Unified Access Case Study: EDF Energy Probes for Customer Insights with SPSS Text Mining for Clementine

This IDC Flash describes how a company with over 5,000,000 accounts can squeeze additional information out of its existing data sources to improve customer understanding. By relying on an integrated approach to text mining, EDF Energy easily analyzes the words and phrases of textual information that it has readily at hand. All too frequently, enterprises looking for insights into their customers rely on incomplete or inaccurate data. It's a familiar garbage-in/garbage-out scenario ? models for improving customer relationships are often based on suspect data in the first place. One reason is that large enterprises frequently begin with their routine (and often legacy) information sources. All too often, all they really know about their customers is captured as structured data within their billing and accounting systems. When companies want to draw inferences about their customers, relying on structured data elements alone won't give them the information they need, nor does it guarantee accuracy. Fortunately, text mining offers a way to unlock the value of the words and phrases captured in customer account records. EDF Energy, one of the United Kingdom's largest energy companies, relies on SPSS Text Mining for Clementine for customer insights. The company verifies the segmentation of its customer accounts based on the textual analysis of their name and address fields. EDF Energy integrates text mining with its ongoing statistical data mining activities.