What Is a Case Study?
When you’re performing research as part of your job or for a school assignment, you’ll probably come across case studies that help you to learn more about the topic at hand. But what is a case study and why are they helpful? Read on to learn all about case studies.
At face value, a case study is a deep dive into a topic. Case studies can be found in many fields, particularly across the social sciences and medicine. When you conduct a case study, you create a body of research based on an inquiry and related data from analysis of a group, individual or controlled research environment.
As a researcher, you can benefit from the analysis of case studies similar to inquiries you’re currently studying. Researchers often rely on case studies to answer questions that basic information and standard diagnostics cannot address.
Study a Pattern
One of the main objectives of a case study is to find a pattern that answers whatever the initial inquiry seeks to find. This might be a question about why college students are prone to certain eating habits or what mental health problems afflict house fire survivors. The researcher then collects data, either through observation or data research, and starts connecting the dots to find underlying behaviors or impacts of the sample group’s behavior.
During the study period, the researcher gathers evidence to back the observed patterns and future claims that’ll be derived from the data. Since case studies are usually presented in the professional environment, it’s not enough to simply have a theory and observational notes to back up a claim. Instead, the researcher must provide evidence to support the body of study and the resulting conclusions.
As the study progresses, the researcher develops a solid case to present to peers or a governing body. Case study presentation is important because it legitimizes the body of research and opens the findings to a broader analysis that may end up drawing a conclusion that’s more true to the data than what one or two researchers might establish. The presentation might be formal or casual, depending on the case study itself.
Once the body of research is established, it’s time to draw conclusions from the case study. As with all social sciences studies, conclusions from one researcher shouldn’t necessarily be taken as gospel, but they’re helpful for advancing the body of knowledge in a given field. For that purpose, they’re an invaluable way of gathering new material and presenting ideas that others in the field can learn from and expand upon.
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National Newspaper Case Study
When Agility in Mind was invited to come and help get things back on track at a national newspaper, we knew a different approach was needed and couldn’t wait to get stuck in.
Falling print circulation, increased competition, digital disruption, declining ad-revenue, diffused readership, there has never been a harder time to make money out of news.
Recovering from a couple of bad hires and facing huge pressure to simply stop being loss-making, this was a challenge like no other we had faced. When Agility in Mind was invited to come and help get things back on track, we knew a different approach was needed.
Crucial to success was better internal relationships, re-establishing and aligning to a new vision and, on a more technical note, increasing page impressions, page speed and reach.
People, people, people. That’s where change has to begin. We started by listening, with a pattern of problems rapidly becoming clear. We then set to tackle them. This was our approach:
- We needed to align everyone behind some key goals. Previously everyone was doing what they thought was the right thing, we needed to align them to something meaningful. We needed to increase digital ad revenue just to survive. So we chose that as an outcome, along with average page views per day as a measure.
- Stakeholder management. Editorial, business, technology, marketing, sales – they all wanted their say and all had targets of their own. We set about explaining how they all couldn’t get what they wanted right now. The transparency and honestly we brought was tough love, but well received on a business level for just being straight and on a personal level for being decent enough to explain reasons personally.
- Product roles: We brought structure. A team for web, a team for mobile and a platform team, all with the ability to craft their own day to day destiny within a wider vision. Colleagues began to feel respected and empowered. A positive change was now underway.
- A simple operating rhythm was established with regular planning against the goal. This tied up in to the fiscal quarterly and annual planning, allowing for seamless reporting to the board. This built confidence and allowed for a change in scrutiny from accusative to collaborative.
- A roadmap for the future was developed. This engaged people right across the business and had an effect of shifting the culture to one of a more product-led, value-driven, experimentation-centred, outcome-focussed team.
- On a technical level, increasing page speed led to more page impressions and therefore more ad impressions, increasing revenue. This required huge amounts of cross department collaboration, especially with commercial and marketing, who were incentivised quite differently to the rest of the business.
- Within 6 months we’d increased average page views per day from a low of 9.2m to 16.69m, smashing our Q1 13m, then Q2 15m targets.
- We decreased article first interactive from 36.52s to 27.71s and article first paint from 5.1s to 3.82s.
- PAMCo, the ‘industry’s data bible’ commented that print and digital led to this becoming Britain’s most read newspaper and website with 33.9m readers per day, acknowledging that news brands such as this were still comfortably ahead of the likes of Facebook and Google in the UK.
- Numbers aside, we left a new culture and way of working. Relationships had been rebuilt and a shared vision for the future was set. There was a new spirit of collaboration and, as we all know, a happier team is a more productive team.
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Are Newspapers Heading Toward Post-Print Obscurity? A case study of The Independent’s transition to online-only
In this paper , Neil Thurman and Richard Fletcher use a case study of The Independent to explore what happens when newspaper brands stop printing and go online only. They estimate that, although its net British readership did not decline in the year after it stopped printing, the total time spent with The Independent by its British audiences fell 81%.
Abstract: With print circulations in decline and the print advertising market shrinking, newspapers in many countries are under pressure. Some—like Finland’s Taloussanomat and Canada’s La Presse—have decided to stop printing and go online-only. Others, like the Sydney Morning Herald, are debating whether to follow. Those newspapers that have made the switch often paint a rosy picture of a sustainable and profitable digital future. This study examines the reality behind the spin via a case study of The Independent, a general-interest UK national newspaper that went digital-only in March 2016. We estimate that, although its net British readership did not decline in the year after it stopped printing, the total time spent with The Independent by its British audiences fell 81%, a disparity caused by huge differences in the habits of online and print readers. This suggests that when newspapers go online-only they may move back into the black, but they also forfeit much of the attention they formerly enjoyed. Furthermore, although The Independent is serving at least 50% more overseas browsers since going online-only, the relative influence on that growth of internal organizational change and external factors—such as the “Trump Bump” in news consumption—is difficult to determine.
Read the full paper
Dr Richard Fletcher
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Hearst Newspapers: Engaging readers with cloud machine learning
About Hearst Newspapers
Hearst is one of the nation’s most diversified media, information, and services companies with more than 360 businesses. Hearst Newspapers is the operating group responsible for newspapers, local digital marketing services, and directories. With more than 4,000 employees, Hearst Newspapers reaches more than 42 million unique visitors with related digital products.
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Hearst newspapers uses google cloud natural language api for content classification, improving speed and accuracy while reducing manual labor, and deriving insights into content consumption., google cloud results.
- Categorizes digital content in real time across 30+ media properties, allowing content creators to see patterns and insights based on categories and entities
- Improves speed and accuracy of content classification, while reducing manual labor
- Helps forecast content performance to increase ad revenue
- Integrates into Customer Data Platform to segment users by reading habits
Categorizing 3,000 articles a day in real time
For newspaper publishers, precision and speed are critical to engaging readers with the right editorial and advertising content on digital properties. At Hearst Newspapers , the newspaper division of Hearst , one of the world’s largest mass media publishers, classifying content used to be a difficult process. Sorting, labeling, and categorizing an average of 3,000 new articles every day was time consuming, and teams often had to prioritize certain content and leave other articles unclassified just to keep up.
“Google Cloud Natural Language API is unmatched in its accuracy for content classification.”
Instead of hiring a larger team, Hearst Newspapers is solving the problem with Google Cloud AI . Using Google Cloud Natural Language API to enable content classification with powerful machine learning models in an easy-to-use REST API, Hearst Newspapers can understand what its content is about, regardless of how it is structured and presented on the company’s many websites. Although Hearst Newspapers previously used a legacy system that attempted to automate the classification process, it was not as fast or as accurate.
“Google Cloud Natural Language API is unmatched in its accuracy for content classification,” says Naveed Ahmad, Senior Director of Data at Hearst Newspapers, who is responsible for data centralization and business intelligence using Google Cloud Platform . At Hearst Newspapers, we publish several thousand articles a day across more than 30 properties. With natural language processing, we can quickly gain insight into what content is being published and how it resonates with our audiences.”
With Google Cloud Platform, Hearst Newspapers is finding new ways to use machine learning to personalize content and engage readers. Google Cloud Natural Language API integrates with DoubleClick for Publishers by Google, helping the Hearst Newspaper editorial team save time previously spent manually tagging content. It also improves ad targeting to increase revenue. This allows Hearst to analyze its past revenue performance in the context of content and predict future ad performance by content categories.
With the ability to classify documents in more than 700 predefined categories—such as news, technology, health, and entertainment—Hearst Newspapers can automatically parse the meaning of articles in real time to organize and present them more efficiently. The company also uses entity recognition in Google Cloud Natural Language API to quickly identify people, places, and events featured in content.
“With the granular data we get from Google Cloud Natural Language API, we can target more specific content segments and associate ads with narrower reader profiles,” says Naveed. “We save a lot of time because we don’t have to manually categorize 3,000 articles daily.”
“Google Cloud Platform and cloud machine learning services open up a new world for Hearst Newspapers, and for all publishers, to get more value from content data.”
This capability will not only save the ad operations team several hours a day, it will also allow for a much larger content inventory for matching ads. Previously, the team manually classified only a portion of the content for better ad targeting. Now, all new content is classified automatically.
The Google Cloud Natural Language API category and entity data is also pushed to the Hearst Newspapers Customer Data Platform, where it is used to create user profiles and create segments based on reading habits. This allows for targeted messaging and offers to these segments.
Hearst Newspapers also pulls all the CMS content, along with the Google Cloud Natural Language API tags, into a Google BigQuery data warehouse. By marrying this data to Google Analytics , the company can do sophisticated analysis on the nature of the content being published and how it is consumed over time. This analysis will help drive the future strategy of how the company can improve and invest into curation of content, and better understand how readers engage.
A new world for publishers
Hearst Newspapers is exploring a broad spectrum of Google Cloud AI machine learning services, from out-of-the-box APIs to a content recommendation engine based on TensorFlow, an open-source library for machine intelligence developed by Google.
Hearst Newspapers also uses Google BigQuery to analyze content performance and calculate the value of ad slots. Google BigQuery Data Transfer Service allows the company to centralize data from Google Analytics and internal data sources, helping the company analyze its advertising campaigns against web traffic trends.
“Google Cloud Platform and cloud machine learning services open up a new world for Hearst, and for all publishers to get more value from data,” says Naveed. “We look forward to the possibilities.”
DigitalCommons@University of Nebraska - Lincoln
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Newspaper reading habits of university students: a case study of chaudhary charan singh university, india.
Devendra Kumar , Chaudhary Charan Singh University Follow Rajkumar Singh , Government Mahila Degree College Jamal Ahmad Siddiqui , Chaudhary Charan Singh University Follow
Date of this Version
The study analyzed the newspapers reading habits of university students of Chaudhary Charan Singh University, India. Structured questionnaires were distributed among P.G. Students, M. Phil. Students, and Research Scholars of Chaudhary Charan Singh University, India. Data was gathered on various aspects of newspaper reading habits of university students including languages read, time spent on newspaper reading; sources of newspapers; and which sections of newspapers are read most.
Since February 16, 2011
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