customers on a more targeted level. The use of dynamic fields and filters when importing lists ensured prospects received the right information, while communication with existing fans became faster with automation.

As a result, Georgia Tech Athletics recorded an 80% increase in season ticket sales as relationships with season ticket holders significantly improved. Employee engagement increased as employees became more energized to connect and communicate with fans.

Why Does This Case Study Work?

In this case study example , Salesloft utilized the key elements of a good case study. Their introduction gave an overview of their customers' challenges and the results they enjoyed after using them. After which they categorized the case study into three main sections: challenge, solution and result.

Salesloft utilized a case study video to increase engagement and invoke human connection.

Incorporating videos in your case study has a lot of benefits. Wyzol’s 2023 state of video marketing report showed a direct correlation between videos and an 87% increase in sales.

The beautiful thing is that creating videos for your case study doesn’t have to be daunting.

With an easy-to-use platform like Visme, you can create top-notch testimonial videos that will connect with your audience. Within the Visme editor, you can access over 1 million stock photos , video templates, animated graphics and more. These tools and resources will significantly improve the design and engagement of your case study.

Simplify content creation and brand management for your team

Collaborate on designs , mockups and wireframes with your non-design colleagues
Lock down your branding to maintain brand consistency throughout your designs
Why start from scratch? Save time with 1000s of professional branded templates

2. WeightWatchers Completely Revamped their Enterprise Sales Process with HubSpot

WeightWatchers, a 60-year-old wellness company, sought a CRM solution that increased the efficiency of their sales process. With their previous system, Weightwatchers had limited automation. They would copy-paste message templates from word documents or recreate one email for a batch of customers.

This required a huge effort from sales reps, account managers and leadership, as they were unable to track leads or pull customized reports for planning and growth.

WeightWatchers transformed their B2B sales strategy by leveraging HubSpot's robust marketing and sales workflows. They utilized HubSpot’s deal pipeline and automation features to streamline lead qualification. And the customized dashboard gave leadership valuable insights.

As a result, WeightWatchers generated seven figures in annual contract value and boosted recurring revenue. Hubspot’s impact resulted in 100% adoption across all sales, marketing, client success and operations teams.

Hubspot structured its case study into separate sections, demonstrating the specific benefits of their products to various aspects of the customer's business. Additionally, they integrated direct customer quotes in each section to boost credibility, resulting in a more compelling case study.

Getting insight from your customer about their challenges is one thing. But writing about their process and achievements in a concise and relatable way is another. If you find yourself constantly experiencing writer’s block, Visme’s AI writer is perfect for you.

Visme created this AI text generator tool to take your ideas and transform them into a great draft. So whether you need help writing your first draft or editing your final case study, Visme is ready for you.

3. Immi’s Ram Fam Helps to Drive Over $200k in Sales

Immi embarked on a mission to recreate healthier ramen recipes that were nutritious and delicious. After 2 years of tireless trials, Immi finally found the perfect ramen recipe. However, they envisioned a community of passionate ramen enthusiasts to fuel their business growth.

This vision propelled them to partner with Shopify Collabs. Shopify Collabs successfully cultivated and managed Immi’s Ramen community of ambassadors and creators.

As a result of their partnership, Immi’s community grew to more than 400 dedicated members, generating over $200,000 in total affiliate sales.

The power of data-driven headlines cannot be overemphasized. Chili Piper strategically incorporates quantifiable results in their headlines. This instantly sparks curiosity and interest in readers.

While not every customer success story may boast headline-grabbing figures, quantifying achievements in percentages is still effective. For example, you can highlight a 50% revenue increase with the implementation of your product.

Take a look at the beautiful case study template below. Just like in the example above, the figures in the headline instantly grab attention and entice your reader to click through.

Having a case study document is a key factor in boosting engagement. This makes it easy to promote your case study in multiple ways. With Visme, you can easily publish, download and share your case study with your customers in a variety of formats, including PDF, PPTX, JPG and more!

4. How WOW! is Saving Nearly 79% in Time and Cost With Visme

This case study discusses how Visme helped WOW! save time and money by providing user-friendly tools to create interactive and quality training materials for their employees. Find out what your team can do with Visme. Request a Demo

WOW!'s learning and development team creates high-quality training materials for new and existing employees. Previous tools and platforms they used had plain templates, little to no interactivity features, and limited flexibility—that is, until they discovered Visme.

Now, the learning and development team at WOW! use Visme to create engaging infographics, training videos, slide decks and other training materials.

This has directly reduced the company's turnover rate, saving them money spent on recruiting and training new employees. It has also saved them a significant amount of time, which they can now allocate to other important tasks.

Visme's customer testimonials spark an emotional connection with the reader, leaving a profound impact. Upon reading this case study, prospective customers will be blown away by the remarkable efficiency achieved by Visme's clients after switching from PowerPoint.

Visme’s interactivity feature was a game changer for WOW! and one of the primary reasons they chose Visme.

“Previously we were using PowerPoint, which is fine, but the interactivity you can get with Visme is so much more robust that we’ve all steered away from PowerPoint.” - Kendra, L&D team, Wow!

Visme’s interactive feature allowed them to animate their infographics, include clickable links on their PowerPoint designs and even embed polls and quizzes their employees could interact with.

By embedding the slide decks, infographics and other training materials WOW! created with Visme, potential customers get a taste of what they can create with the tool. This is much more effective than describing the features of Visme because it allows potential customers to see the tool in action.

To top it all off, this case study utilized relevant data and figures. For example, one part of the case study said, “In Visme, where Kendra’s team has access to hundreds of templates, a brand kit, and millions of design assets at their disposal, their team can create presentations in 80% less time.”

Who wouldn't want that?

Including relevant figures and graphics in your case study is a sure way to convince your potential customers why you’re a great fit for their brand. The case study template below is a great example of integrating relevant figures and data.

This colorful template begins with a captivating headline. But that is not the best part; this template extensively showcases the results their customer had using relevant figures.

The arrangement of the results makes it fun and attractive. Instead of just putting figures in a plain table, you can find interesting shapes in your Visme editor to take your case study to the next level.

5. Lyte Reduces Customer Churn To Just 3% With Hubspot CRM

While Lyte was redefining the ticketing industry, it had no definite CRM system . Lyte utilized 12–15 different SaaS solutions across various departments, which led to a lack of alignment between teams, duplication of work and overlapping tasks.

Customer data was spread across these platforms, making it difficult to effectively track their customer journey. As a result, their churn rate increased along with customer dissatisfaction.

Through Fuelius , Lyte founded and implemented Hubspot CRM. Lyte's productivity skyrocketed after incorporating Hubspot's all-in-one CRM tool. With improved efficiency, better teamwork and stronger client relationships, sales figures soared.

The case study title page and executive summary act as compelling entry points for both existing and potential customers. This overview provides a clear understanding of the case study and also strategically incorporates key details like the client's industry, location and relevant background information.

Having a good summary of your case study can prompt your readers to engage further. You can achieve this with a simple but effective case study one-pager that highlights your customer’s problems, process and achievements, just like this case study did in the beginning.

Moreover, you can easily distribute your case study one-pager and use it as a lead magnet to draw prospective customers to your company.

Take a look at this case study one-pager template below.

This template includes key aspects of your case study, such as the introduction, key findings, conclusion and more, without overcrowding the page. The use of multiple shades of blue gives it a clean and dynamic layout.

Our favorite part of this template is where the age group is visualized.

With Visme’s data visualization tool , you can present your data in tables, graphs, progress bars, maps and so much more. All you need to do is choose your preferred data visualization widget, input or import your data and click enter!

6. How Workato Converts 75% of Their Qualified Leads

Workato wanted to improve their inbound leads and increase their conversion rate, which ranged from 40-55%.

At first, Workato searched for a simple scheduling tool. They soon discovered that they needed a tool that provided advanced routing capabilities based on zip code and other criteria. Luckily, they found and implemented Chili Piper.

As a result of implementing Chili Piper, Workato achieved a remarkable 75–80% conversion rate and improved show rates. This led to a substantial revenue boost, with a 10-15% increase in revenue attributed to Chili Piper's impact on lead conversion.

This case study example utilizes the power of video testimonials to drive the impact of their product.

Chili Piper incorporates screenshots and clips of their tool in use. This is a great strategy because it helps your viewers become familiar with how your product works, making onboarding new customers much easier.

In this case study example, we see the importance of efficient Workflow Management Systems (WMS). Without a WMS, you manually assign tasks to your team members and engage in multiple emails for regular updates on progress.

However, when crafting and designing your case study, you should prioritize having a good WMS.

Visme has an outstanding Workflow Management System feature that keeps you on top of all your projects and designs. This feature makes it much easier to assign roles, ensure accuracy across documents, and track progress and deadlines.

Visme’s WMS feature allows you to limit access to your entire document by assigning specific slides or pages to individual members of your team. At the end of the day, your team members are not overwhelmed or distracted by the whole document but can focus on their tasks.

7. Rush Order Helps Vogmask Scale-Up During a Pandemic

Vomask's reliance on third-party fulfillment companies became a challenge as demand for their masks grew. Seeking a reliable fulfillment partner, they found Rush Order and entrusted them with their entire inventory.

Vomask's partnership with Rush Order proved to be a lifesaver during the COVID-19 pandemic. Rush Order's agility, efficiency and commitment to customer satisfaction helped Vogmask navigate the unprecedented demand and maintain its reputation for quality and service.

Rush Order’s comprehensive support enabled Vogmask to scale up its order processing by a staggering 900% while maintaining a remarkable customer satisfaction rate of 92%.

Rush Order chose one event where their impact mattered the most to their customer and shared that story.

While pandemics don't happen every day, you can look through your customer’s journey and highlight a specific time or scenario where your product or service saved their business.

The story of Vogmask and Rush Order is compelling, but it simply is not enough. The case study format and design attract readers' attention and make them want to know more. Rush Order uses consistent colors throughout the case study, starting with the logo, bold square blocks, pictures, and even headers.

Take a look at this product case study template below.

Just like our example, this case study template utilizes bold colors and large squares to attract and maintain the reader’s attention. It provides enough room for you to write about your customers' backgrounds/introductions, challenges, goals and results.

The right combination of shapes and colors adds a level of professionalism to this case study template.

Fuji Xerox Australia Business Equipment Case Study

8. AMR Hair & Beauty leverages B2B functionality to boost sales by 200%

With limits on website customization, slow page loading and multiple website crashes during peak events, it wasn't long before AMR Hair & Beauty began looking for a new e-commerce solution.

Their existing platform lacked effective search and filtering options, a seamless checkout process and the data analytics capabilities needed for informed decision-making. This led to a significant number of abandoned carts.

Upon switching to Shopify Plus, AMR immediately saw improvements in page loading speed and average session duration. They added better search and filtering options for their wholesale customers and customized their checkout process.

Due to this, AMR witnessed a 200% increase in sales and a 77% rise in B2B average order value. AMR Hair & Beauty is now poised for further expansion and growth.

This case study example showcases the power of a concise and impactful narrative.

To make their case analysis more effective, Shopify focused on the most relevant aspects of the customer's journey. While there may have been other challenges the customer faced, they only included those that directly related to their solutions.

Take a look at this case study template below. It is perfect if you want to create a concise but effective case study. Without including unnecessary details, you can outline the challenges, solutions and results your customers experienced from using your product.

Don’t forget to include a strong CTA within your case study. By incorporating a link, sidebar pop-up or an exit pop-up into your case study, you can prompt your readers and prospective clients to connect with you.

9. How a Marketing Agency Uses Visme to Create Engaging Content With Infographics

SmartBox Dental , a marketing agency specializing in dental practices, sought ways to make dental advice more interesting and easier to read. However, they lacked the design skills to do so effectively.

Visme's wide range of templates and features made it easy for the team to create high-quality content quickly and efficiently. SmartBox Dental enjoyed creating infographics in as little as 10-15 minutes, compared to one hour before Visme was implemented.

By leveraging Visme, SmartBox Dental successfully transformed dental content into a more enjoyable and informative experience for their clients' patients. Therefore enhancing its reputation as a marketing partner that goes the extra mile to deliver value to its clients.

Visme creatively incorporates testimonials In this case study example.

By showcasing infographics and designs created by their clients, they leverage the power of social proof in a visually compelling way. This way, potential customers gain immediate insight into the creative possibilities Visme offers as a design tool.

This example effectively showcases a product's versatility and impact, and we can learn a lot about writing a case study from it. Instead of focusing on one tool or feature per customer, Visme took a more comprehensive approach.

Within each section of their case study, Visme explained how a particular tool or feature played a key role in solving the customer's challenges.

For example, this case study highlighted Visme’s collaboration tool . With Visme’s tool, the SmartBox Dental content team fostered teamwork, accountability and effective supervision.

Visme also achieved a versatile case study by including relevant quotes to showcase each tool or feature. Take a look at some examples;

Visme’s collaboration tool: “We really like the collaboration tool. Being able to see what a co-worker is working on and borrow their ideas or collaborate on a project to make sure we get the best end result really helps us out.”

Visme’s library of stock photos and animated characters: “I really love the images and the look those give to an infographic. I also really like the animated little guys and the animated pictures. That’s added a lot of fun to our designs.”

Visme’s interactivity feature: “You can add URLs and phone number links directly into the infographic so they can just click and call or go to another page on the website and I really like adding those hyperlinks in.”

You can ask your customers to talk about the different products or features that helped them achieve their business success and draw quotes from each one.

10. Jasper Grows Blog Organic Sessions 810% and Blog-Attributed User Signups 400X

Jasper, an AI writing tool, lacked a scalable content strategy to drive organic traffic and user growth. They needed help creating content that converted visitors into users. Especially when a looming domain migration threatened organic traffic.

To address these challenges, Jasper partnered with Omniscient Digital. Their goal was to turn their content into a growth channel and drive organic growth. Omniscient Digital developed a full content strategy for Jasper AI, which included a content audit, competitive analysis, and keyword discovery.

Through their collaboration, Jasper’s organic blog sessions increased by 810%, despite the domain migration. They also witnessed a 400X increase in blog-attributed signups. And more importantly, the content program contributed to over $4 million in annual recurring revenue.

The combination of storytelling and video testimonials within the case study example makes this a real winner. But there’s a twist to it. Omniscient segmented the video testimonials and placed them in different sections of the case study.

Video marketing , especially in case studies, works wonders. Research shows us that 42% of people prefer video testimonials because they show real customers with real success stories. So if you haven't thought of it before, incorporate video testimonials into your case study.

Take a look at this stunning video testimonial template. With its simple design, you can input the picture, name and quote of your customer within your case study in a fun and engaging way.

Try it yourself! Customize this template with your customer’s testimonial and add it to your case study!

11. How Meliá Became One of the Most Influential Hotel Chains on Social Media

Meliá Hotels needed help managing their growing social media customer service needs. Despite having over 500 social accounts, they lacked a unified response protocol and detailed reporting. This largely hindered efficiency and brand consistency.

Meliá partnered with Hootsuite to build an in-house social customer care team. Implementing Hootsuite's tools enabled Meliá to decrease response times from 24 hours to 12.4 hours while also leveraging smart automation.

In addition to that, Meliá resolved over 133,000 conversations, booking 330 inquiries per week through Hootsuite Inbox. They significantly improved brand consistency, response time and customer satisfaction.

The need for a good case study design cannot be over-emphasized.

As soon as anyone lands on this case study example, they are mesmerized by a beautiful case study design. This alone raises the interest of readers and keeps them engaged till the end.

If you’re currently saying to yourself, “ I can write great case studies, but I don’t have the time or skill to turn it into a beautiful document.” Say no more.

Visme’s amazing AI document generator can take your text and transform it into a stunning and professional document in minutes! Not only do you save time, but you also get inspired by the design.

With Visme’s document generator, you can create PDFs, case study presentations , infographics and more!

Take a look at this case study template below. Just like our case study example, it captures readers' attention with its beautiful design. Its dynamic blend of colors and fonts helps to segment each element of the case study beautifully.

12. Tea’s Me Cafe: Tamika Catchings is Brewing Glory

Tamika's journey began when she purchased Tea's Me Cafe in 2017, saving it from closure. She recognized the potential of the cafe as a community hub and hosted regular events centered on social issues and youth empowerment.

One of Tamika’s business goals was to automate her business. She sought to streamline business processes across various aspects of her business. One of the ways she achieves this goal is through Constant Contact.

Constant Contact became an integral part of Tamika's marketing strategy. They provided an automated and centralized platform for managing email newsletters, event registrations, social media scheduling and more.

This allowed Tamika and her team to collaborate efficiently and focus on engaging with their audience. They effectively utilized features like WooCommerce integration, text-to-join and the survey builder to grow their email list, segment their audience and gather valuable feedback.

The case study example utilizes the power of storytelling to form a connection with readers. Constant Contact takes a humble approach in this case study. They spotlight their customers' efforts as the reason for their achievements and growth, establishing trust and credibility.

This case study is also visually appealing, filled with high-quality photos of their customer. While this is a great way to foster originality, it can prove challenging if your customer sends you blurry or low-quality photos.

If you find yourself in that dilemma, you can use Visme’s AI image edit tool to touch up your photos. With Visme’s AI tool, you can remove unwanted backgrounds, erase unwanted objects, unblur low-quality pictures and upscale any photo without losing the quality.

Constant Contact offers its readers various formats to engage with their case study. Including an audio podcast and PDF.

In its PDF version, Constant Contact utilized its brand colors to create a stunning case study design. With this, they increase brand awareness and, in turn, brand recognition with anyone who comes across their case study.

With Visme’s brand wizard tool , you can seamlessly incorporate your brand assets into any design or document you create. By inputting your URL, Visme’s AI integration will take note of your brand colors, brand fonts and more and create branded templates for you automatically.

You don't need to worry about spending hours customizing templates to fit your brand anymore. You can focus on writing amazing case studies that promote your company.

13. How Breakwater Kitchens Achieved a 7% Growth in Sales With Thryv

Breakwater Kitchens struggled with managing their business operations efficiently. They spent a lot of time on manual tasks, such as scheduling appointments and managing client communication. This made it difficult for them to grow their business and provide the best possible service to their customers.

David, the owner, discovered Thryv. With Thryv, Breakwater Kitchens was able to automate many of their manual tasks. Additionally, Thryv integrated social media management. This enabled Breakwater Kitchens to deliver a consistent brand message, captivate its audience and foster online growth.

As a result, Breakwater Kitchens achieved increased efficiency, reduced missed appointments and a 7% growth in sales.

This case study example uses a concise format and strong verbs, which make it easy for readers to absorb the information.

At the top of the case study, Thryv immediately builds trust by presenting their customer's complete profile, including their name, company details and website. This allows potential customers to verify the case study's legitimacy, making them more likely to believe in Thryv's services.

However, manually copying and pasting customer information across multiple pages of your case study can be time-consuming.

To save time and effort, you can utilize Visme's dynamic field feature . Dynamic fields automatically insert reusable information into your designs. So you don’t have to type it out multiple times.

14. Zoom’s Creative Team Saves Over 4,000 Hours With Brandfolder

Zoom experienced rapid growth with the advent of remote work and the rise of the COVID-19 pandemic. Such growth called for agility and resilience to scale through.

At the time, Zoom’s assets were disorganized which made retrieving brand information a burden. Zoom’s creative manager spent no less than 10 hours per week finding and retrieving brand assets for internal teams.

Zoom needed a more sustainable approach to organizing and retrieving brand information and came across Brandfolder. Brandfolder simplified and accelerated Zoom’s email localization and webpage development. It also enhanced the creation and storage of Zoom virtual backgrounds.

With Brandfolder, Zoom now saves 4,000+ hours every year. The company also centralized its assets in Brandfolder, which allowed 6,800+ employees and 20-30 vendors to quickly access them.

Brandfolder infused its case study with compelling data and backed it up with verifiable sources. This data-driven approach boosts credibility and increases the impact of their story.

Bradfolder's case study goes the extra mile by providing a downloadable PDF version, making it convenient for readers to access the information on their own time. Their dedication to crafting stunning visuals is evident in every aspect of the project.

From the vibrant colors to the seamless navigation, everything has been meticulously designed to leave a lasting impression on the viewer. And with clickable links that make exploring the content a breeze, the user experience is guaranteed to be nothing short of exceptional.

The thing is, your case study presentation won’t always sit on your website. There are instances where you may need to do a case study presentation for clients, partners or potential investors.

Visme has a rich library of templates you can tap into. But if you’re racing against the clock, Visme’s AI presentation maker is your best ally.

15. How Cents of Style Made $1.7M+ in Affiliate Sales with LeadDyno

Cents of Style had a successful affiliate and influencer marketing strategy. However, their existing affiliate marketing platform was not intuitive, customizable or transparent enough to meet the needs of their influencers.

Cents of Styles needed an easy-to-use affiliate marketing platform that gave them more freedom to customize their program and implement a multi-tier commission program.

After exploring their options, Cents of Style decided on LeadDyno.

LeadDyno provided more flexibility, allowing them to customize commission rates and implement their multi-tier commission structure, switching from monthly to weekly payouts.

Also, integrations with PayPal made payments smoother And features like newsletters and leaderboards added to the platform's success by keeping things transparent and engaging.

As a result, Cents of Style witnessed an impressive $1.7 million in revenue from affiliate sales with a substantial increase in web sales by 80%.

LeadDyno strategically placed a compelling CTA in the middle of their case study layout, maximizing its impact. At this point, readers are already invested in the customer's story and may be considering implementing similar strategies.

A well-placed CTA offers them a direct path to learn more and take action.

LeadDyno also utilized the power of quotes to strengthen their case study. They didn't just embed these quotes seamlessly into the text; instead, they emphasized each one with distinct blocks.

Are you looking for an easier and quicker solution to create a case study and other business documents? Try Visme's AI designer ! This powerful tool allows you to generate complete documents, such as case studies, reports, whitepapers and more, just by providing text prompts. Simply explain your requirements to the tool, and it will produce the document for you, complete with text, images, design assets and more.

Still have more questions about case studies? Let's look at some frequently asked questions.

How to Write a Case Study?

Choose a compelling story: Not all case studies are created equal. Pick one that is relevant to your target audience and demonstrates the specific benefits of your product or service.
Outline your case study: Create a case study outline and highlight how you will structure your case study to include the introduction, problem, solution and achievements of your customer.
Choose a case study template: After you outline your case study, choose a case study template . Visme has stunning templates that can inspire your case study design.
Craft a compelling headline: Include figures or percentages that draw attention to your case study.
Work on the first draft: Your case study should be easy to read and understand. Use clear and concise language and avoid jargon.
Include high-quality visual aids: Visuals can help to make your case study more engaging and easier to read. Consider adding high-quality photos, screenshots or videos.
Include a relevant CTA: Tell prospective customers how to reach you for questions or sign-ups.

What Are the Stages of a Case Study?

The stages of a case study are;

Planning & Preparation: Highlight your goals for writing the case study. Plan the case study format, length and audience you wish to target.
Interview the Client: Reach out to the company you want to showcase and ask relevant questions about their journey and achievements.
Revision & Editing: Review your case study and ask for feedback. Include relevant quotes and CTAs to your case study.
Publication & Distribution: Publish and share your case study on your website, social media channels and email list!
Marketing & Repurposing: Turn your case study into a podcast, PDF, case study presentation and more. Share these materials with your sales and marketing team.

What Are the Advantages and Disadvantages of a Case Study?

Advantages of a case study:

Case studies showcase a specific solution and outcome for specific customer challenges.
It attracts potential customers with similar challenges.
It builds trust and credibility with potential customers.
It provides an in-depth analysis of your company’s problem-solving process.

Disadvantages of a case study:

Limited applicability. Case studies are tailored to specific cases and may not apply to other businesses.
It relies heavily on customer cooperation and willingness to share information.
It stands a risk of becoming outdated as industries and customer needs evolve.

What Are the Types of Case Studies?

There are 7 main types of case studies. They include;

Illustrative case study.
Instrumental case study.
Intrinsic case study.
Descriptive case study.
Explanatory case study.
Exploratory case study.
Collective case study.

How Long Should a Case Study Be?

The ideal length of your case study is between 500 - 1500 words or 1-3 pages. Certain factors like your target audience, goal or the amount of detail you want to share may influence the length of your case study. This infographic has powerful tips for designing winning case studies

What Is the Difference Between a Case Study and an Example?

Case studies provide a detailed narrative of how your product or service was used to solve a problem. Examples are general illustrations and are not necessarily real-life scenarios.

Case studies are often used for marketing purposes, attracting potential customers and building trust. Examples, on the other hand, are primarily used to simplify or clarify complex concepts.

Where Can I Find Case Study Examples?

You can easily find many case study examples online and in industry publications. Many companies, including Visme, share case studies on their websites to showcase how their products or services have helped clients achieve success. You can also search online libraries and professional organizations for case studies related to your specific industry or field.

If you need professionally-designed, customizable case study templates to create your own, Visme's template library is one of the best places to look. These templates include all the essential sections of a case study and high-quality content to help you create case studies that position your business as an industry leader.

Get More Out Of Your Case Studies With Visme

Case studies are an essential tool for converting potential customers into paying customers. By following the tips in this article, you can create compelling case studies that will help you build trust, establish credibility and drive sales.

Visme can help you create stunning case studies and other relevant marketing materials. With our easy-to-use platform, interactive features and analytics tools , you can increase your content creation game in no time.

There is no limit to what you can achieve with Visme. Connect with Sales to discover how Visme can boost your business goals.

Easily create beautiful case studies and more with Visme

Trusted by leading brands

Create Stunning Content!

Design visual brand experiences for your business whether you are a seasoned designer or a total novice.

About the Author

10 Real World Data Science Case Studies Projects with Example

Top 10 Data Science Case Studies Projects with Examples and Solutions in Python to inspire your data science learning in 2023.

BelData science has been a trending buzzword in recent times. With wide applications in various sectors like healthcare , education, retail, transportation, media, and banking -data science applications are at the core of pretty much every industry out there. The possibilities are endless: analysis of frauds in the finance sector or the personalization of recommendations on eCommerce businesses. We have developed ten exciting data science case studies to explain how data science is leveraged across various industries to make smarter decisions and develop innovative personalized products tailored to specific customers.

Walmart Sales Forecasting Data Science Project

Downloadable solution code | Explanatory videos | Tech Support

Data science case studies in retail , data science case study examples in entertainment industry , data analytics case study examples in travel industry , case studies for data analytics in social media , real world data science projects in healthcare, data analytics case studies in oil and gas, what is a case study in data science, how do you prepare a data science case study, 10 most interesting data science case studies with examples.

So, without much ado, let's get started with data science business case studies !

With humble beginnings as a simple discount retailer, today, Walmart operates in 10,500 stores and clubs in 24 countries and eCommerce websites, employing around 2.2 million people around the globe. For the fiscal year ended January 31, 2021, Walmart's total revenue was $559 billion showing a growth of $35 billion with the expansion of the eCommerce sector. Walmart is a data-driven company that works on the principle of 'Everyday low cost' for its consumers. To achieve this goal, they heavily depend on the advances of their data science and analytics department for research and development, also known as Walmart Labs. Walmart is home to the world's largest private cloud, which can manage 2.5 petabytes of data every hour! To analyze this humongous amount of data, Walmart has created 'Data Café,' a state-of-the-art analytics hub located within its Bentonville, Arkansas headquarters. The Walmart Labs team heavily invests in building and managing technologies like cloud, data, DevOps , infrastructure, and security.

ProjectPro Free Projects on Big Data and Data Science

Walmart is experiencing massive digital growth as the world's largest retailer . Walmart has been leveraging Big data and advances in data science to build solutions to enhance, optimize and customize the shopping experience and serve their customers in a better way. At Walmart Labs, data scientists are focused on creating data-driven solutions that power the efficiency and effectiveness of complex supply chain management processes. Here are some of the applications of data science at Walmart:

i) Personalized Customer Shopping Experience

Walmart analyses customer preferences and shopping patterns to optimize the stocking and displaying of merchandise in their stores. Analysis of Big data also helps them understand new item sales, make decisions on discontinuing products, and the performance of brands.

ii) Order Sourcing and On-Time Delivery Promise

Millions of customers view items on Walmart.com, and Walmart provides each customer a real-time estimated delivery date for the items purchased. Walmart runs a backend algorithm that estimates this based on the distance between the customer and the fulfillment center, inventory levels, and shipping methods available. The supply chain management system determines the optimum fulfillment center based on distance and inventory levels for every order. It also has to decide on the shipping method to minimize transportation costs while meeting the promised delivery date.

Here's what valued users are saying about ProjectPro

Tech Leader | Stanford / Yale University

Anand Kumpatla

Sr Data Scientist @ Doubleslash Software Solutions Pvt Ltd

Not sure what you are looking for?

iii) Packing Optimization

Also known as Box recommendation is a daily occurrence in the shipping of items in retail and eCommerce business. When items of an order or multiple orders for the same customer are ready for packing, Walmart has developed a recommender system that picks the best-sized box which holds all the ordered items with the least in-box space wastage within a fixed amount of time. This Bin Packing problem is a classic NP-Hard problem familiar to data scientists .

Whenever items of an order or multiple orders placed by the same customer are picked from the shelf and are ready for packing, the box recommendation system determines the best-sized box to hold all the ordered items with a minimum of in-box space wasted. This problem is known as the Bin Packing Problem, another classic NP-Hard problem familiar to data scientists.

Here is a link to a sales prediction data science case study to help you understand the applications of Data Science in the real world. Walmart Sales Forecasting Project uses historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and you must build a model to project the sales for each department in each store. This data science case study aims to create a predictive model to predict the sales of each product. You can also try your hands-on Inventory Demand Forecasting Data Science Project to develop a machine learning model to forecast inventory demand accurately based on historical sales data.

Get Closer To Your Dream of Becoming a Data Scientist with 70+ Solved End-to-End ML Projects

Amazon is an American multinational technology-based company based in Seattle, USA. It started as an online bookseller, but today it focuses on eCommerce, cloud computing , digital streaming, and artificial intelligence . It hosts an estimate of 1,000,000,000 gigabytes of data across more than 1,400,000 servers. Through its constant innovation in data science and big data Amazon is always ahead in understanding its customers. Here are a few data analytics case study examples at Amazon:

i) Recommendation Systems

Data science models help amazon understand the customers' needs and recommend them to them before the customer searches for a product; this model uses collaborative filtering. Amazon uses 152 million customer purchases data to help users to decide on products to be purchased. The company generates 35% of its annual sales using the Recommendation based systems (RBS) method.

Here is a Recommender System Project to help you build a recommendation system using collaborative filtering.

ii) Retail Price Optimization

Amazon product prices are optimized based on a predictive model that determines the best price so that the users do not refuse to buy it based on price. The model carefully determines the optimal prices considering the customers' likelihood of purchasing the product and thinks the price will affect the customers' future buying patterns. Price for a product is determined according to your activity on the website, competitors' pricing, product availability, item preferences, order history, expected profit margin, and other factors.

Check Out this Retail Price Optimization Project to build a Dynamic Pricing Model.

iii) Fraud Detection

Being a significant eCommerce business, Amazon remains at high risk of retail fraud. As a preemptive measure, the company collects historical and real-time data for every order. It uses Machine learning algorithms to find transactions with a higher probability of being fraudulent. This proactive measure has helped the company restrict clients with an excessive number of returns of products.

You can look at this Credit Card Fraud Detection Project to implement a fraud detection model to classify fraudulent credit card transactions.

New Projects

Let us explore data analytics case study examples in the entertainment indusry.

Ace Your Next Job Interview with Mock Interviews from Experts to Improve Your Skills and Boost Confidence!

Netflix started as a DVD rental service in 1997 and then has expanded into the streaming business. Headquartered in Los Gatos, California, Netflix is the largest content streaming company in the world. Currently, Netflix has over 208 million paid subscribers worldwide, and with thousands of smart devices which are presently streaming supported, Netflix has around 3 billion hours watched every month. The secret to this massive growth and popularity of Netflix is its advanced use of data analytics and recommendation systems to provide personalized and relevant content recommendations to its users. The data is collected over 100 billion events every day. Here are a few examples of data analysis case studies applied at Netflix :

i) Personalized Recommendation System

Netflix uses over 1300 recommendation clusters based on consumer viewing preferences to provide a personalized experience. Some of the data that Netflix collects from its users include Viewing time, platform searches for keywords, Metadata related to content abandonment, such as content pause time, rewind, rewatched. Using this data, Netflix can predict what a viewer is likely to watch and give a personalized watchlist to a user. Some of the algorithms used by the Netflix recommendation system are Personalized video Ranking, Trending now ranker, and the Continue watching now ranker.

ii) Content Development using Data Analytics

Netflix uses data science to analyze the behavior and patterns of its user to recognize themes and categories that the masses prefer to watch. This data is used to produce shows like The umbrella academy, and Orange Is the New Black, and the Queen's Gambit. These shows seem like a huge risk but are significantly based on data analytics using parameters, which assured Netflix that they would succeed with its audience. Data analytics is helping Netflix come up with content that their viewers want to watch even before they know they want to watch it.

iii) Marketing Analytics for Campaigns

Netflix uses data analytics to find the right time to launch shows and ad campaigns to have maximum impact on the target audience. Marketing analytics helps come up with different trailers and thumbnails for other groups of viewers. For example, the House of Cards Season 5 trailer with a giant American flag was launched during the American presidential elections, as it would resonate well with the audience.

Here is a Customer Segmentation Project using association rule mining to understand the primary grouping of customers based on various parameters.

Get FREE Access to Machine Learning Example Codes for Data Cleaning , Data Munging, and Data Visualization

In a world where Purchasing music is a thing of the past and streaming music is a current trend, Spotify has emerged as one of the most popular streaming platforms. With 320 million monthly users, around 4 billion playlists, and approximately 2 million podcasts, Spotify leads the pack among well-known streaming platforms like Apple Music, Wynk, Songza, amazon music, etc. The success of Spotify has mainly depended on data analytics. By analyzing massive volumes of listener data, Spotify provides real-time and personalized services to its listeners. Most of Spotify's revenue comes from paid premium subscriptions. Here are some of the examples of case study on data analytics used by Spotify to provide enhanced services to its listeners:

i) Personalization of Content using Recommendation Systems

Spotify uses Bart or Bayesian Additive Regression Trees to generate music recommendations to its listeners in real-time. Bart ignores any song a user listens to for less than 30 seconds. The model is retrained every day to provide updated recommendations. A new Patent granted to Spotify for an AI application is used to identify a user's musical tastes based on audio signals, gender, age, accent to make better music recommendations.

Spotify creates daily playlists for its listeners, based on the taste profiles called 'Daily Mixes,' which have songs the user has added to their playlists or created by the artists that the user has included in their playlists. It also includes new artists and songs that the user might be unfamiliar with but might improve the playlist. Similar to it is the weekly 'Release Radar' playlists that have newly released artists' songs that the listener follows or has liked before.

ii) Targetted marketing through Customer Segmentation

With user data for enhancing personalized song recommendations, Spotify uses this massive dataset for targeted ad campaigns and personalized service recommendations for its users. Spotify uses ML models to analyze the listener's behavior and group them based on music preferences, age, gender, ethnicity, etc. These insights help them create ad campaigns for a specific target audience. One of their well-known ad campaigns was the meme-inspired ads for potential target customers, which was a huge success globally.

iii) CNN's for Classification of Songs and Audio Tracks

Spotify builds audio models to evaluate the songs and tracks, which helps develop better playlists and recommendations for its users. These allow Spotify to filter new tracks based on their lyrics and rhythms and recommend them to users like similar tracks ( collaborative filtering). Spotify also uses NLP ( Natural language processing) to scan articles and blogs to analyze the words used to describe songs and artists. These analytical insights can help group and identify similar artists and songs and leverage them to build playlists.

Here is a Music Recommender System Project for you to start learning. We have listed another music recommendations dataset for you to use for your projects: Dataset1 . You can use this dataset of Spotify metadata to classify songs based on artists, mood, liveliness. Plot histograms, heatmaps to get a better understanding of the dataset. Use classification algorithms like logistic regression, SVM, and Principal component analysis to generate valuable insights from the dataset.

Explore Categories

Below you will find case studies for data analytics in the travel and tourism industry.

Airbnb was born in 2007 in San Francisco and has since grown to 4 million Hosts and 5.6 million listings worldwide who have welcomed more than 1 billion guest arrivals in almost every country across the globe. Airbnb is active in every country on the planet except for Iran, Sudan, Syria, and North Korea. That is around 97.95% of the world. Using data as a voice of their customers, Airbnb uses the large volume of customer reviews, host inputs to understand trends across communities, rate user experiences, and uses these analytics to make informed decisions to build a better business model. The data scientists at Airbnb are developing exciting new solutions to boost the business and find the best mapping for its customers and hosts. Airbnb data servers serve approximately 10 million requests a day and process around one million search queries. Data is the voice of customers at AirBnB and offers personalized services by creating a perfect match between the guests and hosts for a supreme customer experience.

i) Recommendation Systems and Search Ranking Algorithms

Airbnb helps people find 'local experiences' in a place with the help of search algorithms that make searches and listings precise. Airbnb uses a 'listing quality score' to find homes based on the proximity to the searched location and uses previous guest reviews. Airbnb uses deep neural networks to build models that take the guest's earlier stays into account and area information to find a perfect match. The search algorithms are optimized based on guest and host preferences, rankings, pricing, and availability to understand users’ needs and provide the best match possible.

ii) Natural Language Processing for Review Analysis

Airbnb characterizes data as the voice of its customers. The customer and host reviews give a direct insight into the experience. The star ratings alone cannot be an excellent way to understand it quantitatively. Hence Airbnb uses natural language processing to understand reviews and the sentiments behind them. The NLP models are developed using Convolutional neural networks .

Practice this Sentiment Analysis Project for analyzing product reviews to understand the basic concepts of natural language processing.

iii) Smart Pricing using Predictive Analytics

The Airbnb hosts community uses the service as a supplementary income. The vacation homes and guest houses rented to customers provide for rising local community earnings as Airbnb guests stay 2.4 times longer and spend approximately 2.3 times the money compared to a hotel guest. The profits are a significant positive impact on the local neighborhood community. Airbnb uses predictive analytics to predict the prices of the listings and help the hosts set a competitive and optimal price. The overall profitability of the Airbnb host depends on factors like the time invested by the host and responsiveness to changing demands for different seasons. The factors that impact the real-time smart pricing are the location of the listing, proximity to transport options, season, and amenities available in the neighborhood of the listing.

Here is a Price Prediction Project to help you understand the concept of predictive analysis which is widely common in case studies for data analytics.

Uber is the biggest global taxi service provider. As of December 2018, Uber has 91 million monthly active consumers and 3.8 million drivers. Uber completes 14 million trips each day. Uber uses data analytics and big data-driven technologies to optimize their business processes and provide enhanced customer service. The Data Science team at uber has been exploring futuristic technologies to provide better service constantly. Machine learning and data analytics help Uber make data-driven decisions that enable benefits like ride-sharing, dynamic price surges, better customer support, and demand forecasting. Here are some of the real world data science projects used by uber:

i) Dynamic Pricing for Price Surges and Demand Forecasting

Uber prices change at peak hours based on demand. Uber uses surge pricing to encourage more cab drivers to sign up with the company, to meet the demand from the passengers. When the prices increase, the driver and the passenger are both informed about the surge in price. Uber uses a predictive model for price surging called the 'Geosurge' ( patented). It is based on the demand for the ride and the location.

ii) One-Click Chat

Uber has developed a Machine learning and natural language processing solution called one-click chat or OCC for coordination between drivers and users. This feature anticipates responses for commonly asked questions, making it easy for the drivers to respond to customer messages. Drivers can reply with the clock of just one button. One-Click chat is developed on Uber's machine learning platform Michelangelo to perform NLP on rider chat messages and generate appropriate responses to them.

iii) Customer Retention

Failure to meet the customer demand for cabs could lead to users opting for other services. Uber uses machine learning models to bridge this demand-supply gap. By using prediction models to predict the demand in any location, uber retains its customers. Uber also uses a tier-based reward system, which segments customers into different levels based on usage. The higher level the user achieves, the better are the perks. Uber also provides personalized destination suggestions based on the history of the user and their frequently traveled destinations.

You can take a look at this Python Chatbot Project and build a simple chatbot application to understand better the techniques used for natural language processing. You can also practice the working of a demand forecasting model with this project using time series analysis. You can look at this project which uses time series forecasting and clustering on a dataset containing geospatial data for forecasting customer demand for ola rides.

Explore More Data Science and Machine Learning Projects for Practice. Fast-Track Your Career Transition with ProjectPro

7) LinkedIn

LinkedIn is the largest professional social networking site with nearly 800 million members in more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking around 1 billion interactions per month. The data science team at LinkedIn works with this massive pool of data to generate insights to build strategies, apply algorithms and statistical inferences to optimize engineering solutions, and help the company achieve its goals. Here are some of the real world data science projects at LinkedIn:

i) LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems

LinkedIn Recruiter helps recruiters build and manage a talent pool to optimize the chances of hiring candidates successfully. This sophisticated product works on search and recommendation engines. The LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The results delivered have to be relevant and specific. The initial search model was based on linear regression but was eventually upgraded to Gradient Boosted decision trees to include non-linear correlations in the dataset. In addition to these models, the LinkedIn recruiter also uses the Generalized Linear Mix model to improve the results of prediction problems to give personalized results.

ii) Recommendation Systems Personalized for News Feed

The LinkedIn news feed is the heart and soul of the professional community. A member's newsfeed is a place to discover conversations among connections, career news, posts, suggestions, photos, and videos. Every time a member visits LinkedIn, machine learning algorithms identify the best exchanges to be displayed on the feed by sorting through posts and ranking the most relevant results on top. The algorithms help LinkedIn understand member preferences and help provide personalized news feeds. The algorithms used include logistic regression, gradient boosted decision trees and neural networks for recommendation systems.

iii) CNN's to Detect Inappropriate Content

To provide a professional space where people can trust and express themselves professionally in a safe community has been a critical goal at LinkedIn. LinkedIn has heavily invested in building solutions to detect fake accounts and abusive behavior on their platform. Any form of spam, harassment, inappropriate content is immediately flagged and taken down. These can range from profanity to advertisements for illegal services. LinkedIn uses a Convolutional neural networks based machine learning model. This classifier trains on a training dataset containing accounts labeled as either "inappropriate" or "appropriate." The inappropriate list consists of accounts having content from "blocklisted" phrases or words and a small portion of manually reviewed accounts reported by the user community.

Here is a Text Classification Project to help you understand NLP basics for text classification. You can find a news recommendation system dataset to help you build a personalized news recommender system. You can also use this dataset to build a classifier using logistic regression, Naive Bayes, or Neural networks to classify toxic comments.

Get confident to build end-to-end projects

Access to a curated library of 250+ end-to-end industry projects with solution code, videos and tech support.

Pfizer is a multinational pharmaceutical company headquartered in New York, USA. One of the largest pharmaceutical companies globally known for developing a wide range of medicines and vaccines in disciplines like immunology, oncology, cardiology, and neurology. Pfizer became a household name in 2010 when it was the first to have a COVID-19 vaccine with FDA. In early November 2021, The CDC has approved the Pfizer vaccine for kids aged 5 to 11. Pfizer has been using machine learning and artificial intelligence to develop drugs and streamline trials, which played a massive role in developing and deploying the COVID-19 vaccine. Here are a few data analytics case studies by Pfizer :

i) Identifying Patients for Clinical Trials

Artificial intelligence and machine learning are used to streamline and optimize clinical trials to increase their efficiency. Natural language processing and exploratory data analysis of patient records can help identify suitable patients for clinical trials. These can help identify patients with distinct symptoms. These can help examine interactions of potential trial members' specific biomarkers, predict drug interactions and side effects which can help avoid complications. Pfizer's AI implementation helped rapidly identify signals within the noise of millions of data points across their 44,000-candidate COVID-19 clinical trial.

ii) Supply Chain and Manufacturing

Data science and machine learning techniques help pharmaceutical companies better forecast demand for vaccines and drugs and distribute them efficiently. Machine learning models can help identify efficient supply systems by automating and optimizing the production steps. These will help supply drugs customized to small pools of patients in specific gene pools. Pfizer uses Machine learning to predict the maintenance cost of equipment used. Predictive maintenance using AI is the next big step for Pharmaceutical companies to reduce costs.

iii) Drug Development

Computer simulations of proteins, and tests of their interactions, and yield analysis help researchers develop and test drugs more efficiently. In 2016 Watson Health and Pfizer announced a collaboration to utilize IBM Watson for Drug Discovery to help accelerate Pfizer's research in immuno-oncology, an approach to cancer treatment that uses the body's immune system to help fight cancer. Deep learning models have been used recently for bioactivity and synthesis prediction for drugs and vaccines in addition to molecular design. Deep learning has been a revolutionary technique for drug discovery as it factors everything from new applications of medications to possible toxic reactions which can save millions in drug trials.

You can create a Machine learning model to predict molecular activity to help design medicine using this dataset . You may build a CNN or a Deep neural network for this data analyst case study project.

Access Data Science and Machine Learning Project Code Examples

9) Shell Data Analyst Case Study Project

Shell is a global group of energy and petrochemical companies with over 80,000 employees in around 70 countries. Shell uses advanced technologies and innovations to help build a sustainable energy future. Shell is going through a significant transition as the world needs more and cleaner energy solutions to be a clean energy company by 2050. It requires substantial changes in the way in which energy is used. Digital technologies, including AI and Machine Learning, play an essential role in this transformation. These include efficient exploration and energy production, more reliable manufacturing, more nimble trading, and a personalized customer experience. Using AI in various phases of the organization will help achieve this goal and stay competitive in the market. Here are a few data analytics case studies in the petrochemical industry:

i) Precision Drilling

Shell is involved in the processing mining oil and gas supply, ranging from mining hydrocarbons to refining the fuel to retailing them to customers. Recently Shell has included reinforcement learning to control the drilling equipment used in mining. Reinforcement learning works on a reward-based system based on the outcome of the AI model. The algorithm is designed to guide the drills as they move through the surface, based on the historical data from drilling records. It includes information such as the size of drill bits, temperatures, pressures, and knowledge of the seismic activity. This model helps the human operator understand the environment better, leading to better and faster results will minor damage to machinery used.

ii) Efficient Charging Terminals

Due to climate changes, governments have encouraged people to switch to electric vehicles to reduce carbon dioxide emissions. However, the lack of public charging terminals has deterred people from switching to electric cars. Shell uses AI to monitor and predict the demand for terminals to provide efficient supply. Multiple vehicles charging from a single terminal may create a considerable grid load, and predictions on demand can help make this process more efficient.

iii) Monitoring Service and Charging Stations

Another Shell initiative trialed in Thailand and Singapore is the use of computer vision cameras, which can think and understand to watch out for potentially hazardous activities like lighting cigarettes in the vicinity of the pumps while refueling. The model is built to process the content of the captured images and label and classify it. The algorithm can then alert the staff and hence reduce the risk of fires. You can further train the model to detect rash driving or thefts in the future.

Here is a project to help you understand multiclass image classification. You can use the Hourly Energy Consumption Dataset to build an energy consumption prediction model. You can use time series with XGBoost to develop your model.

10) Zomato Case Study on Data Analytics

Zomato was founded in 2010 and is currently one of the most well-known food tech companies. Zomato offers services like restaurant discovery, home delivery, online table reservation, online payments for dining, etc. Zomato partners with restaurants to provide tools to acquire more customers while also providing delivery services and easy procurement of ingredients and kitchen supplies. Currently, Zomato has over 2 lakh restaurant partners and around 1 lakh delivery partners. Zomato has closed over ten crore delivery orders as of date. Zomato uses ML and AI to boost their business growth, with the massive amount of data collected over the years from food orders and user consumption patterns. Here are a few examples of data analyst case study project developed by the data scientists at Zomato:

i) Personalized Recommendation System for Homepage

Zomato uses data analytics to create personalized homepages for its users. Zomato uses data science to provide order personalization, like giving recommendations to the customers for specific cuisines, locations, prices, brands, etc. Restaurant recommendations are made based on a customer's past purchases, browsing history, and what other similar customers in the vicinity are ordering. This personalized recommendation system has led to a 15% improvement in order conversions and click-through rates for Zomato.

You can use the Restaurant Recommendation Dataset to build a restaurant recommendation system to predict what restaurants customers are most likely to order from, given the customer location, restaurant information, and customer order history.

ii) Analyzing Customer Sentiment

Zomato uses Natural language processing and Machine learning to understand customer sentiments using social media posts and customer reviews. These help the company gauge the inclination of its customer base towards the brand. Deep learning models analyze the sentiments of various brand mentions on social networking sites like Twitter, Instagram, Linked In, and Facebook. These analytics give insights to the company, which helps build the brand and understand the target audience.

iii) Predicting Food Preparation Time (FPT)

Food delivery time is an essential variable in the estimated delivery time of the order placed by the customer using Zomato. The food preparation time depends on numerous factors like the number of dishes ordered, time of the day, footfall in the restaurant, day of the week, etc. Accurate prediction of the food preparation time can help make a better prediction of the Estimated delivery time, which will help delivery partners less likely to breach it. Zomato uses a Bidirectional LSTM-based deep learning model that considers all these features and provides food preparation time for each order in real-time.

Data scientists are companies' secret weapons when analyzing customer sentiments and behavior and leveraging it to drive conversion, loyalty, and profits. These 10 data science case studies projects with examples and solutions show you how various organizations use data science technologies to succeed and be at the top of their field! To summarize, Data Science has not only accelerated the performance of companies but has also made it possible to manage & sustain their performance with ease.

FAQs on Data Analysis Case Studies

A case study in data science is an in-depth analysis of a real-world problem using data-driven approaches. It involves collecting, cleaning, and analyzing data to extract insights and solve challenges, offering practical insights into how data science techniques can address complex issues across various industries.

To create a data science case study, identify a relevant problem, define objectives, and gather suitable data. Clean and preprocess data, perform exploratory data analysis, and apply appropriate algorithms for analysis. Summarize findings, visualize results, and provide actionable recommendations, showcasing the problem-solving potential of data science techniques.

Access Solved Big Data and Data Science Projects

About the Author

ProjectPro is the only online platform designed to help professionals gain practical, hands-on experience in big data, data engineering, data science, and machine learning related technologies. Having over 270+ reusable project templates in data science and big data with step-by-step walkthroughs,

User policy

Write for ProjectPro

User Preferences

Content preview.

Arcu felis bibendum ut tristique et egestas quis:

Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris
Duis aute irure dolor in reprehenderit in voluptate
Excepteur sint occaecat cupidatat non proident

Keyboard Shortcuts

1.1 - cases & variables.

Throughout the course, we will be using many of the terms introduced in this lesson. Let's start by defining some of the most frequently used terms: case, variable, and constant.

A case is an experimental unit. These are the individuals from which data are collected. When data are collected from humans, we sometimes call them participants . When data are collected from animals, the term subjects is often used. Another synonym is experimental unit .

A variable is a characteristic that is measured and can take on different values. In other words, something that varies between cases. This is in contrast to a constant which is the same for all cases in a research study.

Let's look at a few examples.

Example: Study Time & Grades Section

A teacher wants to know if third grade students who spend more time reading at home get higher homework and exam grades.

The students are the cases . There are three variables : amount of time spent reading at home, homework grades, and exam grades. The grade-level of the students is a constant because all students are in the third grade.

Example: Dog Food Section

A researcher wants to know if dogs who are fed only canned food have different body mass indexes (BMI) than dogs who are fed only hard food. They collect BMI data from 50 dogs who eat only canned food and 50 dogs who eat only hard food.

The cases are the dogs. There are two variables : type of food and BMI. A constant would be subspecies, because all cases are domestic dogs.

Example: Age & Weight of Sea Otters Section

Researchers are studying the relationship between age and weight in a sample of 100 male sea otters ( Enhydra lutris ).

The 100 otters are the cases . There are two variables : age and weight. Biological sex is a constant because all subjects are male. Species is also a constant .

Data Science

12 Data Science Case Studies: Across Various Industries

Home Blog Data Science 12 Data Science Case Studies: Across Various Industries

Data science has become popular in the last few years due to its successful application in making business decisions. Data scientists have been using data science techniques to solve challenging real-world issues in healthcare, agriculture, manufacturing, automotive, and many more. For this purpose, a data enthusiast needs to stay updated with the latest technological advancements in AI. An excellent way to achieve this is through reading industry data science case studies. I recommend checking out Data Science With Python course syllabus to start your data science journey. In this discussion, I will present some case studies to you that contain detailed and systematic data analysis of people, objects, or entities focusing on multiple factors present in the dataset. Almost every industry uses data science in some way. You can learn more about data science fundamentals in this Data Science course content .

Let’s look at the top data science case studies in this article so you can understand how businesses from many sectors have benefitted from data science to boost productivity, revenues, and more.

List of Data Science Case Studies 2024

Hospitality: Airbnb focuses on growth by analyzing customer voice using data science. Qantas uses predictive analytics to mitigate losses
Healthcare: Novo Nordisk is Driving innovation with NLP. AstraZeneca harnesses data for innovation in medicine
Covid 19: Johnson and Johnson use s d ata science to fight the Pandemic
E-commerce: Amazon uses data science to personalize shop p ing experiences and improve customer satisfaction
Supply chain management: UPS optimizes supp l y chain with big data analytics
Meteorology: IMD leveraged data science to achieve a rec o rd 1.2m evacuation before cyclone ''Fani''
Entertainment Industry: Netflix u ses data science to personalize the content and improve recommendations. Spotify uses big data to deliver a rich user experience for online music streaming
Banking and Finance: HDFC utilizes Big D ata Analytics to increase income and enhance the banking experience
Urban Planning and Smart Cities: Traffic management in smart cities such as Pune and Bhubaneswar
Agricultural Yield Prediction: Farmers Edge in Canada uses Data science to help farmers improve their produce
Transportation Industry: Uber optimizes their ride-sharing feature and track the delivery routes through data analysis
Environmental Industry: NASA utilizes Data science to predict potential natural disasters, World Wildlife analyzes deforestation to protect the environment

Top 12 Data Science Case Studies

1. data science in hospitality industry.

In the hospitality sector, data analytics assists hotels in better pricing strategies, customer analysis, brand marketing, tracking market trends, and many more.

Airbnb focuses on growth by analyzing customer voice using data science. A famous example in this sector is the unicorn '' Airbnb '', a startup that focussed on data science early to grow and adapt to the market faster. This company witnessed a 43000 percent hypergrowth in as little as five years using data science. They included data science techniques to process the data, translate this data for better understanding the voice of the customer, and use the insights for decision making. They also scaled the approach to cover all aspects of the organization. Airbnb uses statistics to analyze and aggregate individual experiences to establish trends throughout the community. These analyzed trends using data science techniques impact their business choices while helping them grow further.

Travel industry and data science

Predictive analytics benefits many parameters in the travel industry. These companies can use recommendation engines with data science to achieve higher personalization and improved user interactions. They can study and cross-sell products by recommending relevant products to drive sales and increase revenue. Data science is also employed in analyzing social media posts for sentiment analysis, bringing invaluable travel-related insights. Whether these views are positive, negative, or neutral can help these agencies understand the user demographics, the expected experiences by their target audiences, and so on. These insights are essential for developing aggressive pricing strategies to draw customers and provide better customization to customers in the travel packages and allied services. Travel agencies like Expedia and Booking.com use predictive analytics to create personalized recommendations, product development, and effective marketing of their products. Not just travel agencies but airlines also benefit from the same approach. Airlines frequently face losses due to flight cancellations, disruptions, and delays. Data science helps them identify patterns and predict possible bottlenecks, thereby effectively mitigating the losses and improving the overall customer traveling experience.

How Qantas uses predictive analytics to mitigate losses

Qantas , one of Australia's largest airlines, leverages data science to reduce losses caused due to flight delays, disruptions, and cancellations. They also use it to provide a better traveling experience for their customers by reducing the number and length of delays caused due to huge air traffic, weather conditions, or difficulties arising in operations. Back in 2016, when heavy storms badly struck Australia's east coast, only 15 out of 436 Qantas flights were cancelled due to their predictive analytics-based system against their competitor Virgin Australia, which witnessed 70 cancelled flights out of 320.

2. Data Science in Healthcare

The Healthcare sector is immensely benefiting from the advancements in AI. Data science, especially in medical imaging, has been helping healthcare professionals come up with better diagnoses and effective treatments for patients. Similarly, several advanced healthcare analytics tools have been developed to generate clinical insights for improving patient care. These tools also assist in defining personalized medications for patients reducing operating costs for clinics and hospitals. Apart from medical imaging or computer vision, Natural Language Processing (NLP) is frequently used in the healthcare domain to study the published textual research data. 

A. Pharmaceutical

Driving innovation with NLP: Novo Nordisk. Novo Nordisk uses the Linguamatics NLP platform from internal and external data sources for text mining purposes that include scientific abstracts, patents, grants, news, tech transfer offices from universities worldwide, and more. These NLP queries run across sources for the key therapeutic areas of interest to the Novo Nordisk R&D community. Several NLP algorithms have been developed for the topics of safety, efficacy, randomized controlled trials, patient populations, dosing, and devices. Novo Nordisk employs a data pipeline to capitalize the tools' success on real-world data and uses interactive dashboards and cloud services to visualize this standardized structured information from the queries for exploring commercial effectiveness, market situations, potential, and gaps in the product documentation. Through data science, they are able to automate the process of generating insights, save time and provide better insights for evidence-based decision making.

How AstraZeneca harnesses data for innovation in medicine. AstraZeneca is a globally known biotech company that leverages data using AI technology to discover and deliver newer effective medicines faster. Within their R&D teams, they are using AI to decode the big data to understand better diseases like cancer, respiratory disease, and heart, kidney, and metabolic diseases to be effectively treated. Using data science, they can identify new targets for innovative medications. In 2021, they selected the first two AI-generated drug targets collaborating with BenevolentAI in Chronic Kidney Disease and Idiopathic Pulmonary Fibrosis. 

Data science is also helping AstraZeneca redesign better clinical trials, achieve personalized medication strategies, and innovate the process of developing new medicines. Their Center for Genomics Research uses data science and AI to analyze around two million genomes by 2026. Apart from this, they are training their AI systems to check these images for disease and biomarkers for effective medicines for imaging purposes. This approach helps them analyze samples accurately and more effortlessly. Moreover, it can cut the analysis time by around 30%. 

AstraZeneca also utilizes AI and machine learning to optimize the process at different stages and minimize the overall time for the clinical trials by analyzing the clinical trial data. Summing up, they use data science to design smarter clinical trials, develop innovative medicines, improve drug development and patient care strategies, and many more.

C. Wearable Technology

Wearable technology is a multi-billion-dollar industry. With an increasing awareness about fitness and nutrition, more individuals now prefer using fitness wearables to track their routines and lifestyle choices.

Fitness wearables are convenient to use, assist users in tracking their health, and encourage them to lead a healthier lifestyle. The medical devices in this domain are beneficial since they help monitor the patient's condition and communicate in an emergency situation. The regularly used fitness trackers and smartwatches from renowned companies like Garmin, Apple, FitBit, etc., continuously collect physiological data of the individuals wearing them. These wearable providers offer user-friendly dashboards to their customers for analyzing and tracking progress in their fitness journey.

3. Covid 19 and Data Science

In the past two years of the Pandemic, the power of data science has been more evident than ever. Different pharmaceutical companies across the globe could synthesize Covid 19 vaccines by analyzing the data to understand the trends and patterns of the outbreak. Data science made it possible to track the virus in real-time, predict patterns, devise effective strategies to fight the Pandemic, and many more.

How Johnson and Johnson uses data science to fight the Pandemic 

The data science team at Johnson and Johnson leverages real-time data to track the spread of the virus. They built a global surveillance dashboard (granulated to county level) that helps them track the Pandemic's progress, predict potential hotspots of the virus, and narrow down the likely place where they should test its investigational COVID-19 vaccine candidate. The team works with in-country experts to determine whether official numbers are accurate and find the most valid information about case numbers, hospitalizations, mortality and testing rates, social compliance, and local policies to populate this dashboard. The team also studies the data to build models that help the company identify groups of individuals at risk of getting affected by the virus and explore effective treatments to improve patient outcomes.

4. Data Science in E-commerce

In the e-commerce sector , big data analytics can assist in customer analysis, reduce operational costs, forecast trends for better sales, provide personalized shopping experiences to customers, and many more.

Amazon uses data science to personalize shopping experiences and improve customer satisfaction. Amazon is a globally leading eCommerce platform that offers a wide range of online shopping services. Due to this, Amazon generates a massive amount of data that can be leveraged to understand consumer behavior and generate insights on competitors' strategies. Data science case studies reveal how Amazon uses its data to provide recommendations to its users on different products and services. With this approach, Amazon is able to persuade its consumers into buying and making additional sales. This approach works well for Amazon as it earns 35% of the revenue yearly with this technique. Additionally, Amazon collects consumer data for faster order tracking and better deliveries.   

Similarly, Amazon's virtual assistant, Alexa, can converse in different languages; uses speakers and a camera to interact with the users. Amazon utilizes the audio commands from users to improve Alexa and deliver a better user experience. 

5. Data Science in Supply Chain Management

Predictive analytics and big data are driving innovation in the Supply chain domain. They offer greater visibility into the company operations, reduce costs and overheads, forecasting demands, predictive maintenance, product pricing, minimize supply chain interruptions, route optimization, fleet management, drive better performance, and more. 

Optimizing supply chain with big data analytics: UPS

UPS is a renowned package delivery and supply chain management company. With thousands of packages being delivered every day, on average, a UPS driver makes about 100 deliveries each business day. On-time and safe package delivery are crucial to UPS's success. Hence, UPS offers an optimized navigation tool ''ORION'' (On-Road Integrated Optimization and Navigation), which uses highly advanced big data processing algorithms. This tool for UPS drivers provides route optimization concerning fuel, distance, and time. UPS utilizes supply chain data analysis in all aspects of its shipping process. Data about packages and deliveries are captured through radars and sensors. The deliveries and routes are optimized using big data systems. Overall, this approach has helped UPS save 1.6 million gallons of gasoline in transportation every year, significantly reducing delivery costs.

6. Data Science in Meteorology

Weather prediction is an interesting application of data science . Businesses like aviation, agriculture and farming, construction, consumer goods, sporting events, and many more are dependent on climatic conditions. The success of these businesses is closely tied to the weather, as decisions are made after considering the weather predictions from the meteorological department. 

Besides, weather forecasts are extremely helpful for individuals to manage their allergic conditions. One crucial application of weather forecasting is natural disaster prediction and risk management.

Weather forecasts begin with a large amount of data collection related to the current environmental conditions (wind speed, temperature, humidity, clouds captured at a specific location and time) using sensors on IoT (Internet of Things) devices and satellite imagery. This gathered data is then analyzed using the understanding of atmospheric processes, and machine learning models are built to make predictions on upcoming weather conditions like rainfall or snow prediction. Although data science cannot help avoid natural calamities like floods, hurricanes, or forest fires. Tracking these natural phenomena well ahead of their arrival is beneficial. Such predictions allow governments sufficient time to take necessary steps and measures to ensure the safety of the population.

IMD leveraged data science to achieve a record 1.2m evacuation before cyclone ''Fani'' 

Most d ata scientist’s responsibilities rely on satellite images to make short-term forecasts, decide whether a forecast is correct, and validate models. Machine Learning is also used for pattern matching in this case. It can forecast future weather conditions if it recognizes a past pattern. When employing dependable equipment, sensor data is helpful to produce local forecasts about actual weather models. IMD used satellite pictures to study the low-pressure zones forming off the Odisha coast (India). In April 2019, thirteen days before cyclone ''Fani'' reached the area, IMD (India Meteorological Department) warned that a massive storm was underway, and the authorities began preparing for safety measures.

It was one of the most powerful cyclones to strike India in the recent 20 years, and a record 1.2 million people were evacuated in less than 48 hours, thanks to the power of data science. 

7. Data Science in the Entertainment Industry

Due to the Pandemic, demand for OTT (Over-the-top) media platforms has grown significantly. People prefer watching movies and web series or listening to the music of their choice at leisure in the convenience of their homes. This sudden growth in demand has given rise to stiff competition. Every platform now uses data analytics in different capacities to provide better-personalized recommendations to its subscribers and improve user experience. 

How Netflix uses data science to personalize the content and improve recommendations

Netflix is an extremely popular internet television platform with streamable content offered in several languages and caters to various audiences. In 2006, when Netflix entered this media streaming market, they were interested in increasing the efficiency of their existing ''Cinematch'' platform by 10% and hence, offered a prize of $1 million to the winning team. This approach was successful as they found a solution developed by the BellKor team at the end of the competition that increased prediction accuracy by 10.06%. Over 200 work hours and an ensemble of 107 algorithms provided this result. These winning algorithms are now a part of the Netflix recommendation system.

Netflix also employs Ranking Algorithms to generate personalized recommendations of movies and TV Shows appealing to its users. 

Spotify uses big data to deliver a rich user experience for online music streaming

Personalized online music streaming is another area where data science is being used. Spotify is a well-known on-demand music service provider launched in 2008, which effectively leveraged big data to create personalized experiences for each user. It is a huge platform with more than 24 million subscribers and hosts a database of nearly 20million songs; they use the big data to offer a rich experience to its users. Spotify uses this big data and various algorithms to train machine learning models to provide personalized content. Spotify offers a "Discover Weekly" feature that generates a personalized playlist of fresh unheard songs matching the user's taste every week. Using the Spotify "Wrapped" feature, users get an overview of their most favorite or frequently listened songs during the entire year in December. Spotify also leverages the data to run targeted ads to grow its business. Thus, Spotify utilizes the user data, which is big data and some external data, to deliver a high-quality user experience.

8. Data Science in Banking and Finance

Data science is extremely valuable in the Banking and Finance industry . Several high priority aspects of Banking and Finance like credit risk modeling (possibility of repayment of a loan), fraud detection (detection of malicious or irregularities in transactional patterns using machine learning), identifying customer lifetime value (prediction of bank performance based on existing and potential customers), customer segmentation (customer profiling based on behavior and characteristics for personalization of offers and services). Finally, data science is also used in real-time predictive analytics (computational techniques to predict future events).

How HDFC utilizes Big Data Analytics to increase revenues and enhance the banking experience

One of the major private banks in India, HDFC Bank , was an early adopter of AI. It started with Big Data analytics in 2004, intending to grow its revenue and understand its customers and markets better than its competitors. Back then, they were trendsetters by setting up an enterprise data warehouse in the bank to be able to track the differentiation to be given to customers based on their relationship value with HDFC Bank. Data science and analytics have been crucial in helping HDFC bank segregate its customers and offer customized personal or commercial banking services. The analytics engine and SaaS use have been assisting the HDFC bank in cross-selling relevant offers to its customers. Apart from the regular fraud prevention, it assists in keeping track of customer credit histories and has also been the reason for the speedy loan approvals offered by the bank.

9. Data Science in Urban Planning and Smart Cities

Data Science can help the dream of smart cities come true! Everything, from traffic flow to energy usage, can get optimized using data science techniques. You can use the data fetched from multiple sources to understand trends and plan urban living in a sorted manner.

The significant data science case study is traffic management in Pune city. The city controls and modifies its traffic signals dynamically, tracking the traffic flow. Real-time data gets fetched from the signals through cameras or sensors installed. Based on this information, they do the traffic management. With this proactive approach, the traffic and congestion situation in the city gets managed, and the traffic flow becomes sorted. A similar case study is from Bhubaneswar, where the municipality has platforms for the people to give suggestions and actively participate in decision-making. The government goes through all the inputs provided before making any decisions, making rules or arranging things that their residents actually need.

10. Data Science in Agricultural Prediction

Have you ever wondered how helpful it can be if you can predict your agricultural yield? That is exactly what data science is helping farmers with. They can get information about the number of crops they can produce in a given area based on different environmental factors and soil types. Using this information, the farmers can make informed decisions about their yield and benefit the buyers and themselves in multiple ways.

Data Science in Agricultural Yield Prediction

Farmers across the globe and overseas use various data science techniques to understand multiple aspects of their farms and crops. A famous example of data science in the agricultural industry is the work done by Farmers Edge. It is a company in Canada that takes real-time images of farms across the globe and combines them with related data. The farmers use this data to make decisions relevant to their yield and improve their produce. Similarly, farmers in countries like Ireland use satellite-based information to ditch traditional methods and multiply their yield strategically.

11. Data Science in the Transportation Industry

Transportation keeps the world moving around. People and goods commute from one place to another for various purposes, and it is fair to say that the world will come to a standstill without efficient transportation. That is why it is crucial to keep the transportation industry in the most smoothly working pattern, and data science helps a lot in this. In the realm of technological progress, various devices such as traffic sensors, monitoring display systems, mobility management devices, and numerous others have emerged.

Many cities have already adapted to the multi-modal transportation system. They use GPS trackers, geo-locations and CCTV cameras to monitor and manage their transportation system. Uber is the perfect case study to understand the use of data science in the transportation industry. They optimize their ride-sharing feature and track the delivery routes through data analysis. Their data science case studies approach enabled them to serve more than 100 million users, making transportation easy and convenient. Moreover, they also use the data they fetch from users daily to offer cost-effective and quickly available rides.

12. Data Science in the Environmental Industry

Increasing pollution, global warming, climate changes and other poor environmental impacts have forced the world to pay attention to environmental industry. Multiple initiatives are being taken across the globe to preserve the environment and make the world a better place. Though the industry recognition and the efforts are in the initial stages, the impact is significant, and the growth is fast.

The popular use of data science in the environmental industry is by NASA and other research organizations worldwide. NASA gets data related to the current climate conditions, and this data gets used to create remedial policies that can make a difference. Another way in which data science is actually helping researchers is they can predict natural disasters well before time and save or at least reduce the potential damage considerably. A similar case study is with the World Wildlife Fund. They use data science to track data related to deforestation and help reduce the illegal cutting of trees. Hence, it helps preserve the environment.

Where to Find Full Data Science Case Studies?

Data science is a highly evolving domain with many practical applications and a huge open community. Hence, the best way to keep updated with the latest trends in this domain is by reading case studies and technical articles. Usually, companies share their success stories of how data science helped them achieve their goals to showcase their potential and benefit the greater good. Such case studies are available online on the respective company websites and dedicated technology forums like Towards Data Science or Medium.

Additionally, we can get some practical examples in recently published research papers and textbooks in data science.

What Are the Skills Required for Data Scientists?

Data scientists play an important role in the data science process as they are the ones who work on the data end to end. To be able to work on a data science case study, there are several skills required for data scientists like a good grasp of the fundamentals of data science, deep knowledge of statistics, excellent programming skills in Python or R, exposure to data manipulation and data analysis, ability to generate creative and compelling data visualizations, good knowledge of big data, machine learning and deep learning concepts for model building & deployment. Apart from these technical skills, data scientists also need to be good storytellers and should have an analytical mind with strong communication skills.

Opt for the best business analyst training elevating your expertise. Take the leap towards becoming a distinguished business analysis professional

Conclusion

These were some interesting data science case studies across different industries. There are many more domains where data science has exciting applications, like in the Education domain, where data can be utilized to monitor student and instructor performance, develop an innovative curriculum that is in sync with the industry expectations, etc. 

Almost all the companies looking to leverage the power of big data begin with a SWOT analysis to narrow down the problems they intend to solve with data science. Further, they need to assess their competitors to develop relevant data science tools and strategies to address the challenging issue. Thus, the utility of data science in several sectors is clearly visible, a lot is left to be explored, and more is yet to come. Nonetheless, data science will continue to boost the performance of organizations in this age of big data.

Frequently Asked Questions (FAQs)

A case study in data science requires a systematic and organized approach for solving the problem. Generally, four main steps are needed to tackle every data science case study:

Defining the problem statement and strategy to solve it 
Gather and pre-process the data by making relevant assumptions 
Select tool and appropriate algorithms to build machine learning /deep learning models
Make predictions, accept the solutions based on evaluation metrics, and improve the model if necessary.

Getting data for a case study starts with a reasonable understanding of the problem. This gives us clarity about what we expect the dataset to include. Finding relevant data for a case study requires some effort. Although it is possible to collect relevant data using traditional techniques like surveys and questionnaires, we can also find good quality data sets online on different platforms like Kaggle, UCI Machine Learning repository, Azure open data sets, Government open datasets, Google Public Datasets, Data World and so on.

Data science projects involve multiple steps to process the data and bring valuable insights. A data science project includes different steps - defining the problem statement, gathering relevant data required to solve the problem, data pre-processing, data exploration & data analysis, algorithm selection, model building, model prediction, model optimization, and communicating the results through dashboards and reports.

Devashree Madhugiri

Devashree holds an M.Eng degree in Information Technology from Germany and a background in Data Science. She likes working with statistics and discovering hidden insights in varied datasets to create stunning dashboards. She enjoys sharing her knowledge in AI by writing technical articles on various technological platforms. She loves traveling, reading fiction, solving Sudoku puzzles, and participating in coding competitions in her leisure time.

Something went wrong

Upcoming Data Science Batches & Dates

Name	Date	Fee	Know more

An official website of the United States government

Here’s how you know

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Case studies & examples

Agencies mobilize to improve emergency response in puerto rico through better data.

Federal agencies' response efforts to Hurricanes Irma and Maria in Puerto Rico was hampered by imperfect address data for the island. In the aftermath, emergency responders gathered together to enhance the utility of Puerto Rico address data and share best practices for using what information is currently available.

Federal Data Strategy

BUILDER: A Science-Based Approach to Infrastructure Management

The Department of Energy’s National Nuclear Security Administration (NNSA) adopted a data-driven, risk-informed strategy to better assess risks, prioritize investments, and cost effectively modernize its aging nuclear infrastructure. NNSA’s new strategy, and lessons learned during its implementation, will help inform other federal data practitioners’ efforts to maintain facility-level information while enabling accurate and timely enterprise-wide infrastructure analysis.

Department of Energy

data management , data analysis , process redesign , Federal Data Strategy

Business case for open data

Six reasons why making your agency's data open and accessible is a good business decision.

CDO Council Federal HR Dashboarding Report - 2021

The CDO Council worked with the US Department of Agriculture, the Department of the Treasury, the United States Agency for International Development, and the Department of Transportation to develop a Diversity Profile Dashboard and to explore the value of shared HR decision support across agencies. The pilot was a success, and identified potential impact of a standardized suite of HR dashboards, in addition to demonstrating the value of collaborative analytics between agencies.

Federal Chief Data Officer's Council

data practices , data sharing , data access

CDOC Data Inventory Report

The Chief Data Officers Council Data Inventory Working Group developed this paper to highlight the value proposition for data inventories and describe challenges agencies may face when implementing and managing comprehensive data inventories. It identifies opportunities agencies can take to overcome some of these challenges and includes a set of recommendations directed at Agencies, OMB, and the CDO Council (CDOC).

data practices , metadata , data inventory

DSWG Recommendations and Findings

The Chief Data Officer Council (CDOC) established a Data Sharing Working Group (DSWG) to help the council understand the varied data-sharing needs and challenges of all agencies across the Federal Government. The DSWG reviewed data-sharing across federal agencies and developed a set of recommendations for improving the methods to access and share data within and between agencies. This report presents the findings of the DSWG’s review and provides recommendations to the CDOC Executive Committee.

data practices , data agreements , data sharing , data access

Data Skills Training Program Implementation Toolkit

The Data Skills Training Program Implementation Toolkit is designed to provide both small and large agencies with information to develop their own data skills training programs. The information provided will serve as a roadmap to the design, implementation, and administration of federal data skills training programs as agencies address their Federal Data Strategy’s Agency Action 4 gap-closing strategy training component.

data sharing , Federal Data Strategy

Data Standdown: Interrupting process to fix information

Although not a true pause in operations, ONR’s data standdown made data quality and data consolidation the top priority for the entire organization. It aimed to establish an automated and repeatable solution to enable a more holistic view of ONR investments and activities, and to increase transparency and effectiveness throughout its mission support functions. In addition, it demonstrated that getting top-level buy-in from management to prioritize data can truly advance a more data-driven culture.

Office of Naval Research

data governance , data cleaning , process redesign , Federal Data Strategy

Data.gov Metadata Management Services Product-Preliminary Plan

Status summary and preliminary business plan for a potential metadata management product under development by the Data.gov Program Management Office

data management , Federal Data Strategy , metadata , open data

PDF (7 pages)

Department of Transportation Case Study: Enterprise Data Inventory

In response to the Open Government Directive, DOT developed a strategic action plan to inventory and release high-value information through the Data.gov portal. The Department sustained efforts in building its data inventory, responding to the President’s memorandum on regulatory compliance with a comprehensive plan that was recognized as a model for other agencies to follow.

Department of Transportation

data inventory , open data

Department of Transportation Model Data Inventory Approach

This document from the Department of Transportation provides a model plan for conducting data inventory efforts required under OMB Memorandum M-13-13.

data inventory

PDF (5 pages)

FEMA Case Study: Disaster Assistance Program Coordination

In 2008, the Disaster Assistance Improvement Program (DAIP), an E-Government initiative led by FEMA with support from 16 U.S. Government partners, launched DisasterAssistance.gov to simplify the process for disaster survivors to identify and apply for disaster assistance. DAIP utilized existing partner technologies and implemented a services oriented architecture (SOA) that integrated the content management system and rules engine supporting Department of Labor’s Benefits.gov applications with FEMA’s Individual Assistance Center application. The FEMA SOA serves as the backbone for data sharing interfaces with three of DAIP’s federal partners and transfers application data to reduce duplicate data entry by disaster survivors.

Federal Emergency Management Agency

data sharing

Federal CDO Data Skills Training Program Case Studies

This series was developed by the Chief Data Officer Council’s Data Skills & Workforce Development Working Group to provide support to agencies in implementing the Federal Data Strategy’s Agency Action 4 gap-closing strategy training component in FY21.

FederalRegister.gov API Case Study

This case study describes the tenets behind an API that provides access to all data found on FederalRegister.gov, including all Federal Register documents from 1994 to the present.

National Archives and Records Administration

PDF (3 pages)

Fuels Knowledge Graph Project

The Fuels Knowledge Graph Project (FKGP), funded through the Federal Chief Data Officers (CDO) Council, explored the use of knowledge graphs to achieve more consistent and reliable fuel management performance measures. The team hypothesized that better performance measures and an interoperable semantic framework could enhance the ability to understand wildfires and, ultimately, improve outcomes. To develop a more systematic and robust characterization of program outcomes, the FKGP team compiled, reviewed, and analyzed multiple agency glossaries and data sources. The team examined the relationships between them, while documenting the data management necessary for a successful fuels management program.

metadata , data sharing , data access

Government Data Hubs

A list of Federal agency open data hubs, including USDA, HHS, NASA, and many others.

Helping Baltimore Volunteers Find Where to Help

Bloomberg Government analysts put together a prototype through the Census Bureau’s Opportunity Project to better assess where volunteers should direct litter-clearing efforts. Using Census Bureau and Forest Service information, the team brought a data-driven approach to their work. Their experience reveals how individuals with data expertise can identify a real-world problem that data can help solve, navigate across agencies to find and obtain the most useful data, and work within resource constraints to provide a tool to help address the problem.

Census Bureau

geospatial , data sharing , Federal Data Strategy

How USDA Linked Federal and Commercial Data to Shed Light on the Nutritional Value of Retail Food Sales

Purchase-to-Plate Crosswalk (PPC) links the more than 359,000 food products in a comercial company database to several thousand foods in a series of USDA nutrition databases. By linking existing data resources, USDA was able to enrich and expand the analysis capabilities of both datasets. Since there were no common identifiers between the two data structures, the team used probabilistic and semantic methods to reduce the manual effort required to link the data.

Department of Agriculture

data sharing , process redesign , Federal Data Strategy

How to Blend Your Data: BEA and BLS Harness Big Data to Gain New Insights about Foreign Direct Investment in the U.S.

A recent collaboration between the Bureau of Economic Analysis (BEA) and the Bureau of Labor Statistics (BLS) helps shed light on the segment of the American workforce employed by foreign multinational companies. This case study shows the opportunities of cross-agency data collaboration, as well as some of the challenges of using big data and administrative data in the federal government.

Bureau of Economic Analysis / Bureau of Labor Statistics

data sharing , workforce development , process redesign , Federal Data Strategy

Implementing Federal-Wide Comment Analysis Tools

The CDO Council Comment Analysis pilot has shown that recent advances in Natural Language Processing (NLP) can effectively aid the regulatory comment analysis process. The proof-ofconcept is a standardized toolset intended to support agencies and staff in reviewing and responding to the millions of public comments received each year across government.

Improving Data Access and Data Management: Artificial Intelligence-Generated Metadata Tags at NASA

NASA’s data scientists and research content managers recently built an automated tagging system using machine learning and natural language processing. This system serves as an example of how other agencies can use their own unstructured data to improve information accessibility and promote data reuse.

National Aeronautics and Space Administration

metadata , data management , data sharing , process redesign , Federal Data Strategy

Investing in Learning with the Data Stewardship Tactical Working Group at DHS

The Department of Homeland Security (DHS) experience forming the Data Stewardship Tactical Working Group (DSTWG) provides meaningful insights for those who want to address data-related challenges collaboratively and successfully in their own agencies.

Department of Homeland Security

data governance , data management , Federal Data Strategy

Leveraging AI for Business Process Automation at NIH

The National Institute of General Medical Sciences (NIGMS), one of the twenty-seven institutes and centers at the NIH, recently deployed Natural Language Processing (NLP) and Machine Learning (ML) to automate the process by which it receives and internally refers grant applications. This new approach ensures efficient and consistent grant application referral, and liberates Program Managers from the labor-intensive and monotonous referral process.

National Institutes of Health

standards , data cleaning , process redesign , AI

FDS Proof Point

National Broadband Map: A Case Study on Open Innovation for National Policy

The National Broadband Map is a tool that provide consumers nationwide reliable information on broadband internet connections. This case study describes how crowd-sourcing, open source software, and public engagement informs the development of a tool that promotes government transparency.

Federal Communications Commission

National Renewable Energy Laboratory API Case Study

This case study describes the launch of the National Renewable Energy Laboratory (NREL) Developer Network in October 2011. The main goal was to build an overarching platform to make it easier for the public to use NREL APIs and for NREL to produce APIs.

National Renewable Energy Laboratory

Open Energy Data at DOE

This case study details the development of the renewable energy applications built on the Open Energy Information (OpenEI) platform, sponsored by the Department of Energy (DOE) and implemented by the National Renewable Energy Laboratory (NREL).

open data , data sharing , Federal Data Strategy

Pairing Government Data with Private-Sector Ingenuity to Take on Unwanted Calls

The Federal Trade Commission (FTC) releases data from millions of consumer complaints about unwanted calls to help fuel a myriad of private-sector solutions to tackle the problem. The FTC’s work serves as an example of how agencies can work with the private sector to encourage the innovative use of government data toward solutions that benefit the public.

Federal Trade Commission

data cleaning , Federal Data Strategy , open data , data sharing

Profile in Data Sharing - National Electronic Interstate Compact Enterprise

The Federal CDO Council’s Data Sharing Working Group highlights successful data sharing activities to recognize mature data sharing practices as well as to incentivize and inspire others to take part in similar collaborations. This Profile in Data Sharing focuses on how the federal government and states support children who are being placed for adoption or foster care across state lines. It greatly reduces the work and time required for states to exchange paperwork and information needed to process the placements. Additionally, NEICE allows child welfare workers to communicate and provide timely updates to courts, relevant private service providers, and families.

Profile in Data Sharing - National Health Service Corps Loan Repayment Programs

The Federal CDO Council’s Data Sharing Working Group highlights successful data sharing activities to recognize mature data sharing practices as well as to incentivize and inspire others to take part in similar collaborations. This Profile in Data Sharing focuses on how the Health Resources and Services Administration collaborates with the Department of Education to make it easier to apply to serve medically underserved communities - reducing applicant burden and improving processing efficiency.

Profile in Data Sharing - Roadside Inspection Data

The Federal CDO Council’s Data Sharing Working Group highlights successful data sharing activities to recognize mature data sharing practices as well as to incentivize and inspire others to take part in similar collaborations. This Profile in Data Sharing focuses on how the Department of Transportation collaborates with the Customs and Border Patrol and state partners to prescreen commercial motor vehicles entering the US and to focus inspections on unsafe carriers and drivers.

Profiles in Data Sharing - U.S. Citizenship and Immigration Service

The Federal CDO Council’s Data Sharing Working Group highlights successful data sharing activities to recognize mature data sharing practices as well as to incentivize and inspire others to take part in similar collaborations. This Profile in Data Sharing focuses on how the U.S. Citizenship and Immigration Service (USCIS) collaborated with the Centers for Disease Control to notify state, local, tribal, and territorial public health authorities so they can connect with individuals in their communities about their potential exposure.

SBA’s Approach to Identifying Data, Using a Learning Agenda, and Leveraging Partnerships to Build its Evidence Base

Through its Enterprise Learning Agenda, Small Business Administration’s (SBA) staff identify essential research questions, a plan to answer them, and how data held outside the agency can help provide further insights. Other agencies can learn from the innovative ways SBA identifies data to answer agency strategic questions and adopt those aspects that work for their own needs.

Small Business Administration

process redesign , Federal Data Strategy

Supercharging Data through Validation as a Service

USDA's Food and Nutrition Service restructured its approach to data validation at the state level using an open-source, API-based validation service managed at the federal level.

data cleaning , data validation , API , data sharing , process redesign , Federal Data Strategy

The Census Bureau Uses Its Own Data to Increase Response Rates, Helps Communities and Other Stakeholders Do the Same

The Census Bureau team produced a new interactive mapping tool in early 2018 called the Response Outreach Area Mapper (ROAM), an application that resulted in wider use of authoritative Census Bureau data, not only to improve the Census Bureau’s own operational efficiency, but also for use by tribal, state, and local governments, national and local partners, and other community groups. Other agency data practitioners can learn from the Census Bureau team’s experience communicating technical needs to non-technical executives, building analysis tools with widely-used software, and integrating efforts with stakeholders and users.

open data , data sharing , data management , data analysis , Federal Data Strategy

The Mapping Medicare Disparities Tool

The Centers for Medicare & Medicaid Services’ Office of Minority Health (CMS OMH) Mapping Medicare Disparities Tool harnessed the power of millions of data records while protecting the privacy of individuals, creating an easy-to-use tool to better understand health disparities.

Centers for Medicare & Medicaid Services

geospatial , Federal Data Strategy , open data

The Veterans Legacy Memorial

The Veterans Legacy Memorial (VLM) is a digital platform to help families, survivors, and fellow veterans to take a leading role in honoring their beloved veteran. Built on millions of existing National Cemetery Administration (NCA) records in a 25-year-old database, VLM is a powerful example of an agency harnessing the potential of a legacy system to provide a modernized service that better serves the public.

Veterans Administration

data sharing , data visualization , Federal Data Strategy

Transitioning to a Data Driven Culture at CMS

This case study describes how CMS announced the creation of the Office of Information Products and Data Analytics (OIPDA) to take the lead in making data use and dissemination a core function of the agency.

data management , data sharing , data analysis , data analytics

PDF (10 pages)

U.S. Department of Labor Case Study: Software Development Kits

The U.S. Department of Labor sought to go beyond merely making data available to developers and take ease of use of the data to the next level by giving developers tools that would make using DOL’s data easier. DOL created software development kits (SDKs), which are downloadable code packages that developers can drop into their apps, making access to DOL’s data easy for even the most novice developer. These SDKs have even been published as open source projects with the aim of speeding up their conversion to SDKs that will eventually support all federal APIs.

Department of Labor

open data , API

U.S. Geological Survey and U.S. Census Bureau collaborate on national roads and boundaries data

It is a well-kept secret that the U.S. Geological Survey and the U.S. Census Bureau were the original two federal agencies to build the first national digital database of roads and boundaries in the United States. The agencies joined forces to develop homegrown computer software and state of the art technologies to convert existing USGS topographic maps of the nation to the points, lines, and polygons that fueled early GIS. Today, the USGS and Census Bureau have a longstanding goal to leverage and use roads and authoritative boundary datasets.

U.S. Geological Survey and U.S. Census Bureau

data management , data sharing , data standards , data validation , data visualization , Federal Data Strategy , geospatial , open data , quality

USA.gov Uses Human-Centered Design to Roll Out AI Chatbot

To improve customer service and give better answers to users of the USA.gov website, the Technology Transformation and Services team at General Services Administration (GSA) created a chatbot using artificial intelligence (AI) and automation.

General Services Administration

AI , Federal Data Strategy

resources.data.gov

An official website of the Office of Management and Budget, the General Services Administration, and the Office of Government Information Services.

This section contains explanations of common terms referenced on resources.data.gov.

Free Samples
Premium Essays
Editing Services Editing Proofreading Rewriting
Extra Tools Essay Topic Generator Thesis Generator Citation Generator GPA Calculator Study Guides Donate Paper
Essay Writing Help
About Us About Us Testimonials FAQ
Statistics Case Study
Samples List

An case study examples on statistics is a prosaic composition of a small volume and free composition, expressing individual impressions and thoughts on a specific occasion or issue and obviously not claiming a definitive or exhaustive interpretation of the subject.

Some signs of statistics case study:

the presence of a specific topic or question. A work devoted to the analysis of a wide range of problems in biology, by definition, cannot be performed in the genre of statistics case study topic.
The case study expresses individual impressions and thoughts on a specific occasion or issue, in this case, on statistics and does not knowingly pretend to a definitive or exhaustive interpretation of the subject.
As a rule, an essay suggests a new, subjectively colored word about something, such a work may have a philosophical, historical, biographical, journalistic, literary, critical, popular scientific or purely fiction character.
in the content of an case study samples on statistics , first of all, the author’s personality is assessed - his worldview, thoughts and feelings.

The goal of an case study in statistics is to develop such skills as independent creative thinking and writing out your own thoughts.

Writing an case study is extremely useful, because it allows the author to learn to clearly and correctly formulate thoughts, structure information, use basic concepts, highlight causal relationships, illustrate experience with relevant examples, and substantiate his conclusions.

Studentshare

Examples List on Statistics Case Study

TERMS & CONDITIONS
PRIVACY POLICY
COOKIES POLICY

A counterfactual analysis quantifying the COVID-19 vaccination impact in Sweden

Find this author on Google Scholar
Find this author on PubMed
Search for this author on this site
ORCID record for Fanny Bergström
For correspondence: [email protected]
Info/History
Supplementary material
Preview PDF

Background Vaccination was the single most effective measure in mitigating the impact of the COVID-19 pandemic. Our study aims to quantify the impact of vaccination programmes during this initial year of vaccination by estimating the number of case fatalities avoided, having Sweden as a case study.

Methods Using Swedish data on age-specific reported incidence, vaccination uptake, and contact structures, along with age-specific estimates on the vaccine efficacies and under-reporting from the literature, we fit a Bayesian SEIR epidemic model with time-varying community contact rate β ( t ) for COVID-19 incidence. By adding age-specific infection fatality rates, we obtain an estimate of about 5,540 (95% PI: 5,390-5,690) for the number of case fatalities from the fitted model. This estimate aligns closely with the reported 5,430 case fatalities during the same period. We then use the estimated contact rate β ( t ) in a counterfactual analysis where the population is unvaccinated, leading to more infections and fatalities.

Findings The counterfactual analysis result in a severe epidemic outbreak during the early autumn of 2021, resulting in about 52,600 (49,900-55,500) number of case fatalities. Consequently, the number of lives saved by the vaccination program is estimated to be about 47,100 (44,500-49,800), out of which 6,460 are directly saved and 40,600 are indirectly saved, mainly by drastically reducing the severe outbreak in the early autumn of 2021, which would have occurred without vaccination and unchanged community contact rate.

Interpretation Our mathematical model is used to analyze the impact of COVID-19 vaccination on lives saved in Sweden during 2021, but the same methodology can be applied to other countries. The counterfactual analysis offers insights into an alternative trajectory of the pandemic without vaccination. By incorporating estimated vaccination-related parameters, age-specific infection fatality ratio and under-reporting, our model estimates the number of case fatalities avoided. The results not only show the direct impact of vaccination on reducing deaths for infected individuals but also shed light on the indirect effects of reduced transmission dynamics.

Funding NordForsk (project number 105572).

Evidence before this study Vaccination against COVID-19 has been proven to reduce infection rates, hospitalizations, and mortality. Observational studies and clinical trials have demonstrated the efficacy COVID-19 vaccines, and mathematical modeling studies have been conducted to predict the potential outcomes of vaccination programmes under different scenarios. These models have provided insights into the benefits of achieving high vaccination coverage and the consequences of delays or interruptions in vaccine distribution. There remains a gap in analyses that quantify the impact of the vaccination programme in Sweden by comparing actual outcomes with a scenario without vaccination. This study aims to fill this gap by employing a robust counterfactual analysis method to provide a clearer picture of the COVID-19 vaccination impact in Sweden. This approach offers a understanding of the impact of vaccination by isolating the effects of the vaccination from other interventions. For our literature review, we have searched PubMed for “vaccination counterfactual analysis”, “SEIR model vaccination impact”, “quantifying effect of COVID-19 vaccination” and “COVID-19 IFR”. The Swedish Public Health Agency has provided country-specific data for Sweden.

Added value of this study The Bayesian SEIR model presented in this paper provides a flexible and data-driven framework to assess the effectiveness of vaccination strategies. Using age- and country-specific data and parameters on reported cases, under-reporting and infection fatality rate (IFR), we can quantify the effect of vaccination in Sweden 2021. This study compares the case fatalities in a factual analysis with a counterfactual analysis, which has the same contact rates but with a unvaccinated population. The comparison allows an estimate of the number of lives saved from the vaccination. The methodology can be used to evaluate the effect of vaccination in other countries, but also for other counterfactual scenarios such as different vaccination schemes.

Implications of all the available evidence Our findings highlight the critical role of vaccination in mitigating COVID-19-related mortality. Through the counterfactual analysis provided by the SEIR epidemic model, we gain insights into the effects of vaccination programmes. Beyond reducing deaths directly attributable to COVID-19 infection vaccination also exerts a broader societal impact by curbing transmission rates and easing strain on healthcare systems. Moreover, by quantifying the number of lives saved through vaccination efforts, we offer policymakers and public health officials invaluable data for optimizing future vaccination strategies and reinforcing the importance of widespread vaccine uptake. The insights gained from this study not only show the effectiveness of vaccination in saving lives but also provide a robust framework of a data-driven approach to guide evidence-based decision-making and shaping vaccination policies.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

NordForsk (project number 105572).

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Data used for our analysis were publicly avaliable prior to the initialisation of this study. COVID-19 case data and vaccination is avaliable from the Swedish public health agency (PHA): https://www.folkhalsomyndigheten.se/sm ittskydd-beredskap/utbrott/aktuella-utbrott/covid-19/statistik-och-analyser/ Report on Infection fatality rate from the PHA: https://www.folkhalsomyndigheten.se/contentassets/da0321b738ee4f0686d758e069e18caa/skattning-letalitet-covid-19-stockholms-lan.pdf/. Demographics for Sweden is avalable from Statistics Sweden: https://www.scb.se/hitta-statistik/sverige-i-siffror/manniskorna-i-sverige/befolkningspyramid-for-sverige/

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

↵ * Felix Günther changed affiliation after his work on this project and now works at the Department of Infectious Disease Epidemiology, Robert Koch Institute, Germany.

Data Availability

All data used for our analysis are available online at https://github.com/fannybergstrom/vaccination_sweden/

https://www.folkhalsomyndigheten.se/smittskydd-beredskap/utbrott/aktuella-utbrott/covid-19/statistik-och-analyser/

https://www.folkhalsomyndigheten.se/contentassets/da0321b738ee4f0686d758e069e18caa/skattning-letalitet-covid-19-stockholms-lan.pdf/

https://www.scb.se/hitta-statistik/sverige-i-siffror/manniskorna-i-sverige/befolkningspyramid-for-sverige/

View the discussion thread.

Supplementary Material

Thank you for your interest in spreading the word about medRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Citation Manager Formats

EndNote (tagged)
EndNote 8 (xml)
RefWorks Tagged
Ref Manager
Tweet Widget
Facebook Like
Google Plus One

Subject Area

Infectious Diseases (except HIV/AIDS)
Addiction Medicine (343)
Allergy and Immunology (666)
Anesthesia (180)
Cardiovascular Medicine (2629)
Dentistry and Oral Medicine (314)
Dermatology (222)
Emergency Medicine (397)
Endocrinology (including Diabetes Mellitus and Metabolic Disease) (932)
Epidemiology (12181)
Forensic Medicine (10)
Gastroenterology (756)
Genetic and Genomic Medicine (4070)
Geriatric Medicine (387)
Health Economics (678)
Health Informatics (2627)
Health Policy (998)
Health Systems and Quality Improvement (981)
Hematology (361)
HIV/AIDS (845)
Infectious Diseases (except HIV/AIDS) (13662)
Intensive Care and Critical Care Medicine (792)
Medical Education (399)
Medical Ethics (109)
Nephrology (431)
Neurology (3839)
Nursing (209)
Nutrition (571)
Obstetrics and Gynecology (735)
Occupational and Environmental Health (691)
Oncology (2011)
Ophthalmology (582)
Orthopedics (239)
Otolaryngology (304)
Pain Medicine (250)
Palliative Medicine (74)
Pathology (471)
Pediatrics (1109)
Pharmacology and Therapeutics (460)
Primary Care Research (448)
Psychiatry and Clinical Psychology (3405)
Public and Global Health (6504)
Radiology and Imaging (1390)
Rehabilitation Medicine and Physical Therapy (808)
Respiratory Medicine (869)
Rheumatology (401)
Sexual and Reproductive Health (407)
Sports Medicine (341)
Surgery (441)
Toxicology (52)
Transplantation (185)
Urology (165)

Global Report on Food Crises (GRFC) 2024

Published by the Food Security Information Network (FSIN) in support of the Global Network against Food Crises (GNAFC), the GRFC 2024 is the reference document for global, regional and country-level acute food insecurity in 2023. The report is the result of a collaborative effort among 16 partners to achieve a consensus-based assessment of acute food insecurity and malnutrition in countries with food crises and aims to inform humanitarian and development action.

FSIN and Global Network Against Food Crises. 2024. GRFC 2024 . Rome.

When citing this report online please use this link:

https://www.fsinplatform.org/report/global-report-food-crises-2024/

Document	File
Global Report on Food Crises 2023 - mid-year update
Global Report on Food Crises 2023
Global Report on Food Crises 2022
Global Report on Food Crises 2021 - September update
Global Report on Food Crises 2021
Global Report on Food Crises 2021 (In brief)
Global Report on Food Crises 2020 - September update In times of COVID-19
Global Report on Food Crises 2020
Global Report on Food Crises 2019 - September update
Global Report on Food Crises 2019
Global Report on Food Crises 2019 (In brief)
Global Report on Food Crises 2019 (Key Messages)
Global Report on Food Crises 2019 (Key Messages) - French
Global Report on Food Crises 2019 (Key Messages) - Arabic

Advanced search

IMAGES

(PDF) A case study report on integrating statistics, problem-based
49 Free Case Study Templates ( + Case Study Format Examples + )
Case Analysis: Examples + How-to Guide & Writing Tips
😱 Business case analysis report. How to Write a Business Case: Template
statistics case study examples with solutions pdf
49 Free Case Study Templates ( + Case Study Format Examples + )

COMMENTS

What is a Case Study? Definition & Examples
A case study is an in-depth investigation of a single person, group, event, or community. This research method involves intensively analyzing a subject to understand its complexity and context. The richness of a case study comes from its ability to capture detailed, qualitative data that can offer insights into a process or subject matter that ...
Chapter 16 Case Studies
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).
5 Statistics Case Studies That Will Blow Your Mind
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.
Top 40 Most Popular Case Studies of 2021
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 ...
How to Use Case Studies in Research: Guide and Examples
1. Select a case. Once you identify the problem at hand and come up with questions, identify the case you will focus on. The study can provide insights into the subject at hand, challenge existing assumptions, propose a course of action, and/or open up new areas for further research. 2.
What Is a Case Study?
Revised on November 20, 2023. A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research. A case study research design usually involves qualitative methods, but quantitative methods are ...
PDF Case Study Applications of Statistics in Institutional Research
For example in this case study, an appropriate conclusion statement might. For example, a corelation coeficient of greater than or equal to A .196 with a sample size of 10 will be significant. In Institutional Research, larger sample sizes often exist, which does in some cases assist with statistical power. 40.
Open Case Studies: Statistics and Data Science Education through Real
ence and statistics is the limited availability of courses ... However, Background is listed ﬁrst here to more easily map to our Open Case Studies model. example topics covered in all case studies (TableS1). 1. Motivation. Each case study begins with a motivating data visualization. This idea originated from Dr. Mine
(PDF) Open Case Studies: Statistics and Data Science ...
Open Case Studies: Statistics and Data Science Education. through Real-W orld Applications. Carrie W right 1, Qier Meng 1, Michael R. Breshock 2, Lyla Atta 2, Margaret A. T aub 1, Leah R. Jager 1 ...
Case Study: Definition, Types, Examples and Benefits
Researchers, economists, and others frequently use case studies to answer questions across a wide spectrum of disciplines, from analyzing decades of climate data for conservation efforts to developing new theoretical frameworks in psychology. Learn about the different types of case studies, their benefits, and examples of successful case studies.
20: Case Studies
This page titled 20: Case Studies is shared under a Public Domain license and was authored, remixed, and/or curated by David Lane via source content that was edited to the style and standards of the LibreTexts platform.
Case Study, Examples and Statistics
Examples of what real organizations are doing with it in real-life situations make it easier to grasp the scale of this advance and apply the learnings to your own situation. . Learning analytics examples. Case study 1: Measuring behavior change at InterContinental Hotels Group with xAPI. . Case Study 98.
27 Case Study Examples Every Marketer Should See
19. " Bringing an Operator to the Game ," by Redapt. This case study example by Redapt is another great demonstration of the power of summarizing your case study's takeaways right at the start of the study. Redapt includes three easy-to-scan columns: "The problem," "the solution," and "the outcome.".
Data Science Case Studies: Solved and Explained
4 min read. ·. Feb 21, 2021. 1. Solving a Data Science case study means analyzing and solving a problem statement intensively. Solving case studies will help you show unique and amazing data ...
10 Real-World Data Science Case Studies Worth Reading
Real-world data science case studies differ significantly from academic examples. While academic exercises often feature clean, well-structured data and simplified scenarios, real-world projects tackle messy, diverse data sources with practical constraints and genuine business objectives.
15 Real-Life Case Study Examples & Best Practices
To ensure you're making the most of your case studies, we've put together 15 real-life case study examples to inspire you. These examples span a variety of industries and formats. We've also included best practices, design tips and templates to inspire you. Let's dive in!
10 Real World Data Science Case Studies Projects with Example
Here are some of the examples of case study on data analytics used by Spotify to provide enhanced services to its listeners: i) Personalization of Content using Recommendation Systems. Spotify uses Bart or Bayesian Additive Regression Trees to generate music recommendations to its listeners in real-time. Bart ignores any song a user listens to ...
1.1
1.1 - Cases & Variables. Throughout the course, we will be using many of the terms introduced in this lesson. Let's start by defining some of the most frequently used terms: case, variable, and constant. A case is an experimental unit. These are the individuals from which data are collected.
Statistical Case Studies (Student Edition)
Evaluating the Effects of Nonresponse and the Number of Response Levels on Survey Samples. Robert K. Smidt, Robert Tortora. pp. 79-85. Excerpt. PDF. Excerpt. 12. Designing an Experiment to Obtain a Target Value in the Chemical Processes Industry.
Home
A Series of Case Studies. Resources for Statistics Teachers developed by: Richard D. De Veaux, Williams College Deborah Nolan and Jasjeet Sekhon, UC Berkeley ... They can be used as examples in class, or just as guides for what a statistical analysis might entail. Each case is presented in 2 versions: An R version, written in R Markdown ...
12 Data Science Case Studies: Across Various Industries
Top 12 Data Science Case Studies. 1. Data Science in Hospitality Industry. In the hospitality sector, data analytics assists hotels in better pricing strategies, customer analysis, brand marketing, tracking market trends, and many more. Airbnb focuses on growth by analyzing customer voice using data science. A famous example in this sector is ...
Case studies & examples
Case studies & examples. Articles, use cases, and proof points describing projects undertaken by data managers and data practitioners across the federal government ... Bureau of Economic Analysis / Bureau of Labor Statistics Keywords. data sharing, workforce development, process redesign , Federal Data ... This case study describes how crowd ...
Open Case Studies: Statistics and Data Science Education through Real
This case study also investigates how CO2 emission rates may relate to increasing temperatures and increasing rates of natural disasters in the United States (US). We also describe four other case studies (Table 2) and give example topics covered in all case studies (Table S1). 1. Motivation. Currently, each case study begins with a motivating ...
Free Statistics Case Study Samples and Examples List
An case study examples on statistics is a prosaic composition of a small volume and free composition, expressing individual impressions and thoughts on a specific occasion or issue and obviously not claiming a definitive or exhaustive interpretation of the subject. Some signs of statistics case study: the presence of a specific topic or question.
10 Statistics Questions to Ace Your Data Science Interview
And after interviewing for multiple data science positions, I've found that most statistics interview questions followed a similar pattern. In this article, I'm going to list 10 of the most popular statistics questions I've encountered in data science interviews, along with sample answers to these questions. Question 1: What is a p-value?
A counterfactual analysis quantifying the COVID-19 vaccination impact
Vaccination was the single most effective measure in mitigating the impact of the COVID-19 pandemic. Our study aims to quantify the impact of vaccination programmes during this initial year of vaccination by estimating the number of case fatalities avoided, having Sweden as a case study. Using Swedish data on age-specific reported incidence, vaccination uptake, and contact structures, along ...
Adobe Workfront
ADOBE WORKFRONT Plan, assign, and execute work from one place. Build a marketing system of record by centralizing and integrating work across teams and applications with the industry-leading enterprise marketing work management application.
Global Report on Food Crises (GRFC) 2024
The Global Report on Food Crises (GRFC) 2024 confirms the enormity of the challenge of achieving the goal of ending hunger by 2030. In 2023, nearly 282 million people or 21.5 percent of the analysed population in 59 countries/territories faced high levels of acute food insecurity requiring urgent food and livelihood assistance. This additional 24 million people since 2022 is explained by ...

What is a Case Study? Definition & Examples

Case Study Definition

Case Study Examples

Types of Case Studies

Pros and Cons

Crafting a Good Case Study: Methodology

Share this:

Reader Interactions

Introduction to Statistical Thinking

16.2 A Review

16.3 Case Studies

16.3.1 Physicians’ Reactions to the Size of a Patient

16.3.2 Physical Strength and Job Performance

16.4 Summary

16.4.2 Discussion in the Forum

5 Statistics Case Studies That Will Blow Your Mind

Introduction

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

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

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

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

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

Recommended Articles

Frequently Asked Questions

Similar Posts

Inter-Class Correlation: Mastering the Art of Evaluating Rater Agreement

Think Coin Flips Are 50/50? This Study Will Make You Think Again

Avoiding the Pitfall of Jumping to Conclusions in Data Science

Join the Data Revolution: A Layman’s Guide to Statistical Learning

Confounding Variables in Statistics: Strategies for Identifying and Adjusting

How Statistics Can Change Your Life: A Guide for Beginners

Leave a Reply Cancel reply

Top 40 Most Popular Case Studies of 2021

What is case study research?

Analyze case study research

Explanatory

Descriptive

Exploratory

Critical instance

Instrumental

1. Select a case

2. Create a theoretical framework

3. Collect the data

4. Analyze your case

Case study topics

Should you be using a customer insights hub?

Editor’s picks

Latest articles

Have a language expert improve your writing

What Is a Case Study? | Definition, Examples & Methods

Table of contents

Here's why students love Scribbr's proofreading services

Cite this Scribbr article

Is this article helpful?

Shona McCombes

Learning analytics examples: 5 case studies of data insights in action

Case Study, Scenario, Story: What’s the Difference?

Trending Sources

How Two Businesses Scored Big with Gamification (Case Studies)

Creating Quality Leadership Development Training for New Leaders

How Educators Can Leverage Generative AI to Augment Teaching and Learning

Creating Inclusive Designs: Universal Design Principles Explained

A 5-Step Guide to Create an Employee Onboarding Plan

Using ChatGPT to Develop Course Content for E-Learning

Recycle for a Greater ROI: How to Future-Proof Your Corporate Training Content

Keep Learners Guessing to Increase Engagement and Retention

What is Gamification in Marketing? How to Use Gamification to Boost Your Marketing Campaigns?

How to introduce your new LMS to the company

Why statistics are not enough

Benefits of Educational Explainer Videos for K-12 Learning

15m microlearning lessons delivered. What we have learned.

Why Fun in Learning is Important

Quiz Funnel: Examples, Benefits, & Steps to Create One

How to Create an Online Course Outline | Complete Guide & Template

eBooks and Professional Development for K-12 Teachers

The Benefits of Personalization in E-Learning

Predictive Analytics VS Traditional Business Forecasting: A Comprehensive Guide

Harnessing the Power of Assessment for Data-Driven Curriculum Optimization

Mastering the craft: A comprehensive guide to technical skills training

Case study: Lehigh Valley’s investment in talent pipeline attracts people, business