The three pillars of insurance analytics

Published April 8, 2016   |   

The insurance industry is all about assessing risk and managing the same successfully. Life insurance industry operates intrinsically by balancing risk assessment and risk management. Compiled with a large volume of data the insurance industry operates with, arriving at meaningful information can be a challenging task. Also, the insurance industry is growing competitive with each passing year and numbers of insurance service providers are constantly on the rise. In such a scenario only those companies that can increase their top and bottom growth line can stay competitive and profitable in the long run.

Understanding Insurance Analytics

Insurance companies use a large chunk of data available with them and develop new tools to understand and analyze the data for their profitability. This process of using large chunks of data to understand the market dynamic and risk assessment is known as insurance analytics. Insurance analytics can go a long way in helping insurance companies develop their business model and various insurance products which allow the companies to stay profitable while attracting new clients and retaining existing ones.

Insurance Analytics and Business Blueprint

For New Entrants: Analytics can help insurance companies who plan to enter new market segments. For example, with FDI in the Indian insurance sector now increased to 49%, more and more insurance companies are likely to enter the Indian markets. Before entering to a new market, they need offer products that have been developed for the local population, making them attractive for Indians as a whole.

For Staying Profitable: On the other hand, the companies also have to take note that they remain profitable and do not end up losing money while offering protective services to the clients. Insurance analytics data helps companies to balance such a dilemma allowing them to grow and sustain business in the long run.

Three Significant Pillars of Insurance Analytics

The three most important aspects of a well defined insurance analytics data is to acquire new customers, retain the existing ones and analyzing risk management to stay profitable in the long term.
• Analytics and Customer Acquisition: Insurance analytics if performed optimally can reduce the cost of customer acquisition. Each insurance company spends a big amount on their marketing programs. An insurance analytical tool offers solutions to the company listing the marketing tool offering best return on investment. Using such analytical tools, companies can acquire new customers staying well within their marketing budgets across all market segments.
• Analytics and Customer Retention: Insurance analytics helps companies devise a strategy to make sure that they retain their customers over a long period of time and lowering policy lapses. The sooner a policy lapse happens, the chances of insurance company losing money is substantially higher. Since most policy lapses that happen within the first year by directly impacting the acquisition costs for the insurance company, having a customer retention program holds paramount significance. Insurance analytics data can help companies make sure they sustain their customers over a long period of time resulting in both brand development and cutting out any losses.
• Analytics and Risk Management: Insurance analytics can help insurance companies understand risk management in various markets. This can be a useful tool while developing new insurance products driving revenue growth. Rather than using a predictive analysis, companies are beginning to use area specific market analytics to understand the risks associated with introduction of a particular insurance product allowing companies to judge their profitability before venturing into the market.

Since market uncertainty and the rising costs mean that finding profitable customers remains a challenge for the industry overall, balancing both new customer acquisitions while retaining the older ones is an important element of success.