The insurance industry is continuously on the lookout for ways to leverage the wealth of data it collects effectively. When Sun Life realized it was stewarding terabytes of data about its end clients, VP of distribution operations Tom Gilligan and his team set out to turn some of that information into a consumable form for brokers of its group insurance products.

“We have a really big database of customers and their plan designs because we quote on about 20,000 employers a year,” Gilligan says. “We’re also going after more and different external data sources. We’re all about helping brokers make better decisions with their employer customers using data.”

The end result was Sun Life’s proprietary Benefit Profile Tool, which incorporates all kinds of data from demographics to plan design information to claim data, in order to help brokers provide prospects and clients with an elaborate presentation on which products are best for their company.

“A lot of people can grab data and prepare a report. We’ve achieved scale with a very professional benchmarking report, combined with demographic data, to really help the employer and the broker say, ‘How do my plan designs stack up against my peers?’” Gilligan explains.

Creating the tool allows Sun Life to get some mileage out of data that previously had been used only once or simply sat languishing on a server, Gilligan adds. The tool also benefits from frequent data calls to the Group MarketShare clearinghouse for market share information.

Key to the success of the tool – and a complementary Stop-Loss Benchmarking tool for self-insured clients – isn’t just providing the analytics, but representing them graphically in an easy-to-understand way for brokers and clients.

“Every group carrier in the country produces demographic information, but they’re all just sitting on it,” Gilligan says. “Using good graphics and data visualization, we help them do a side-by-side breakdown on any existing customer or prospect.”

For Sun Life organizationally, the success of the two distribution analytics tools has spurred greater exploration of big data in other business areas as well.

“We have a really strong team of data people -- I personally call them the data ninjas – that are very fast and effective,” Gilligan says. “We’ve all started talking about sources of data and leveraging data, people started to realize look what they did, then everyone kind of woke up internally it didn’t take the functional areas long to start coming up with ideas.”

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