How Assurant Solutions Saved Its Customers From Call Center Hell

New analytics tools help one insurance company pair its customers with the best call center representative for their needs, providing better service and retaining clients.

By Stephanie Overby
Wed, October 27, 2010

CIO — There’s no shortage of technology at work when you call a customer service line, including customer relationship management systems and voice-recognition programs. From the second you dial that toll-free number until the moment the agent answers, a slew of tools is whirring in the background to make the interaction as helpful as possible.

But anyone who’s endured a less-than-productive 1-800 experience knows these tools don’t always get you to the right person—let alone the best person—right away.

“It’s a thousand-mile journey between when a customer picks up the phone and when they land on a customer service representative’s desktop,” says Cameron Hurst, a 15-year veteran of call-center-technology management. “The problem is, we’ve overlooked the last 500 feet.”

Assurant Solutions, where Hurst is vice president of targeted solutions, provides specialized insurance products from its base in Atlanta, and it has developed a better way to match customers and call center agents using analytics. Most call centers are heavy users of rules-based analytics, such as systems that route calls based on “if-then” formulas. (If your account balance is greater than $10,000, then you are sent to a premier agent. If the call is made between 8 a.m. and 5 p.m., you’re routed to Boise instead of Bangalore.)

This wasn’t enough for Assurant Solutions, which sells and supports debt-cancellation products, credit card insurance, and other financial-protection products through Fortune 50 financial institutions. The company has a high acquisition rate—most of its products come with a trial period—and a high customer churn rate. Seven years ago, it retained just 16 percent of its customers.

So it turned to model-based analytics. The company created a proprietary system that analyzed the attributes of all calls over six months to create models of the successful ones. It then examined a host of customer variables—such as age, account balance, credit-to-balance ratio, and persistence in calling—to create models of different customer “clusters.” “The models are reduced to algorithms [which become] Java code, and that code powers a matching engine,” Hurst explains. “It scans thousands of agents and finds that one optimal match. It’s kind of like eHarmony—there’s someone for everyone.” Unlike most matching systems, Assurant’s looks at agents who are already on a call—not just those who are available—and determines if they will be wrapping up in time to deal with their next customer soul mate.

Since implementing the system, Assurant’s customer-retention rate has jumped 190 percent to around 47 percent. Most customer-service-related technologies promise a 10 percent to 25 percent improvement in customer retention, says Hurst, who was formerly a CIO at a large community bank.

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