6 Big Data Use Cases- How Companies Use Big Data?

From Retail To Finance, Explore These Real-World Big Data Use Cases To Know How Big Data Is Reshaping Various Industries. | ProjectPro

6 Big Data Use Cases- How Companies Use Big Data?
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Are you curious about how big data is transforming businesses? Join us as we explore real-world examples of big data use cases in various industries.


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According to Market Data Forecast reports, the global Big Data market will likely reach USD 268.4 billion by 2026.

With this rapidly growing big data market, organizations are leveraging big data to gain insights that help them make better decisions, improve operations and ultimately drive optimal growth. From healthcare to finance, retail to telecom, big data is being used to transform how industries function, enabling business enterprises to create new revenue streams, enhance customer experiences, and increase operational efficiency. This comprehensive blog will explore the exciting realm of big data use cases, exploring how business organizations leverage data to gain insights, drive innovation, and achieve immense success. So, let's dive in and discover how big data transforms industries and creates new business possibilities.

Real-Time Big Data Use Cases Across Industries

Big data technology enables the storage, analysis, and management of vast amounts of data, thus enabling businesses to develop smart solutions. It is used in various areas like medicine, agriculture, and environmental protection. By incorporating big data into their systems, companies enhance operations, deliver superior customer service, craft personalized marketing campaigns, and undertake initiatives that can ultimately amplify revenue and drive greater profits.

Several industries employ various big data analytics use cases to achieve business success by analyzing massive amounts of unstructured data to gain actionable insights. Some examples of big data use cases among organizations include-

  • Retailers analyze big data to understand customer preferences and buying patterns, enabling targeted marketing campaigns and personalized recommendations.

  • Healthcare organizations leverage big data to improve patient outcomes by identifying trends, predicting disease outbreaks, and optimizing treatment plans based on large-scale data analysis.

  • Financial institutions utilize big data to detect fraudulent activities, manage risk, and make data-driven investment decisions.

  • Manufacturing firms employ big data to optimize production processes, reduce downtime, and predict maintenance needs, resulting in increased productivity and reduced costs.

  • Government agencies utilize big data for policy-making, urban planning, and resource allocation, enabling evidence-based decision-making and improving public services.

  • Energy companies leverage big data to optimize energy generation and distribution, identify consumption patterns, and promote energy efficiency.

Let us understand in further detail the key big data use cases across various industries to understand how these industries harness the potential of big data.

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Top Big Data Use Cases In Healthcare

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Big data analytics has been a game-changer for the healthcare industry, revolutionizing how medical treatment is provided, enhancing patient outcomes, and driving medical innovation. For instance, in the fight against COVID-19, the healthcare sector has used big data to enhance patient outcomes. Public health experts have been able to determine hotspots, monitor disease transmission, etc., due to real-time data analysis of COVID-19 cases. This is just one example of how big data analytics is used in healthcare to address complex health challenges and drive innovation in the healthcare industry.

Let us look at some other key use cases of big data analytics in healthcare:

1. Predictive Analytics

Big data analytics is used to analyze vast amounts of patient data, including electronic health records (EHRs), genomic data, and real-time monitoring data, to predict disease outcomes and identify patients at high risk of developing certain health conditions. This enables healthcare providers to take early actions and offer personalized healthcare plans, leading to better patient treatment outcomes. For instance, analyzing data from wearable devices to predict health issues, such as heart attacks or failures, allows for timely interventions.

2. Personalized Medicine

Big data enables personalized medicine, which includes personalizing medical treatments based on an individual's unique genetic profile, lifestyle, and other factors. By analyzing large datasets of genomic data, clinical data, and other relevant information, big data is helping healthcare providers to identify targeted treatments for patients with complex medical conditions, such as cancer, cardiovascular diseases, rare genetic disorders, etc. For instance, medical care facilities can use genomic data to identify targeted treatment alternatives for cancer patients based on their genetic mutations.

3. Telemedicine And Remote Patient Monitoring

Big data facilitates telemedicine and remote patient monitoring, allowing healthcare providers to monitor patients' health conditions and collect real-time data remotely. Big data analytics can be used to analyze this and other patient data to find patterns and trends, allowing the early identification of possible health risks and timely treatment. For instance, hospitals may offer virtual consultations and follow-up treatment for patients with chronic diseases, reducing hospital visits and enhancing patient outcomes. Hospitals can also employ telemedicine to provide mental health treatments in far-off places, enhancing underprivileged people's access to healthcare.

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4. Health Data Analytics

Big data analytics is helping healthcare organizations analyze large volumes of data to gain valuable business insights into population health patterns, disease prevalence, and treatment efficiency. Healthcare centers can use this data to create evidence-based treatment guidelines, allocate resources more effectively, and assist public health activities like disease surveillance and outbreak control. For instance, medical centers can analyze population health data to identify trends and patterns, enabling healthcare officials to develop targeted interventions to prevent disease outbreaks.

5. Drug Discovery And Development

Big data is used to analyze massive amounts of biological, chemical, and clinical data to accelerate drug discovery and development. This involves analyzing genetic, molecular, clinical trials, and real-world data to find new drugs, forecast efficacy and safety, and improve clinical trial designs. For instance, pharma companies can implement machine learning algorithms to predict drug efficacy and toxicity, speeding up the drug development process and reducing the cost of clinical trials.

6. Operational Efficiency

Big data analytics allows healthcare organizations to optimize their operational efficiency by analyzing data from various sources, such as patient scheduling, resource allocation, and supply chain management. This allows healthcare providers to streamline operations, reduce expenses, and improve patient flow, ultimately leading to better patient care and outcomes. For instance, healthcare facilities can optimize staff scheduling based on patient demand and acuity levels, improving the quality of care and reducing staff burnout.

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Top Big Data Use Cases In Retail

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The retail sector has increasingly used big data analytics to obtain valuable business insights and improve business processes, including customer experiences, inventory management, pricing strategies, and supply chain management. For instance, Amazon, the biggest online retailer in the world, utilizes big data to analyze customer information and behavior, including browsing and purchase history, to tailor the shopping experience for each customer. Amazon also uses big data to optimize its supply chain management, accurately forecasting demand and optimizing inventory levels to reduce costs and ensure timely deliveries. By leveraging big data, retailers like Amazon can gain a competitive edge and deliver a better customer experience.

Here are some other key big data use cases in the retail industry:

1. Personalized Recommendations

Retailers use big data to analyze customer data, such as browsing history, purchase behavior, and social media activity, to personalize the shopping experience. This includes personalized recommendations, targeted promotions, and customized offers based on customer preferences and behaviors. For instance, a clothing retailer analyzes a customer's browsing and purchase history to provide personalized recommendations and promotions tailored to their style and preferences.

2. Inventory Optimization

Retailers use big data analytics to optimize inventory management by analyzing historical and real-time log data on sales, returns, and stock levels. This helps retailers accurately forecast demand, optimize product assortment, and reduce stockouts or overstocks, ultimately leading to improved sales and reduced costs. For instance, a home goods retailer uses big data analytics to forecast demand for seasonal products and optimize inventory levels to prevent overstock and stockouts.

3. Price Optimization

Retailers are leveraging big data analytics for price optimization by analyzing data on competitor pricing, historical sales data, customer demand, and market trends. This helps retailers identify the optimal price points for their products or services to maximize revenue and profitability. For instance, a travel booking website implements price optimization and prepares dynamic pricing strategies based on demand, competition, and customer behavior to optimize revenue.

4. Supply Chain Management

Retailers use big data to optimize their supply chain operations by analyzing log data on logistics, transportation, and inventory levels. This helps retailers streamline their supply chain processes, reduce lead times, and minimize stockouts or excess inventory, improving operational efficiency and cost savings. For instance, a department store optimizes its supply chain by analyzing product transportation and inventory levels data, reducing lead times and stockouts.

5. Fraud Detection

Retailers use data analytics to detect and prevent fraud in online transactions, credit card processing, and loyalty programs. By analyzing large volumes of data, including transaction patterns, customer behavior, and historical fraud data, retailers can identify potential fraud patterns and take preventive measures to mitigate risks and protect their business. For instance, an e-commerce website uses big data analytics to detect and prevent fraudulent transactions, analyzing customer behavior, transaction history, and fraud patterns to identify potential risks.

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6. Market Trend Analysis

Retailers leverage big data to analyze market trends, customer preferences, and competitor data to gain insights into consumer demand and make informed business decisions. This includes analyzing data generated from social media channels, customer reviews, and online forums to understand customer sentiment and preferences, which can inform product development, marketing strategies, and merchandising decisions. For instance, a fashion retailer analyzes sales data and competitor information to identify emerging trends and optimize product offerings.

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Top Big Data Use Cases In Banking And Financial Services

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The banking and financial services sector has used big data analytics to enhance customer experiences, control risks, and increase operational efficiency. Big data has become crucial for institutions to make informed decisions, identify patterns, and gain a competitive edge. One of the biggest US banks, JPMorgan Chase, has been using big data analytics to mitigate fraudulent activities and enhance compliance with legal requirements. The bank has been able to detect and control fraudulent activity while improving its regulatory reporting processes by analyzing billions of daily transactions. 

Let us look at some key use cases for big data in banking and financial services to understand how big data analytics is reshaping the banking and financial services sector.

1. Fraud Detection And Prevention

Big data analytics can help detect and prevent fraudulent activities, such as identity theft, unauthorized transactions, etc. Banking institutions can analyze historical data, transaction history, and behavior patterns to identify potential risks and take appropriate action. For instance, credit card companies can analyze real-time transaction data to identify suspicious patterns, such as transactions from numerous locations or huge transactions. Additionally, they can employ big data analytics tools and machine learning algorithms to spot anomalies in consumer behavior, such as unexpected changes in purchasing patterns or frequent alterations to account information.

2. Risk Management

Big data analytics can help financial institutions better manage risks, such as credit risk, market risk, and operational risk. By analyzing market trends, economic indicators, and customer behavior, institutions can identify potential risks and take proactive measures to mitigate them. For instance, financial organizations might use historical market data analysis to find trends and patterns to make informed decisions concerning risk exposure. Additionally, they can monitor operational processes in real-time to spot potential operational hazards like system issues or processing mistakes.

3. Customer Analytics

Big data analytics help banks and financial institutions gain valuable insights into customer behavior, preferences, etc. This can help institutions improve customer experience, personalize their services, and identify new business opportunities. Banking organizations, for instance, may use big data analytics tools to conduct customer data analysis to discover cross-selling and upselling prospects and personalize offers and promotions. They may also employ sentiment analysis to identify customer preferences and sentiments towards the institution by reviewing customer feedback.

4. Compliance And Regulatory Reporting

Big data analytics can help banking institutions comply with regulatory requirements by offering real-time data on transactions, account activity, and customer behavior. This can help financial institutions identify and report suspicious activities, monitor compliance, and avoid penalties. For instance, financial institutions can automate the process of gathering and analyzing regulatory data to guarantee compliance with regulations like Anti-Money Laundering (AML) and Know Your Customer (KYC).

5. Trading And Investment Analytics

Banks can use big data to analyze market trends, financial data, and investment strategies, enabling institutions to make more informed trading and investment decisions. For instance, financial institutions can analyze market data, including stock prices, trading volumes, etc., to identify potential investment opportunities and improve trading strategies.

6. Loan Management

Financial institutions can use big data to analyze credit risk, assess borrower eligibility, and predict loan default rates. This can help institutions improve their loan management processes and reduce the risk of default. Financial institutions, for instance, can assess borrower eligibility and determine loan repayment terms by looking at credit data such as credit scores, payment histories, and financial ratios. They can use big data analytics to automate loan approval processes, increasing productivity and decreasing manual errors in loan administration.

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Top Big Data Use Cases In Media And Entertainment

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Big data analytics is becoming increasingly significant in fostering development and innovation in the media and entertainment sector. Massive data is generated daily, allowing media companies to understand their audience better and customize their content to maximize engagement and revenue. For instance, Netflix generates personalized content recommendations for its users based on their viewing preferences and history. This has led to higher user engagement and retention rates for the streaming platform. 

Below are some more significant big data use cases in the media and entertainment industry-

1. Content Recommendation

Media platforms use big data analytics to analyze user behavior and recommend content that interests them. For instance, leveraging big data analytics, Spotify generates song and playlist recommendations based on user listening habits. Additionally, Amazon Prime Video implements big data analytics to generate content recommendations based on past viewing patterns and user reviews.

2. Advertising Optimization

Big data can be used to analyze user behavior and preferences, allowing companies to serve more targeted and effective advertisements. This can lead to increased ad revenue and better ROI for advertisers. For instance, video streaming platforms employ big data analytics to target the right viewers by optimizing their ad placement. They use big data to offer advertisements relevant to viewers' interests and preferences.

3. Predictive Analytics

By analyzing data on user behavior and content consumption patterns, media companies can predict what content will be successful in the future. This can help them make better investment decisions and reduce the risk of content flops. For instance, media companies like Warner Bros. and NBCUniversal use big data analytics to predict the box office performance of upcoming movies and make investment decisions on new TV shows.

4. Performance Tracking

Media platforms use big data to track content performance across various platforms, such as social media, streaming services, and websites. This can help companies identify trends and optimize their content strategy. For instance, media companies like Disney track the performance of their movies and TV shows across various platforms to understand audience engagement and optimize their content strategy.

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Top Big Data Use Cases In Telecom Industry

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The introduction of big data analytics has drastically revolutionized the telecom sector. The telecom industry generates vast amounts of data, from call records and network performance to customer behavior and preferences. Telecom service providers use this data to gain insights into consumer behavior, optimize network performance, identify and prevent fraud, and create customized marketing campaigns. For instance, Verizon, a leading player in the telecom industry, employs big data analytics to analyze consumer behavior trends and preferences to develop customized marketing strategies.

Here are some other key use cases of big data analytics in the telecom industry-

1. Network Optimization

Telecom companies use big data analytics to monitor and optimize network performance. Analyzing network traffic data, bandwidth usage, and other metrics can identify bottlenecks and improve network capacity, resulting in better customer service quality. For instance, telecom companies leverage big data to analyze traffic patterns and user behavior, forecast network congestion, and deploy resources proactively to reduce it.

2. Enhanced Customer Experience

The telecom industry also uses big data analytics to improve customer experience. Telecom companies analyze customer behavior and preferences data to personalize their offerings and provide more targeted and relevant services. For instance, telecom companies leverage big data to analyze customer interactions with customer service channels, such as call centers and chatbots, to identify areas for improvement and enhance the customer experience.

3. Fraud Prevention And Detection

Big data analytics helps telecom companies detect and prevent fraudulent activities, such as unauthorized network access, hacking, and subscription fraud. By analyzing data on call patterns, usage behavior, and device characteristics, they can detect anomalies and take proactive measures to prevent fraud. For instance, telecom companies can spot anomalies and suspicious activity and take action to detect fraud by analyzing call patterns and usage metrics. Big data analytics also helps telecom businesses discover and prevent fraud such as subscription fraud, call fraud, and unauthorized network access.

4. Marketing And Sales

Telecom companies use big data analytics to gain valuable insights into customer preferences and behavior and develop more effective marketing and sales strategies. For instance, telecom businesses employ big data to learn more about their customers' demographics, usage patterns, and location data to create more effective marketing and sales strategies. They also utilize big data to monitor customer churn and create customized retention strategies to increase customer lifetime value and loyalty.

5. Predictive Maintenance

Big data analytics is also used for predictive maintenance in the telecom industry. By analyzing data on network performance, equipment usage, and environmental conditions, telecom companies can predict when equipment will need maintenance or replacement, therefore reducing downtime and improving network availability. Big data analytics is used by telecom businesses, for instance, to optimize maintenance schedules and minimize operating costs, ensuring optimal network performance while lowering maintenance expenses.

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Top Big Data Use Cases In Supply Chain And Manufacturing

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One of the most complex and dynamic sectors is supply chain and manufacturing, where the smooth flow of products and services is crucial for a company's success. Big data analytics is revolutionizing supply chain and manufacturing businesses by offering insights into their operations, identifying inefficiencies, and enhancing performance. For instance, UPS, a well-known logistics and shipping business, employs big data analytics to optimize its delivery routes, resulting in significant cost savings and less environmental impact. Toyota leverages big data analytics to enhance production workflows and minimize errors, which enhances product quality and customer satisfaction. 

Let us look at some other significant big data analytics use cases in the supply chain and manufacturing industry-

1. Predictive Maintenance

Predictive maintenance is a big data application that helps manufacturers detect potential equipment failures. Using real-time data collected from sensors and other sources, manufacturers can identify patterns and trends that indicate when a machine will likely break down. This helps them to schedule maintenance proactively and minimize downtime. For instance, manufacturing companies use sensor data analysis to predict equipment breakdowns and schedule maintenance in advance, minimizing downtime and production losses.

2. Quality Control

Big data analytics help manufacturers identify quality issues early in production and take corrective action before products are shipped to customers. For example, automotive manufacturers may track sensor data in real-time to identify quality standards violations and trigger alarms for rapid corrective measures.

3. Inventory Management

Big data analytics can help manufacturers optimize inventory levels by predicting demand, reducing stock-outs, and improving order fulfillment rates. By analyzing historical data, manufacturers can identify patterns and trends in demand, which allows them to adjust inventory levels accordingly. Manufacturing firms, for instance, use supply chain data analysis to optimize lead times, reorder points, and safety stock levels, thereby improving order fulfillment rates and avoiding stockouts.

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4. Supply Chain Visibility

Companies can use big data analytics to gain visibility into their supply chain by providing real-time data on supplier performance, inventory levels, and order status. This helps to identify bottlenecks and inefficiencies in the supply chain, enabling companies to optimize their operations. For instance, manufacturing companies may use big data analytics to manage shipments, analyze supplier performance, and plan logistics routes to increase supply chain transparency and minimize additional costs.

5. Better Product Design

Big data analytics can help manufacturers to design better products by analyzing customer feedback, usage data, and other internal and external sources of information. For example, companies like Caterpillar use data analytics to collect customer feedback on their heavy machinery and use that feedback to improve product design.

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Big Data Use Cases Examples- Companies That Are Using Big Data

This section will discuss some real-world examples and big data analytics use cases across various industries-

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The big data universe is filled with conversations, customer reviews, feedback, and comments. With increasing customer communication channels like social media platforms, product review forums, etc., business organizations must understand and analyze what customers say about their products or services to ensure customer satisfaction. Big data analytics and social media channels help analyze customer sentiments giving a business organization a clear picture of what it must do to outperform its competitors.

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  • Delta Airlines

Airline companies are using sentiment analysis to analyze flyer tracking experience. Large airlines like Delta monitors tweets to find out how their customers feel about delays, upgrades, in-flight entertainment, and more. For example, when a customer tweets negatively about his lost baggage with the airline before boarding his connecting flight. The airline identifies such negative tweets and forwards them to their support team. The support team sends a representative to the passenger's destination, presenting him a free first-class upgrade ticket on his return along with the information about the tracked baggage promising to deliver it as soon as he or she steps out of the plane. The customer tweets like a happy camper rest of his trip, helping the airlines build positive brand recognition.

  • Thomson Reuters

Thomson Reuters uses sentiment analysis from Twitter data for its trading platform and Eikon (a financial solution by Thomson Reuters) market analysis. Financial experts can gain a competitive advantage by analyzing Twitter sentiment data by tracking specific tweets from various companies and people. Users of the Eikon application can intuitively identify trends and any potential signals from massive amounts of unstructured data. This helps financial professionals get an overview of the number of positive and negative sentiments related to any company. Sentiment analysis and other advanced big data analytics solutions help financial professionals spot the financial market and any events impacting the company as they happen.

  • Macy’s

US-based retailer Macy’s collects big data about customer preferences and interests based on seasonality, price range, demographics, color, geography, and other characteristics. The analytics systems then measure customers' positive and negative sentiments on social media about a particular product to apply predictive analytics that helps identify novel opportunities and forecast trends that can impact their business. For instance, through sentiment analysis of big data, Macy’s finds out that people who are sharing tweets about “Jackets” are also making use of the terms “Michael Kors” and “Louis Vuitton” frequently. This information helps the retailer to identify what brands of jackets should be offered discounts in their future advertising campaigns to attract customers. Macy’s analytics systems are empowered to predict what a customer wants by segregating customers to gain their attention and keep them engaged until the interactions lead to the purchase of the product.

  • Salesforce

Salesforce product Radian6 performs social media analysis to identify customer trends. Radian6 identifies conversations on social media about a particular company, its products, or its competitors. This social media data is analyzed for sentiments, trends, and demographics by aggregating negative, positive, and neutral sentiments. The sentiment analysis helps social media managers streamline workflows easily while responding to hundreds of messages daily.

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48% of organizations use big data to unlock meaningful insights from customer behavioral data. Organizations are harnessing the power of big data through behavioral analytics to deliver big value to businesses. Amazon has mastered the recommendation of products quite some time back based on customers' interests, and various other companies like  Spotify, Pinterest, and Netflix follow the same suit.

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  • Bank of America

Bank of America's rewards program, named BankAmeriDeals, rewards its customer with different cashback offers by analyzing their previous credit and debit card purchase histories.

  • Target

Target employs behavioral analytics to predict life changes their customers are going through, such as divorce, marriage, and pregnancy. Leveraging big data analytics, Target identified 25 products like vitamin supplements and unscented lotion, which, when analyzed collectively, helped determine a pregnancy prediction score. Thus, based on the prediction score for each woman retailer Target sent promotions focused on baby-related products. This helped retailer Target boost their sales of baby products after launching their novel advertising campaigns based on customers' shopping behavior.

  • Nordstrom

With 225 retail outlets, Nordstrom generates petabytes of data from its 4.5 million Pinterest followers, 300,000 Twitter followers, and 2 million likes on Facebook. Their analytics system monitors customer behavior by tracking – How many people enter the store, which section they walk in, how long they stay there, and for how long they shop in a particular section. This helps Nordstrom decide what products should be promoted to which customers, when, and through what advertising channel. Nordstrom is providing its customers with a personalized shopping experience by analyzing the shopping behaviors of its customers.

  • McDonald’s

With more than 34K local restaurants serving 69 million customers across 118 countries, 62 million daily customer traffic, selling 75 burgers every second, $27 billion annual revenue- McDonald's is using big data analytics to gain a lot more insight to improve operations at its various stores and enhance customer experience. McDonald’s analytics system analyzes data about various factors such as wait times, information on the menu, the size of the orders, and customer ordering patterns to optimize the operations of its restaurants at specific locations.

  • Kohl

Retail chain Kohl’s pushes personalized offers to users’ Smartphones when they enter the store. Kohl’s tracks customers' browsing history and sends them offers based on their browsing history. If a shopper is lingering in the Trousers section, Kohl’s offers customers for the trousers they searched for on their website but never bought. Customers generally have an increased probability to respond to an offer when they receive it at the moment of purchase while they are shopping, thus helping Kohl’s earn a sale.

With increasing customer acquisition costs, it has become important for business enterprises to target marketing promotions effectively through customer segmentation. The information about a customer comes from various internal and external sources like transactional data, social media, etc. Organizations correlate the profile information of customers’ behavior on social media websites and purchase history - to reduce customer acquisition costs by targeting their customers with personalized offers they would be interested in. Companies have successfully reduced their customer acquisition costs by 30% through big data analytics. A Harvard Business Review publication stated that organizations had attained a 70% improvement in their conversion rates through targeted marketing promotions.

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  • Time Warner

Time Warner, the giant media company, operates in 15 different markets and has close to 14 million customers, of which 7.9 million are subscribing customers. It collects approximately 0.6 TB of data every day. Time Warner leverages big data analytics to create personalized advertising campaigns. Time Warner’s analytics systems combine local viewing data and demographic data sets with other data like voter registration and real estate records to understand the personalized preferences of their customers by gaining insights into political preferences, income, and the local environment. This helps Time Warner target their marketing campaigns and advertisements using various mediums such as websites, radio, television, social media, and mobile apps. This helps Time Warner make further adjustments to their advertising campaigns based on how people respond to each advertising platform.

  • Amazon

Browse through the Amazon e-commerce website and see what products it recommends you buy. The products recommended by Amazon are probably different for you and your friend. How do they do it? Every time a user logs into his or her Amazon account and purchases or browses various products on the site, Amazon collects this data. The next time customers return, they offer them products based on their previous purchases and browsing history. This also helps Amazon identify various trends amongst people who make similar purchases. For instance, if 75% of the people who buy an Apple iPhone 6s also buy a power bank, then Amazon offers a power bank as a recommendation whenever somebody purchases an iPhone 6s. By segmenting the customers based on their interests and purchase patterns, Amazon provides people with more choices, even if they are not looking to buy other products, thereby tempting them to make additional purchases.

  • Pandora

With 72 million users and the data for approximately 200 million users’ listening habits, Pandora is a name to reckon with in the music industry for providing music recommendations that people love. Apart from the data like gender, age, and zip code that users provide at sign-up, Pandora tracks all the songs that a particular user likes and dislikes. From this location, they listen, from which devices they listen, and more - to provide customers with a curated music catalog based on interests and demographics.

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Businesses nowadays want to peer into the future to boost revenues. By leveraging big data analytics, industries are developing predictive models as a top priority.

  • Utica National Insurance Group

Utica National Insurance Group uses predictive analytics to monitor continuously incoming credit reports that can measure the risk appetite depending on a range of existing data rather than just considering the credit score alone.

  • Volkswagen

Volkswagen uses big data to support predictive marketing that helps Volkswagen build brand loyalty by boosting its aftermarket service revenues. Volkswagen analyzed customer data from multiple sources, vehicle data, and the qualitative notes written by technicians to entice Volkswagen owners to come to its service centers.

  • Ayasdi

Ayasdi uses 20-year-old breast cancer data through its IRIS product that combines machine learning and topology. Ayasdi is leveraging big data analytics to discover new relationships and new questions that can be answered with the help of the 20-year-old dataset. Ayasdi developed topologies for leukemia and breast cancer patients’ data by analyzing the data to find similarities that can help predict and find novel cancer treatments and therapies.

  • Purdue University

Purdue University in the USA is using big data analytics to increase the success rate of its students. The signals application leverages big data analytics to track students’ performance in different classes, which helps identify students with low performance. The predictive analytics system will provide data-driven alerts to warn students, notifying them about the potential pitfalls that are likely to occur during their higher education experience at the university.

Financial crimes, fraudulent claims, and data breaches are the most common challenges businesses across several industries face. Fraud prevention and detection was a global problem across all industries impacting the business of all organizations before the advent of big data analytics. Big data analytics helps organizations detect, prevent and eliminate internal and external fraud. For instance, the analysis algorithms can alert a bank that a debit or credit card has been stolen by someone by identifying unusual behavior patterns on the card transactions. This helps banks temporarily hold any further card transactions while they contact the card owner.

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  • VISA

The credit and debit card processing giant VISA uses big data analytics to potential frauds. Big data analytics at VISA helped them identify $2 billion in probable incremental fraud opportunities and helped it address those fraudulent vulnerabilities before the money was at stake.

  • The Insurance Bureau of Canada

The Insurance Bureau of Canada, which represents Canada’s car, home, and business insurers, leveraged IBM’s big data analytics solution to identify fraudulent claims and show a red flag for suspicious claims. IBC analyzed unstructured data of more than 233000 claims from the past 6 years. IBC could identify fraudulent claims worth 41 million CAD. IBC states that big data analytics solutions can help the Ontario automobile industry save approximately 200 million CAD annually.

  • JPMorgan Chase

JPMorgan Chase analyses emails, phone calls, and transaction data to detect the possibilities of fraud that would otherwise be difficult to detect.  JPMorgan uses analytics software developed by Palantirto to keep track of employee communications to identify any indications of internal fraud.

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Big data has an immense potential to drive growth and boost productivity across businesses and industries, including healthcare, finance, media, telecom, supply chain, and manufacturing. Companies can reinvent their business models and maintain competitiveness in a rapidly evolving digital economy by leveraging the potential of big data.

To gain a deeper understanding of real-world big data use cases, data professionals must try working on big data projects offered by ProjectPro. ProjectPro allows you to work on real-world big data projects, gaining practical experience and enhancing your knowledge of big data technologies. By working on diverse big data projects across industries, you can learn how big data is used to solve complex challenges, make informed decisions, and achieve business objectives. This hands-on approach allows professionals to build a strong foundation in big data tools and stay up-to-date with the latest big data technologies and industry trends, thus landing a successful career in the big data industry.

So, whether you are a data scientist, analyst, engineer, or business intelligence professional, exploring real-world big data use cases through projects offered by ProjectPro can be a valuable learning experience. Start your big data journey with ProjectPro today!

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FAQs On Big Data Use Cases

An example of a big data use case is personalized marketing. Businesses can target certain audiences with personalized offers and promotions by analyzing large volumes of customer data, boosting customer engagement and loyalty.

The use cases of data analytics include better business decision-making, risk identification and mitigation, process and operation optimization, improved customer satisfaction, and gaining valuable insights into market trends and possibilities.

Various industries, including healthcare, finance, retail, manufacturing, transportation, and marketing, can benefit from big data use cases by using data insights for enhancing operations, boosting customer experience, streamlining the supply chain, etc.

Numerous real-life examples of successful big data use cases across various industries exist. For instance, Netflix personalizes content suggestions for each user using big data, which boosts user retention and engagement. Walmart optimizes its supply chain management with big data to cut costs and boost productivity. The New York Times optimizes its digital subscription strategy using big data to boost sales and lower customer churn.

 

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