Big data analytics involves examining large amounts of data. This is done so as to uncover the hidden patterns and correlations, and to give insights so as to make proper business decisions.
Basically, organizations have realized the need to evolve from a knowing organization to a learning organization. Βusinesses want to be more objective and data-driven, so they are embracing the power of data and technology.
The big data concept has been around for many years. Decades before the first mention of big data, businesses applied analytics to the data they collected to gain insights and uncover trends.
This involved capturing numbers on a spreadsheet and manually examining the numbers.
But now, big data analytics is done using advanced software systems. Coupled with the advancements of AI in digital marketing, this allows businesses to reduce the analytics time for speedy decision-making.
Μodern big data analytics systems allow for swift and efficient analytical procedures.
This ability to work faster and achieve agility offers a competitive advantage to businesses. In the meantime, businesses enjoy lower costs using big data analytics software.
Organizations have invested in big data analytics. In this article, we give five real-world examples of big data analytics done by well-known brands. Keep reading to gain more insights.
Updated on 16.07.2025
Table of Contents
- What is Considered Big Data Analytics?
- #1 Big Data Analytics Example: Boost Customer Acquisition and Retention
- #2 Big Data Analytics Example: Offer Marketing Insights
- #3 Big Data Analytics Example: Risk Management
- #4 Big Data Analytics Example: Innovations and Development
- #5 Business Big Data Analytics Example: Supply Chain Management
What is Considered Big Data Analytics?
Big data analytics is considered to be the complex process of examining large and varied data sets — called big data — to uncover trends, hidden patterns, correlations, and other useful business insights, such as customer preferences, for example.
It involves advanced analytics techniques (e.g., machine learning, predictive modeling) applied to massive, often unstructured or semi-structured data (e.g., images, social media posts), at a scale and speed that goes beyond traditional data-processing tools, like spreadsheets.
The goal is to turn raw data into actionable intelligence that supports decision-making, optimizes operations, identifies opportunities, and predicts future outcomes.
#1 Big Data Analytics Example: Boost Customer Acquisition and Retention
The first example of big data analytics is about customers, who are the most important asset of any business.
There is no single business that can claim success without first having to establish a solid customer base. However, even with a customer base, a business cannot afford to disregard the high competition it faces.
If a business is slow to learn what customers are looking for, then it is very easy to begin offering poor-quality products or services. In the end, a loss of clientele will be the result, and this creates an adverse overall effect on business success.
The use of big data allows businesses to observe various customer-related patterns and trends.
For example, social mentions of niche keywords can tell you what your customers are discussing online regarding your product category or specific service.
See, for instance, below a Reddit conversation Mentionlytics picked up about project management tools. And there are hundreds more!
Observing customer behavior is important to trigger loyalty.
Theoretically, the more data that a business collects, the more patterns and trends the business can be able to identify.
Today, a business can easily collect all the customer data it needs. This means that it is very easy to understand your customers. All that is necessary is to have a big data analytics strategy to maximize the data at your disposal.
With a proper customer data analytics mechanism in place, a business will have the capability to derive critical behavioral insights that it needs to act on so as to retain its customer base.
Understanding customer insights will allow your business to be able to deliver what the customers want from you. This is the most basic step to attaining high customer retention.
Coca-Cola Big Data Analytics Example
A real example of a company that uses big data analytics to drive customer retention is Coca-Cola.
In 2015, Coca-Cola managed to strengthen its data strategy by building a digital-led loyalty program. The Coca-Cola director of data strategy was interviewed by the ADMA managing editor.
The interview made it clear that big data analytics is strongly behind customer retention at Coca-Cola. Ten years after the interview, Coca-Cola achieved a whopping 85% of their drinkers becoming loyal customers, according to 2025 Statista insights.
#2 Big Data Analytics Example: Offer Marketing Insights
Big data analytics can help change all business operations. This includes the ability to match customer expectations, change the company’s product line, and, of course, ensure that marketing campaigns are powerful.
Let’s face the truth here: Businesses have spent millions running ads that are not fruitful. Why is this happening? There is a high possibility that they skipped the research phase.
After years of cautious enthusiasm, the marketing and advertising technology sector is now able to embrace big data in an effective way and conduct a more sophisticated analysis.
This involves social listening, monitoring sales transactions, and ensuring on-the-fly detection of dynamic changes in customer trends. Gaining insights into customer behavior takes collecting and analyzing customer data.
More targeted and personalized marketing campaigns mean that businesses can save money and ensure efficiency.
This is because they target high-potential clients with the right products. Social media analytics tools can provide a wealth of intelligence, helping companies that use big data understand customers’ purchasing behavior.
Netflix Big Data Examples for Targeted Ads
Netflix is a good example of big data analytics usage by a brand for targeted advertising. With over 301.6 million subscribers in 2024, the company collects huge amounts of data, which is the key to achieving the industry status that Netflix boasts.
If you are a subscriber, you are familiar with how they send you suggestions for the next movie you should watch.
Basically, this is done using your past search and watch data. This data is used to give them insights into what interests the subscriber most.
#3 Big Data Analytics Example: Risk Management
The unprecedented times and highly risky business environment call for better risk management processes. Basically, a risk management plan is a critical investment for any business, regardless of the sector.
Being able to foresee potential risk and mitigate it before it occurs is critical if the business is to remain profitable.
Business consultants will advise that enterprise risk management encompasses much more than ensuring your business has the right type of insurance.
So far, big data analytics has contributed greatly to the development of risk management solutions. The tools available allow businesses to quantify and model the risks that they face every day.
Considering the increasing availability and diversity of statistics, big data analytics has huge potential for enhancing the quality of risk management models.
Therefore, a business can be able to achieve smarter risk mitigation strategies and make strategic decisions.
However, organizations need to be able to implement a structured evolution so as to accommodate the broad scope of big data. To achieve this, organizations collect internal data first to gain clear insights that will benefit them.
More important is the integrated process of analysis that a company uses. A proper big data analytics system helps ensure that areas of weakness or potential risks are identified.
Big Data Analytics for Risk Management: UOB Singapore
UOB Singapore is an example of a brand that uses big data to drive risk management.
Being a financial institution, there is a huge potential for incurring losses if risk management is not well thought out. UOB Singapore tested a risk management system based on big data in 2014, according to Banking Hub.
The big data risk management system enables the bank to reduce the calculation time of the value at risk. Initially, it took about 18 hours, but with the risk management system that uses big data, it only takes a few minutes.
Through this initiative, the bank was aiming to carry out real-time risk analysis.
#4 Big Data Analytics Example: Innovations and Development
Another huge advantage of big data is the ability to help companies innovate and redevelop their products.
Basically, big data has become an avenue for creating additional revenue streams by enabling innovations and product improvement.
Organizations begin by correcting as much data as would be technically possible before designing new product lines and redesigning the existing products.
Every design process has to begin by establishing what exactly fits the customers.
There are various channels through which an organization can study customer needs. Then the business can identify the best approach to capitalize on that need based on big data analytics.
Gone are the days when you could go with your gut. To improve the quality and streamline your manufacturing performance, you need to collect huge data. The gut instinct is no longer reliable if an organization wants to compete in the 21st century.
This means that these organizations must come up with means for tracking their products, analyzing competitors, and collecting customer feedback.
For example, having a competitive analysis dashboard can be such a means.
Once the data is available, an analysis is then conducted to ensure logical reasoning is applied before an action plan is devised.
Luckily, product manufacturers of every size have a unique advantage when it comes to gathering and harnessing big data. This, therefore, means that organizations can easily improve their product line by producing innovative products.
Example of Big Data usage by Amazon to Drive Innovations
You have probably heard of Amazon Fresh and Whole Foods. This is a perfect example of how big data can help improve innovation and product development.
Amazon leverages big data analytics to move into a large market. The data-driven logistics gives Amazon the required expertise to enable the creation and achievement of greater value.
Focusing on big data analytics, Amazon Whole Foods can understand how customers buy groceries and how suppliers interact with the grocer.
This data gives insights whenever there is a need to implement further changes. You can also use the data of the best products to sell on Amazon for a more effective sales strategy.
#5 Business Big Data Analytics Example: Supply Chain Management
Big data offers supplier networks greater accuracy, clarity, and insights. Through the application of big data analytics, suppliers achieve contextual intelligence across their supply chains.
Basically, through big data analytics, suppliers are able to escape the constraints faced earlier.
In the past, suppliers faced many challenges because of traditional enterprise management systems and supply chain management systems.
These legacy applications didn’t leverage big data analytics, and therefore, suppliers incurred huge losses and were prone to making errors.
However, through modern approaches built on big data, suppliers can be able to leverage higher levels of contextual intelligence, which is necessary for supply chain success.
Modern supply chain systems based on big data enable more complex supplier networks. These are built on knowledge sharing and high-level collaboration to achieve contextual intelligence.
It is also essential to note that supply chain executives consider big data analytics as a disruptive technology. This is based on the thinking that it will set a foundation for change management in the organization.
Example of Big Data Usage by PepsiCo to Manage Supply Chain
PepsiCo is a consumer packaged goods company that relies on huge volumes of data for efficient supply chain management. The company is committed to ensuring it replenishes the retailers’ shelves with appropriate volumes and types of products.
The company’s clients provide reports that include their warehouse inventory and the POS inventory to the company, and this data is used to reconcile and forecast the production and shipment needs.
This way, the company ensures retailers have the right products, in the right volumes, and at the right time.
Invest in Big Data Analytics for Your Business
Big data analytics is an important investment for a growing business. Through implementing big data analytics, businesses can achieve a competitive advantage, reduce the cost of operation, and drive customer retention.
There are various sources of customer data that businesses can leverage. As technological advancements continue, data is becoming readily available to all organizations.
Technically, it is fair enough to say that organizations already have data at their disposal. It is up to the individual organizations to ensure they implement appropriate data analysis systems that can handle the huge amount of data.
Using Mentionlytics, you can handle some of this big data that is actually external. Mentionlytics uses AI to gather and analyze data about your brand from all over social media platforms and the web, so that you have a comprehensive understanding of the status of your:
- brand awareness
- share of voice
- sentiment analysis
- and many more important KPIs
Step up your game with two weeks of free access to Mentionlytics and start harnessing big data analytics for your business.
FAQ
What are the 4 types of data analytics?
According to Harvard Business School, these are the 4 types of data analytics:
- Descriptive analytics: Summarizes past data to reveal trends.
- Diagnostic analytics: Explores why events occurred by identifying patterns and causes.
- Predictive analytics: Forecasts future outcomes using historical data.
- Prescriptive analytics: Offers recommendations based on all data insights, guiding next steps.
What are the top 5 sources of big data?
The top five sources of big data are:
- Social media: e.g., posts, images, videos, mentions, engagement metrics
- IoT devices: e.g., sensor readings, GPS data, telemetry, usage logs, environmental data
- Transactional systems: e.g., e-commerce transactions, banking records, retail POS systems, stock trades
- Web and mobile activity: e.g., website logs, app usage, clickstreams, browsing behavior
- Machine logs: e.g., server logs, error logs, system health logs, cybersecurity logs
These sources generate vast amounts of diverse data that can be analyzed to extract insights into customer behavior, performance, and trends to inform business decisions.
What are data analysis tools?
Data analysis tools are software used to collect, process, and interpret data. They help identify patterns, trends, and insights to support decision-making.
Such tools can range from simple spreadsheets to more elaborate software like Tableau or Mentionlytics. These tools streamline data handling, visualization, and statistical analysis for businesses, researchers, and analysts.
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