For insurance providers, one of the biggest operational challenges is connecting with clients and improving the customer experience. The emergence of predictive analytics helps providers optimize their use of big data to give their workflow a more personal touch, targeting clients’ individual needs more effectively and offering them the policies and claims management services needed to get the most out of their insurance.
According to Econsultancy, the effects of personalization are much broader than optimized policy management. Even simple things like getting a client’s gender wrong on paperwork can affect his or her experience and reduce overall satisfaction with service- something the provider wants to avoid at all costs.
“Send duplicate catalogs to a customer, get their gender wrong, or try to sell them lawn mowers when they live in an apartment, and you’ll soon compromise your marketing ROI,” Dominique Levin, an industry expert, told the news source.
The trick is to utilize all of the available information regarding a claim or policy and use it properly to maximize service quality during every interaction with that client. As data grows more sophisticated, firms will need to use a high-end business intelligence platform to continue improving and delivering the insights necessary to meet customer expectations.
Within the sphere of insurance analytics, the need doesn’t lie in customer service, however, it affects every area of operations. Underwriting, claims management software, even basic information management, can all be improved by deploying predictive analytics to streamline data usage and enhance the ability of employees to assess and report on trends. This will allow them to shift through data faster and ensure they are meeting customer needs on a personal level, while promoting overall growth throughout operations.
According to the Huffington Post, predictive analytics helps bring a personal touch to business intelligence because it helps produce more timely, actionable reports on information, ensuring that trends are acted on when they are still relevant, rather than missing an opportunity- or worse, a risk. This change is particularly advantageous in the health care professional liability field, where claims fraud and other risks continue to grow.
Beyond the general improvement that taking data usage personal with predictive analytics provides, the medical industry is advancing by leaps and bounds because of advanced business analytics solutions. These tools provide a framework, according to the news source, which aligns the evolving needs of healthcare staff with the capabilities of the insurance provider, minimizing risks and eliminating fraudulent claims in order to enhance the quality of service that firms are able to offer legitimate clients. This shifts the focus of insurer operations from risk management to risk prevention, allowing for more time spent on the claims and policies that need it.
Of course, providers will need to consider the data they are using as well. A high-quality predictive analytics strategy is nothing without the data to fuel it, and firms will need to ensure they are optimizing their data gathering and management solutions as well. This will ensure a more well-rounded approach to analytics practices and data-related operations as a whole.
The best thing an insurance provider can do to enhance its opportunities for growth, and minimize the risks that big data presents, is to invest in a business intelligence solution now and streamline its operations around providing higher-quality service to its clients moving forward. Such a change will support future demands and reduce the chances for loss as the insurance industry continues to evolve.