Today’s businesses rely on data analytics more than ever before, and to keep up with the competition, it’s necessary to gather, analyze, and take action on information the moment it’s received — while interactions and conversations are still happening. This blog offers 25 real-time analytics examples to illustrate how this business intelligence technology is impacting practically every facet of modern life. We’ll discuss real-time analytics use cases such as:

• Real-time credit scoring
• Predictive equipment maintenance
• Route optimization
• Next best action and next best offer
• Dynamic pricing
• Omnichannel marketing
• Medication adherence
• Wildlife conservation
• …and more

We’ll discuss these and other real-time analytics examples later in this article. First, let’s review what real-time analytics is and how it works.

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What is real-time analytics?

You have certainly encountered something in life that’s driven by real-time analytics — the process of capturing, analyzing, and acting on data in the moment, as it happens — but you may not have realized it at the time. The real-time element of real-time analytics refers to the immediacy with which data is analyzed when it’s received. The analytics element of real-time analytics involves aggregating data from various sources, interpreting it, and transforming it into something actionable that humans can understand and take action on.

By analyzing data the moment it’s collected – such as a conversation in a contact or customer service center – businesses can take immediate action to drive outcomes, seize opportunities, and even identify and resolve problems before they actually happen.

Advances in technology now enable businesses to collect massive volumes of data from many disparate sources, leveraging powerful analytical tools to combine the information and apply algorithms and logic, artificial intelligence (AI), and machine learning (ML) to produce actionable and contextual insights mere moments after data is received. Done right, businesses can gain a competitive advantage by extracting value from their data as it’s being collected.

Real-time analytics is used by every business sector, from manufacturing to healthcare, marketing, public safety, and customer service. It’s also used in many business processes, from raw materials sourcing to production planning, logistics, and customer service. It’s even used to identify new untapped markets and inform new product development and innovation.

For example, real-time analytics is used to analyze conversations in a contact center, identifying signals that the conversation is heading in a positive or negative direction and providing immediate, in-call guidance to help agents steer those interactions in the right direction. It’s also used to analyze consumer behaviors and interactions to identify signs that a customer is about to churn, giving businesses the opportunity to take action and right the ship before losing a customer to the competition.

Examples of real-time analytics in action

Now that you have an idea of what real-time analytics is and how it works, you can gain a deeper understanding of this technology by learning about its many use cases across various industries. Keep reading to learn some of the many ways real-time analytics is being used to shape various facets of modern life.

Real-time analytics examples in financial services

1. Regulatory compliance. “The mortgage industry is highly regulated, both at the federal and state level. This means that certain disclosures must be communicated to consumers. And when that doesn’t happen, it doesn’t just erode consumer trust, it can result in serious fines. The right technology can help mortgage lenders understand how and when the right (or wrong) disclosures are being said across 100% of consumer communications. Particularly in the cases where required disclosures aren’t being said, mortgage lenders can find those interactions and mitigate the issue before audits. They can also better coach and train employees who are regularly missing the required statements. Meeting compliance regulations and reducing risk benefits your bottom line, which is always important, but even more so as the industry changes.” - Chris Stanley, Four key conversation intelligence use cases in the mortgage industry, CallMiner; Twitter: @CallMiner

2. Credit scoring. “For example, Equifax introduced machine learning modeling (neural network) into an explainable artificial intelligence credit score method to generate actionable explanations that are tailored to individual consumers. Equifax is not the only bureau dabbling in machine learning solutions. Experian augmented their analytics tools with machine learning functionalities to generate deeper, on-demand insight. TransUnion and FICO also incorporated machine learning to spot high risk identity behaviors and generate more accurate, understandable scorecards for credit applications. The more recent VantageScore uses machine learning to assess risks and assign scores, even for ‘credit invisible’ consumers without recently updated credit files. Other bureaus such as Creditinfo are working on machine learning model generation platforms.” - Nan Jiang and Nadia Novik, Leveraging big data and machine learning in credit reporting, World Bank Blogs; Twitter: @WorldBank

3. Financial trading. “In rapidly changing capital markets, it is no longer adequate to measure risk as an end of day process. Trading decisions can significantly alter exposures in a millisecond as traders with exposures to Bear Stearns found out the hard way in March 2008. In order to assess risks to market portfolios and take corrective measures in real-time, capital markets are now moving towards intra-day value at risk computations.

“Streaming analytics can be leveraged to support these risk computations and aide banks to minimize and manage risk. With streaming analytics, banks can obtain a low latency, high-performance solution that listens to market prices as well as real-time changes to portfolios and compute value at risk on the fly. By employing risk calculations in a streaming fashion, financial institutions can stay several steps ahead of its competition by ensuring that portfolios are safe from intraday market fluctuations.” - Seshika Fernando, Real-Time Analytics in Banking & Finance: Use Cases, WSO2; Twitter: @wso2

4. Detecting and blocking fraudulent transactions. “Catching fraud can be done by leveraging real-time analytics. Prominent banks and capital markets have started to deploy real-time analytics, in addition to machine learning, for risk management, fraud detection, compliance, consumer metrics, and, of course, to distinguish themselves from the competition. Artificial intelligence has great potential when it comes to reducing financial fraud. A great many practical ideas involving AI, in particular, machine learning, have been developed. Speaking of machine learning, it’s getting better and better at identifying potential cases of deception. Machine learning makes it possible to establish which transactions are likely to be fraudulent and to reduce false positives.” - Kristijan, How Real-Time Analytics Tackles Fraud Detection, The Future of Things; Twitter: @Future0fThings

Real-time analytics examples in manufacturing & logistics

5. Responsive processes in transport logistics. “There is a growing need for better visibility along transportation lines in the industry. Shipping managers are changing how they manage the lines to ensure better output. Real-time freight analytics is mandatory from the starting point to the final destination. Managers can use the continuous flow of real-time data to identify any gaps and inefficiencies and correct them quickly.

“At the same time, the data systems provide detailed and more regular updates, which results in automatic updating and notifying all the concerned parties. The supply chain can be made fully disruption-proof by providing an automated and responsive process that works right through real-time freight analytics.” - Why Real-Time Data Is Important In Shipping And Logistics, AutomationFactory.ai; Twitter: @automation_ai

6. Predictive maintenance. “Predictive maintenance analytics is a tool that enterprises in the industrial sphere can use to better anticipate equipment failure and avoid unnecessary downtime. Predictive maintenance analytics marries real-time equipment data with data analytics and machine learning to evaluate current and future equipment performance. By monitoring equipment conditions in real-time, companies can gain a deeper understanding of equipment status to identify factors that may indicate a malfunction is about to occur. In turn, companies can utilize this information to develop a data-driven maintenance strategy and reduce equipment downtime to improve productivity.” - Predictive Maintenance Analytics, Aspen Technology; Twitter: @AspenTech

7. Inventory management. “Predictive analytics is a subset of advanced analytics. In most cases, the term is used interchangeably with machine learning. A large quantity of data about a given question is collected and an answer is spit out. Take this example:

• Based on all of the data about this customer, they will unsubscribe within 3 months.
• Based on all of the data about your product and the market, you should focus your marketing efforts on new moms in urban areas who hold advanced degrees.
• Based on all of the data about this account holder, their behavior indicates they will or have already committed fraud.

“Naturally, this extends to inventory management. Based on what we know about this product, online interactions, and your historical data, demand will go up by 40 percent over the next quarter.” - Real Time Warehouse Inventory Management Software Tools, Logiwa; Twitter: @LogiwaWms

8. Production planning. “Real-time analytics drive overall equipment effectiveness (OEE), which is one of the most powerful measurement tools in an industrial environment. It exposes all manufacturing losses, so automation professionals can make objective business decisions that improve the performance, capacity, and utilization of plant assets. It can help companies achieve desired outcomes, such as reduced changeover time, improved quality and throughput, greater supply chain predictability, and reduced costs. When measured and adopted correctly, OEE allows managers to make effective, accurate, and objective decisions in real time.” - Jennifer Bennett, How to Use Real-Time Analytics to Achieve Operational Excellence, ISA Interchange; Twitter: @ISA_Interchange

9. Fleet management and driver safety. “Fleet management data analytics is based on a variety of data types, such as telematics data, data from various cloud or edge devices, GPS, vehicle cameras, traffic cameras, and driver monitoring applications. The collected data can reveal the most common causes of accidents and give insight into how to avoid them. For example, if the major cause of accidents is risky driving behavior, the problem can be addressed with a suitable training program. Or, if accidents are being caused by the vehicles themselves, fleet managers can predict accidents before they occur and prevent them from happening through predictive maintenance.” - How to Use Predictive Analysis in Fleet Management?, Cprime Studios; Twitter: @CprimeStudios

10. Route optimization. “It goes without saying that route optimisation is essential to finding cost efficiencies in transportation while maintaining margins. You’re probably already using a routing system to calculate where you’re going. Big Data does this based on shipment data, traffic situations, weather, holidays, delivery sequences and other factors.

“Intelligent route optimization also plays a crucial part in the case of determining which vehicles to choose over possible routes and junction points in order to optimize the flow throughout the chain in terms of cost and time. Truly intelligent solution optimises many hundred thousand master route stops in one optimisation. Second by second, it optimises highly dynamic and real-time-based routes.” - Wim Hoek, The impact of Big Data on route planning, AMCS Group; Twitter: @AMCSGroup1

Real-time analytics examples in retail & eCommerce

11. Omnichannel experiences. “For many retailers, there is not enough time or resources to invest in integrating and unifying their data across all customer interaction channels.

“Even so, siloed information and a disconnected CX are top causes of customer concern. Eighty-nine percent of customers report becoming frustrated when they must repeat the issue they already explained in chat or to another agent. To mitigate these risks, leverage technology like AI-powered conversation intelligence to map out and understand the entire customer journey. Customer journey mapping is a critical step toward spanning silos of intelligence and empowering agents with information as customers move between channels.” - Five Critical Trends That Omnichannel Retailers Must Understand, CallMiner; Twitter: @CallMiner

12. Dynamic pricing. “It’s essential to present competitive prices to maximize revenue without diverting visitors from your retail company.

“Use dynamic pricing to manage offers. It’s a method of adapting prices to the market. Current dynamic pricing relies on historical data, artificial intelligence, and machine learning to determine the best prices for a store. Ride-sharing businesses like Uber and Lyft employ dynamic pricing all the time. Days with unfavorable weather conditions or rush hour affect the service costs to gain additional profits from these conditions.

“eCommerce isn’t an exception. We expect prices to go higher on trendy products, while basic items are usually stable. Below are the most notable retailers with integrated dynamic pricing.” - Alex Husar, How Real-Time eCommerce Analytics Impacts Your Business, RTInsights.com; Twitter: @RTInsights

13. Next best offer and next best action. “Personalization is table-stakes for optimal digital experiences. However, many companies implement personalization at the persona or audience level, using information like demographics, website traffic, location data, and other similar attributes. It can work, but it’s not true one-to-one personalization.

“Next-best action is a technique that uses data-driven insights and analytics from marketing, sales, customer service, and other departments to predict the next action brands should take with a consumer. By pulling together data from all interactions across all departments, and analyzing that data using machine learning and AI, a company can more accurately predict the right content, message, or offer a consumer might want or need next.” - Next-Best Action: How To Use AI For Predictive Personalization, CDP.com; Twitter: @CDPdotcom

14. Personalized in-store experiences. “According to a 2019 Gartner study, brands risk losing 38 percent of customers because of poor marketing personalization efforts. The beauty of real-time in-store analytics is its ability to easily pinpoint exactly what works and what doesn’t. It takes the guesswork out of how retailers can deliver powerful and meaningful personalization for shoppers by understanding which customers shop at specific zones, the correlation between dwell engagement to dwell conversion and the products actually purchased to create customized shopping experiences. This can help to isolate performance opportunities between products and locations or best practices for floor layouts and in-store designs.” - Judith Subban, Real-Time In-Store Analytics Will Grow Your Business, RetailNext; Twitter: @RetailNext

Real-time analytics examples in marketing, social media & digital technology

15. Real-time agent guidance. “Conversation analytics and conversation intelligence software, powered by AI, can help you improve the frontline agent experience by providing real-time monitoring and guidance agents can implement to drive positive outcomes from every interaction. Conversation analytics solutions like CallMiner analyze every interaction across multiple channels to give you a better understanding of your customers and what drives their behaviors. With CallMiner, you can create a culture of self-improvement by providing real-time feedback and next-best-action guidance to help them turn negative interactions into great customer experiences.” - Tips & strategies to improve frontline agent experience, CallMiner; Twitter: @CallMiner

16. Artwork and image selection. “Some say marketing is more art than science. When it comes to the visual imagery that Netflix uses to entice viewers, it’s a marriage of both. Using Artwork Visual Analysis (AVA), ‘a collection of tools and algorithms designed to surface high quality imagery from videos,’ is able to predict which merchandising still will resonate most with individual users based on their age and general preferences.

“Surprisingly complex, AVA uses computer vision to analyze visual data such as composition metadata (heuristic characteristics that make up the images overall aesthetic) and contextual metadata (facial expressions, objects) to drive image selection. In addition to automatically generating thumbnail images for Netflix’s user interface, AVA is also used to select artwork for general marketing and social media campaigns.” - Elizabeth Mixson, Data Science at Netflix: How Advanced Data & Analytics Helps Netflix Generate Billions, AI, Data & Analytics Network; Twitter: @AiiA_Network

17. Virtual reality. “According to Facebook, virtual reality (VR) and augmented reality (AR) for their Oculus Quest headsets depend upon ‘positional tracking that [is] precise, accurate, and available in real-time.’ Moreover, the company claims that this positional tracking system must be compact and energy-efficient enough for a standalone headset.

“Meta claims that its ‘Oculus Insight’ (also called its ‘insight stack’) machine learning models leverage the latest computer vision systems and visual-inertial simultaneous localization and mapping, or SLAM. SLAM is used to track the position of the user’s head, while constellation mapping is used to track head movements. Other applications that use SLAM include autonomous driving and mobile AR apps.” - Daniel Faggella, Artificial Intelligence at Meta (Facebook) – Two Current Use-Cases, Emerj Artificial Intelligence Research; Twitter: @Emerj

18. Prioritizing social media news feed content. “When picking posts for each person who logs on to Facebook, the News Feed algorithm takes into account hundreds of variables — and can predict whether a given user will Like, click, comment, share, hide, or even mark a post as spam.

“More specifically, the algorithm predicts each of these outcomes with a certain degree of confidence. This prediction is quantified into a single number called a ‘relevancy score’ that's specific both to you and to that post.

“Once every post that could potentially show up in your feed has been assigned a relevancy score, Facebook's sorting algorithm ranks them and puts them in the order they end up appearing in your feed. This means that every time you log in, the post you see at the top of your News Feed was chosen over thousands of others as the one most likely to make you react and engage.” - Lindsay Kolowich Cox, 5 Social Media Algorithms Marketers Need to Know About in 2022, HubSpot; Twitter: @HubSpot

Real-time analytics examples in healthcare

19. Benefit verifications and prior authorizations. “A pioneer of hyperautomation, CVS Health has long leveraged RPA, AI and other business process automation tools to optimize its support functions. For example, Using a combination of AI, RPA, machine learning, data analytics and natural language processes (NLP), CVS Health was able to automate its prescription intake, benefits administration and revenue cycle management processes.

“As CEO Karen Lynch explained in a August 2021 earnings call, ‘Our technology-driven programs are leveraging blockchain, driving cloud migration, and intelligent automation, and streamlining processes, to accelerate results and generate greater impact. One example is a specialty pharmacy script automation program that uses artificial intelligence to yield better results more quickly, while eliminating more than 30 manual steps, such as benefit verification and prior authorization.’” - Elizabeth Mixson, CVS Health goes from digital transformation to digital optimization, Intelligent Automation Network; Twitter: @AiiA_Network

20. Medication adherence. “Specialty therapies are complex and patients often face challenges including side effects or ineffective treatment, which require active monitoring and personalized engagement to ensure patients stay adherent. If the side effects are significant, or if the patient no longer feels they are getting the benefit of the medication, they may stop taking them. The right monitoring to detect if this is happening, and appropriate interventions are critical to saving payors money.

“Our Intelligent Medication Monitoring solution uses data analytics and our digital infrastructure to identify when patients may no longer be benefiting from their treatment and intervene appropriately. Our proactive surveillance enables us to identify gaps in care, and monitor efficacy, symptoms, pain and exacerbations. Given our high level of digital engagement we can adapt our message to members and reach them through a channel of their preference. When appropriate, we can work with providers to deliver targeted interventions, including stopping treatment or changing to a different therapy.” - Christine Sawicki, Intelligent Medication Monitoring for Targeted Specialty Interventions, CVS Health Payor Solutions; Twitter: @CVSHealthPBM

21. Faster, more accurate breast cancer screening and diagnosis. “A new set of real-world data from the 3D mammogram developer iCAD showed that its artificial intelligence-powered screening programs were able to increase breast cancer detection rates and help cut down the number of false positives. … Researchers found that after installing the AI system, the average rate of cancers detected per 1,000 patients screened rose from 3.8 to 6.2, compared to the findings of a team of radiologists. False interpretations of results also dropped, from a rate of 9.6% down to 7.3%.

“At the same time, the researchers said the addition of AI helped nearly double the positive predictive value of screenings where biopsies were performed to confirm the disease, from 29% to 57%.” - Conor Hale, iCAD's 3D mammography AI catches more breast cancers in real-world study, Fierce Biotech; Twitter: @FierceBiotech

Other real-time analytics examples

22. Weather forecasting. “As HPC-driven [high-performance computing] analytics fuels progress in weather and climate research, many scientists are looking to artificial intelligence (AI) capabilities to analyze data even more quickly and accurately. Deep learning, a subset of AI, leverages a series of trained algorithms that learn to make predictions based on past insights. Deep learning tools are designed to process massive data sets in order to identify patterns, and because learning can be supervised or unsupervised (using algorithms to reach specific answers or learning without a specific answer in mind), scientists can extract critical insights without exhausting their IT resources.

“According to a research paper on deep hybrid models for weather forecasting, IT architectures based on deep learning demonstrated an improved ability to predict the accumulated daily precipitation for the next day. Using supervised learning, the architecture was able to forecast the daily accumulated rainfall at a specific meteorological station, and outperformed all other analytical approaches.” - Pankaj Goyal, Improving Weather Forecasting with Real-Time, Data-Driven Insights, The Next Platform; Twitter: @pango, @TheNextPlatform

23. Wildlife conservation. “Equipped with cutting edge artificial intelligence technology, Wildlife Insights can automatically identify hundreds of wildlife photos in minutes, a task that traditionally takes researchers weeks or months to complete. Now scientists can access photos and data on any device, whether in an office or out in the field. By sharing data in one place, Wildlife Insight is helping to facilitate collaboration and answer larger conservation questions.

“With access to reliable and timely information on wildlife, scientists, land managers and other stakeholders can better anticipate threats, understand where and why wildlife populations are changing, and take action to protect our most endangered species.” - Wildlife Insights, World Wildlife Fund; Twitter: @World_Wildlife

24. Public safety. “Another initiative being trialled in Singapore and Thailand involves the use of computer vision at service station forecourts. Computer vision – cameras which can ‘think’ and understand what they are filming – are trained to watch out for the potential hazard of customers lighting cigarettes in the vicinity of pumps and refuelling vehicles.

“Camera data is processed by what is essentially the same technology powering Google’s reverse image search, which allows the content of the picture to be labelled and categorised.

“When an image is detected that matches what the algorithms ‘know’ (through training) is a person lighting a cigarette, alerts can be issued allowing the forecourt staff to close down nearby pumps and reduce the risk of fires or explosions.” - Bernard Marr, The Incredible Ways Shell Uses Artificial Intelligence To Help Transform The Oil And Gas Giant, Bernard Marr & Co.; Twitter: @BernardMarr

25. Politics and elections. “AI and machine learning can be used to engage voters in election campaigns and help them be more informed about important political issues happening in the country. Based on statistical techniques, machine learning algorithms can automatically identify patterns in data. By analyzing the online behaviour of voters which includes their data consumption patterns, relationships, and social media patterns, unique psychographic and behavioural user profiles could be created. Targeted advertising campaigns could then be sent to each voter based on their individual psychology. This helps in persuading voters to vote for the party that meets their expectations.

“Apart from using intelligent algorithms, autonomous bots can also be used to spread information on a large scale. Bots are automated programs that can be programmed to run certain tasks over the internet. They can also be employed to detect fake news and misinformation. Whenever fake news is detected, they could issue a warning that the information is incorrect, thereby stopping it from influencing the voter.” - Manu Siddharth Jha, Want to Win an Election? Use AI And Machine Learning, Great Learning; Twitter: @Great_Learning

Deciding what posts users see in their social media feeds, wildlife conservation, breast cancer screening, virtual reality, personalized in-store experiences, and more — these real-time analytics examples merely scratch the surface of what real-time analytics has impacted in modern life and its potential for the future. If you want to put real-time analytics to use for your business, understanding the myriad ways it can be applied is crucial for making strategic investments.