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Information SWAT Team

Pat Speer February 2007

No one ever said guiding a distribution network to optimal performance is easy. The insurance and financial services industry's supply chain is as complex as ever, and carriers across all lines want to exploit its value.

How do you know how much business you may be missing? How do you hold your distribution network accountable for increased growth and profitability? For many carriers, it becomes the classic "what to do with all that data" dilemma.

Knowing that a virtual sea of data is available about products, the distribution network, customers, prospects and even the competition, insurers are challenged with finding ways to leverage its inherent-and often obscure-knowledge for improving overall business processes and gaining market share.

And no one ever said getting an entire marketing organization to rally around a data mining initiative that would ultimately improve your company's multifaceted distribution network would be easy, either.

Rachel Alt-Simmons, director, Business Intelligence Division at Hartford Life Insurance Co., Simsbury, Conn., found this out first-hand approximately five years ago when she and her team delved into a massive business intelligence initiative that studied the performance-and potential opportunities for its distribution network. "We recognized we had an analytic need that wasn't being met," she says.

Under the umbrella of The Hartford's Financial Services Group, Hartford Life sells retail and institutional financial service products. On the retail side, variable annuity, mutual funds, 401(k) and 529 college savings plans contribute to $155 billion in retail product assets; variable annuity products represent more than $100 billion in assets. The stakes are as high as the business model is complex.

TOP-DOWN SUPPORT

"We wanted to be able to analyze profiles and preferences of existing customers and then predict buying behavior," says Alt-Simmons. "And, we wanted to make sure our marketing efforts were what they needed to be."

For the past eight years most of those marketing efforts have been managed by PLANCO, Hartford Life's Philadelphia-based wholesale subsidiary. With 300-plus wholesalers across three Hartford product lines, PLANCO markets to financial advisors and registered brokers and dealers, who in turn sell to the client. (See Business Model chart, p. 36.)

"There were some indications that if we built a model that would allow us to look at the data in certain ways, we could help PLANCO recognize and actually predict which brokers were 'endangered'-those brokers that have not sold in six months or are considered at risk of stopping their selling efforts," Alt-Simmons says. To do that, the group wanted to bring data in from the company's annuities lines and look at how brokers function.

Hartford Life's business intelligence team recognized that the challenge would entail selling the project first on the inside - essentially becoming cheerleaders for business intelligence. "We took a bottom-up approach and then pushed the idea up to the executive level, seeking a top-down blessing," she recalls. Once the team received visible support from executives, the challenges before them seemed to unfold, one by one.

TESTING THE THEORY

At the start of the multiphase project, a group of 10 users worked with Enterprise Guide, a Microsoft Windows front-end application from Cary, N.C.-based SAS that provides visual access to enterprise data sources supported by SAS and native Windows data types. As a dedicated, intuitive interface for analyzing business information stored in OLAP data cubes, it guides the user to access data across the enterprise on multiple platforms, operating systems and databases.

"We ended up being really good programmers," Alt-Simmons recalls, "but it helped us understand our need for expanded data management." SAS Account Executive John McDonald recalls that when Alt-Simmons said the team wanted to start performing data analytics, they decided to add SAS' data mining product, Enterprise Miner, to the mix.

A proof of concept, which tested the theory across all lines and all marketing-related functions, along with capability prototyping, followed, says McDonald, "but it became obvious early on that it was going to be tough to get to all the data."

Alt-Simmons admits limited access to data and data quality issues were a challenge, but so was aligning overall business strategy with business analytics.

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