Adapting to changing policyholder behaviors & expectations

There is a need to evolve data analytics alongside changing customer behaviors and expectations.

While consumers are more willing to share personal data in exchange for more personalized services and pricing, they do have concerns about their insurers’ ability to use their data responsibly. For instance, just under a third (32%) of consumers say they significantly trust insurers to look after their data. (Credit: Knssr/stock.adobe.com)

The insurance industry has always been based on data. Risk models and actuarial analytics have been and will continue to be essential to how the industry allocates capital as well as how it assesses and prices risk.

Leading insurance companies are reinventing their product and customer engagement strategies to meet the evolving needs of customers in real-time — with data at the core. To make it work, they need both customer data from connected and Internet of Things (IoT) devices and advanced data analytics.

As customer behaviors and expectations continue to adapt, there is a need to evolve data analytics. For example, the ever-increasing volume of customer-generated data coming from the “internet of everything” is driving demand for insurers to collect and use it in new ways.  

Customers are seeking new & better solutions

Across every industry, the companies that are delivering relevant offers in real-time through advanced data analytics are the ones winning in the market. Customers are willing to share their data when it is used to deliver value back to them. As reported in our Insurance Consumer Survey 2021, 69% of consumers said they would share significant data on their health, exercise and driving habits in exchange for lower prices from their insurers and 66% of consumers said they would also share significant data for personalized services to prevent injury and loss.

Insurers that mature their analytics capabilities are better positioned to offer this kind of customer relevance. They can provide continuous support to customers at every touchpoint — from underwriting to policy servicing to claims.  

Building trust through responsible use of customer data

While consumers are more willing to share personal data in exchange for more personalized services and pricing, they do have concerns about their insurers’ ability to use their data responsibly. For instance, just under a third (32%) of consumers say they significantly trust insurers to look after their data.

The three levels of data analytics

  1. Descriptive analytics are routinely combined with automation solutions to underwrite risk and process claims. Such analytics are based on specific data attributes from the past and present, historic risk models, and current market conditions.  
  1. Predictive analytics allow insurers to look into the future and, using behavioral models, better understand how a customer is likely to respond to potential risks. As more customer data feed into the model, the more complete the individual risk profile and more accurate the predictions become.  
  1. Prescriptive analytics are how insurers start creating strategies to help the customer mitigate and manage risk. That requires large-scale, real-time optimization of customer data and the insurer’s product portfolio to present a contextualized real-time recommendation in the moment.

Overall, the use of customer data to generate real time usage- and behavior-based offers that help customers mitigate, manage, and recover from loss can help insurers build trust with customers. That’s the value that advanced data analytics can deliver both to the insurance customer and to the insurer.

Kenneth Saldanha is the global insurance lead for Accenture.

Opinions expressed here are the author’s own.

Related:

Recommended Stories