Big Data Analytics Gold for the Call Center

There's valuable data in those contact center logs that have largely been gathering dust, and companies are using new tools to mine them to boost customer satisfaction and revenue and lower costs.

By Stephanie Overby
Fri, May 11, 2012

CIO — There may be no corporate function that throws off more data than the corporate call center. "Every contact is counted, routed, measured and scored. Agent performance is actively measured," says Tony Filippone, executive vice president of research for sourcing analyst firm HfS Research. "Other key process owners, like finance and accounting or claims adjudication, wish their data was as rich."

Throughout the history of the contact center, much of the analysis of that data has been quantitative in nature—calls received, average hold time, call length, resolution rate.

"Over time, companies added more sophisticated workforce management tools including global scheduling information—to help with network call handling, scheduling, real-time adherence—but the data collected was agent performance- and efficiency-related," says John Magliocca, principal consultant for contact center service at outsourcing and management consultancy ISG.

That's beginning to change. Corporate call centers—and call center providers—are embracing new analytic tools to dig deeper into the big data they generate. There are a number of business factors driving the change, says Filippone. Contact center agents are being asked to handle a wider breadth of issues from more channels, including contacts from social media and online forums.

"This requires more advanced skills, better training and great real-time guidance," Filippone says. At the same time, companies that have been disappointed with the quality of offshored call centers are now looking to emerging end-to-end CRM tools to take costs out of their operations instead.

Analyzing Unstructured Data

Some of the most interesting software being implemented today attempts to take unstructured voice recordings and analyze them for content and sentiment. "There have been efforts underway to put contact data to work to best understand the current mood of the customer and other information that can immediately mold client strategy and direction [for some time]," says Magilocca. "But until recently the systems were not robust and the information wasn't useful." These tools could enable the call center professional to understand what's really bugging his next contact before he even says hello.

"Companies are applying text and sentiment analysis to this unstructured data, and looking for patterns and trends," says Deepek Advani, vice president of predictive analytics for IBM, which has implemented its Text Analysis and Knowledge Mining (TAKMI) tool at it sown call centers to analyze call center agent records, identify customer concerns, highlight trends and patterns, and provides early warning capabilities.

"Many companies are integrating this call center data with their transactional data warehouse to reduce customer churn, and drive up-sell and cross-sell [activity]," Advani says. "Call center logs can provide invaluable insight on what customers were calling about, and can also provide insights for future product requirements."

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