5 Big Data Analytics Case Studies You Should Know

The Importance of Big Data Analytics

Today, big data analytics is essential for businesses. It provides a way to discover trends and patterns in a large data set. This process enables organizations to make data driven decisions. For instance, Netflix uses big data analytics to recommend a show based on user preference. As a result, viewer activity has increased by 75%.

Big data analytics also enhances efficiencies and profitability. For example, Walmart uses data mining to find patterns and improve its supply chain. Walmart has been able to stay on top as the world’s largest retailer because of this. Big data analytics also assists in personalized marketing. Additionally, PepsiCo uses data analytics to create targeted ads based on customer browsing history.

In addition, big data analytics is a must to remain competitive. So JPMorgan Chase uses advanced analytics to catch fraudulent transactions. This has cut down considerable losses suffered as a result of fraud. Moreover, big data analytics is changing industries such as health care and finance. This is useful to predict the outcome of patients, manage financial risks.

To sum up, big data analytics is a great tool for business. It gives valuable insights, gets better efficiency, and it helps stay competitive. For more detailed information, you can refer to reports from IBM and Coursera.

5 Notable Big Data Analytics Case Studies

The analytics of big data has been a game changer in how industries work and can produce unprecedented insights from huge data sets. According to recent studies, businesses that use big data analytics have seen productivity rise by 5 percent and profitability rise by 6 percent. Below are five case studies of how big data analytics can transform.

Netflix

Netflixm the world’s largest streaming platform to change how we consume entertainment, utilizes big data analytics. Using analytics on large amounts of user data, Netflix can make content recommendations and optimize user experience in order to increase customer retention.

International viewers, including those from 190 countries, numbered 222 million for Netflix in 2022. This massive user base creates a lot of data that Netflix uses to create and distribute its content. For example, “Stranger Things” was helped along by data determined audience tastes for sci-fi and nostalgic themes.

Netflix’s data analytics also change with the times, such as binge watching. With a view into viewing habits, Netflix releases to drive engagement and keep users coming back. The strategy had contributed something towards Netflix’s impressive customer retention rate, which is growing year after year.

For example, to run its business globally, Netflix uses cloud services like Amazon Web Services (AWS). Netflix can deploy thousands of servers and terabytes of storage in minutes on AWS for seamless streaming experiences to users all over the world.

To get more detailed insights, you can read the Netflix Case Study on AWS.

Walmart

Walmart, the world’s largest retailer, uses big data analytics to improve its operations and customer experience. The company, via big data analytics to analyze enormous data sets and can predict demand, overstock, and sales.

For example, Walmart has been using predictive analytics, machine learning to improve demand forecasting. The whole system analyzes historical sales data, the seasonal trends and weather patters to predict demand more accurately for both the store and at regional level.

Big data is also used by Walmart to enhance customer emotional intelligence quotient (EQ). Walmart uses the competence of analyzing social mentions as well as their online activities to gain insights into the customer preferences and customer behavior in order to allow for more targeted marketing and better customer satisfaction.

For more details, you can read the ProjectPro article on Walmart’s big data analysis.

Uber

Ride sharing giant Uber uses big data analytics to improve its services and operations. The company processes over 100 petabytes of data daily, making data driven decisions at every level. This data allows Uber to predict rider demand during high traffic events and to identify bottlenecks in the driver partner sign up process.

Big data is used by Uber’s surge pricing algorithm to estimate consumer surplus. Uber can use individual level data to calculate demand elasticities and improve supply efficiency. Millions of queries are handled by the company’s data warehouse team every day, and 95 percent of them return responses in less than 15 seconds.

If you want more details, you can read Uber’s blog on solving big data challenges and their Hadoop journey.

eBay

The global e-commerce giant, eBay, uses big data analytics to increase customer experience and business growth. eBay can recommend personalized products and target marketing campaigns by analyzing customer behavior and behavior patterns. It has resulted in significant increase in customer satisfaction and sales.

In 2021, eBay moved its analytics platform from a vendor based data warehouse to an open source solution. This helped eBay for the flexibility and innovation to process and analyze data in a more efficient way for masses. With the new platform, over 20 petabytes of data can be harnessed to extract insight into customer trends, market dynamics, and other pieces of information that can help shape your decision process.

One such case of eBay applying big data analytics is the usage of machine learning algorithms to find false transactions. eBay can identify suspicious patterns in transaction data and prevent fraud in order to make its users’ shopping experience secure.

If you want more details, you can read the MIT Sloan article on eBay’s data and analytics strategy.

Procter & Gamble

Procter & Gamble (P&G) is a leading consumer packaged goods company. They have adopted big data analytics to understand consumer behavior and make better business decisions. P&G’s big data strategy has helped save costs and speed up product roll outs.

For example, P&G uses Google Cloud for advanced data analytics. They have been able to provide more personalized consumer experiences, from baby care to beauty products. The company collects consumer data from multiple touch points and uses it to make real time business decisions.

For more detailed information, you can refer to the Big Data Strategy of Procter & Gamble: Big Data into Big Value case study.

What You Can Learn from the Big Data Analytics Case Studies

It is important to understand the roles of Data Analysts and Business Analysts. In our project with a retail company building an e-commerce website, we could see this. At first, the team had a hard time finding features that meet user needs. To solve this issue, we combined both roles.

Using Business Analyst Skills

Understanding customer needs is a key role of Business Analysts. They interview and analyze feedback. This allows them to identify issues and opportunities. Our Business Analyst skills helped us to collect the right customer data and make the right decisions.

Using Data Analyst Techniques

Data Analysts are interested in user behavior and website analytics. For this project, we looked at site traffic, time spent on pages, feature clicks, and purchase rates. We used descriptive analysis to identify user behavior patterns. For example, we discovered which features were popular and which were not.

Achieving Significant Results

We adjusted the website features based on our analysis. We removed unnecessary features and added new ones that customers needed. The results were remarkable. From 100 to 20,000 page visits per month. The average time spent on the site increased from 1.2 minutes to 9.3 minutes. The new search bar and filter was used by 70 percent of users. Overall sales increased from 12% to 33% purchase rates.

At Designveloper, we excel in combining Data Analyst and Business Analyst roles. This project is just one example of our successful big data analytics case studies. For more insights on how we can help your business grow, visit our services page.

Conclusion

Big data analytics case studies offer valuable insights into how organizations leverage data to drive success. By looking at these examples, businesses can learn more about what works and what doesn’t work, and what is innovative and what isn’t.

In conclusion, big data analytics case studies highlight the transformative power of data-driven decision-making. Through real life examples from real companies across different industries, we can see how data analytics make operations better, improve customer experience, and remain competitive for the best of companies. With these lessons learned, businesses can more effectively steer through the difficult waters of the modern data landscape in search of additional opportunities for growth and innovation.