The need for Big Data analytics keeps growing constantly. Today, according to G2, as many as 95% of businesses say they need to manage unstructured data on a frequent basis. As a result, the Big Data market will get more popular among investors, having reached $77 billion by 2023.
Different businesses find different applications for Big Data analytics according to their needs and goals. But what is Big Data mainly used for?
General Applications of Big Data
Big Data analytics brings out the insights that were previously unknown. As a result, with such analytics, companies are able to find solutions to many issues that they couldn’t handle before. Below are a few practical uses of Big Data that your business can take advantage of.
Improved Logistics and Location Tracking
This application of Big Data analytics is relevant for many industries that depend on logistics and supply chain.
With the help of predictive analysis powered by Big Data, a business can optimize logistical routes by detecting bottlenecks and tracking equipment, and adjust schedules accordingly.
One of the latest applications of Big Data analytics is the data-driven shipment process.
In an interview with Wall Street Journal, Matthias Winkenbach, the director of Megacity Logistics Lab at MIT, says that Big Data improves the shipment and delivery processes with the help of the ‘last mile’ analytics.
Using GPS and the Internet of Things to collect data, the shippers can now see the delivery process from start to finish. As a result, Big Data is what makes it possible to ensure the security of every delivered package.
Data Security and Fraud Detection
Cybersecurity and fraud detection have always been major concerns not only for businesses but also for banks and governmental institutions.
Looking for possible ways to solve this problem and decrease their susceptibility to cybercrimes, businesses and governments turned to Big Data analytics, which, using predictive analysis models, helps them detect fraud and data breaches early.
The Securities Exchange Commission (SEC) and the Financial Industry Regulatory Authority (FINRA) introduced Big Data analytics to monitor the financial market for illegal activity some time ago.
FINRA’s data surveillance includes data-driven analysis of trading across the U.S., helping the institution detect illegal financial speculations. Such activities get flagged immediately, and FINRA adds them to its database to prevent them from happening again in the future.
Big Data analytics has also found its many applications in marketing, helping companies improve relationships with customers.
Among the many practical uses of Big Data in marketing, the following three stand out significantly:
keeping track of users’ behavior;
analyzing social data to form the demographics;
targeting consumers with the right content at the right time.
Netflix reached success thanks to all these three Big Data applications. The company collects and draws insights from Big Data to make personalized content offers.
Besides, its smart data-based algorithms steer users away from popular blockbusters to not so famous movies based on consumer behavior and preferences. This is what helped Netflix promote their original series over the years, and made it one of the media moguls of today.
4 Ways Insurtech Companies Are Using Big Data
Almost all the above-mentioned practical uses of Big Data have also found their application in the activity of insurance companies. But improving customer experience has so far been the most important benefit of Big Data for this industry.
Insurance companies use Big Data analytics to collect data about customer behavior in real-time and improve their marketing efforts by catering precisely to their customers’ needs.
So, let’s take a closer look at how insurance companies leverage Big Data analytics to improve customer experience, illustrating our points with a few interesting use cases.
1. Tracking Consumer Behavior to Offer Personalized Insurance Plans
You already know that Big Data analytics has great potential to improve your relationships with customers by tracking their behavior.
To track and analyze consumer behavior, the data is collected from three main sources:
transactional data – usually involves purchase or shipment details, as well as insurance transactions and other expenses;
machine data – data generated by processes initiated on a computer or other device;
social data – information usually obtained from social networks, which includes information about user demographics and other details.
Big Data analysis can help you collect insights that, according to the research from the American Journal of Industrial and Business Management, can help businesses:
recognize the needs of their customers;
find out content preferences to tailor personalized content offers;
determine product or content alternatives;
determine how to influence customer purchase decisions;
find out how to nurture leads during the post-purchase period.
For insurance companies, these benefits of Big Data analytics mean a great deal. They offer insurance companies a solution to tailor insurance plans to the needs of every customer.
Use Case: Lemonade and Personalized Policy Offers
Lemonade, the NYC based company that offers insurance to renters and homeowners, built its success on Big Data analytics.
Big Data analytics was Lemonade’s primary growth strategy from the beginning, ultimately helping them outcompete other renter’s insurance giants, like Allstate and Liberty, and get the accounting rate of return of over $100 million:
The company uses its chatbot called Maya to interact with a client and gather essential information about them. Maya can have hundreds of simultaneous conversations at once with potential customers. During these conversations, the chatbot collects and organizes their queries and finds personalized insurance plans that fit their needs.
As a result, with Maya, it only takes 90 seconds to get a potential customer insured and about 3 minutes for them to receive insurance funds. Customers also get to switch to a different insurance plan if their living situation changes.
All these features are possible thanks to Big Data analytics, using which Lemonade crawls the social, transactional, and machine data provided by their customers to offer personalized insurance plans.
2. Using the Internet of Things to Assess Security Risks
Insurance companies strive to improve customer experience and better predict and prevent the risks associated with the type of insurance policy.
This is where IoT devices often come into play, helping insurers collect and analyze consumer data to predict possible risks.
IoT devices, such as sensors, wearables, and telematics, rely on different types of data analytics:
status data – when the appliance turns on and off, when it connects to and disconnects from the internet, and so on;
location data – data coming from sensors that detect movement and change of location;
actionable data – data used from forecasting and prediction analysis, which benefits decision making in the long term;
automation data – the type of data that usually runs IoT systems, such as smart homes and vehicles, and ensures their security based on automatically performed tasks.
Insurance companies can employ the IoT devices to determine how suitable the choice of an insurance policy is for a particular customer, as well as determine and prevent risks.