SPSS Inc., a leading provider of predictive analytics technology and services, announced the immediate availability of Predictive Text Analytics, a new solution focused on unlocking the customer insight trapped in widely collected, but rarely used, unstructured text data. Predictive Text Analytics is one of the first text mining solutions specifically designed for analytical customer relationship management (aCRM) applications. Predictive Text Analytics integrates text mining technologies with predictive analytic approaches to enable organizations to unify and analyze both unstructured text data and structured data across all interaction channels, including call centers, branch locations and Web sites.
"The majority of direct customer communication, including phone conversations, inbound e-mails, and Web form responses, is captured as raw, unstructured text," says Sue Feldman, research vice president, content technologies, at IDC, an industry analyst firm. "If CRM customers only analyze the data in their collections, they are missing the bulk of the information they have about their customers. Text mining and predictive analytics technologies are two sides of the same aCRM coin, and competitive organizations need to analyze, draw conclusions and take definitive action based on everything they know about their customers."
CIM01, a French market research company working for several pharmaceutical companies, is using Predictive Text Analytics. "This type of application will be key to many industries, and the pharmaceutical sector is no different," said Sandra Cohen, CIM01 general manager. "SPSS' Predictive Text Analytics has enabled us to improve the targeting of our research so that we get more meaningful answers. This is helping us stay ahead of the competition and provide a more tailored solution to our customers. It gives us a competitive advantage like never before."
Today, in efforts to predict likely customer behaviors such as cross-sell potential and propensity to churn, many organizations apply predictive analytic techniques to their structured customer data assets, including demographic and behavioral information. However, this analysis does not involve unstructured customer feedback, such as call center notes, e-mail requests and open-ended survey responses, as this feedback data cannot be accurately summarized and integrated with traditional structured data. "By integrating text mining with predictive analytics, Predictive Text Analytics has helped major mobile telecommunications providers and other types of businesses significantly reduce the number of profitable customers who defect to competitors," said Olivier Jouve, senior director, text mining at SPSS Inc., a 15-year veteran of the text mining field.
Through incorporating specialized, automated text mining technologies, Predictive Text Analytics analyzes customer feedback to uncover and summarize key concepts such as overall satisfaction level, service frustration, price sensitivity, switching considerations and more. Once identified and extracted, these concepts are then integrated with the existing structured customer data. Then the entire data set can be evaluated using data mining to cluster, classify, segment, profile and predict the probability of this customer's next action.
Available now, Predictive Text Analytics combines the technologies of SPSS Inc.'s data mining workbench, Clementine, with LexiQuest, its linguistics- based text extraction technology that finds the patterns and relationships in unstructured text. U.S. pricing for the customized solution starts at $100,000. For additional information, please visit www.spss.com or contact SPSS sales at firstname.lastname@example.org or 1-800-543-2185.
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