Big data analytics involves examining large amounts of data. This is done so as to uncover the hidden patterns, correlations and also to give insights so as to make proper business decisions. Basically, organizations have realized the need for evolving from a knowing organization to a learning organization.Essentially, businesses want to be more objective and data-driven, and 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 on the data they collected so as to gain insights and uncover trends. This involved capturing numbers on a spreadsheet and manually examining the numbers.
Big data analytics is done using advanced software systems. This allows businesses to reduce the analytics time for speedy decision making. Basically, the modern big data analytics systems allow for speedy 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. Think of a business you know that depends on quick and agile decisions to remain competitive. In this article, we give five real-world examples of how big brands are using big data analytics. Keep reading to gain more insights.
#1 Using Big Data Analytics to Boost Customer Acquisition and Retention
The customer is the most important asset any business depends on.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. In the end, loss of clientele will 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. Observing customer behaviour 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. In the modern business world and the current technology age, a business can easily collect all the customer data it needs. This means that it is very easy to understand the modern-day client. Basically, all that is necessary is having 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 behavioural insights that it needs to act on so as to retain the customer base.
Understanding the customer insights will allow your business to be able to deliver what the customers want from you. This is the most basic step to attain high customer retention.
Example of a Company that uses Big Data for Customer Acquisition and Retention
A real example of a company that uses big data analytics to drive customer retention is Coca-Cola. In the year 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. Below is an abstract of the full interview on what Coca-Cola had to say about the role of big data in achieving customer retention.
How much does the role of data play in Coca-Cola remaining relevant and staying connected to its consumers in the age of digital transformation? What about data and product development?
Data plays an increasingly important role in marketing and product development. Consumers do a great job of sharing their opinions with us – either by phone, email or social networks – that allow us to hear their voice and adjust our approach. We often talk about why we have two ears and one mouth – it’s better to listen more than we speak. This holds true with our approach on consumer input. Data is also helping us create more relevant content for different audiences. We want to focus on creating advertising content that speaks differently to different audiences. Some people love music. Other people watch every sport no matter what time of year. Our brands are already visible in those spaces, and we’re working hard to use data to bring branded content that aligns with people’s passions.See Alsospencer klavan boyfriend joshWhat is a data leak and what can you do about it?[3-Mar-2022] New Security+ SY0-601 Dumps with VCE and PDF from PassLeader (New Questions) - New CompTIA A+, CASP+, Security+, Network+, Server+, CySA+, Cloud+, Linux+, Project+ Exam Dumps VCE & PDF & Practice Test from PassLeader for FreeTop 5 AWS Misconfigurations That Led to Data Leaks in 2021 | Spiceworks
#2 Use of Big Data Analytics to Solve Advertisers’ Problem and Offer Marketing Insights
Big data analytics can help change all business operations. This includes the ability to match customer expectations, changing the company’s product line and of course ensuring that the marketing campaigns are powerful. Let’s face the naked truth here. Businesses have lost millions spent in running advertisements 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 a big way. The marketing and advertising sector is able to make a more sophisticated analysis. This involves observing the online activity, monitoring the point of sale transactions, and ensuring on the fly detection of dynamic changes in customer trends. Gaining insights on customer behaviour takes collecting and analyzing the customer’s data. This is done through the similar approach used by marketers and advertisers as illustrated. This result in the capability to achieve focused and targeted campaigns.
A more targeted and personalized campaign means that businesses can save money and ensure efficiency. This is because they target high potential clients with the right products. Big data analytics is good for advertisers since the companies can use this data to understand customers purchasing behaviour. We can’t ignore the huge ad fraud problem. Through predictive analytics, it is possible for organizations to define their target clients. Therefore, businesses can have an appropriate and effective reach avoiding the huge losses incurred as a result of Ad fraud.
Example of a Brand that uses Big Data for Targeted Adverts
Netflix is a good example of a big brand that uses big data analytics for targeted advertising. With over 100 million subscribers, the company collects huge data, which is the key to achieving the industry status Netflix boosts. If you are a subscriber, you are familiar with how they send you suggestions of 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. See the screenshot below showing how Netflix gathers big data.
#3 Big Data Analytics for Risk Management
The unprecedented times and highly risky business environment calls 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 mitigating 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 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 risks that they face every day. Considering the increasing availability and diversity of statistics, big data analytics has a 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 evolutionary so as to accommodate the broad scope of big data. To achieve this, organizations collect the internal data first so as 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 weaknesses or potential risks are identified.
Example of Brand that uses Big Data Analytics for Risk Management
UOB bank from Singapore is an example of a brand that uses big data to drive risk management. Being a financial institution, there is huge potential for incurring losses if risk management is not well thought of. UOB bank recently tested a risk management system that is based on big data. 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 will possibly be able to carry out real-time risk analysis in the near future.
#4 Big Data Analytics As a Driver of Innovations and Product 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 through 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 intuition is basically 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, competitors and customer feedback.
Once the data is availed, 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 these organizations can easily improve their product line by producing innovative products.
Example of use of Big Data 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 are able to 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 Use of Big Data in 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 the supply chains. Basically, through big data analytics suppliers are able to escape the constraints faced earlier.
This was through the use of the traditional enterprise management systems and the 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, the 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 organizations.
Example of a Brand that uses Big Data for Supply Chain Efficiency
PepsiCo is a consumer packaged goods company that relies on huge volumes of data for the efficient supply chain management. The company is committed to ensuring they replenish 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. Listen to this webinar where the company’s Customer Supply Chain Analyst talks about the importance of big data analytics in the PepsiCo Supply chain.
Big data analytics is an important investment for a growing business. Through implementing big data analytics businesses can achieve a competitive advantage, reduced 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 data. Does your business have a big data analysis mechanism in place? Learn from the above examples of successful brands and implement one today.
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Banking and Financial Services
- Fraud detection. ...
- Risk management. ...
- Customer relationship optimization. ...
- Personalized marketing.
Modern big data analytics and operations anticipate the patterns of consumers. After that, they use those patterns to motivate brand loyalty as they can collect more data to observe more trends and also the ways to make consumers satisfied. It helps in delivering smarter services and products.How do companies use big data analytics in real world? ›
It can be used to collect and analyze the vast internal data available in the company archives that can help in developing both short term and long term risk management models. Using these, the company can identify future risks and make much more strategic business decisions.What are real life examples of the application of big data analytics? ›
When you think of big data, you usually think of applications related to banking, healthcare analytics, or manufacturing. After all, these are some pretty massive industries with many examples of big data analytics, and the rise of business intelligence software is answering what data management needs.How does Netflix use big data? ›
Netflix itself automatically collects other forms of data, such as the platform used to watch Netflix, a user's watch history, search queries, and time spent watching a show. The company also collects some bits of data from other sources, such as demographic data, interest-based data, and Internet browsing behavior.Is Netflix an example of big data? ›
Netflix, a giant streaming platform has made it big using big data analytics. Netflix is one of the most prominent examples of how advancements in technology have helped brands like Netflix to grow into becoming famous and successful. It is not only Netflix that is making use of big data analytics like Amazon.How does Zara use big data? ›
Zara's strategy to optimize the supply chain involves applying big data from gathered customer interests, feedback, surveys, and other insights used to guide the design and production processes. Hence, a lower production lead time.How is Burberry using big data? ›
Burberry may use the project to create client profiles depending on which clothing they've tried on in the past, allowing them to customise tailored situations in the future. Burberry saw an 11% increase in revenue and a 14% increase year over year as a result of these and other digital efforts.How does Spotify use big data? ›
Using Big Data to create value. One of Spotify's funnest feature is “Wrapped”. Every December, “Wrapped” gives users a roundup of their favourite or most listened to song/artist of the entire year. “Wrapped” also lets users know if they were in the leading 1% of an artist's most loyal listeners.How does Apple use big data? ›
Using big data Apple can discover how people are using apps in real life and alter future designs to fit with customer tendencies. The Apple Watch is a good example of Apple's strategy of capitalizing on big data.
Walmart uses data mining to discover patterns in point of sales data. Data mining helps Walmart find patterns that can be used to provide product recommendations to users based on which products were bought together or which products were bought before the purchase of a particular product.What would Amazon use big data for? ›
In order to fulfill orders quickly, Amazon connects with manufacturers and uses data to track their inventories. They also use Big data to locate the closest warehouse to a customer to reduce the overall shipping costs.What is data analytics real life examples? ›
Most search engines like Google, Bing, Yahoo, AOL, Duckduckgo, etc. use data analytics. These search engines use different algorithms to deliver the best result for a search query, and they do so within a few milliseconds. Google is said to process about 20 petabytes of data every day.What is big data analytics why it is important explain by using real world example? ›
Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers. Businesses that use big data with advanced analytics gain value in many ways, such as: Reducing cost.How Spotify uses business analytics? ›
More specifically, Spotify utilizes machine learning (ML) to analyze users' behaviors and group users with similar music taste into groups. The ML algorithms then searches for what users are listening to that those other users aren't listening to, and recommends those songs to the other users (Mixson, 2021).What is big data with examples? ›
What are examples of big data? Big data comes from myriad sources -- some examples are transaction processing systems, customer databases, documents, emails, medical records, internet clickstream logs, mobile apps and social networks.How many GB of data is a Netflix movie? ›
Data used per hour, per device:
High: Best video quality: Standard definition: up to1 GB. High definition: up to 3 GB. Ultra high definition (4K): up to 7 GB.
Microsoft is taking Big Data to a billion people by providing easy access to all data, big or small, and enabling end users to analyze all data with familiar tools like Excel. We also give IT a complete data platform to scale insights across their organizations with confidence.What business analytics does Netflix use? ›
Netflix uses AI-powered algorithms to make predictions based on the user's watch history, search history, demographics, ratings, and preferences. These predictions shows with 80% accuracy what the user might be interested in seeing next.What is one more example of how Netflix has used analytics to help make a decision? ›
Predictive analytics is a kind of data mining that uses gathered data to make predictions based on the actions of individuals. Netflix benefits from predictive analytics by using it to predict its users' viewing habits. For example, it uses data collected from its users to determine what movies they'll watch next.
Sephora Gathers Marketing Data to Emphasize Customer Experience. As an added benefit of the mobile app, Sephora can collect more information about audience habits through the data that shoppers provide while using it, which helps the brand influence future purchasing decisions.How does Nordstrom use big data? ›
Those data sets include the items a customer is interested in, when a SKU might change, when a price drop occurs and when an item comes back in stock. To track them all, Nordstrom uses a number of event-streaming technologies, including Apache's Kafka Streams.What is big data in fashion industry? ›
Big data will allow the fashion industry to examine popularity patterns at a granular level, allowing them to understand who is buying what and why. Labels may focus their efforts on items with emerging promise once they have this information.How does Zara measure success? ›
Its core values are found in four simple terms: beauty, clarity, functionality and sustainability. The secret to Zara's success has largely being driven by its ability to keep up with rapidly changing fashion trends and showcase it in its collections with very little delay.Does ASOS use big data? ›
Another use of those advanced analytics is the “Style and match” feature, where the app shows you suggestions on how to pair the clothes you have scanned and how to improve their fashion appearance. With such use of Big data, ASOS becomes a preferred brand for shopping and fashion advice.How does Singapore use big data? ›
More companies are embracing big data.
Singaporean banks also use data analytics to streamline their operations. DBS Bank began overhauling its HR operations as early as 2011, introducing data analytics to boost recruitment, staff productivity and reduce attrition rates.
Data analytics is providing the music industry with a leg up on what listeners are listening to, from where, when, and how many times they are listening to a specific song or a genre.What analytics tools does Spotify use? ›
Using machine learning (ML) algorithms, natural language processing (NLP) and convolutional neural networks (CNN), Spotify is able to transform historical listening data into personalized playlists and music recommendations.How much data does 30 minutes of Spotify use? ›
Spotify data use per hour
High-quality streams use around 12MB for every 10 minutes of streaming, or 75MB per hour. For premium users, that doubles to 150MB for an hour. If you're streaming at Spotify's lowest quality, you'll only use about 10MB per hour.
The system manages data processing and storage for big data applications by providing high throughput access to application data. LinkedIn's records are aggregated across more than 50 offline data flows, making its huge dataset applicable for Hadoop.
Apple is in a league above Amazon in protecting user privacy. It is the most privacy-conscious firm out there. Apple only stores the information that is necessary to maintain users' accounts. This is because their website is not as reliant on advertising revenue as are Google, Twitter, and Facebook.How does Kohl's use big data? ›
For example, Kohl's uses a combination of customer spending algorithms to predict the next best offer to a customer based on their recent purchases. Much of that is based on first-party data of Kohl's customers online and in stores.How grocery stores use big data? ›
For example, grocery stores can test different pricing strategies and analyze the effect on sales data, allowing them to identify products where a price raise won't necessarily affect buyers' decisions (e.g. luxury food, fresh produce).How big data is used in supermarkets? ›
Big Data analytics is now being applied at every step of the retail process - right from predicting the popular products to identifying the customers who are likely to be interested in these products and what to sell them next.How does Amazon use big data and AI? ›
Amazon uses the large amount of data stored in its databases over the cloud about information related to customers and provides it to feed the data into the machine learning algorithms so that they can drive some meaningful decisions and analysis from it which will further increase its business.Why did Amazon use data analytics? ›
Data analytics is the main factor that contributes to sellers improving their business and increasing their revenue. They are able to better understand the market trends and customers' buying behaviors and help them cater to what the customers really want. It is the safest way to make any business decisions.How do Amazon use big data for their e business activities? ›
Amazon leverages its data via its recommendation engine. Everytime a user searches for a specific product, this data helps the platform to guess what else the user can have interest in. This in turn allows Amazon to enhance their procedure of convincing the consumer into purchasing it.What are 5 data examples? ›
- Number of females per 1000 males in various states of our country.
- Production of wheat in the last 10 years in our country.
- Number of plants in our locality.
- Rainfall in our city in the last 10 years.
- Marks obtained by students.
- Predictive data analytics. Predictive analytics may be the most commonly used category of data analytics. ...
- Prescriptive data analytics. ...
- Diagnostic data analytics. ...
- Descriptive data analytics.
- Integer. Integer data types often represent whole numbers in programming. ...
- Character. In coding, alphabet letters denote characters. ...
- Date. This data type stores a calendar date with other programming information. ...
- Floating point (real) ...
- Long. ...
- Short. ...
- String. ...
Big data analytics describes the process of uncovering trends, patterns, and correlations in large amounts of raw data to help make data-informed decisions. These processes use familiar statistical analysis techniques—like clustering and regression—and apply them to more extensive datasets with the help of newer tools.What are the top 3 business applications of big data? ›
Here is the list of the top 10 industries using big data applications: Banking and Securities. Communications, Media and Entertainment. Healthcare Providers.What are the 5 Vs of big data What are the types of analytics explain in detail? ›
Big data is a collection of data from many different sources and is often describe by five characteristics: volume, value, variety, velocity, and veracity.What are the 5 Vs of big data analytics Why is value the most important V? ›
The 5 V's of big data (velocity, volume, value, variety and veracity) are the five main and innate characteristics of big data. Knowing the 5 V's allows data scientists to derive more value from their data while also allowing the scientists' organization to become more customer-centric.What can big data be used for in business? ›
Using those disciplines, big data analytics applications help businesses better understand customers, identify operational issues, detect fraudulent transactions and manage supply chains, among other uses.Where is big data used in a business? ›
Companies use big data in their systems to improve operations, provide better customer service, create personalized marketing campaigns and take other actions that, ultimately, can increase revenue and profits.What is big data and how is it used within business? ›
Big data is a term that describes large, hard-to-manage volumes of data – both structured and unstructured – that inundate businesses on a day-to-day basis. But it's not just the type or amount of data that's important, it's what organizations do with the data that matters.How is big data being used to support businesses? ›
Companies analyse huge swathes of data to understand how their customers behave and what their preferences are. This, in turn, allows them to predict the products that they want to see, as well as to target customers with more relevant and personalised marketing.What is big data give examples? ›
Bigdata is a term used to describe a collection of data that is huge in size and yet growing exponentially with time. Big Data analytics examples includes stock exchanges, social media sites, jet engines, etc.How does target use big data? ›
Target collects customer data through guest satisfaction surveys, which help the company identify buying trends and develop initiatives to drive more customers into stores.
Big data analytics in retail enables companies to create customer recommendations based on their purchase history, resulting in personalized shopping experiences and improved customer service. These super-sized data sets also help with forecasting trends and making strategic decisions based on market analysis.How is big data used in restaurants? ›
Big Data is changing the restaurant industry by allowing the opportunity to objectively analyze data and influence business strategy decisions. It allows restaurants to collect information on long-term trends. It helps build a foundation for strategies. Restaurant chains have begun exploring this more in-depth.What is big data and how is it used in marketing? ›
In marketing, big data comprises gathering, analyzing, and using massive amounts of digital information to improve business operations, such as: Getting a 360-degree view of their audiences. The concept of “know your customer” (KYC) was initially conceived many years ago to prevent bank fraud.How does big data analytics add value to business organizations? ›
It helps optimize business processes to generate cost savings, boost productivity and increase customer satisfaction. Hiring and HR management can become more effective. Better fraud detection, risk management and cybersecurity planning help organizations reduce financial losses and avoid potential business threats.What is an example of big data Accenture? ›
Examples of big data applications are :
Transportation. Advertising and Marketing. Banking and Financial Services.
Big data analytics benefits
Cost savings, which can result from new business process efficiencies and optimizations. A better understanding of customer needs, behavior and sentiment, which can lead to better marketing insights, as well as provide information for product development.
1 Answer. Industries can benefit from the huge amount of data available. For example, in the tourism industry, through Big Data travel agencies and hotels can identify the times when there are more crowds and hence more demand for a certain tourist spot.