Software Detects Unhappy Callers - InformationWeek

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Software // Enterprise Applications
10:55 AM

Software Detects Unhappy Callers

Smart call-center tool is designed to help companies improve customer service by identifying callers who are upset

Not long ago, an elderly man distressed over high medical premiums phoned the call center at Wisconsin Physician Services Insurance Corp. The caller was so frustrated he hung up before an agent could address his problem. But the call center's IT system was aware of the customer's exasperation and automatically E-mailed a supervisor, who immediately listened to a digital recording of the conversation.

Moments later, the supervisor called the customer and suggested ways to lower the premium. The customer agreed to the policy changes. "All in all, we ended up with a happy customer," says Sharon Whitwam, the insurer's VP of member services.

The Madison, Wis., health insurer did that with new emotion-detection software called Perform, created by Nice Systems Ltd., which began widely marketing the product last month.

Using algorithms, the system determines a baseline of emotion during the first five to 10 seconds of a call, when most people usually aren't excited or frustrated. Any deviation from that baseline can trigger an alert.

The software examines 200 elements to give a holistic picture of the customer experience. It lets users set parameters to determine which supervisor should be contacted and how, and create lexicons of words and phrases a caller may say that could raise red flags: cancellation, frustration, a competitor's name. Perform also tracks the history of a call and lets callers rate their experience. Licensing fees vary, but start at $100,000 for up to 150 seats.

What's next for emotion-detection software? Artificial intelligence. For example, instead of users defining keywords and emotions, the software will figure things out by analyzing the caller's voice. By analyzing pitch, tone, tempo, and inflection, the software in the not-too-distant future could be used to detect fraud. It already can differentiate between real anger and someone mimicking anger.

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