What is Data Analytics?
Each day, an estimated 2.5 quintillion bytes of data are created. The past two years alone accounted for 90% of this data creation. But what should be done with it all? This is where data analytics comes in.
Data analytics is the set of techniques used to analyze raw data (unprocessed data) in order to extract relevant information, trends, and insights. This process includes collecting data, organizing it, and storing it, then performing statistical analysis on the data. Once the information is collected, conclusions can be drawn from it, which can be used for problem-solving, business processing, decision-making, and predictions that can inform what a company’s next steps should be. This process relies on disciplines like mathematics, statistics, and computer programming.
The field of data analytics doesn’t just provide valuable insights into current trends, but it helps to predict what may occur based on past and current actions. There are four kinds of data analytics: descriptive, diagnostic, prescriptive, and predictive, which can be used independently, as well as in concert, for a company’s specific analytic needs.
What is Predictive Analytics?
Predictive analytics draws on summarized data, as well as past trends and behaviors, to offer logical predictions on what may occur in the future. This branch of analytics incorporates statistical modeling to make forecasts that seek to answer the question: “What happens if?” The accuracy of these forecasted estimates depends on the quality of the data.
Predictive models have many real-world applications. This article will explore the various ways predictive models can be applied to the insurance sector.
Applying Predictive Analytics to the Insurance Sector
The following are some of the main ways predictive data analytics is being applied to the insurance sector to help both the insurers, as well as the customers, have a better overall experience:
- Speeding up claims processing. One of the most important factors that influences the efficiency of an insurer is how fast it can settle claims. However, processing claims requires a variety of time-intensive steps that each can present its own challenges. Automated data processing services are now being used by insurance companies to process all relevant documents into digital formats. This drastically speeds up claims processing and increases overall efficiency.
- Improving risk selection and pricing. Because the data sets being collected from insurers now come from a variety of data sources, the information gathered from this data is more actionable. This data largely comes from firsthand information, such as smart devices, social media, and correspondences between customers and claims specialists, which makes it more reliable and direct than information gathered from outside sources like criminal records or credit histories. It is estimated that insurers are able to collect over 10 MB of data from IoT-enabled devices in a household in a day, a number that is likely to increase in the coming years as more people turn to smart devices. This information ultimately leads to more accurate risk and pricing selection.
- Providing a better understanding of the customer. Companies can now aggregate data from a variety of touchpoints customers use when reaching out to a company to make a purchase or leave a review. Predictive analytics enables insurers to consolidate data in order to gain a more complete understanding of the customer. Information pertaining to their risk profile, purchasing habits, and the likelihood of a customer purchasing new coverage are just a few variables that can now be studied in order to offer customers better services and products, and ultimately increase profits.
- Spotting those at risk of terminating coverage. Carriers can use predictive analytic techniques to identify customers who are most likely to elect to lower their coverage or cancel it altogether. Because these customers tend to be dissatisfied with their current coverage, carriers can preemptively reach out to those who are shown to be most at risk for cancellation and offer them additional attention before issues can arise.
- Personalizing the customer experience. When selecting an insurance policy, customers often value a personalized experience. With the help of predictive analytics, insurance providers can use data from the IoT to gain a more robust understanding of what their customers want and find better ways to provide it.
- Targeting potential markets. Insurers apply predictive analytics to locate and reach out to potential markets. By studying the purchasing and commenting habits of over 3 billion social media users around the globe, it’s possible for insurers to better spot potential markets.
- Automating the underwriting process. Advanced data analytics techniques, such as virtual underwriters, play an important role in helping insurers streamline and quicken the underwriting process. Automated techniques help insurers avoid typical hurdles to the data-gathering process, such as collecting data from different types of applications that have different formats, as well as manually gathering data.
- Identifying outlier claims. Insurance providers refer to claims that unexpectedly grow into high-cost losses as “outlier claims.” Predictive analytic tools allow insurers to analyze prior claims to spot similarities, then automatically alert claims specialists of any overlaps. Spotting patterns in these potential losses can help to reduce other outlier claims in the future.
- Improving the process of pricing premiums. One of the main challenges insurance companies face is to provide every policyholder with an accurate price for their premium. In some instances, policyholders are charged premiums above what they should be, though it is not the customer’s fault. If an insurance company wishes to remain competitive in the market, it’s crucial for them to devise new ways to apply insurance analytics to decide customer premiums. This process typically involves analyzing customer data in order to extract actionable insights, as well as tracking the past behavior of individuals seeking a policy. This process ultimately results in more accurate premiums for customers.
As new technologies such as automation and machine learning continue to be incorporated into the insurance sector, it will be possible for insurers to provide a better, more customized customer experience, fairer premiums, and faster claims. In addition, insurers will be able to reduce manual work, improve accuracy, and increase revenue by doing so.
Hands-On Data Analytics Classes
Do you want to learn more about Data Analytics? If so, Noble Desktop’s data analytics classes are a great starting point. Courses are currently available in topics such as Excel, Python, and data analytics, among others skills necessary for analyzing data.
In addition, more than 130 live online data analytics courses are also available from top providers. Courses range from three hours to six months and cost from $219 to $60,229. Students can study from the comfort of their own home or office space and still receive industry-relevant data analytics training.
Those who are committed to learning in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely instruction on how to handle large sets of data. Over 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python, among others.
For those searching for a data analytics class nearby, Noble’s Data Analytics Classes Near Me tool provides an easy way to locate and browse the 400 or so data analytics classes currently offered in the in-person and live online formats. Course lengths vary from three hours to 36 weeks and cost $119-$60,229.