MIT SLoan Management Review published a case-study interview with Cameron Hurst from Assurant Solutions.
The excellent article by authors Michael S. Hopkins and Leslie Brokaw, takes a deeper look into how Assurant—who sells credit insurance and debt protection products—attained a three-fold success rate in its call center. It also reveals some interesting 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.
Here’s just one interesting highlight from the article:
… “we operated under the fallacy — and I believe it’s fallacious reasoning — that if we improve the operational experience to the nth degree, squeeze every operational improvement we can out of the business, our customers will reflect these improvements by their satisfaction, and that satisfaction will be reflected in retention. And that was fundamentally wrong. We learned that operational efficiency and those traditional metrics of customer experience like abandon rate, service levels and average speed to answer are not the things that keep a customer on the books.” Assurant Solutions was looking for the key to customer retention — but was looking in the wrong place.
What they found surprised them. In a sense, it was simple: They found that technology could assist the company in retaining customers by leveraging the fact that some customer service reps are extremely successful at dealing with certain types of customers. Matching each specific in-calling customer to a specific CSR made a difference. Not just an incremental difference. A huge difference. Science and analytics couldn’t quite establish why a particular rapport would be likely to happen, but they could look at past experience and predict with a lot of accuracy that a rapport would be likely to happen.”
Is your call center using traditional skills-based routing? If yes, you should read this article to learn why that might not be good enough.