Matchmaking With Math: How Analytics Beats Intuition to Win Customers

In sales, the rapport between a prospective buyer and seller can be the deciding factor. Using analytics, Assurant Solutions has tripled its success. (A case-study interview.)

Assurant Solutions sells credit insurance and debt protection products. Maybe you’ve bought a product like theirs. If you lose your job or have medical problems and are unable to make a credit card payment, Assurant Solutions will help you cover it. Like a lot of insurance products, payment protection is a discretionary add-on often made at the point of purchase. But when customers get the bill and see the additional fee of, say, $10.95 per month for payment protection, maybe they think, “Well, I’ll take my chances” and decide to cancel. When those customers call, they reach Assurant Solutions customer service representatives, because the company manages insurance activation, claims, underwriting and customer retention (for many industry-leading banks and lending institutions). It’s in that last piece — that attempt to retain customers, beat the churn and stem a high exit rate — that Assurant Solutions faced a now-universal management challenge. As a call center positioned as the pivot point of all customer interaction for its clients, Assurant had access to hoards of data as well as the ability to create the kinds of rules and systems that any operationally optimized call center would deploy. With skills-based routing, customized desktops with screen pops, and high-end voice recording and quality assurance tools, its efforts were state-of-the-art.

The Leading Question

If analytics are brought to bear on a call center, how are operations and results affected?

Findings
  • Many conventional beliefs about call centers prove to be wrong. For instance, customers will wait longer than expected.
  • Evidence trumps intuition when predicting outcomes.
  • Conflicting goals can be reconciled in real time by analytically driven models.

But it wanted to do better. Its 16% retention rate was consistent with the best industry standards, but that still meant that 5 out of 6 customers weren’t convinced to keep their coverage, let alone consider other products. That’s a lot of room for opportunity. So Assurant Solutions tried something new: deep analytics. And it invented an operations system that capitalized on what the analytics prescribed. The result? The success rate of its call center nearly tripled. What Assurant Solutions found was that all the conventional tenets about contact centers “are not necessarily wrong, but they’re obsolete,” says Cameron Hurst, vice president of Targeted Solutions at Assurant.

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3 Comments On: Matchmaking With Math: How Analytics Beats Intuition to Win Customers

  • DAVID SHORT | January 4, 2011

    This article literally begs for the follow up article: Mapping Your Call Center Hires to Optimize Outcomes. Even if the “talent/skill” that produces the desired outcomes has not been defined,plotting the service reps and clients on a multi-dimensional space should indicate quadrants where “gaps” exist and “balancing” could take place. I foresee some innovative new services in the area of human resource selection consultation based on this article. For instance, some car dealership chains have “media groups” that advertise and then arrange client appointments with sales personnel in the appropriate dealerships. Not all the sales personnel are equally effective in utilizing these appointments and, like in your article, the outcome may be related to currently undefined factors. Based on this article, the mix or composition of auto sales personnel could radically change.

  • Cassandra Van Buren | January 4, 2011

    @David Short – I concur. As the article indicates, these data don’t explain the ‘why’ of the results, which is a clear next step for research.

  • Chris Bracken | January 14, 2011

    David,

    This is exactly the kind of work my company does. We focus exclusively on human capital management for the call center industry. In recruiting and screening call center agents, we look to match the skills, personality types, and aptitude scores of successful agents to those in the applicant pool.

    Funny thing is, many call centers still hire on “gut feel”. Our aim is to change that!

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