Schwab has piloted the project with the transcriptions from recorded feedback that customers leave on its interactive voice response system. The next step is to unleash text analytics on the millions of records in its CRM system and the sales and customer interaction notes. Ultimately, the firm wants to use text analytics to help establish a client promoter score, similar to the net promoter score -- the likelihood that a customer will recommend a company's product or service to someone else.
"As an advanced analytic group, we're most interested in the ability to combine structured information with unstructured information for input into new modeling," Lee said. "We're expecting that to improve our predictive modeling strengths by adding new data sources."
For now, Schwab is analyzing only internal data, but Lee said they're intrigued by leveraging external sources as well. In fact, the ever-increasing amount of text-based information promises good things for the industry, according to IDC's Feldman.
"If you think of Web 2.0 and interaction, the future of text analytics is pretty bright because in order to manage the exchange of information as we move to conversational systems, you really need text analytics in the next generation of these applications," Feldman said. "Those are on people's minds and drawing boards right now."
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