Data Science Case Studies – Why is Data Science regarded as a revolution?
One of my favourite ways to demonstrate Data Science is through case studies. Through case studies, we will easily understand the use of a particular technology. In this article, we are going to see some real-time Data Science case studies of top industries.
Using this Data Science case studies helped me to understand the real-world importance of Data Science in today’s world. That’s why I am sharing this article with you. I hope it might help you too in understanding the importance of Data Science.
Data Science Case Studies
Data Science is a continuously evolving field. In today’s world, almost every industry is using various techniques of Data Science to cope up with the competition in the market. The industries have realized the importance of data and are utilizing it in one or the other way for increasing their profits.
Data Science is all about extracting insights from the data and applying it to your problems. Different industries are using Data Science for different purposes.
For example, the finance industry is using it for controlling frauds, the e-commerce industry is using it for providing a personalized experience, and there are many such more examples.
In this article, we will see different Data Science case studies that will help you to understand how different industries are using Data Science. This will help them in improving their business strategies, revenue generation, customer experience, and much more.
Check out the applications of Data Science in various industries with TechVidvan.
Let’s have a look at various Data Science case studies in various industries one by one.
Music plays an important role in the lives of people of almost all age groups. We frequently listen to our favorite songs in our daily routine such as while traveling, in leisure time, etc to release our stress and relax.
Today, there are many music playing applications in the market. You all might have heard the name “Spotify” at least once or maybe you might have used it also. So you must have observed that as soon as we start using it on a regular basis, it starts giving us personalized music recommendations and options to create customized playlists. This is what people like about it.
But how does Spotify do all this? The answer is “data”. At the core of these personalized services lies a large amount of user data that Spotify, actually not only Spotify but most of the music playing applications are using. Spotify is using this data for optimizing their algorithms, improving user music experience, providing targeted ads, and making some good business strategies.
In the present scenario, Spotify has around 108 million subscribers and around 124 million free users. The main goal of Spotify is to provide such a great experience to every user that will make them continue listening for hours. To achieve this they are using many advanced Data Science and Machine Learning techniques to extract insights from the user data for matching with the music taste of their individual customer.
A. Discover Weekly
This is one of the most loved features of Spotify. It provides customized playlists to the users based on their previous activities by using a Machine Learning Algorithm. This algorithm examines the previous songs played by the user and creates new playlists similar to those songs.
Along with providing customized playlist Spotify also analyzes the users’ reactions to individual songs. They observe whether the user played any song on repeat or changed it after a few seconds which helps them to develop an overall idea of the taste of different users.
B. Daily Mixes
Daily mixes are those playlists that Spotify generates by itself. These playlists include the songs that are either saved by the users or are of the artists followed by them or maybe something new that they may like.
In today’s era of digitalization, people spend hours on various social networking sites like Facebook, Instagram, WhatsApp, etc. Around 1.2 million people all over the globe upload 136,000 photos and update their status 293,000 times per minute. Our various activities whether it be commenting, tweeting, uploading something or anything else generates a large amount of data.
In 2012, Facebook stated that it generates more than 500 terabytes of data every day. Data Science and many other Big Data technologies are helping Facebook to deal with such a large amount of data. Some of the examples that show how Facebook is using user data for developing smarter ideas and better products for bringing the world closer are:
A. The Flashback
You might have observed that on some special occasions like on your birthday, Facebook offers you to share a video that consists of some of the photos from your previous history. This is called “Flashback” which is a video collection of some of your previous uploads that received the most number of likes and comments.
B. Celebrate Pride
Facebook introduced a feature called “ Celebrate Pride” for supporting the supreme court’s judgment empowering same-sex marriage. This feature enables people to decorate their photos with the seven colors of the rainbow. The success of this step taken by Facebook was unbelievable. Millions of users changed their profile pictures in just a few hours.
The Data Science team of Facebook analyzed the user data and observed that a large number of people were showing their support for the judgment. The success of “ Celebrate Pride” was the result of this analysis.
3. Data Science Case Study – Dickey’s Barbecue Pit
Dickey Barbecue Pit is one of the most successful restaurant chains in America. They started their business in 1941 and today includes more than 500 restaurants in their food chain in the US. They are using a software named “Smoke Stack”. This software collects the user data from loyalty programs, promotional events, online and offline surveys, etc and gives them the real-time feedback of the customers which helps them in improving their sales and business strategies.
The CEO of Dickey’s Barbecue Pit, Laura Rea Dickey has stated that the Smoke Stack tool has helped them to cope up with the other food industries in the US. The Smoke Stack tool analyzes the user data and gives decisions based on the insights extracted from it, in every 20 minutes. This is helping the industry in better planning and implementation of various business and other strategies.
For example, if the Smoke Stack analyzed the customer data and indicated them about the lower sales of the chicken ribs in a particular area and that the demand is not matching up with the production then they start sending offers and invitations to the customers of that area for some special rib dishes. This overall and regular analysis of the customer data helps them to be one step ahead of their competitors.
LinkedIn is one of the most successful social media platforms that is connecting professionals across the globe. It also uses customer data for providing better services and customized user experience. LinkedIn stores a large amount of user data including several details like their contact information, previous history, interests, activities on different social networking sites, etc in its data warehouse for being aware of the trends and patterns.
Using the insights gained from the user data, LinkedIn connects individual users with their friends and people related to their areas of interest. It also helps them to make some decisions regarding the business. According to the different trends, LinkedIn provides various articles and other services that might match user interests.
LinkedIn also enables users to promote their business to the right people by making use of targeting. Also while using the customers’ data, LinkedIn makes sure that the data is secure and no scrapping of data takes place from their site.
Uber is one of the fastest-growing companies and has established its roots across 449 cities in 66 countries. Uber is completely ruling over the market with millions of users and around 1 billion uber rides. The key behind this mesmerizing success story is the use of Big Data and Data Science for extracting insights and making some smart game-changing business decisions.
Data Science has helped Uber to deal with issues like pricing policies, better cars, fake user accounts, fake rides, ranking and much more. Uber uses technologies like Hadoop and Spark for collecting data of each and every ride taken on uber.
Data Scientists at Uber use this data to understand the customers’ point of view for solving their problems efficiently. The Data Science team at Uber performs a detailed analysis of data for various purposes like predicting the demand of rides, deciding the fares, identifying the cities with poor transportation services, etc.
For maximizing their profit and the number of rides uber uses the idea of surge pricing. With the help of real-time analysis of data, they always provide rides at times when you are running late but they charge more than around two or three times the usual fares. This is done by the use of surge pricing algorithms.
They are now moving towards the use of Machine Learning algorithms for implementing surge pricing to predict the areas of higher demand. This will help them to divert more and more drivers there. There are many other use cases that show the significance of Data Science at Uber.
This was all about TechVidvan’s Data Science case studies article. In this, we have seen some of the examples of how the leading companies used Data Science for taking some data-driven decisions that proved to be a game-changer for them. Data Science can help companies to achieve new heights in their respective fields. The major goal of all the industries is to fulfill the requirements of the customers in an efficient way and using Data Science helps them to achieve this by understanding their customers.
If you know more interesting Data Science case studies and want us to add that in our article, do let us know in the comment section.