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Data Mining and Explorative Multivariate Data Analysis for Customer Satisfaction Study

Data Mining and Explorative Multivariate Data Analysis for Customer Satisfaction Study

Rosaria Lombardo
Copyright: © 2011 |Pages: 24
ISBN13: 9781616928650|ISBN10: 1616928654|EISBN13: 9781616928674
DOI: 10.4018/978-1-61692-865-0.ch013
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MLA

Lombardo, Rosaria. "Data Mining and Explorative Multivariate Data Analysis for Customer Satisfaction Study." Surveillance Technologies and Early Warning Systems: Data Mining Applications for Risk Detection, edited by Ali Serhan Koyuncugil and Nermin Ozgulbas, IGI Global, 2011, pp. 243-266. https://doi.org/10.4018/978-1-61692-865-0.ch013

APA

Lombardo, R. (2011). Data Mining and Explorative Multivariate Data Analysis for Customer Satisfaction Study. In A. Koyuncugil & N. Ozgulbas (Eds.), Surveillance Technologies and Early Warning Systems: Data Mining Applications for Risk Detection (pp. 243-266). IGI Global. https://doi.org/10.4018/978-1-61692-865-0.ch013

Chicago

Lombardo, Rosaria. "Data Mining and Explorative Multivariate Data Analysis for Customer Satisfaction Study." In Surveillance Technologies and Early Warning Systems: Data Mining Applications for Risk Detection, edited by Ali Serhan Koyuncugil and Nermin Ozgulbas, 243-266. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-61692-865-0.ch013

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Abstract

By the early 1990s, the term “data mining” had come to mean the process of finding information in large data sets. In the framework of the Total Quality Management, earlier studies have suggested that enterprises could harness the predictive power of Learning Management System (LMS) data to develop reporting tools that identify at-risk customers/consumers and allow for more timely interventions (Macfadyen & Dawson, 2009). The Learning Management System data and the subsequent Customer Interaction System data can help to provide “early warning system data” for risk detection in enterprises. This chapter confirms and extends this proposition by providing data from an international research project investigating on customer satisfaction in services to persons of public utility, like education, training services and health care services, by means of explorative multivariate data analysis tools as Ordered Multiple Correspondence Analysis, Boosting regression, Partial Least Squares regression and its generalizations.

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