KXEN Analytic Framework Version 4 Pushes Data Mining Automation to Text Data Sources
KXEN, the global provider of automated data mining technology, today announced the launch of KXEN Analytic Framework version 4.0. The new version extends KXEN’s proven expertise in automating predictive analytic processes to improve the results of direct marketing activities.
Through its new component, KXEN Text Coder, version 4.0 allows users to tap into the wealth of customer knowledge held in free text form. Today’s operational databases contain a lot of textual information, such as the messages or a synopsis of the mails sent to a support line, the free forms contained in marketing surveys results, or even the free text captured in call centre tools through direct discussions with customers. This specific type of data can be used to improve the accuracy and reliability of predictive models for decreasing customer attrition, improving cross-sell rates, or acquiring more customers.
“This data can be of immense value to an organisation but it normally lies buried in otherwise unusable text fields,” said KXEN’s founder and CEO Roger Haddad. “With KXEN Analytic Framework version 4.0, companies can take advantage of this information to increase the effectiveness of their marketing initiatives.” KXEN’s text categorization automatically identifies and works with multiple languages and business domains.
KXEN clients have been quick to point out the benefits of the KXEN 4th release, and the new text mining functionality. Among them is Damien Weldon, director of collateral risk analytics at First American LoanPerformance, the leading provider of performance data and credit risk decision support tools for the US residential mortgage industry. “Text-based modeling is the next step forward in data mining automation and opens up a rich new seam of customer intelligence,” he said. “KXEN’s advance means organisations can liberate valuable new information and use it to enhance their risk management, customer service and sales and marketing efforts.”
