The insurance industry is one of the most flourishing sectors of today. Recent research shows that with the increasing level of market competition, insurance companies are formulating new ways to differentiate themselves. Big data analytics is one of the promising strategies that has taken the insurance industry by storm. Insurance companies are employing big data analytics to provide tailored and specialized services to clients to withstand the rising competition. There are different types of big data analytics methodologies that are being exploited by companies. Let’s take a look at some of them here.
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Descriptive analytics consists of any data that can be analyzed and synthesized for the further benefit of the business such as the client’s page views, web activities, blog mentions, etc. It involves collecting historical and other basic information about the client. This is one of the most popularly used analytics methods used in the insurance industry. An example of the successful implementation of descriptive analytics is the car insurance industry. The insurance companies provide their clients with a device that have to be plugged into their vehicles, and this tool helps determine the car insurance pricing based on the driving safety measures adopted by the client.
Unlike descriptive analytics which is based on raw data, diagnostic analytics mainly deals with regression analysis based on the big data available, which is then used to estimate the correlation between variables. An in-depth data mining is done to discover different correlations. This is highly beneficial for the insurance industry players as it helps companies to assist their clients in making various informed decisions based on the client’s data analysis.
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The key to a satisfied customer is providing them with tailor-made services to meet their needs. Predictive analysis has proved to be more helpful for companies in the insurance industry since it uses big data for predicting future events and planning accordingly. This helps save more time and money. A few prominent players in the insurance industry have used this analytical technique to avoid fraudulent practices.