case study topic for data structure

Data Structures Case Studies

Optimizing data structures is different from optimizing algorithms as data structure problems have more dimensions: you may be optimizing for throughput , for latency , for memory usage , or any combination of those — and this complexity blows up exponentially when you need to process multiple query types and consider multiple query distributions.

This makes simply defining benchmarks much harder, let alone the actual implementations. In this chapter, we will try to navigate all this complexity and learn how to design efficient data structures with extensive case studies.

A brief review of the CPU cache system is strongly advised.

13 Interesting Data Structure Projects Ideas and Topics For Beginners [2023]

In the world of computer science, understanding data structures is essential, especially for beginners. These structures serve as the foundation for organizing and manipulating data effectively. To assist newcomers in grasping these concepts, I’ll provide you with data structure projects ideas for beginners . These projects are tailored to offer hands-on learning experiences, allowing beginners to explore various data structures while honing their programming skills. By working on these projects, beginners can gain practical insights into data organization and algorithmic thinking, laying a solid foundation for their journey into computer science.

Let’s delve into some exciting data structure projects ideas designed specifically for beginners. . These projects are tailored to offer hands-on learning experiences, allowing beginners to explore various data structures while honing their programming skills. By working on these projects, beginners can gain practical insights into data organization and algorithmic thinking, laying a solid foundation for their journey into computer science. Let’s delve into some exciting data structure project ideas designed specifically for beginners.

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Data Structure Basics

Data structures can be classified into the following basic types:

Linked Lists
Hash tables

What is Data Structure? The topic of data structure revolves around the organization, management, and storage of data in a way that enables efficient access and modification. It includes various ways of structuring data such as arrays, linked lists, trees, graphs, stacks, queues, and hash tables, each with unique properties and specific use cases.

Understanding data structures is crucial for developing efficient algorithms that can manage large volumes of data, perform complex data analysis, and optimize software applications for speed and performance. This foundational concept is essential in computer science, helping to solve problems related to data management and algorithm design effectively.

Selecting the appropriate setting for your data is an integral part of the programming and problem-solving process. And you can observe that data structures organize abstract data types in concrete implementations. To attain that result, they make use of various algorithms, such as sorting, searching, etc. Learning data structures is one of the important parts in data science courses .

With the rise of big data and analytics , learning about these fundamentals has become almost essential for data scientists. The training typically incorporates various topics in data structure to enable the synthesis of knowledge from real-life experiences. Here is a list of dsa topics to get you started!

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Benefits of Data structures:

Data structures are fundamental building blocks in computer science and programming. They are important tools that helps inorganizing, storing, and manipulating data efficiently. On top of that it provide a way to represent and manage information in a structured manner, which is essential for designing efficient algorithms and solving complex problems.

So, let’s explore the numerous benefits of Data Structures and dsa topics list in the below post: –

1. Efficient Data Access

Data structures enable efficient access to data elements. Arrays, for example, provide constant-time access to elements using an index. Linked lists allow for efficient traversal and modification of data elements. Efficient data access is crucial for improving the overall performance of algorithms and applications.

2. Memory Management

Data structures help manage memory efficiently. They helps in allocating and deallocating memory resources as per requirement, reducing memory wastage and fragmentation. Remember, proper memory management is important for preventing memory leaks and optimizing resource utilization.

3. Organization of Data

Data structures offers a structured way to organize and store data. For example, a stack organizes data in a last-in, first-out (LIFO) fashion, while a queue uses a first-in, first-out (FIFO) approach. These organizations make it easier to model and solve specific problems efficiently.

4. Search and Retrieval

Efficient data search and retrieval are an important aspect in varied applications, like, databases and information retrieval systems. Data structures like binary search trees and hash tables enable fast lookup and retrieval of data, reducing the time complexity of search operations.

Sorting is a fundamental operation in computer science. Data structures like arrays and trees can implement various sorting algorithms. Efficient sorting is crucial for maintaining ordered data lists and searching for specific elements.

6. Dynamic Memory Allocation

Many programming languages and applications require dynamic memory allocation. Data structures like dynamic arrays and linked lists can grow or shrink dynamically, allowing for efficient memory management in response to changing data requirements.

7. Data Aggregation

Data structures can aggregate data elements into larger, more complex structures. For example, arrays and lists can create matrices and graphs, enabling the representation and manipulation of intricate data relationships.

8. Modularity and Reusability

Data structures promote modularity and reusability in software development. Well-designed data structures can be used as building blocks for various applications, reducing code duplication and improving maintainability.

9. Complex Problem Solving

Data structures play a crucial role in solving complex computational problems. Algorithms often rely on specific data structures tailored to the problem’s requirements. For instance, graph algorithms use data structures like adjacency matrices or linked lists to represent and traverse graphs efficiently.

10. Resource Efficiency

Selecting the right data structure for a particular task can impact the efficiency of an application. Regards to this, Data structures helps in minimizing resource usage, such as time and memory, leading to faster and more responsive software.

11. Scalability

Scalability is a critical consideration in modern software development. Data structures that efficiently handle large datasets and adapt to changing workloads are essential for building scalable applications and systems.

12. Algorithm Optimization

Algorithms that use appropriate data structures can be optimized for speed and efficiency. For example, by choosing a hash table data structure, you can achieve constant-time average-case lookup operations, improving the performance of algorithms relying on data retrieval.

13. Code Readability and Maintainability

Well-defined data structures contribute to code readability and maintainability. They provide clear abstractions for data manipulation, making it easier for developers to understand, maintain, and extend code over time.

14. Cross-Disciplinary Applications

Data structures are not limited to computer science; they find applications in various fields, such as biology, engineering, and finance. Efficient data organization and manipulation are essential in scientific research and data analysis.

Other benefits:

It can store variables of various data types.
It allows the creation of objects that feature various types of attributes.
It allows reusing the data layout across programs.
It can implement other data structures like stacks, linked lists, trees, graphs, queues, etc.

Why study data structures & algorithms?

They help to solve complex real-time problems.
They improve analytical and problem-solving skills.
They help you to crack technical interviews.
Topics in data structure can efficiently manipulate the data.

Studying relevant DSA topics increases job opportunities and earning potential. Therefore, they guarantee career advancement.

What are DSA Projects?

DSA projects, or Data Structures and Algorithms projects, involve creating software applications that emphasize the use and implementation of various data structures and algorithms to solve complex problems efficiently. An example could be developing a search engine using trie data structures for fast text retrieval or crafting a route optimization application using graph algorithms like Dijkstra’s or A*. These projects help students and professionals demonstrate their proficiency in coding, optimizing data handling, and solving algorithmic challenges, which are crucial skills in software development and computer science.

Data Structures Projects Ideas

1. obscure binary search trees.

Items, such as names, numbers, etc. can be stored in memory in a sorted order called binary search trees or BSTs. And some of these data structures can automatically balance their height when arbitrary items are inserted or deleted. Therefore, they are known as self-balancing BSTs. Further, there can be different implementations of this type, like the BTrees, AVL trees, and red-black trees. But there are many other lesser-known executions that you can learn about. Some examples include AA trees, 2-3 trees, splay trees, scapegoat trees, and treaps.

You can base your project on these alternatives and explore how they can outperform other widely-used BSTs in different scenarios. For instance, splay trees can prove faster than red-black trees under the conditions of serious temporal locality.

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2. BSTs following the memoization algorithm

Memoization related to dynamic programming. In reduction-memoizing BSTs, each node can memoize a function of its subtrees. Consider the example of a BST of persons ordered by their ages. Now, let the child nodes store the maximum income of each individual. With this structure, you can answer queries like, “What is the maximum income of people aged between 18.3 and 25.3?” It can also handle updates in logarithmic time.

Moreover, such data structures are easy to accomplish in C language. You can also attempt to bind it with Ruby and a convenient API. Go for an interface that allows you to specify ‘lambda’ as your ordering function and your subtree memoizing function. All in all, you can expect reduction-memoizing BSTs to be self-balancing BSTs with a dash of additional book-keeping.

Dynamic coding will need cognitive memorisation for its implementation. Each vertex in a reducing BST can memorise its sub–trees’ functionality. For example, a BST of persons is categorised by their age.

This DSA topics based project idea allows the kid node to store every individual’s maximum salary. This framework can be used to answer the questions like “what’s the income limit of persons aged 25 to 30?”

Checkout: Types of Binary Tree

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3. Heap insertion time

When looking for data structure projects , you want to encounter distinct problems being solved with creative approaches. One such unique research question concerns the average case insertion time for binary heap data structures. According to some online sources, it is constant time, while others imply that it is log(n) time. It is one of great examples of data science project.

But Bollobas and Simon give a numerically-backed answer in their paper entitled, “Repeated random insertion into a priority queue.” First, they assume a scenario where you want to insert n elements into an empty heap. There can be ‘n!’ possible orders for the same. Then, they adopt the average cost approach to prove that the insertion time is bound by a constant of 1.7645.

When looking for Data Structures tasks in this project idea, you will face challenges that are addressed using novel methods. One of the interesting research subjects is the mean response insertion time for the sequential heap DS.

Inserting ‘n’ components into an empty heap will yield ‘n!’ arrangements which you can use in suitable DSA projects in C++ . Subsequently, you can implement the estimated cost approach to specify that the inserting period is limited by a fixed constant.

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4. Optimal treaps with priority-changing parameters

Treaps are a combination of BSTs and heaps. These randomized data structures involve assigning specific priorities to the nodes. You can go for a project that optimizes a set of parameters under different settings. For instance, you can set higher preferences for nodes that are accessed more frequently than others. Here, each access will set off a two-fold process:

Choosing a random number
Replacing the node’s priority with that number if it is found to be higher than the previous priority

As a result of this modification, the tree will lose its random shape. It is likely that the frequently-accessed nodes would now be near the tree’s root, hence delivering faster searches. So, experiment with this data structure and try to base your argument on evidence.

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5. Research project on k-d trees

K-dimensional trees or k-d trees organize and represent spatial data. These data structures have several applications, particularly in multi-dimensional key searches like nearest neighbor and range searches. It is example of one of the advanced data science projects. Here is how k-d trees operate:

Every leaf node of the binary tree is a k-dimensional point
Every non-leaf node splits the hyperplane (which is perpendicular to that dimension) into two half-spaces
The left subtree of a particular node represents the points to the left of the hyperplane. Similarly, the right subtree of that node denotes the points in the right half.

You can probe one step further and construct a self-balanced k-d tree where each leaf node would have the same distance from the root. Also, you can test it to find whether such balanced trees would prove optimal for a particular kind of application.

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With this, we have covered five interesting ideas that you can study, investigate, and try out. Now, let us look at some more projects on data structures and algorithms .

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6. Knight’s travails

In this project, we will understand two algorithms in action – BFS and DFS. BFS stands for Breadth-First Search and utilizes the Queue data structure to find the shortest path. Whereas, DFS refers to Depth-First Search and traverses Stack data structures.

For starters, you will need a data structure similar to binary trees. Now, suppose that you have a standard 8 X 8 chessboard, and you want to show the knight’s movements in a game. As you may know, a knight’s basic move in chess is two forward steps and one sidestep. Facing in any direction and given enough turns, it can move from any square on the board to any other square.

If you want to know the simplest way your knight can move from one square (or node) to another in a two-dimensional setup, you will first have to build a function like the one below.

knight_plays([0,0], [1,2]) == [[0,0], [1,2]]
knight_plays([0,0], [3,3]) == [[0,0], [1,2], [3,3]]
knight_plays([3,3], [0,0]) == [[3,3], [1,2], [0,0]]

Furthermore, this project would require the following tasks:

Creating a script for a board game and a night
Treating all possible moves of the knight as children in the tree structure
Ensuring that any move does not go off the board
Choosing a search algorithm for finding the shortest path in this case
Applying the appropriate search algorithm to find the best possible move from the starting square to the ending square.

7. Fast data structures in non-C systems languages

Programmers usually build programs quickly using high-level languages like Ruby or Python but implement data structures in C/C++. And they create a binding code to connect the elements. However, the C language is believed to be error-prone, which can also cause security issues. Herein lies an exciting project idea.

You can implement a data structure in a modern low-level language such as Rust or Go, and then bind your code to the high-level language. With this project, you can try something new and also figure out how bindings work. If your effort is successful, you can even inspire others to do a similar exercise in the future and drive better performance-orientation of data structures.

Also read: Data Science Project Ideas for Beginners

8. Search engine for data structures

The software aims to automate and speed up the choice of data structures for a given API. This project not only demonstrates novel ways of representing different data structures but also optimizes a set of functions to equip inference on them. We have compiled its summary below.

The data structure search engine project requires knowledge about data structures and the relationships between different methods.
It computes the time taken by each possible composite data structure for all the methods.
Finally, it selects the best data structures for a particular case.

Read: Data Mining Project Ideas

9. Phone directory application using doubly-linked lists

This project can demonstrate the working of contact book applications and also teach you about data structures like arrays, linked lists, stacks, and queues. Typically, phone book management encompasses searching, sorting, and deleting operations. A distinctive feature of the search queries here is that the user sees suggestions from the contact list after entering each character. You can read the source-code of freely available projects and replicate the same to develop your skills.

This project demonstrates how to address the book programs’ function. It also teaches you about queuing, stacking, linking lists, and arrays. Usually, this project’s directory includes certain actions like categorising, scanning, and removing. Subsequently, the client shows recommendations from the address book after typing each character. This is the web searches’ unique facet. You can inspect the code of extensively used DSA projects in C++ and applications and ultimately duplicate them. This helps you to advance your data science career.

10. Spatial indexing with quadtrees

The quadtree data structure is a special type of tree structure, which can recursively divide a flat 2-D space into four quadrants. Each hierarchical node in this tree structure has either zero or four children. It can be used for various purposes like sparse data storage, image processing, and spatial indexing.

Spatial indexing is all about the efficient execution of select geometric queries, forming an essential part of geo-spatial application design. For example, ride-sharing applications like Ola and Uber process geo-queries to track the location of cabs and provide updates to users. Facebook’s Nearby Friends feature also has similar functionality. Here, the associated meta-data is stored in the form of tables, and a spatial index is created separately with the object coordinates. The problem objective is to find the nearest point to a given one.

You can pursue quadtree data structure projects in a wide range of fields, from mapping, urban planning, and transportation planning to disaster management and mitigation. We have provided a brief outline to fuel your problem-solving and analytical skills.

QuadTrees are techniques for indexing spatial data. The root node signifies the whole area and every internal node signifies an area called a quadrant which is obtained by dividing the area enclosed into half across both axes. These basics are important to understand QuadTrees-related data structures topics.

Objective: Creating a data structure that enables the following operations

Insert a location or geometric space
Search for the coordinates of a specific location
Count the number of locations in the data structure in a particular contiguous area

One of the leading applications of QuadTrees in the data structure is finding the nearest neighbor. For example, you are dealing with several points in a space in one of the data structures topics . Suppose somebody asks you what’s the nearest point to an arbitrary point. You can search in a quadtree to answer this question. If there is no nearest neighbor, you can specify that there is no point in this quadrant to be the nearest neighbor to an arbitrary point. Consequently, you can save time otherwise spent on comparisons.

Spatial indexing with Quadtrees is also used in image compression wherein every node holds the average color of each child. You get a more detailed image if you dive deeper into the tree. This project idea is also used in searching for the nods in a 2D area. For example, you can use quadtrees to find the nearest point to the given coordinates.

Follow these steps to build a quadtree from a two-dimensional area:

Divide the existing two-dimensional space into four boxes.
Create a child object if a box holds one or more points within. This object stores the box’s 2D space.
Don’t create a child for a box that doesn’t include any points.
Repeat these steps for each of the children.
You can follow these steps while working on one of the file structure mini project topics .

11. Graph-based projects on data structures

You can take up a project on topological sorting of a graph. For this, you will need prior knowledge of the DFS algorithm. Here is the primary difference between the two approaches:

We print a vertex & then recursively call the algorithm for adjacent vertices in DFS.
In topological sorting, we recursively first call the algorithm for adjacent vertices. And then, we push the content into a stack for printing.

Therefore, the topological sort algorithm takes a directed acyclic graph or DAG to return an array of nodes.

Let us consider the simple example of ordering a pancake recipe. To make pancakes, you need a specific set of ingredients, such as eggs, milk, flour or pancake mix, oil, syrup, etc. This information, along with the quantity and portions, can be easily represented in a graph.

But it is equally important to know the precise order of using these ingredients. This is where you can implement topological ordering. Other examples include making precedence charts for optimizing database queries and schedules for software projects. Here is an overview of the process for your reference:

Call the DFS algorithm for the graph data structure to compute the finish times for the vertices
Store the vertices in a list with a descending finish time order
Execute the topological sort to return the ordered list

12. Numerical representations with random access lists

In the representations we have seen in the past, numerical elements are generally held in Binomial Heaps. But these patterns can also be implemented in other data structures. Okasaki has come up with a numerical representation technique using binary random access lists. These lists have many advantages:

They enable insertion at and removal from the beginning
They allow access and update at a particular index

Know more: The Six Most Commonly Used Data Structures in R

13. Stack-based text editor

Your regular text editor has the functionality of editing and storing text while it is being written or edited. So, there are multiple changes in the cursor position. To achieve high efficiency, we require a fast data structure for insertion and modification. And the ordinary character arrays take time for storing strings.

You can experiment with other data structures like gap buffers and ropes to solve these issues. Your end objective will be to attain faster concatenation than the usual strings by occupying smaller contiguous memory space.

This project idea handles text manipulation and offers suitable features to improve the experience. The key functionalities of text editors include deleting, inserting, and viewing text. Other features needed to compare with other text editors are copy/cut and paste, find and replace, sentence highlighting, text formatting, etc.

This project idea’s functioning depends on the data structures you determined to use for your operations. You will face tradeoffs when choosing among the data structures. This is because you must consider the implementation difficulty for the memory and performance tradeoffs. You can use this project idea in different file structure mini project topics to accelerate the text’s insertion and modification.

Data structure skills are foundational in software development, especially for managing vast data sets in today’s digital landscape. Top companies like Adobe, Amazon, and Google seek professionals proficient in data structures and algorithms for lucrative positions. During interviews, recruiters evaluate not only theoretical knowledge but also practical skills. Therefore, practicing data structure project ideas for beginners is essential to kickstart your career.

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Frequently Asked Questions (FAQs)

There are certain types of containers that are used to store data. These containers are nothing but data structures. These containers have different properties associated with them, which are used to store, organize, and manipulate the data stored in them. There can be two types of data structures based on how they allocate the data. Linear data structures like arrays and linked lists and dynamic data structures like trees and graphs.

In linear data structures, each element is linearly connected to each other having reference to the next and previous elements whereas in non-linear data structures, data is connected in a non-linear or hierarchical manner. Implementing a linear data structure is much easier than a non-linear data structure since it involves only a single level. If we see memory-wise then the non-linear data structures are better than their counterpart since they consume memory wisely and do not waste it.

You can see applications based on data structures everywhere around you. The google maps application is based on graphs, call centre systems use queues, file explorer applications are based on trees, and even the text editor that you use every day is based upon stack data structure and this list can go on. Not just applications, but many popular algorithms are also based on these data structures. One such example is that of the decision trees. Google search uses trees to implement its amazing auto-complete feature in its search bar.

For those working with C++, data structures project ideas can be quite robust due to the language's flexibility and performance capabilities. Examples include implementing a memory-efficient linked list, designing a binary search tree with self-balancing capabilities, or building a graph-based navigation system. Projects might also involve creating a custom hash table to explore collision resolution techniques, or developing a priority queue to understand heap operations in depth.

Python's simplicity and vast library support make it ideal for data structures projects aimed at both learning and solving practical problems. Project ideas could include building a text-based search engine using trie structures, designing a recommendation system with graph algorithms, or implementing various sorting algorithms to understand their efficiency.

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Table of Contents

1. Arrays in Inventory Management

2. stacks in web browsing, 3. queues in print job scheduling, 4. linked lists in music playlists, 5. trees in hierarchical file systems.

Introduction Data structures are fundamental entities in the world of programming, allowing us to organize, process, and store data efficiently. To truly grasp their significance, it's important to see how they are used in real-world applications. This article aims to provide clear examples of how different types of data structures are used in everyday situations.

One of the simplest forms of data structures is the array, which is a collection of items stored at contiguous memory locations. Imagine you're managing a convenience store and you need to keep track of the products in your inventory. An array can be useful in this situation where each element of the array could represent an item in your store.
To avoid errors, always ensure to check that the index you are accessing is within the bounds of the array. For example, accessing inventory[101] will result in an error because our array only has 100 elements.
A stack is a type of data structure that stores items in a Last-In-First-Out (LIFO) manner. This means that the last item added to the stack will be the first one to be removed. A real-world example of a stack is the history feature in web browsers. Each time you visit a new page, it's added to the top of the stack. When you click the 'back' button, you're effectively 'popping' the top element off the stack.
A common error with stacks is a Stack Overflow, which occurs when you try to push an element onto a full stack. Always ensure your stack has enough capacity for your needs.
A queue is another type of data structure that operates in a First-In-First-Out (FIFO) manner. Think about a print queue. When you send multiple print jobs to a printer, they're handled in the order they were received. In this case, a queue data structure can be used to manage the print jobs.
An error to avoid when working with queues is underflow, which occurs when you try to remove an element from an empty queue. Always check if the queue is empty before performing a remove operation.
A linked list is a linear data structure where each element is a separate object. Each element (node) contains data and a link (reference) to the next node in the sequence. A good real-world example is a music playlist, where each song is linked to the next one.
A common error-prone case is accessing a null reference, which occurs when you try to access the next element of the last node in the list. Always check if the next element exists before trying to access it.
Trees are hierarchical data structures with a root value and subtrees of children, represented as a set of linked nodes. A classic real-world example is a computer's file system, where directories have files and subdirectories, which further have their own files and subdirectories.
A common pitfall when dealing with trees is the accidental creation of a circular reference, where a child node references its parent as its child. This can lead to infinite loops, so always be careful when linking nodes.

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

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Allen B. Downey
Olin College via Green Tea Press

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4.1: Word Frequency Analysis
4.2: Random Numbers
4.3: Word Histogram
4.4: Most Common Words
4.5: Optional Parameters
4.6: Dictionary Subtraction
4.7: Random Words
4.8: Markov Analysis
4.9: Data Structures
4.10: Debugging
4.11: Glossary
4.12: Exercises

Data Structures and Algorithms in Everyday Life

Aswin Shrestha

Jun 16, 2020

10 min read

From the origin of the first programming languages to the modern programming languages currently in use, computer programming has evolved quite a lot. It has now become more powerful, efficient, and advanced. However, the fundamental concepts and use of data structure and algorithms in computer programming have not changed. DSA has been the core of computer programming from the beginning.

You might have heard DSA being used mainly in the field of computer science. However, the use of DSA is not limited to the field of computing. We can also find the concept of DSA being used in day to day life. In this blog, we will discuss the common concept of DSA that is used in everyday life. But before that, let's learn the basics of Data Structure and Algorithms first.

What is Data Structure and Algorithm (DSA)?

Data structure and algorithms is a branch of computer science that deals with creating machine-efficient and optimized computer programs. The term Data Structure refers to the storage and organization of data, and Algorithm refers to the step by step procedure to solve a problem. By combining "data structure" and "algorithm", we optimize the codes in software engineering.

DSA in Software Development

Data structure and Algorithm (DSA) is applied in all disciplines of software development. DSA is the building block of the software development process. It is not limited to a single programming language. Although programming languages evolve or get dormant over time, DSA is incorporated into all of these languages.

The efficiency of software development depends on the choice of an appropriate data structure and algorithm.

There might be cases when you are provided with the most efficient data structure to work with a robust algorithm. However, if the two are not compatible with each other, the code will not produce the expected outcome. Thus, selecting an appropriate data structure for an algorithm is an essential part of software development.

Take, for example, the imperial system of measurement used in the US. Why is it so dreadful? The US has been using measuring units like inches, yard, miles, ounce, and pound for measurements. If you need to convert a yard into inches, you have to multiply it by 36. However, in the metric system, you can simply multiply by 1000 to convert meter into kilometer. It is thus easier for the mind to do the conversion in the metric system. That is why most people find the imperial system to be inconvenient. Another example of this inconvenience is that "ounce" is used for solid or liquid depending on the context.

The ease of conversion from one to another metric is the most important factor here. In this example, we can compare the measurement systems (i.e. the metric system and the Imperial system) to "data structures", while the process of conversion from one unit to another can be thought of as the algorithm. This shows that choosing the right data structure has a great impact on the algorithm, and vice-versa.

Another critical facet of DSA usage in software development is the time and space constraints. These constraints check the availability of time and space for the algorithm. An optimized algorithm addresses both of these constraints based on the availability of resources. If memory is not an issue for the hardware, DSA focuses more on optimizing the running time of the algorithm. Similarly, if the hardware has both the constraints, then DSA must address both of them. You can learn more about the representation of these complexities on Asymptotics Analysis .

How can you relate DSA to your day to day life?

Let's dive into some of the examples of the usage of DSA.

Data Structures

Stack data structure to reverse a string.

A stack is a linear data structure, "linear" meaning the elements are placed one after the other. An element can be accessed only after accessing the previous elements.

We can visualize a stack like a pile of plates placed on top of each other. Each plate below the topmost plate cannot be directly accessed until the plates above are removed. Plates can be added and removed from the top only.

Each plate is an element and the pile is the stack. In the programming terms, each plate is a variable and the pile is a data structure.

Why do we need a stack representation?

You might be wondering why a programmer needs to learn how to put a plate on a pile and take the plate out from the pile. Let's find the answer to it. You are assigned a task of reversing a string. How would you do it?

Start selecting a character from the string and copy it into the new location one by one.

Now, let us copy these items from the top into the original location.

Great, we have successfully reversed a string using the property of stack (the new memory). Inserting and removing was only allowed from the top. This way stack is used in programming.

Queue Data Structure while Boarding a Bus

A Queue is also a linear data structure in which the elements are arranged based on FIFO (First In First Out) rule. It is like the passengers standing in a queue to board a bus. The person who first gets into the queue is the one who first gets on the bus. The new passengers can join the queue from the back whereas passengers get on the bus from the front.

Why do we need a Queue representation?

You may ask where a queue is used on a computer. Assume that you are in your office and there is a network of five computers. You have connected all these computers to a single printer. Suppose an employee wants to print his documents and sends a command to the printer through his computer. The printer receives the commands and starts printing the documents. At the same time, another employee sends commands to the printer. The printer puts the second command to the queue. The second command is executed only after the execution of the first command. This follows the FIFO rule.

Graph Data Structure in Social Media and Google Map

A Graph is a network of interconnected items. Each item is known as a node and the connection between them is known as the edge.

You probably use social media like Facebook, LinkedIn, Instagram, and so on. Social media is a great example of a graph being used. Social media uses graphs to store information about each user. Here, every user is a node just like in Graph. And, if one user, let's call him Jack, becomes friends with another user, Rose, then there exists an edge (connection) between Jack and Rose. Likewise, the more we are connected with people, the nodes and edges of the graph keep on increasing.

Similarly, Google Map is another example where Graphs are used. In the case of the Google Map, every location is considered as nodes, and roads between locations are considered as edges. And, when one has to move from one location to another, the Google Map uses various Graph-based algorithms to find the shortest path. We will discuss this later in this blog.

Sorting Algorithm to Arrange Books in the Shelf

In simple terms, sorting is a process of arranging similar items systematically. For example, suppose you are arranging books on a shelf, based on the height of the books. In this case we can keep the taller books on the left followed by the shorter books or we can do vice versa.

This same concept is implemented in Sorting algorithms. Different sorting algorithms are available in DSA. Although the purpose of every algorithm remains the same, each algorithm works differently based on various criteria.

In the above example, if we want to sort the books as fast as we can then there are few points to be considered.

Can the books be easily shuffled on the shelf? If the books are heavy, it may take us more time. Similarly, there may be other constraints. ( accessibility )
What is the number of books? ( data size )
How fast can we access them? ( hardware's ability )

Algorithms are built considering all these constraints to produce an optimal solution. Some of the examples of these algorithms are Bubble Sort , Selection Sort , Merge Sort , Heap Sort , and Quick Sort .

Searching Algorithm to Find a Book in a Shelf

Searching, as its name suggests, helps in finding an item.

Suppose you want to search for a specific book on a shelf. The books in the self are not arranged in a specific way. If you need to find the book in the shortest possible time, how would you do that? The solution to this is provided by DSA.

You may be thinking "I will look for the book from the beginning and locate it". In this case, you will be searching for books one by one from the start to the end of the shelf. This same concept is implemented in Linear Search .

But, what if the book is at the other end of the shelf? The above process might take a long time and will not provide a feasible solution.

Now, let's try another procedure. Firstly, sort the books in ascending alphabetical order then search for the book in the middle. We are searching for a book that starts with J.

Since we are always looking at the middle position, the middle position between A and Z is M, not J.

Now, compare J with M. We know that J lies before M. So let's start searching for J in the middle position of A and M. G is the mid element, again J is not found.

Since J lies between G and M, let's find the mid element between them. Yeah, we have found J. Congratulations!!! ?.

And, you have just implemented Binary Search .

Shortest Path Finding Algorithms to Find the Shortest Path in Google Map

Have you ever thought about how Google Maps is able to show you the shortest path to your destination? Applications such as Google Maps are able to do that using a class of algorithms called Shortest Path Finding Algorithms.

These algorithms deal with finding the shortest path in a graph. As in the example discussed in the Graph data structure above, we can use graph algorithms to find the shortest path between two given locations on a map.

To illustrate the problem, let's find the shortest distance between A and F in the following map.

What are the possible solutions to this problem? Let's figure out the possible routes along with their path length.

We can see that the shortest path is Path-3. But, we have wasted time calculating other paths as well, which we are not going to use. In order to solve this problem without wasting time, we can start from A and check for the possible shortest neighboring paths (AC and AB). We have AC as the shortest path.

Now we are at C, again select the shortest path among its neighboring paths CE and CD, which is CD.

From D, we have a single path to F. From D, we can go to B as well but, B is already visited, so it is not considered. Select the path DF and we reach the destination.

Congratulations one more time?. You have implemented Dijkstra's Algorithm . In this way, the graph finds its use in our life.

When I was in the final year of my undergraduate studies and applying for software engineering positions, there was one thing common between the hiring procedure of all companies. They all tested me on problems that involved the use of data structures and algorithms. DSA has great importance in the recruitment process of software companies as well. Recruiters use DSA to test the ability of the programmer because it shows the problem-solving capability of the candidate.

As you can see from the above examples, we are able to relate DSA with our day to day life and make it more fun to study. For those who are from non-technical backgrounds, they can also learn the techniques used in the algorithms for solving their daily problems.

Furthermore, one cannot neglect the importance of DSA in any programming language. DSA never gets extinct, rather it is evolving because the evolving computers, in the 21st century, need evolving algorithms to solve a complex problem.

Not to mention, a programmer should know how to use an appropriate data structure in the right algorithm. There is a famous saying:

A warrior should not just possess a weapon, he must know when and how to use it.

Best wishes to all the new programmers out there. Start your learning from today.

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Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, 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 analyse the case.

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 in the US
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

Prevent plagiarism, run a free check.

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

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.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible.

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

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 .

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 analyse 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.

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McCombes, S. (2023, January 30). Case Study | Definition, Examples & Methods. Scribbr. Retrieved 9 June 2024, from https://www.scribbr.co.uk/research-methods/case-studies/

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How to Write a Case Study: Bookmarkable Guide & Template

Published: November 30, 2023

Earning the trust of prospective customers can be a struggle. Before you can even begin to expect to earn their business, you need to demonstrate your ability to deliver on what your product or service promises.

company conducting case study with candidate after learning how to write a case study

Sure, you could say that you're great at X or that you're way ahead of the competition when it comes to Y. But at the end of the day, what you really need to win new business is cold, hard proof.

One of the best ways to prove your worth is through a compelling case study. In fact, HubSpot’s 2020 State of Marketing report found that case studies are so compelling that they are the fifth most commonly used type of content used by marketers.

Below, I'll walk you through what a case study is, how to prepare for writing one, what you need to include in it, and how it can be an effective tactic. To jump to different areas of this post, click on the links below to automatically scroll.

Case Study Definition

Case study templates, how to write a case study.

How to Format a Case Study

Business Case Study Examples

A case study is a specific challenge a business has faced, and the solution they've chosen to solve it. Case studies can vary greatly in length and focus on several details related to the initial challenge and applied solution, and can be presented in various forms like a video, white paper, blog post, etc.

In professional settings, it's common for a case study to tell the story of a successful business partnership between a vendor and a client. Perhaps the success you're highlighting is in the number of leads your client generated, customers closed, or revenue gained. Any one of these key performance indicators (KPIs) are examples of your company's services in action.

When done correctly, these examples of your work can chronicle the positive impact your business has on existing or previous customers and help you attract new clients.

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

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Click this link to access this resource at any time.

Why write a case study?

I know, you’re thinking “ Okay, but why do I need to write one of these? ” The truth is that while case studies are a huge undertaking, they are powerful marketing tools that allow you to demonstrate the value of your product to potential customers using real-world examples. Here are a few reasons why you should write case studies.

1. Explain Complex Topics or Concepts

Case studies give you the space to break down complex concepts, ideas, and strategies and show how they can be applied in a practical way. You can use real-world examples, like an existing client, and use their story to create a compelling narrative that shows how your product solved their issue and how those strategies can be repeated to help other customers get similar successful results.

2. Show Expertise

Case studies are a great way to demonstrate your knowledge and expertise on a given topic or industry. This is where you get the opportunity to show off your problem-solving skills and how you’ve generated successful outcomes for clients you’ve worked with.

3. Build Trust and Credibility

In addition to showing off the attributes above, case studies are an excellent way to build credibility. They’re often filled with data and thoroughly researched, which shows readers you’ve done your homework. They can have confidence in the solutions you’ve presented because they’ve read through as you’ve explained the problem and outlined step-by-step what it took to solve it. All of these elements working together enable you to build trust with potential customers.

4. Create Social Proof

Using existing clients that have seen success working with your brand builds social proof . People are more likely to choose your brand if they know that others have found success working with you. Case studies do just that — putting your success on display for potential customers to see.

All of these attributes work together to help you gain more clients. Plus you can even use quotes from customers featured in these studies and repurpose them in other marketing content. Now that you know more about the benefits of producing a case study, let’s check out how long these documents should be.

How long should a case study be?

The length of a case study will vary depending on the complexity of the project or topic discussed. However, as a general guideline, case studies typically range from 500 to 1,500 words.

Whatever length you choose, it should provide a clear understanding of the challenge, the solution you implemented, and the results achieved. This may be easier said than done, but it's important to strike a balance between providing enough detail to make the case study informative and concise enough to keep the reader's interest.

The primary goal here is to effectively communicate the key points and takeaways of the case study. It’s worth noting that this shouldn’t be a wall of text. Use headings, subheadings, bullet points, charts, and other graphics to break up the content and make it more scannable for readers. We’ve also seen brands incorporate video elements into case studies listed on their site for a more engaging experience.

Ultimately, the length of your case study should be determined by the amount of information necessary to convey the story and its impact without becoming too long. Next, let’s look at some templates to take the guesswork out of creating one.

To help you arm your prospects with information they can trust, we've put together a step-by-step guide on how to create effective case studies for your business with free case study templates for creating your own.

Tell us a little about yourself below to gain access today:

And to give you more options, we’ll highlight some useful templates that serve different needs. But remember, there are endless possibilities when it comes to demonstrating the work your business has done.

1. General Case Study Template

Do you have a specific product or service that you’re trying to sell, but not enough reviews or success stories? This Product Specific case study template will help.

This template relies less on metrics, and more on highlighting the customer’s experience and satisfaction. As you follow the template instructions, you’ll be prompted to speak more about the benefits of the specific product, rather than your team’s process for working with the customer.

4. Bold Social Media Business Case Study Template

case study templates: bold social media business

You can find templates that represent different niches, industries, or strategies that your business has found success in — like a bold social media business case study template.

In this template, you can tell the story of how your social media marketing strategy has helped you or your client through collaboration or sale of your service. Customize it to reflect the different marketing channels used in your business and show off how well your business has been able to boost traffic, engagement, follows, and more.

5. Lead Generation Business Case Study Template

case study templates: lead generation business

It’s important to note that not every case study has to be the product of a sale or customer story, sometimes they can be informative lessons that your own business has experienced. A great example of this is the Lead Generation Business case study template.

If you’re looking to share operational successes regarding how your team has improved processes or content, you should include the stories of different team members involved, how the solution was found, and how it has made a difference in the work your business does.

Now that we’ve discussed different templates and ideas for how to use them, let’s break down how to create your own case study with one.

Get started with case study templates.
Determine the case study's objective.
Establish a case study medium.
Find the right case study candidate.
Contact your candidate for permission to write about them.
Ensure you have all the resources you need to proceed once you get a response.
Download a case study email template.
Define the process you want to follow with the client.
Ensure you're asking the right questions.
Layout your case study format.
Publish and promote your case study.

1. Get started with case study templates.

Telling your customer's story is a delicate process — you need to highlight their success while naturally incorporating your business into their story.

If you're just getting started with case studies, we recommend you download HubSpot's Case Study Templates we mentioned before to kickstart the process.

2. Determine the case study's objective.

All business case studies are designed to demonstrate the value of your services, but they can focus on several different client objectives.

Your first step when writing a case study is to determine the objective or goal of the subject you're featuring. In other words, what will the client have succeeded in doing by the end of the piece?

The client objective you focus on will depend on what you want to prove to your future customers as a result of publishing this case study.

Your case study can focus on one of the following client objectives:

Complying with government regulation
Lowering business costs
Becoming profitable
Generating more leads
Closing on more customers
Generating more revenue
Expanding into a new market
Becoming more sustainable or energy-efficient

3. Establish a case study medium.

Next, you'll determine the medium in which you'll create the case study. In other words, how will you tell this story?

Case studies don't have to be simple, written one-pagers. Using different media in your case study can allow you to promote your final piece on different channels. For example, while a written case study might just live on your website and get featured in a Facebook post, you can post an infographic case study on Pinterest and a video case study on your YouTube channel.

Here are some different case study mediums to consider:

Written Case Study

Consider writing this case study in the form of an ebook and converting it to a downloadable PDF. Then, gate the PDF behind a landing page and form for readers to fill out before downloading the piece, allowing this case study to generate leads for your business.

Video Case Study

Plan on meeting with the client and shooting an interview. Seeing the subject, in person, talk about the service you provided them can go a long way in the eyes of your potential customers.

Infographic Case Study

Use the long, vertical format of an infographic to tell your success story from top to bottom. As you progress down the infographic, emphasize major KPIs using bigger text and charts that show the successes your client has had since working with you.

Podcast Case Study

Podcasts are a platform for you to have a candid conversation with your client. This type of case study can sound more real and human to your audience — they'll know the partnership between you and your client was a genuine success.

4. Find the right case study candidate.

Writing about your previous projects requires more than picking a client and telling a story. You need permission, quotes, and a plan. To start, here are a few things to look for in potential candidates.

Product Knowledge

It helps to select a customer who's well-versed in the logistics of your product or service. That way, he or she can better speak to the value of what you offer in a way that makes sense for future customers.

Remarkable Results

Clients that have seen the best results are going to make the strongest case studies. If their own businesses have seen an exemplary ROI from your product or service, they're more likely to convey the enthusiasm that you want prospects to feel, too.

One part of this step is to choose clients who have experienced unexpected success from your product or service. When you've provided non-traditional customers — in industries that you don't usually work with, for example — with positive results, it can help to remove doubts from prospects.

Recognizable Names

While small companies can have powerful stories, bigger or more notable brands tend to lend credibility to your own. In fact, 89% of consumers say they'll buy from a brand they already recognize over a competitor, especially if they already follow them on social media.

Customers that came to you after working with a competitor help highlight your competitive advantage and might even sway decisions in your favor.

5. Contact your candidate for permission to write about them.

To get the case study candidate involved, you have to set the stage for clear and open communication. That means outlining expectations and a timeline right away — not having those is one of the biggest culprits in delayed case study creation.

Most importantly at this point, however, is getting your subject's approval. When first reaching out to your case study candidate, provide them with the case study's objective and format — both of which you will have come up with in the first two steps above.

To get this initial permission from your subject, put yourself in their shoes — what would they want out of this case study? Although you're writing this for your own company's benefit, your subject is far more interested in the benefit it has for them.

Benefits to Offer Your Case Study Candidate

Here are four potential benefits you can promise your case study candidate to gain their approval.

Brand Exposure

Explain to your subject to whom this case study will be exposed, and how this exposure can help increase their brand awareness both in and beyond their own industry. In the B2B sector, brand awareness can be hard to collect outside one's own market, making case studies particularly useful to a client looking to expand their name's reach.

Employee Exposure

Allow your subject to provide quotes with credits back to specific employees. When this is an option for them, their brand isn't the only thing expanding its reach — their employees can get their name out there, too. This presents your subject with networking and career development opportunities they might not have otherwise.

Product Discount

This is a more tangible incentive you can offer your case study candidate, especially if they're a current customer of yours. If they agree to be your subject, offer them a product discount — or a free trial of another product — as a thank-you for their help creating your case study.

Backlinks and Website Traffic

Here's a benefit that is sure to resonate with your subject's marketing team: If you publish your case study on your website, and your study links back to your subject's website — known as a "backlink" — this small gesture can give them website traffic from visitors who click through to your subject's website.

Additionally, a backlink from you increases your subject's page authority in the eyes of Google. This helps them rank more highly in search engine results and collect traffic from readers who are already looking for information about their industry.

6. Ensure you have all the resources you need to proceed once you get a response.

So you know what you’re going to offer your candidate, it’s time that you prepare the resources needed for if and when they agree to participate, like a case study release form and success story letter.

Let's break those two down.

Case Study Release Form

This document can vary, depending on factors like the size of your business, the nature of your work, and what you intend to do with the case studies once they are completed. That said, you should typically aim to include the following in the Case Study Release Form:

A clear explanation of why you are creating this case study and how it will be used.
A statement defining the information and potentially trademarked information you expect to include about the company — things like names, logos, job titles, and pictures.
An explanation of what you expect from the participant, beyond the completion of the case study. For example, is this customer willing to act as a reference or share feedback, and do you have permission to pass contact information along for these purposes?
A note about compensation.

Success Story Letter

As noted in the sample email, this document serves as an outline for the entire case study process. Other than a brief explanation of how the customer will benefit from case study participation, you'll want to be sure to define the following steps in the Success Story Letter.

7. Download a case study email template.

While you gathered your resources, your candidate has gotten time to read over the proposal. When your candidate approves of your case study, it's time to send them a release form.

A case study release form tells you what you'll need from your chosen subject, like permission to use any brand names and share the project information publicly. Kick-off this process with an email that runs through exactly what they can expect from you, as well as what you need from them. To give you an idea of what that might look like, check out this sample email:

sample case study email release form template

8. Define the process you want to follow with the client.

Before you can begin the case study, you have to have a clear outline of the case study process with your client. An example of an effective outline would include the following information.

The Acceptance

First, you'll need to receive internal approval from the company's marketing team. Once approved, the Release Form should be signed and returned to you. It's also a good time to determine a timeline that meets the needs and capabilities of both teams.

The Questionnaire

To ensure that you have a productive interview — which is one of the best ways to collect information for the case study — you'll want to ask the participant to complete a questionnaire before this conversation. That will provide your team with the necessary foundation to organize the interview, and get the most out of it.

The Interview

Once the questionnaire is completed, someone on your team should reach out to the participant to schedule a 30- to 60-minute interview, which should include a series of custom questions related to the customer's experience with your product or service.

The Draft Review

After the case study is composed, you'll want to send a draft to the customer, allowing an opportunity to give you feedback and edits.

The Final Approval

Once any necessary edits are completed, send a revised copy of the case study to the customer for final approval.

Once the case study goes live — on your website or elsewhere — it's best to contact the customer with a link to the page where the case study lives. Don't be afraid to ask your participants to share these links with their own networks, as it not only demonstrates your ability to deliver positive results and impressive growth, as well.

9. Ensure you're asking the right questions.

Before you execute the questionnaire and actual interview, make sure you're setting yourself up for success. A strong case study results from being prepared to ask the right questions. What do those look like? Here are a few examples to get you started:

What are your goals?
What challenges were you experiencing before purchasing our product or service?
What made our product or service stand out against our competitors?
What did your decision-making process look like?
How have you benefited from using our product or service? (Where applicable, always ask for data.)

Keep in mind that the questionnaire is designed to help you gain insights into what sort of strong, success-focused questions to ask during the actual interview. And once you get to that stage, we recommend that you follow the "Golden Rule of Interviewing." Sounds fancy, right? It's actually quite simple — ask open-ended questions.

If you're looking to craft a compelling story, "yes" or "no" answers won't provide the details you need. Focus on questions that invite elaboration, such as, "Can you describe ...?" or, "Tell me about ..."

In terms of the interview structure, we recommend categorizing the questions and flowing them into six specific sections that will mirror a successful case study format. Combined, they'll allow you to gather enough information to put together a rich, comprehensive study.

Open with the customer's business.

The goal of this section is to generate a better understanding of the company's current challenges and goals, and how they fit into the landscape of their industry. Sample questions might include:

How long have you been in business?
How many employees do you have?
What are some of the objectives of your department at this time?

Cite a problem or pain point.

To tell a compelling story, you need context. That helps match the customer's need with your solution. Sample questions might include:

What challenges and objectives led you to look for a solution?
What might have happened if you did not identify a solution?
Did you explore other solutions before this that did not work out? If so, what happened?

Discuss the decision process.

Exploring how the customer decided to work with you helps to guide potential customers through their own decision-making processes. Sample questions might include:

How did you hear about our product or service?
Who was involved in the selection process?
What was most important to you when evaluating your options?

Explain how a solution was implemented.

The focus here should be placed on the customer's experience during the onboarding process. Sample questions might include:

How long did it take to get up and running?
Did that meet your expectations?
Who was involved in the process?

Explain how the solution works.

The goal of this section is to better understand how the customer is using your product or service. Sample questions might include:

Is there a particular aspect of the product or service that you rely on most?
Who is using the product or service?

End with the results.

In this section, you want to uncover impressive measurable outcomes — the more numbers, the better. Sample questions might include:

How is the product or service helping you save time and increase productivity?
In what ways does that enhance your competitive advantage?
How much have you increased metrics X, Y, and Z?

10. Lay out your case study format.

When it comes time to take all of the information you've collected and actually turn it into something, it's easy to feel overwhelmed. Where should you start? What should you include? What's the best way to structure it?

To help you get a handle on this step, it's important to first understand that there is no one-size-fits-all when it comes to the ways you can present a case study. They can be very visual, which you'll see in some of the examples we've included below, and can sometimes be communicated mostly through video or photos, with a bit of accompanying text.

Here are the sections we suggest, which we'll cover in more detail down below:

Title: Keep it short. Develop a succinct but interesting project name you can give the work you did with your subject.
Subtitle: Use this copy to briefly elaborate on the accomplishment. What was done? The case study itself will explain how you got there.
Executive Summary : A 2-4 sentence summary of the entire story. You'll want to follow it with 2-3 bullet points that display metrics showcasing success.
About the Subject: An introduction to the person or company you served, which can be pulled from a LinkedIn Business profile or client website.
Challenges and Objectives: A 2-3 paragraph description of the customer's challenges, before using your product or service. This section should also include the goals or objectives the customer set out to achieve.
How Product/Service Helped: A 2-3 paragraph section that describes how your product or service provided a solution to their problem.
Results: A 2-3 paragraph testimonial that proves how your product or service specifically benefited the person or company and helped achieve its goals. Include numbers to quantify your contributions.
Supporting Visuals or Quotes: Pick one or two powerful quotes that you would feature at the bottom of the sections above, as well as a visual that supports the story you are telling.
Future Plans: Everyone likes an epilogue. Comment on what's ahead for your case study subject, whether or not those plans involve you.
Call to Action (CTA): Not every case study needs a CTA, but putting a passive one at the end of your case study can encourage your readers to take an action on your website after learning about the work you've done.

When laying out your case study, focus on conveying the information you've gathered in the most clear and concise way possible. Make it easy to scan and comprehend, and be sure to provide an attractive call-to-action at the bottom — that should provide readers an opportunity to learn more about your product or service.

11. Publish and promote your case study.

Once you've completed your case study, it's time to publish and promote it. Some case study formats have pretty obvious promotional outlets — a video case study can go on YouTube, just as an infographic case study can go on Pinterest.

But there are still other ways to publish and promote your case study. Here are a couple of ideas:

Lead Gen in a Blog Post

As stated earlier in this article, written case studies make terrific lead-generators if you convert them into a downloadable format, like a PDF. To generate leads from your case study, consider writing a blog post that tells an abbreviated story of your client's success and asking readers to fill out a form with their name and email address if they'd like to read the rest in your PDF.

Then, promote this blog post on social media, through a Facebook post or a tweet.

Published as a Page on Your Website

As a growing business, you might need to display your case study out in the open to gain the trust of your target audience.

Rather than gating it behind a landing page, publish your case study to its own page on your website, and direct people here from your homepage with a "Case Studies" or "Testimonials" button along your homepage's top navigation bar.

Format for a Case Study

The traditional case study format includes the following parts: a title and subtitle, a client profile, a summary of the customer’s challenges and objectives, an account of how your solution helped, and a description of the results. You might also want to include supporting visuals and quotes, future plans, and calls-to-action.

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The title is one of the most important parts of your case study. It should draw readers in while succinctly describing the potential benefits of working with your company. To that end, your title should:

State the name of your custome r. Right away, the reader must learn which company used your products and services. This is especially important if your customer has a recognizable brand. If you work with individuals and not companies, you may omit the name and go with professional titles: “A Marketer…”, “A CFO…”, and so forth.
State which product your customer used . Even if you only offer one product or service, or if your company name is the same as your product name, you should still include the name of your solution. That way, readers who are not familiar with your business can become aware of what you sell.
Allude to the results achieved . You don’t necessarily need to provide hard numbers, but the title needs to represent the benefits, quickly. That way, if a reader doesn’t stay to read, they can walk away with the most essential information: Your product works.

The example above, “Crunch Fitness Increases Leads and Signups With HubSpot,” achieves all three — without being wordy. Keeping your title short and sweet is also essential.

2. Subtitle

Your subtitle is another essential part of your case study — don’t skip it, even if you think you’ve done the work with the title. In this section, include a brief summary of the challenges your customer was facing before they began to use your products and services. Then, drive the point home by reiterating the benefits your customer experienced by working with you.

The above example reads:

“Crunch Fitness was franchising rapidly when COVID-19 forced fitness clubs around the world to close their doors. But the company stayed agile by using HubSpot to increase leads and free trial signups.”

We like that the case study team expressed the urgency of the problem — opening more locations in the midst of a pandemic — and placed the focus on the customer’s ability to stay agile.

3. Executive Summary

The executive summary should provide a snapshot of your customer, their challenges, and the benefits they enjoyed from working with you. Think it’s too much? Think again — the purpose of the case study is to emphasize, again and again, how well your product works.

The good news is that depending on your design, the executive summary can be mixed with the subtitle or with the “About the Company” section. Many times, this section doesn’t need an explicit “Executive Summary” subheading. You do need, however, to provide a convenient snapshot for readers to scan.

In the above example, ADP included information about its customer in a scannable bullet-point format, then provided two sections: “Business Challenge” and “How ADP Helped.” We love how simple and easy the format is to follow for those who are unfamiliar with ADP or its typical customer.

4. About the Company

Readers need to know and understand who your customer is. This is important for several reasons: It helps your reader potentially relate to your customer, it defines your ideal client profile (which is essential to deter poor-fit prospects who might have reached out without knowing they were a poor fit), and it gives your customer an indirect boon by subtly promoting their products and services.

Feel free to keep this section as simple as possible. You can simply copy and paste information from the company’s LinkedIn, use a quote directly from your customer, or take a more creative storytelling approach.

In the above example, HubSpot included one paragraph of description for Crunch Fitness and a few bullet points. Below, ADP tells the story of its customer using an engaging, personable technique that effectively draws readers in.

case study format: storytelling about the business

5. Challenges and Objectives

case study format: challenges and objectives

The challenges and objectives section of your case study is the place to lay out, in detail, the difficulties your customer faced prior to working with you — and what they hoped to achieve when they enlisted your help.

In this section, you can be as brief or as descriptive as you’d like, but remember: Stress the urgency of the situation. Don’t understate how much your customer needed your solution (but don’t exaggerate and lie, either). Provide contextual information as necessary. For instance, the pandemic and societal factors may have contributed to the urgency of the need.

Take the above example from design consultancy IDEO:

“Educational opportunities for adults have become difficult to access in the United States, just when they’re needed most. To counter this trend, IDEO helped the city of South Bend and the Drucker Institute launch Bendable, a community-powered platform that connects people with opportunities to learn with and from each other.”

We love how IDEO mentions the difficulties the United States faces at large, the efforts its customer is taking to address these issues, and the steps IDEO took to help.

6. How Product/Service Helped

case study format: how the service helped

This is where you get your product or service to shine. Cover the specific benefits that your customer enjoyed and the features they gleaned the most use out of. You can also go into detail about how you worked with and for your customer. Maybe you met several times before choosing the right solution, or you consulted with external agencies to create the best package for them.

Whatever the case may be, try to illustrate how easy and pain-free it is to work with the representatives at your company. After all, potential customers aren’t looking to just purchase a product. They’re looking for a dependable provider that will strive to exceed their expectations.

In the above example, IDEO describes how it partnered with research institutes and spoke with learners to create Bendable, a free educational platform. We love how it shows its proactivity and thoroughness. It makes potential customers feel that IDEO might do something similar for them.

The results are essential, and the best part is that you don’t need to write the entirety of the case study before sharing them. Like HubSpot, IDEO, and ADP, you can include the results right below the subtitle or executive summary. Use data and numbers to substantiate the success of your efforts, but if you don’t have numbers, you can provide quotes from your customers.

We can’t overstate the importance of the results. In fact, if you wanted to create a short case study, you could include your title, challenge, solution (how your product helped), and result.

8. Supporting Visuals or Quotes

Let your customer speak for themselves by including quotes from the representatives who directly interfaced with your company.

Visuals can also help, even if they’re stock images. On one side, they can help you convey your customer’s industry, and on the other, they can indirectly convey your successes. For instance, a picture of a happy professional — even if they’re not your customer — will communicate that your product can lead to a happy client.

In this example from IDEO, we see a man standing in a boat. IDEO’s customer is neither the man pictured nor the manufacturer of the boat, but rather Conservation International, an environmental organization. This imagery provides a visually pleasing pattern interrupt to the page, while still conveying what the case study is about.

9. Future Plans

This is optional, but including future plans can help you close on a more positive, personable note than if you were to simply include a quote or the results. In this space, you can show that your product will remain in your customer’s tech stack for years to come, or that your services will continue to be instrumental to your customer’s success.

Alternatively, if you work only on time-bound projects, you can allude to the positive impact your customer will continue to see, even after years of the end of the contract.

10. Call to Action (CTA)

Not every case study needs a CTA, but we’d still encourage it. Putting one at the end of your case study will encourage your readers to take an action on your website after learning about the work you've done.

It will also make it easier for them to reach out, if they’re ready to start immediately. You don’t want to lose business just because they have to scroll all the way back up to reach out to your team.

To help you visualize this case study outline, check out the case study template below, which can also be downloaded here .

You drove the results, made the connection, set the expectations, used the questionnaire to conduct a successful interview, and boiled down your findings into a compelling story. And after all of that, you're left with a little piece of sales enabling gold — a case study.

To show you what a well-executed final product looks like, have a look at some of these marketing case study examples.

1. "Shopify Uses HubSpot CRM to Transform High Volume Sales Organization," by HubSpot

What's interesting about this case study is the way it leads with the customer. This reflects a major HubSpot value, which is to always solve for the customer first. The copy leads with a brief description of why Shopify uses HubSpot and is accompanied by a short video and some basic statistics on the company.

Notice that this case study uses mixed media. Yes, there is a short video, but it's elaborated upon in the additional text on the page. So, while case studies can use one or the other, don't be afraid to combine written copy with visuals to emphasize the project's success.

2. "New England Journal of Medicine," by Corey McPherson Nash

When branding and design studio Corey McPherson Nash showcases its work, it makes sense for it to be visual — after all, that's what they do. So in building the case study for the studio's work on the New England Journal of Medicine's integrated advertising campaign — a project that included the goal of promoting the client's digital presence — Corey McPherson Nash showed its audience what it did, rather than purely telling it.

Notice that the case study does include some light written copy — which includes the major points we've suggested — but lets the visuals do the talking, allowing users to really absorb the studio's services.

3. "Designing the Future of Urban Farming," by IDEO

Here's a design company that knows how to lead with simplicity in its case studies. As soon as the visitor arrives at the page, he or she is greeted with a big, bold photo, and two very simple columns of text — "The Challenge" and "The Outcome."

Immediately, IDEO has communicated two of the case study's major pillars. And while that's great — the company created a solution for vertical farming startup INFARM's challenge — it doesn't stop there. As the user scrolls down, those pillars are elaborated upon with comprehensive (but not overwhelming) copy that outlines what that process looked like, replete with quotes and additional visuals.

4. "Secure Wi-Fi Wins Big for Tournament," by WatchGuard

Then, there are the cases when visuals can tell almost the entire story — when executed correctly. Network security provider WatchGuard can do that through this video, which tells the story of how its services enhanced the attendee and vendor experience at the Windmill Ultimate Frisbee tournament.

5. Rock and Roll Hall of Fame Boosts Social Media Engagement and Brand Awareness with HubSpot

In the case study above , HubSpot uses photos, videos, screenshots, and helpful stats to tell the story of how the Rock and Roll Hall of Fame used the bot, CRM, and social media tools to gain brand awareness.

6. Small Desk Plant Business Ups Sales by 30% With Trello

This case study from Trello is straightforward and easy to understand. It begins by explaining the background of the company that decided to use it, what its goals were, and how it planned to use Trello to help them.

It then goes on to discuss how the software was implemented and what tasks and teams benefited from it. Towards the end, it explains the sales results that came from implementing the software and includes quotes from decision-makers at the company that implemented it.

7. Facebook's Mercedes Benz Success Story

Facebook's Success Stories page hosts a number of well-designed and easy-to-understand case studies that visually and editorially get to the bottom line quickly.

Each study begins with key stats that draw the reader in. Then it's organized by highlighting a problem or goal in the introduction, the process the company took to reach its goals, and the results. Then, in the end, Facebook notes the tools used in the case study.

Showcasing Your Work

You work hard at what you do. Now, it's time to show it to the world — and, perhaps more important, to potential customers. Before you show off the projects that make you the proudest, we hope you follow these important steps that will help you effectively communicate that work and leave all parties feeling good about it.

Editor's Note: This blog post was originally published in February 2017 but was updated for comprehensiveness and freshness in July 2021.

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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.

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

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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.

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

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

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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

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About the Author

Data Structures
Linked List
Binary Tree
Binary Search Tree
Segment Tree
Disjoint Set Union
Fenwick Tree
Red-Black Tree
Advanced Data Structures

Complete Roadmap To Learn DSA From Scratch

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How to use ChatGPT to learn DSA
How to Start Learning DSA?
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Maths for Data Structure and Algorithms (DSA) | A Complete Guide
Which is the best YouTube channel to Learn DSA?
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Top 50 Searching Coding Problems for Interviews
Data Scientist Roadmap - A Complete Guide
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Is Data Science Hard to Learn?

Today’s world is highly reliable on data and their appropriate management through widely used apps and software. The backbone for appropriate management of data is Data Structure and Algorithms (for convenience here we will use the term DSA). It is a dream for many to achieve expertise in handling and creating these apps and software. With this target in mind, they set out on the journey of learning DSA. The very first step in the journey is the creation of a complete roadmap to learn data structure and algorithms.

Complete Roadmap to Learn Data Structure and Algorithms

Here in this article, we will try to make that task easy for you. We will be providing here with a complete roadmap for learning data structure and algorithms for anyone keen to learn DSA , from scratch.

Table of Contents/Roadmap

Learn at least one Programming Language
Learn about Complexities
3) Linked List
4) Searching Algorithm
5) Sorting Algorithm
6) Divide and Conquer Algorithm
9) Tree Data Structure
10) Graph Data Structure
11) Greedy Methodology
12) Recursion
13) Backtracking Algorithm
14) Dynamic Programming
Practice, practice and practice more
Compete and become a pro
Learn the Fundamentals of chosen Language properly

Get a good grasp of the Complexity Analysis

Focus on logic building.

Don’t worry if stuck on a problem

Be consistent

DSA – Self Paced Course From creating Games to building Social Media Algorithms . DSA plays an integral part whether you want to build something of your own or either may be willing to get a job in big tech giants like Google, Microsoft, Netflix and more. This time, learn DSA with us, with our most popular DSA course , trusted by over 75,000 students! Designed by leading experts having years of industry expertise, which gives you a complete package of video lectures, practice problems, quizzes, and contests. Get started!

5 Steps to learn DSA from scratch

The first and foremost thing is dividing the total procedure into little pieces which need to be done sequentially.

The complete process to learn DSA from scratch can be broken into 5 parts:

Learn a programming language of your choice
Learn about Time and Space complexities
Learn the basics of individual Data Structures and Algorithms
Practice, Practice, and Practice more
Compete and Become a Pro

Before starting any data structure or algorithm you need to know the means to express it or implement it. So, the first task is to learn any programming language. Then you should learn about one of the most important and most used concepts about DSA, the complexity of a program. Now equipped with the prerequisites, you can start learning DSA and at the same time practice it regularly and compete in challenges to gauge and sharpen your ability. In the following sections, we will discuss each of the steps in detail.

1. Learn at least one Programming language

This should be your first step while starting to learn data structure and algorithms. We as human beings, before learning to write a sentence or an essay on a topic, first try to learn that language: the alphabet, letters, and punctuations in it, how and when to use them. The same goes for programming also.

Firstly, select a language of your choice , be it Java, C, C++, Python, or any other language of your choice. Before learning how to code in that language you should learn about the building pieces of the language: the basic syntax, the data types, variables, operators, conditional statements, loops, functions, etc. You may also learn the concept of OOP (Object Oriented Programming) .

To help you get started with the language of your choice, we have created a complete course to start as a beginner, such as:

C Programming (Basic to Advanced) – Self Paced
Fork CPP Programming – Self Paced
Fork Java Programming – Self Paced
Fork Python Programming – Self Paced
Fork Javascript -Self Paced

You can also explore our other courses for Programming languages on our Practice portal.

2. Learn about Complexities

Here comes one of the interesting and important topics. The primary motive to use DSA is to solve a problem effectively and efficiently. How can you decide if a program written by you is efficient or not? This is measured by complexities. Complexity is of two types:

Time Complexity : Time complexity is used to measure the amount of time required to execute the code.
Space Complexity : Space complexity means the amount of space required to execute successfully the functionalities of the code. You will also come across the term Auxiliary Space very commonly in DSA, which refers to the extra space used in the program other than the input data structure.

Both of the above complexities are measured with respect to the input parameters. But here arises a problem. The time required for executing a code depends on several factors, such as:

The number of operations performed in the program,
The speed of the device, and also
The speed of data transfer if being executed on an online platform.

So how can we determine which one is efficient? The answer is the use of asymptotic notation.

Asymptotic notation is a mathematical tool that calculates the required time in terms of input size and does not require the execution of the code.

It neglects the system-dependent constants and is related to only the number of modular operations being performed in the whole program. The following 3 asymptotic notations are mostly used to represent the time complexity of algorithms:

Big-O Notation (Ο) – Big-O notation specifically describes the worst-case scenario.
Omega Notation (Ω) – Omega(Ω) notation specifically describes the best-case scenario.
Theta Notation (θ) – This notation represents the average complexity of an algorithm.

Rate of Growth of Algorithms

The most used notation in the analysis of a code is the Big O Notation which gives an upper bound of the running time of the code (or the amount of memory used in terms of input size).

To learn about complexity analysis in detail, you can refer to our complete set of articles on the Analysis of Algorithms .

3. Learn Data Structures and Algorithms

Here comes the most crucial and the most awaited stage of the roadmap for learning data structure and algorithm – the stage where you start learning about DSA. The topic of DSA consists of two parts:

Algorithms

Though they are two different things, they are highly interrelated, and it is very important to follow the right track to learn them most efficiently. If you are confused about which one to learn first, we recommend you to go through our detailed analysis on the topic: What should I learn first- Data Structures or Algorithms?

Here we have followed the flow of learning a data structure and then the most related and important algorithms used by that data structure.

Roadmap to learn DSA

The most basic yet important data structure is the array. It is a linear data structure. An array is a collection of homogeneous data types where the elements are allocated contiguous memory. Because of the contiguous allocation of memory, any element of an array can be accessed in constant time. Each array element has a corresponding index number.

Array Data Structure

To learn more about arrays, refer to the article “ Introduction to Arrays “.

Here are some topics about array which you must learn:

Rotation of Array – Rotation of array means shifting the elements of an array in a circular manner i.e., in the case of right circular shift the last element becomes the first element, and all other element moves one point to the right.
Rearranging an array – Rearrangement of array elements suggests the changing of an initial order of elements following some conditions or operations.
Range queries in the array – Often you need to perform operations on a range of elements. These functions are known as range queries.
Multidimensional array – These are arrays having more than one dimension. The most used one is the 2-dimensional array, commonly known as a matrix.
Kadane’s algorithm
Dutch national flag algorithm

3.2. String

A string is also a type of array. It can be interpreted as an array of characters. But it has some special characteristics like the last character of a string is a null character to denote the end of the string. Also, there are some unique operations, like concatenation which concatenates two strings into one.

String Data Structure

Here we are providing you with some must-know concepts of string:

Subsequence and substring – A subsequence is a sequence that can be derived from a string deleting one or more elements. A substring is a contiguous segment of the string.
Reverse and rotation in a string – Reverse operation is interchanging the position of characters of a string such that the first becomes the last, the second becomes the second last, and so on.
Binary String – A binary string is a string made up of only two types of characters.
Palindrome – A palindrome string is a string in which the elements at the same distance from the center of the string are the same.
Lexicographic pattern – Lexicographical pattern is the pattern based on the ASCII value or can be said in dictionary order.
Pattern searching – Pattern searching is searching a given pattern in the string. It is an advanced topic of string.

3.3. Linked List

As the above data structures, the linked list is also a linear data structure. But Linked List is different from Array in its configuration. It is not allocated to contiguous memory locations. Instead, each node of the linked list is allocated to some random memory space and the previous node maintains a pointer that points to this node. So no direct memory access of any node is possible and it is also dynamic i.e., the size of the linked list can be adjusted at any time. To learn more about linked lists refer to the article “ Introduction to Linked List “.

Linked List Data Structure

The topics which you must want to cover are:

Singly Linked List – In this, each node of the linked list points only to its next node.
Circular Linked List – This is the type of linked list where the last node points back to the head of the linked list.
Doubly Linked List – In this case, each node of the linked list holds two pointers, one point to the next node and the other points to the previous node.

3.4. Searching Algorithm

Now we have learned about some linear data structures and is time to learn about some basic and most used algorithms which are hugely used in these types of data structures. One such algorithm is the searching algorithm.

Searching algorithms are used to find a specific element in an array, string, linked list, or some other data structure.

The most common searching algorithms are:

Linear Search – In this searching algorithm, we check for the element iteratively from one end to the other.
Binary Search – In this type of searching algorithm, we break the data structure into two equal parts and try to decide in which half we need to find for the element.
Ternary Search – In this case, the array is divided into three parts, and based on the values at partitioning positions we decide the segment where we need to find the required element.

Besides these, there are other searching algorithms also like

Jump Search
Interpolation Search
Exponential Search

3.5. Sorting Algorithm

Here is one other most used algorithm. Often we need to arrange or sort data as per a specific condition. The sorting algorithm is the one that is used in these cases. Based on conditions we can sort a set of homogeneous data in order like sorting an array in increasing or decreasing order.

Sorting Algorithm is used to rearrange a given array or list elements according to a comparison operator on the elements. The comparison operator is used to decide the new order of element in the respective data structure.

An example to show Sorting

There are a lot of different types of sorting algorithms. Some widely used algorithms are:

Bubble Sort
Selection Sort
Insertion Sort

There are several other sorting algorithms also and they are beneficial in different cases. You can learn about them and more in our dedicated article on Sorting algorithms .

3.6. Divide and Conquer Algorithm

This is one interesting and important algorithm to be learned in your path of programming. As the name suggests, it breaks the problem into parts, then solves each part and after that again merges the solved subtasks to get the actual problem solved.

Divide and Conquer is an algorithmic paradigm. A typical Divide and Conquer algorithm solves a problem using following three steps. Divide: Break the given problem into subproblems of same type. Conquer: Recursively solve these subproblems Combine: Appropriately combine the answers

This is the primary technique mentioned in the two sorting algorithms Merge Sort and Quick Sort which are mentioned earlier. To learn more about the technique, the cases where it is used, and its implementation and solve some interesting problems, please refer to the dedicated article Divide and Conquer Algorithm .

Now you should move to some more complex data structures, such as Stack and Queue.

Stack is a linear data structure which follows a particular order in which the operations are performed. The order may be LIFO(Last In First Out) or FILO(First In Last Out) .

Stack Data Structure

The reason why Stack is considered a complex data structure is that it uses other data structures for implementation, such as Arrays, Linked lists, etc. based on the characteristics and features of Stack data structure.

Another data structure that is similar to Stack, yet different in its characteristics, is Queue.

A Queue is a linear structure which follows First In First Out (FIFO) approach in its individual operations.

Queue Data Structure

A queue can be of different types like

Circular queue – In a circular queue the last element is connected to the first element of the queue
Double-ended queue (or known as deque) – A double-ended queue is a special type of queue where one can perform the operations from both ends of the queue.
Priority queue – It is a special type of queue where the elements are arranged as per their priority. A low priority element is dequeued after a high priority element.

3.9. Tree Data Structure

After having the basics covered about the linear data structure , now it is time to take a step forward to learn about the non-linear data structures. The first non-linear data structure you should learn is the tree.

Tree data structure is similar to a tree we see in nature but it is upside down. It also has a root and leaves. The root is the first node of the tree and the leaves are the ones at the bottom-most level. The special characteristic of a tree is that there is only one path to go from any of its nodes to any other node.

Tree Data Structure

Based on the maximum number of children of a node of the tree it can be –

Binary tree – This is a special type of tree where each node can have a maximum of 2 children.
Ternary tree – This is a special type of tree where each node can have a maximum of 3 children.
N-ary tree – In this type of tree, a node can have at most N children.

Based on the configuration of nodes there are also several classifications. Some of them are:

Complete Binary Tree – In this type of binary tree all the levels are filled except maybe for the last level. But the last level elements are filled as left as possible.
Perfect Binary Tree – A perfect binary tree has all the levels filled
Binary Search Tree – A binary search tree is a special type of binary tree where the smaller node is put to the left of a node and a higher value node is put to the right of a node
Ternary Search Tree – It is similar to a binary search tree, except for the fact that here one element can have at most 3 children.

3.10. Graph Data Structure

Another important non-linear data structure is the graph. It is similar to the Tree data structure, with the difference that there is no particular root or leaf node, and it can be traversed in any order.

A Graph is a non-linear data structure consisting of a finite set of vertices(or nodes) and a set of edges that connect a pair of nodes.

Graph Data Structure

Each edge shows a connection between a pair of nodes. This data structure helps solve many real-life problems. Based on the orientation of the edges and the nodes there are various types of graphs.

Here are some must to know concepts of graphs:

Types of graphs – There are different types of graphs based on connectivity or weights of nodes.
Introduction to BFS and DFS – These are the algorithms for traversing through a graph
Cycles in a graph – Cycles are a series of connections following which we will be moving in a loop.
Topological sorting in the graph
Minimum Spanning tree in graph

3.11. Greedy methodology

As the name suggests, this algorithm builds up the solution one piece at a time and chooses the next piece which gives the most obvious and immediate benefit i.e., which is the most optimal choice at that moment. So the problems where choosing locally optimal also leads to the global solutions are best fit for Greedy.

For example, consider the Fractional Knapsack Problem . The local optimal strategy is to choose the item that has maximum value vs weight ratio. This strategy also leads to a globally optimal solution because we are allowed to take fractions of an item.

Fractional Knapsack Problem

Here is how you can get started with the Greedy algorithm with the help of relevant sub-topics:

Standard greedy algorithms
Greedy algorithms in graphs
Greedy Algorithms in Operating Systems
Greedy algorithms in array
Approximate greedy algorithms for NP-complete problems

3.12. Recursion

Recursion is one of the most important algorithms which uses the concept of code reusability and repeated usage of the same piece of code.

The point which makes Recursion one of the most used algorithms is that it forms the base for many other algorithms such as:

Tree traversals
Graph traversals
Divide and Conquers Algorithms
Backtracking algorithms

In Recursion, you can follow the below articles/links to get the most out of it:

Recursive Functions
Tail Recursion
Towers of Hanoi (TOH)

3.13. Backtracking Algorithm

As mentioned earlier, the Backtracking algorithm is derived from the Recursion algorithm, with the option to revert if a recursive solution fails, i.e. in case a solution fails, the program traces back to the moment where it failed and builds on another solution. So basically it tries out all the possible solutions and finds the correct one.

Backtracking is an algorithmic technique for solving problems recursively by trying to build a solution incrementally, one piece at a time, removing those solutions that fail to satisfy the constraints of the problem at any point of time

Some important and most common problems of backtracking algorithms, that you must solve before moving ahead, are:

Knight’s tour problem
Rat in a maze
N-Queen problem
Subset sum problem
m-coloring problem
Hamiltonian cycle

3.14. Dynamic Programming

Another crucial algorithm is dynamic programming. Dynamic Programming is mainly an optimization over plain recursion. Wherever we see a recursive solution that has repeated calls for the same inputs, we can optimize it using Dynamic Programming.

The main concept of the Dynamic Programming algorithm is to use the previously calculated result to avoid repeated calculations of the same subtask which helps in reducing the time complexity.

Dynamic Programming

To learn more about dynamic programming and practice some interesting problems related to it, refer to the following articles:

Tabulation vs Memoization
Optimal Substructure Property
Overlapping Subproblems Property
How to solve a Dynamic Programming Problem?
Bitmasking and Dynamic Programming | Set 1
Bitmasking and Dynamic Programming | Set-2 (TSP)
Digit DP | Introduction

4. Practice, Practice and Practice more

With this, we have completed the basics of major Data structure and Algorithms, and now it’s time to try our hands on each of them.

“Practice makes a man perfect.”

This is highly applicable for learning DSA. You have learned a lot of data structures and algorithms and now you need a lot of practice. This may be seen as a separate step or an integrated part of the process of learning DSA. Because of its importance, we are discussing it as a separate step.

For practicing problems on individual data structures and algorithms, you can use the following links:

Practice problems on Arrays
Practice problems on Strings
Practice problems on Linked Lists
Practice problems on Searching algorithm
Practice problems on Sorting algorithm
Practice problems on Divide And Conquer algorithm
Practice problems on Stack
Practice problems on Queue
Practice problems on Tree
Practice problems on Graph
Practice problems on Greedy algorithm
Practice problems on Recursion algorithm
Practice problems on Backtracking algorithm
Practice problems on Dynamic Programming algorithm

Apart from these, there are many other practice problems that you can refer based on their respective difficulties:

School-level
Basic level
Medium level

You can also try to solve the most asked interview questions based on the list curated by us at:

Must-Do Coding Questions for Companies
Top 50 Array Coding Problems for Interviews
Top 50 String Coding Problems for Interviews
Top 50 Tree Coding Problems for Interviews
Top 50 Dynamic Programming Coding Problems for Interviews

You can also try our curated lists of problems from below articles:

SDE SHEET – A Complete Guide for SDE Preparation
DSA Sheet by Love Babbar

5. Compete and Become A Pro

Now it is time to test out your skills and efficiency. The best possible way is to compete with others. This will help you find out your position among others and also give you a hint on the areas you are lacking.

There are several online competitive platforms available where you can participate regularly. Also, some online challenges are held from time to time in a year which also provides lots of prizes and opportunities, such as:

Monthly Job-a-thon : It is a contest for individual participants. Participants get the opportunity to get hired by a bunch of companies that shortlist for interviews as per their criteria.
Bi-Wizard Coding : A coding competition exclusively for students. The top 100 students get chances of winning exciting rewards and also access to free courses.
Interview Series : A weekly challenge that gives a great opportunity for aspirants to practice a lot of questions based on important data structure and algorithms concepts for the preparation of interviews.
Problem of the Day : A new problem every day to strengthen the base of data structure and algorithm.

To learn more about where to compete, you can refer to our detailed article Top 15 Websites for Coding Challenges and Competitions .

Tips to boost your learning

By far we have discussed in-depth the 5 crucial steps to learning DSA from scratch. During the complete journey on the roadmap to learn DSA , here are some tips which will surely help you:

Learn the Fundamentals of chosen Programming Language thoroughly

Implement each small concept that you are learning. Make sure to learn the following concepts:

Basic Syntax
Operators, Variables, functions
Conditional Statement, loops
OOP(Object-Oriented Programming)

Understand how the complexity is calculated, and try to solve multiple questions to find the complexities of programs. You can also try out our quiz on Analysis of Algorithms for better practice.

The best way to do this is to solve as many problems as you can from scratch, without looking into solutions or editorials. The more you solve, the more strong your logic building will be.

Stuck on a problem/topic? Don’t worry, you are not alone

It is a brainer that you can solve all the problems all by yourself.

There will be problems, hours, and even days when you will be stuck and won’t be able to find any solution.

Don’t worry, it happens with everyone. If stuck on any problem try to read hints and approaches for solutions. If still unable then see the logic only and code it on your own. If getting stuck on similar types of problems you should probably revise the concept before trying to solve similar types of problems again.

You can also try out our 24×7 Doubt Assistance program to let us help you tackle such situations without breaking a sweat.

Every monument is built brick by brick by working daily, consistently, and so is the case for DSA. You must try to learn at least 1 new topic every day and solve at least 1 new problem related to it every day. Making this a practice for each day every day will help you master DSA in the best possible manner.

Make sure to give coding challenges at regular intervals as well. You might face challenges in solving even 1 problem in the beginning, but in the end, it will be all worth it. You can try GeeksforGeeks POTD to solve one problem based on DSA every day and here you can also use the discussion forums to help you make sure you get the logic properly. To know more about the discussion portals read the article – Stuck in Programming: Get The Solution From These 10 Best Websites .

Is that it? Is this all required to master Data Structures and Algorithms and become Hero from Zero in DSA? Well if you have gone through the above-mentioned roadmap for learning DSA, then this is it. You have successfully started, learned, practiced, and competed enough to call yourself a DSA Pro .

But, like the universe, learning is endless . You can never learn everything about any topic. So make sure to keep practicing and keep updating yourself with new competitions, topics, and problems.

How to start learning DSA?
What Should I Learn First: Data Structures or Algorithms?
Why Data Structures and Algorithms are Important to learn?

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Google published a case study that shows how using structured data and following best practices improved discoverability and brought more search traffic. The case study was about the use of Video structured data but the insights shared are applicable across a range of content types.

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Implementing The Correct Structured Data

Google highlighted the importance of using the correct structured data and validating it with Google’s structured data testing tool.

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Indexing + Structured Data = More Visibility

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Case Study Research Method in Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews).

The case study research method originated in clinical medicine (the case history, i.e., the patient’s personal history). In psychology, case studies are often confined to the study of a particular individual.

The information is mainly biographical and relates to events in the individual’s past (i.e., retrospective), as well as to significant events that are currently occurring in his or her everyday life.

The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.

Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

This makes it clear that the case study is a method that should only be used by a psychologist, therapist, or psychiatrist, i.e., someone with a professional qualification.

There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e., abnormal) behavior or atypical development.

Famous Case Studies

Anna O – One of the most famous case studies, documenting psychoanalyst Josef Breuer’s treatment of “Anna O” (real name Bertha Pappenheim) for hysteria in the late 1800s using early psychoanalytic theory.
Little Hans – A child psychoanalysis case study published by Sigmund Freud in 1909 analyzing his five-year-old patient Herbert Graf’s house phobia as related to the Oedipus complex.
Bruce/Brenda – Gender identity case of the boy (Bruce) whose botched circumcision led psychologist John Money to advise gender reassignment and raise him as a girl (Brenda) in the 1960s.
Genie Wiley – Linguistics/psychological development case of the victim of extreme isolation abuse who was studied in 1970s California for effects of early language deprivation on acquiring speech later in life.
Phineas Gage – One of the most famous neuropsychology case studies analyzes personality changes in railroad worker Phineas Gage after an 1848 brain injury involving a tamping iron piercing his skull.

Clinical Case Studies

Studying the effectiveness of psychotherapy approaches with an individual patient
Assessing and treating mental illnesses like depression, anxiety disorders, PTSD
Neuropsychological cases investigating brain injuries or disorders

Child Psychology Case Studies

Studying psychological development from birth through adolescence
Cases of learning disabilities, autism spectrum disorders, ADHD
Effects of trauma, abuse, deprivation on development

Types of Case Studies

Explanatory case studies : Used to explore causation in order to find underlying principles. Helpful for doing qualitative analysis to explain presumed causal links.
Exploratory case studies : Used to explore situations where an intervention being evaluated has no clear set of outcomes. It helps define questions and hypotheses for future research.
Descriptive case studies : Describe an intervention or phenomenon and the real-life context in which it occurred. It is helpful for illustrating certain topics within an evaluation.
Multiple-case studies : Used to explore differences between cases and replicate findings across cases. Helpful for comparing and contrasting specific cases.
Intrinsic : Used to gain a better understanding of a particular case. Helpful for capturing the complexity of a single case.
Collective : Used to explore a general phenomenon using multiple case studies. Helpful for jointly studying a group of cases in order to inquire into the phenomenon.

Where Do You Find Data for a Case Study?

There are several places to find data for a case study. The key is to gather data from multiple sources to get a complete picture of the case and corroborate facts or findings through triangulation of evidence. Most of this information is likely qualitative (i.e., verbal description rather than measurement), but the psychologist might also collect numerical data.

1. Primary sources

Interviews – Interviewing key people related to the case to get their perspectives and insights. The interview is an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person’s friends, parents, employer, workmates, and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.
Observations – Observing behaviors, interactions, processes, etc., related to the case as they unfold in real-time.
Documents & Records – Reviewing private documents, diaries, public records, correspondence, meeting minutes, etc., relevant to the case.

2. Secondary sources

News/Media – News coverage of events related to the case study.
Academic articles – Journal articles, dissertations etc. that discuss the case.
Government reports – Official data and records related to the case context.
Books/films – Books, documentaries or films discussing the case.

3. Archival records

Searching historical archives, museum collections and databases to find relevant documents, visual/audio records related to the case history and context.

Public archives like newspapers, organizational records, photographic collections could all include potentially relevant pieces of information to shed light on attitudes, cultural perspectives, common practices and historical contexts related to psychology.

4. Organizational records

Organizational records offer the advantage of often having large datasets collected over time that can reveal or confirm psychological insights.

Of course, privacy and ethical concerns regarding confidential data must be navigated carefully.

However, with proper protocols, organizational records can provide invaluable context and empirical depth to qualitative case studies exploring the intersection of psychology and organizations.

Organizational/industrial psychology research : Organizational records like employee surveys, turnover/retention data, policies, incident reports etc. may provide insight into topics like job satisfaction, workplace culture and dynamics, leadership issues, employee behaviors etc.
Clinical psychology : Therapists/hospitals may grant access to anonymized medical records to study aspects like assessments, diagnoses, treatment plans etc. This could shed light on clinical practices.
School psychology : Studies could utilize anonymized student records like test scores, grades, disciplinary issues, and counseling referrals to study child development, learning barriers, effectiveness of support programs, and more.

How do I Write a Case Study in Psychology?

Follow specified case study guidelines provided by a journal or your psychology tutor. General components of clinical case studies include: background, symptoms, assessments, diagnosis, treatment, and outcomes. Interpreting the information means the researcher decides what to include or leave out. A good case study should always clarify which information is the factual description and which is an inference or the researcher’s opinion.

1. Introduction

Provide background on the case context and why it is of interest, presenting background information like demographics, relevant history, and presenting problem.
Compare briefly to similar published cases if applicable. Clearly state the focus/importance of the case.

2. Case Presentation

Describe the presenting problem in detail, including symptoms, duration,and impact on daily life.
Include client demographics like age and gender, information about social relationships, and mental health history.
Describe all physical, emotional, and/or sensory symptoms reported by the client.
Use patient quotes to describe the initial complaint verbatim. Follow with full-sentence summaries of relevant history details gathered, including key components that led to a working diagnosis.
Summarize clinical exam results, namely orthopedic/neurological tests, imaging, lab tests, etc. Note actual results rather than subjective conclusions. Provide images if clearly reproducible/anonymized.
Clearly state the working diagnosis or clinical impression before transitioning to management.

3. Management and Outcome

Indicate the total duration of care and number of treatments given over what timeframe. Use specific names/descriptions for any therapies/interventions applied.
Present the results of the intervention,including any quantitative or qualitative data collected.
For outcomes, utilize visual analog scales for pain, medication usage logs, etc., if possible. Include patient self-reports of improvement/worsening of symptoms. Note the reason for discharge/end of care.

4. Discussion

Analyze the case, exploring contributing factors, limitations of the study, and connections to existing research.
Analyze the effectiveness of the intervention,considering factors like participant adherence, limitations of the study, and potential alternative explanations for the results.
Identify any questions raised in the case analysis and relate insights to established theories and current research if applicable. Avoid definitive claims about physiological explanations.
Offer clinical implications, and suggest future research directions.

5. Additional Items

Thank specific assistants for writing support only. No patient acknowledgments.
References should directly support any key claims or quotes included.
Use tables/figures/images only if substantially informative. Include permissions and legends/explanatory notes.
Provides detailed (rich qualitative) information.
Provides insight for further research.
Permitting investigation of otherwise impractical (or unethical) situations.

Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of ‘averaging’.

Because of their in-depth, multi-sided approach, case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.

Research that only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension of experience, which is important to psychoanalytic and humanistic psychologists.

Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person’s life are related to each other.

The method is, therefore, important for psychologists who adopt a holistic point of view (i.e., humanistic psychologists ).

Limitations

Lacking scientific rigor and providing little basis for generalization of results to the wider population.
Researchers’ own subjective feelings may influence the case study (researcher bias).
Difficult to replicate.
Time-consuming and expensive.
The volume of data, together with the time restrictions in place, impacted the depth of analysis that was possible within the available resources.

Because a case study deals with only one person/event/group, we can never be sure if the case study investigated is representative of the wider body of “similar” instances. This means the conclusions drawn from a particular case may not be transferable to other settings.

Because case studies are based on the analysis of qualitative (i.e., descriptive) data , a lot depends on the psychologist’s interpretation of the information she has acquired.

This means that there is a lot of scope for Anna O , and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.

For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit particular behavioral theories (e.g., Little Hans ).

This is also true of Money’s interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.

Breuer, J., & Freud, S. (1895). Studies on hysteria . Standard Edition 2: London.

Curtiss, S. (1981). Genie: The case of a modern wild child .

Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine , 151(3), 298-304

Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306

Freud, S. (1909b). Bemerkungen über einen Fall von Zwangsneurose (Der “Rattenmann”). Jb. psychoanal. psychopathol. Forsch ., I, p. 357-421; GW, VII, p. 379-463; Notes upon a case of obsessional neurosis, SE , 10: 151-318.

Harlow J. M. (1848). Passage of an iron rod through the head. Boston Medical and Surgical Journal, 39 , 389–393.

Harlow, J. M. (1868). Recovery from the Passage of an Iron Bar through the Head . Publications of the Massachusetts Medical Society. 2 (3), 327-347.

Money, J., & Ehrhardt, A. A. (1972). Man & Woman, Boy & Girl : The Differentiation and Dimorphism of Gender Identity from Conception to Maturity. Baltimore, Maryland: Johns Hopkins University Press.

Money, J., & Tucker, P. (1975). Sexual signatures: On being a man or a woman.

Further Information

Case Study Approach
Case Study Method
Enhancing the Quality of Case Studies in Health Services Research
“We do things together” A case study of “couplehood” in dementia
Using mixed methods for evaluating an integrative approach to cancer care: a case study

Research Methodology

Qualitative Data Coding

What Is a Focus Group?

Cross-Cultural Research Methodology In Psychology

What Is Internal Validity In Research?

Research Methodology , Statistics

What Is Face Validity In Research? Importance & How To Measure

Criterion Validity: Definition & Examples

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Human capital, life satisfaction, and the floating population’s urban settlement intention in cities—a case study of six cities in the pearl river delta.

1. Introduction

2. concepts and measurement indicators of model structure of human capital, life satisfaction, and urban settlement intention, 2.1. the concept and measurement indicators of life satisfaction, 2.2. indicators for measuring the human capital of the floating population, 2.3. model structure of human capital, life satisfaction, and urban settlement intention for the floating population, 3. study area, data, and model, 3.1. study area and survey target, 3.2. urban life satisfaction of floating population in pearl river delta, 3.3. measurements for structural equation modeling, 3.3.1. measurement models, 3.3.2. structural models, 4.1. correlation analysis of observed variables in life satisfaction, 4.2. model estimation results: human capital, life satisfaction, and urban settlement intention, 4.3. outcome analysis: human capital, life satisfaction, and urban settlement intention, 4.3.1. human capital, economic life satisfaction, and social security satisfaction among the floating population exert a discernible influence on its urban settlement intentions, 4.3.2. a negative correlation between the economic life satisfaction of the floating population and multiple latent variables exists, 4.3.3. a robust correlation among satisfaction with social security, emotional life, and public services exists, 5. conclusions and policy recommendations, author contributions, informed consent statement, data availability statement, conflicts of interest.

	Household Economy	Work Situation	Housing Situation	Physical Health	Medical Condition	Children’s Education	Daily Transportation	Leisure and Recreation	Married Life	Social Interaction	Financial Services	Overall Situation
Dongguan City	6.2	6.72	6.03	7.87	6.58	6.44	7.11	6.8	7.9	6.96	6.81	7.13
Northeast Cluster	5.83	6.32	5.83	7.7	6.11	6.09	6.83	6.62	7.95	6.77	6.31	6.85
Southeast Cluster	6.43	7.12	6.12	8.17	6.63	6.68	7.27	7.09	8.18	7.22	7.06	7.21
Northwest Cluster	6.22	6.42	6.03	7.42	6.68	6.33	6.89	6.31	8	6.78	6.61	7.06
Southwest Cluster	6	6.42	5.78	7.83	6.39	6.12	6.87	6.48	7.87	6.66	6.7	7.02
Center Cluster	6.4	7.13	6.32	8.08	6.9	6.99	7.54	7.36	7.64	7.3	7.09	7.37
Foshan City	6.24	6.68	6.04	7.78	6.69	6.84	7.1	6.58	7.91	7.11	7.03	7.29
Chancheng District	6.61	6.57	6.07	7.84	7.27	7.03	7.54	7.16	7.51	7.45	7.11	7.45
Gaoming District	6.5	7.43	6.38	7.88	6.4	7.54	7	5.76	8.68	7.55	7.02	7.55
Nanhai District	6.53	6.85	6.18	8.04	7.04	6.58	7.15	6.87	7.91	7.13	7.16	7.36
Sanshui District	5.91	7.04	6.35	7.52	6.13	7	6.74	6.13	8.19	6.26	6.78	7.13
Shunde District	5.76	6.26	5.72	7.5	6.3	6.64	6.95	6.38	7.73	6.95	6.92	7.1
Guangzhou City	5.99	6.07	5.68	7.77	6.51	6.08	7.04	6.51	7.6	6.96	6.71	7.01
Baiyun District	6.39	6.26	5.61	7.13	6.3	6.37	6.97	6.23	7.79	7.03	6.88	7.11
Conghua District	5.68	6.48	5.88	7.64	6.4	6.32	7.24	6.16	7.59	7.28	6.48	6.92
Panyu District	5.67	5.95	5.67	7.89	6.31	5.52	7.62	6.69	8.24	7.23	6.43	7
Haizhu District	5.94	5.52	5.09	7.33	6.46	6.07	6.48	6.15	6.78	6.6	6.33	6.75
Huadu District	5.67	6.07	5.62	7.68	5.95	5.79	6.42	6.14	6.29	6.28	6.55	6.95
Huangpu District	6.23	6.35	6.16	8.25	6.98	5.45	6.98	7	6.93	6.97	6.87	7.3
Liwan District	6.25	5.48	5.61	8.39	6.39	6.24	8	6.46	7.86	7.5	7.04	6.96
Nansha District	5.52	6.11	6.04	8.26	6.69	6.05	7.06	6.76	8.55	7.02	7.22	6.91
Tianhe District	6.32	6.07	5.42	7.68	6.74	7	6.86	6.42	7.53	6.89	6.5	6.96
Yuexiu District	6.2	6.35	5.35	8.35	7.3	7	7.75	6.95	9	7.8	7.3	7.3
Zengcheng District	6.09	6.18	6.12	7.5	6.69	5.85	6.73	6.77	7.8	6.74	6.9	6.91
Shenzhen City	6.25	6.88	5.94	7.93	7.08	6.87	7.25	7.23	7.85	7.3	7.37	7.3
Baoan District	6.03	6.72	5.78	7.72	6.77	6.92	7.29	7.06	7.91	7.1	7.22	7.08
Futian District	7.24	7.33	6.43	8.14	7.19	7.62	8.19	8.05	7.88	7.33	7.67	7.55
Guangming New District	7.83	7.17	5.83	8.83	7.83	6.25	7	8.5	8.25	7.67	8.5	7.83
Longgang District	5.93	6.77	5.59	7.82	7.03	6.59	7.1	7.02	7.79	7.39	7.48	7.22
Longhua District	6.25	6.9	6.17	7.93	7.26	6.97	7.11	7.32	7.78	7.35	7.54	7.49
Luohu District	6.68	7.16	6.21	8.21	7.74	8.3	6.84	7.79	7.73	7.26	7.26	7.63
Nanshan District	6.47	7.13	6.51	8.06	6.98	6.83	7.51	7.19	7.96	7.23	6.87	7.28
Pingshan District	6.94	6.83	5.83	8.72	7.67	6.13	7.17	7.33	7.75	7.72	7.56	7.56
Zhongshan City	6.36	6.68	6.43	7.91	6.67	6.49	7.19	6.8	7.77	7.1	6.89	7.26
Eastern Cluster	5.97	5.8	5.64	7.38	6.08	5.83	6.68	6.36	7.75	6.74	6.45	6.87
Southern Cluster	6.1	6.63	6.68	8.21	6.66	7.03	7.44	6.67	8	7.3	6.9	7.26
Northwest Cluster	6.66	7.07	6.69	8.08	6.88	6.9	7.22	6.93	7.69	7.26	7.38	7.46
Center Cluster	6.29	6.51	6.23	7.7	6.64	5.95	7.18	6.91	7.75	6.86	6.38	7.14
Zhuhai City	6.28	6.49	6.11	7.8	6.59	6.83	7.32	6.81	7.97	7.18	6.77	7.24
Doumen District	5.93	6.55	6.19	7.81	6.71	6.85	6.77	6.18	7.54	7	6.36	7.12
Jinwan District	6.55	6.64	6.49	7.78	6.49	7.17	7.3	6.81	8.38	7.35	6.93	7.25
Xiangzhou District	6.25	6.32	5.72	7.81	6.6	6.52	7.67	7.2	7.85	7.15	6.88	7.3
Total	6.2	6.56	6	7.84	6.68	6.55	7.15	6.77	7.82	7.08	6.92	7.18

Attribute	Classification	Frequency	Proportion(%)	Attribute	Classification	Frequency	Proportion(%)
Personal income	≤30	684	29.9	Daily transportation	0–2	61	2.7
	30–60	1105	48.4		3–4	126	5.5
	60–100	337	14.8		5–6	597	26.1
	>100	158	6.9		7–8	960	42.0
Personal education	Primary school and below	327	14.3		9–10	540	23.6
	Junior and senior high school	1424	62.3		Average	7.13
	College and above	533	23.3	Leisure and recreation	0–2	109	4.8
Household economy	0–2	133	5.8		3–4	171	7.5
	3–4	242	10.6		5–6	680	29.8
	5–6	915	40.1		7–8	910	39.8
	7–8	744	32.6		9–10	414	18.1
	9–10	250	10.9		Average	6.73
	Average	6.15		Married life	0–2	109	4.8
Work situation	0–2	90	3.9		3–4	171	7.5
	3–4	158	6.9		5–6	680	29.8
	5–6	828	36.3		7–8	910	39.8
	7–8	908	39.8		9–10	414	18.1
	9–10	300	13.1		Average	7.83
	Average	6.55		Social interaction	0–2	53	2.3
Housing situation	0–2	167	7.3		3–4	143	6.3
	3–4	351	15.4		5–6	634	27.8
	5–6	818	35.8		7–8	961	42.1
	7–8	672	29.4		9–10	493	21.6
	9–10	276	12.1		Average	7.06
	Average	5.97		Financial services	0–2	90	3.9
Physical health	0–2	33	1.4		3–4	130	5.7
	3–4	60	2.6		5–6	631	27.6
	5–6	388	17.0		7–8	965	42.3
	7–8	952	41.7		9–10	468	20.5
	9–10	851	37.3		Average	6.93
	Average	7.83		Overall situation	0–2	11	0.5
Medical condition	0–2	109	4.8		3–4	67	2.9
	3–4	169	7.4		5–6	589	25.8
	5–6	709	31.0		7–8	1300	56.9
	7–8	933	40.8		9–10	317	13.9
	9–10	364	15.9		Average	7.17
	Average	6.65
Children’s education	0–2	90	3.9
	3–4	123	5.4
	5–6	402	17.6
	7–8	1423	62.3
	9–10	246	10.8
	Average	6.53

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	Dongguan	Foshan	Guangzhou	Shenzhen	Zhongshan	Zhuhai	Total
Number of questionnaires	499	384	560	363	317	293	2416
Proportion of males (%)	58.52%	51.82%	56.43%	53.99%	49.21%	50.17%	54.06%
Avg. age	36.32	34.15	34.81	32.97	33.22	35.30	34.59
S.D. of age	11.84	9.62	11.01	10.15	10.58	10.16	10.75
Avg. years of schooling	9.56	9.99	11.35	12.00	10.36	10.60	10.64
S.D. of years of schooling	3.61	3.73	4.00	3.44	3.56	3.54	3.78
Proportion of rural household registrations (%)	87.17%	83.85%	76.25%	76.31%	79.50%	78.16%	80.38%
Avg. monthly individual income (unit: Yuan)	4086.38	4288.14	4743.45	6934	4987.12	3922.46	4802.72
S.D. of monthly individual income (unit: Yuan)	3144.05	2912.86	4264.92	11,691.35	6879.08	2682.78	6040.65

	Household Economy	Work Situation	Housing Situation	Physical Health	Medical Condition	Children’s Education	Daily Transportation	Leisure and Recreation
Household Economy	1	0.605 **	0.529 **	0.330 **	0.394 **	0.345 **	0.284 **	0.338 **	0.248 **	0.304 **	0.336 **
Work Situation	0.605 **	1	0.544 **	0.329 **	0.415 **	0.347 **	0.295 **	0.339 **	0.245 **	0.330 **	0.346 **
Housing Situation	0.529 **	0.544 **	1	0.331 **	0.386 **	0.365 **	0.295 **	0.284 **	0.191 **	0.296 **	0.304 **
Physical Health	0.330 **	0.329 **	0.331 **	1	0.420 **	0.266 **	0.278 **	0.254 **	0.285 **	0.332 **	0.258 **
Medical Condition	0.394 **	0.415 **	0.386 **	0.420 **	1	0.395 **	0.390 **	0.357 **	0.189 **	0.285 **	0.380 **
Children’s Education	0.345 **	0.347 **	0.365 **	0.266 **	0.395 **	1	0.341 **	0.324 **	0.309 **	0.299 **	0.336 **
Daily Transportation	0.284 **	0.295 **	0.295 **	0.278 **	0.390 **	0.341 **	1	0.427 **	0.228 **	0.295 **	0.375 **
Leisure and Recreation	0.338 **	0.339 **	0.284 **	0.254 **	0.357 **	0.324 **	0.427 **	1	0.254 **	0.413 **	0.379 **
Married Life	0.248 **	0.245 **	0.191 **	0.285 **	0.189 **	0.309 **	0.228 **	0.254 **	1	0.394 **	0.281 **
Social Interaction	0.304 **	0.330 **	0.296 **	0.332 **	0.285 **	0.299 **	0.295 **	0.413 **	0.394 **	1	0.397 **
Financial Services	0.336 **	0.346 **	0.304 **	0.258 **	0.380 **	0.336 **	0.375 **	0.379 **	0.281 **	0.397 **	1
Overall Situation	0.533 **	0.518 **	0.476 **	0.404 **	0.459 **	0.414 **	0.424 **	0.424 **	0.332 **	0.486 **	0.504 **

	Path Coefficient (Estimate)	Standard Error (S.E.)	Critical Ratio (st./S.E.)	Significance Probability (p-Value)
Economic life satisfaction by household economy	0.069	0.023	2.935	0.003
Economic life satisfaction by work situation	−0.768	0.013	−58.439	0.000
Economic life satisfaction by housing situation	−0.788	0.013	−61.090	0.000
Social security satisfaction by physical health	0.661	0.015	43.322	0.000
Social security satisfaction by medical condition	0.520	0.019	28.023	0.000
Social security satisfaction by children’s education	0.625	0.017	37.770	0.000
Emotional life satisfaction by leisure and recreation	0.295	0.097	3.041	0.002
Emotional life satisfaction by married life	0.645	0.049	13.100	0.000
Emotional life satisfaction by social interaction	−0.023	0.026	−0.876	0.381
Public service satisfaction by daily transportation	0.639	0.036	17.768	0.000
Public service satisfaction by leisure and recreation	0.307	0.096	3.182	0.001
Public service satisfaction by financial services	0.035	0.026	1.366	0.172
Human capital factors by personal income	0.890	0.087	10.203	0.000
Human capital factors by personal occupation	0.625	0.104	6.017	0.000
Human capital factors by personal education	0.439	0.041	10.723	0.000
Economic life satisfaction → urban settlement intention	0.176	0.089	1.964	0.050
Social security satisfaction → urban settlement intention	0.199	0.088	2.267	0.023
Human capital factors → urban settlement intention	0.560	0.169	3.321	0.001
Personal income → urban settlement intention	−0.229	0.177	−1.296	0.195
Personal occupation → personal income	−0.209	0.075	−2.784	0.005
Personal education → personal occupation	−0.067	0.067	−1.009	0.313
Economic life satisfaction ↔ human capital factors	−0.146	0.033	−4.431	0.000
Social security satisfaction ↔ human capital factors	0.142	0.032	4.497	0.000
Social security satisfaction ↔ economic life satisfaction	−0.905	0.017	−53.303	0.000
Emotional life satisfaction ↔ human capital factors	0.210	0.037	5.616	0.000
Emotional life satisfaction ↔ economic life satisfaction	−0.620	0.052	−11.975	0.000
Emotional life satisfaction ↔ social security satisfaction	0.722	0.058	12.513	0.000
Public service satisfaction ↔ human capital factors	0.028	0.038	0.729	0.466
Public service satisfaction ↔ economic life satisfaction	−0.619	0.043	−14.533	0.000
Public service satisfaction ↔ social security satisfaction	0.843	0.046	18.142	0.000
Public service satisfaction ↔ emotional life satisfaction	0.790	0.094	8.447	0.000

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Jiang, Q.; Wang, Y.; Ye, X.; Li, X.; Pan, W.; Wang, Y. Human Capital, Life Satisfaction, and the Floating Population’s Urban Settlement Intention in Cities—A Case Study of Six Cities in the Pearl River Delta. Land 2024 , 13 , 817. https://doi.org/10.3390/land13060817

Jiang Q, Wang Y, Ye X, Li X, Pan W, Wang Y. Human Capital, Life Satisfaction, and the Floating Population’s Urban Settlement Intention in Cities—A Case Study of Six Cities in the Pearl River Delta. Land . 2024; 13(6):817. https://doi.org/10.3390/land13060817

Jiang, Qinyi, Yuanyuan Wang, Xiaomei Ye, Xinger Li, Weimin Pan, and Yuqu Wang. 2024. "Human Capital, Life Satisfaction, and the Floating Population’s Urban Settlement Intention in Cities—A Case Study of Six Cities in the Pearl River Delta" Land 13, no. 6: 817. https://doi.org/10.3390/land13060817

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The state of AI in 2023: Generative AI’s breakout year

You have reached a page with older survey data. please see our 2024 survey results here ..

The latest annual McKinsey Global Survey on the current state of AI confirms the explosive growth of generative AI (gen AI) tools . Less than a year after many of these tools debuted, one-third of our survey respondents say their organizations are using gen AI regularly in at least one business function. Amid recent advances, AI has risen from a topic relegated to tech employees to a focus of company leaders: nearly one-quarter of surveyed C-suite executives say they are personally using gen AI tools for work, and more than one-quarter of respondents from companies using AI say gen AI is already on their boards’ agendas. What’s more, 40 percent of respondents say their organizations will increase their investment in AI overall because of advances in gen AI. The findings show that these are still early days for managing gen AI–related risks, with less than half of respondents saying their organizations are mitigating even the risk they consider most relevant: inaccuracy.

The organizations that have already embedded AI capabilities have been the first to explore gen AI’s potential, and those seeing the most value from more traditional AI capabilities—a group we call AI high performers—are already outpacing others in their adoption of gen AI tools. 1 We define AI high performers as organizations that, according to respondents, attribute at least 20 percent of their EBIT to AI adoption.

The expected business disruption from gen AI is significant, and respondents predict meaningful changes to their workforces. They anticipate workforce cuts in certain areas and large reskilling efforts to address shifting talent needs. Yet while the use of gen AI might spur the adoption of other AI tools, we see few meaningful increases in organizations’ adoption of these technologies. The percent of organizations adopting any AI tools has held steady since 2022, and adoption remains concentrated within a small number of business functions.

It’s early days still, but use of gen AI is already widespread
Leading companies are already ahead with gen AI
AI-related talent needs shift, and AI’s workforce effects are expected to be substantial
With all eyes on gen AI, AI adoption and impact remain steady

About the research

1. it’s early days still, but use of gen ai is already widespread.

The findings from the survey—which was in the field in mid-April 2023—show that, despite gen AI’s nascent public availability, experimentation with the tools is already relatively common, and respondents expect the new capabilities to transform their industries. Gen AI has captured interest across the business population: individuals across regions, industries, and seniority levels are using gen AI for work and outside of work. Seventy-nine percent of all respondents say they’ve had at least some exposure to gen AI, either for work or outside of work, and 22 percent say they are regularly using it in their own work. While reported use is quite similar across seniority levels, it is highest among respondents working in the technology sector and those in North America.

Organizations, too, are now commonly using gen AI. One-third of all respondents say their organizations are already regularly using generative AI in at least one function—meaning that 60 percent of organizations with reported AI adoption are using gen AI. What’s more, 40 percent of those reporting AI adoption at their organizations say their companies expect to invest more in AI overall thanks to generative AI, and 28 percent say generative AI use is already on their board’s agenda. The most commonly reported business functions using these newer tools are the same as those in which AI use is most common overall: marketing and sales, product and service development, and service operations, such as customer care and back-office support. This suggests that organizations are pursuing these new tools where the most value is. In our previous research , these three areas, along with software engineering, showed the potential to deliver about 75 percent of the total annual value from generative AI use cases.

In these early days, expectations for gen AI’s impact are high : three-quarters of all respondents expect gen AI to cause significant or disruptive change in the nature of their industry’s competition in the next three years. Survey respondents working in the technology and financial-services industries are the most likely to expect disruptive change from gen AI. Our previous research shows that, while all industries are indeed likely to see some degree of disruption, the level of impact is likely to vary. 2 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. Industries relying most heavily on knowledge work are likely to see more disruption—and potentially reap more value. While our estimates suggest that tech companies, unsurprisingly, are poised to see the highest impact from gen AI—adding value equivalent to as much as 9 percent of global industry revenue—knowledge-based industries such as banking (up to 5 percent), pharmaceuticals and medical products (also up to 5 percent), and education (up to 4 percent) could experience significant effects as well. By contrast, manufacturing-based industries, such as aerospace, automotives, and advanced electronics, could experience less disruptive effects. This stands in contrast to the impact of previous technology waves that affected manufacturing the most and is due to gen AI’s strengths in language-based activities, as opposed to those requiring physical labor.

Responses show many organizations not yet addressing potential risks from gen AI

According to the survey, few companies seem fully prepared for the widespread use of gen AI—or the business risks these tools may bring. Just 21 percent of respondents reporting AI adoption say their organizations have established policies governing employees’ use of gen AI technologies in their work. And when we asked specifically about the risks of adopting gen AI, few respondents say their companies are mitigating the most commonly cited risk with gen AI: inaccuracy. Respondents cite inaccuracy more frequently than both cybersecurity and regulatory compliance, which were the most common risks from AI overall in previous surveys. Just 32 percent say they’re mitigating inaccuracy, a smaller percentage than the 38 percent who say they mitigate cybersecurity risks. Interestingly, this figure is significantly lower than the percentage of respondents who reported mitigating AI-related cybersecurity last year (51 percent). Overall, much as we’ve seen in previous years, most respondents say their organizations are not addressing AI-related risks.

2. Leading companies are already ahead with gen AI

The survey results show that AI high performers—that is, organizations where respondents say at least 20 percent of EBIT in 2022 was attributable to AI use—are going all in on artificial intelligence, both with gen AI and more traditional AI capabilities. These organizations that achieve significant value from AI are already using gen AI in more business functions than other organizations do, especially in product and service development and risk and supply chain management. When looking at all AI capabilities—including more traditional machine learning capabilities, robotic process automation, and chatbots—AI high performers also are much more likely than others to use AI in product and service development, for uses such as product-development-cycle optimization, adding new features to existing products, and creating new AI-based products. These organizations also are using AI more often than other organizations in risk modeling and for uses within HR such as performance management and organization design and workforce deployment optimization.

AI high performers are much more likely than others to use AI in product and service development.

Another difference from their peers: high performers’ gen AI efforts are less oriented toward cost reduction, which is a top priority at other organizations. Respondents from AI high performers are twice as likely as others to say their organizations’ top objective for gen AI is to create entirely new businesses or sources of revenue—and they’re most likely to cite the increase in the value of existing offerings through new AI-based features.

As we’ve seen in previous years , these high-performing organizations invest much more than others in AI: respondents from AI high performers are more than five times more likely than others to say they spend more than 20 percent of their digital budgets on AI. They also use AI capabilities more broadly throughout the organization. Respondents from high performers are much more likely than others to say that their organizations have adopted AI in four or more business functions and that they have embedded a higher number of AI capabilities. For example, respondents from high performers more often report embedding knowledge graphs in at least one product or business function process, in addition to gen AI and related natural-language capabilities.

While AI high performers are not immune to the challenges of capturing value from AI, the results suggest that the difficulties they face reflect their relative AI maturity, while others struggle with the more foundational, strategic elements of AI adoption. Respondents at AI high performers most often point to models and tools, such as monitoring model performance in production and retraining models as needed over time, as their top challenge. By comparison, other respondents cite strategy issues, such as setting a clearly defined AI vision that is linked with business value or finding sufficient resources.

The findings offer further evidence that even high performers haven’t mastered best practices regarding AI adoption, such as machine-learning-operations (MLOps) approaches, though they are much more likely than others to do so. For example, just 35 percent of respondents at AI high performers report that where possible, their organizations assemble existing components, rather than reinvent them, but that’s a much larger share than the 19 percent of respondents from other organizations who report that practice.

Many specialized MLOps technologies and practices may be needed to adopt some of the more transformative uses cases that gen AI applications can deliver—and do so as safely as possible. Live-model operations is one such area, where monitoring systems and setting up instant alerts to enable rapid issue resolution can keep gen AI systems in check. High performers stand out in this respect but have room to grow: one-quarter of respondents from these organizations say their entire system is monitored and equipped with instant alerts, compared with just 12 percent of other respondents.

3. AI-related talent needs shift, and AI’s workforce effects are expected to be substantial

Our latest survey results show changes in the roles that organizations are filling to support their AI ambitions. In the past year, organizations using AI most often hired data engineers, machine learning engineers, and Al data scientists—all roles that respondents commonly reported hiring in the previous survey. But a much smaller share of respondents report hiring AI-related-software engineers—the most-hired role last year—than in the previous survey (28 percent in the latest survey, down from 39 percent). Roles in prompt engineering have recently emerged, as the need for that skill set rises alongside gen AI adoption, with 7 percent of respondents whose organizations have adopted AI reporting those hires in the past year.

The findings suggest that hiring for AI-related roles remains a challenge but has become somewhat easier over the past year, which could reflect the spate of layoffs at technology companies from late 2022 through the first half of 2023. Smaller shares of respondents than in the previous survey report difficulty hiring for roles such as AI data scientists, data engineers, and data-visualization specialists, though responses suggest that hiring machine learning engineers and AI product owners remains as much of a challenge as in the previous year.

Looking ahead to the next three years, respondents predict that the adoption of AI will reshape many roles in the workforce. Generally, they expect more employees to be reskilled than to be separated. Nearly four in ten respondents reporting AI adoption expect more than 20 percent of their companies’ workforces will be reskilled, whereas 8 percent of respondents say the size of their workforces will decrease by more than 20 percent.

Looking specifically at gen AI’s predicted impact, service operations is the only function in which most respondents expect to see a decrease in workforce size at their organizations. This finding generally aligns with what our recent research suggests: while the emergence of gen AI increased our estimate of the percentage of worker activities that could be automated (60 to 70 percent, up from 50 percent), this doesn’t necessarily translate into the automation of an entire role.

AI high performers are expected to conduct much higher levels of reskilling than other companies are. Respondents at these organizations are over three times more likely than others to say their organizations will reskill more than 30 percent of their workforces over the next three years as a result of AI adoption.

4. With all eyes on gen AI, AI adoption and impact remain steady

While the use of gen AI tools is spreading rapidly, the survey data doesn’t show that these newer tools are propelling organizations’ overall AI adoption. The share of organizations that have adopted AI overall remains steady, at least for the moment, with 55 percent of respondents reporting that their organizations have adopted AI. Less than a third of respondents continue to say that their organizations have adopted AI in more than one business function, suggesting that AI use remains limited in scope. Product and service development and service operations continue to be the two business functions in which respondents most often report AI adoption, as was true in the previous four surveys. And overall, just 23 percent of respondents say at least 5 percent of their organizations’ EBIT last year was attributable to their use of AI—essentially flat with the previous survey—suggesting there is much more room to capture value.

Organizations continue to see returns in the business areas in which they are using AI, and they plan to increase investment in the years ahead. We see a majority of respondents reporting AI-related revenue increases within each business function using AI. And looking ahead, more than two-thirds expect their organizations to increase their AI investment over the next three years.

The online survey was in the field April 11 to 21, 2023, and garnered responses from 1,684 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 913 said their organizations had adopted AI in at least one function and were asked questions about their organizations’ AI use. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

The survey content and analysis were developed by Michael Chui , a partner at the McKinsey Global Institute and a partner in McKinsey’s Bay Area office, where Lareina Yee is a senior partner; Bryce Hall , an associate partner in the Washington, DC, office; and senior partners Alex Singla and Alexander Sukharevsky , global leaders of QuantumBlack, AI by McKinsey, based in the Chicago and London offices, respectively.

They wish to thank Shivani Gupta, Abhisek Jena, Begum Ortaoglu, Barr Seitz, and Li Zhang for their contributions to this work.

This article was edited by Heather Hanselman, an editor in the Atlanta office.

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13: Case study
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Data Structure Basics

Benefits of Data structures:

1. Efficient Data Access

2. Memory Management

3. Organization of Data

4. Search and Retrieval

6. Dynamic Memory Allocation

7. Data Aggregation

8. Modularity and Reusability

9. Complex Problem Solving

10. Resource Efficiency

11. Scalability

12. Algorithm Optimization

13. Code Readability and Maintainability

14. Cross-Disciplinary Applications

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