Data Mining and Explorative Multivariate Data Analysis for Customer Satisfaction Study

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Author(s): Rosaria Lombardo (Second University of Naples, Italy)
Copyright: 2011
Pages: 24
Source title: Surveillance Technologies and Early Warning Systems: Data Mining Applications for Risk Detection
Source Author(s)/Editor(s): Ali Serhan Koyuncugil (Capital Markets Board of Turkey, Turkey, and Baskent University, Turkey)and Nermin Ozgulbas (Baskent University, Turkey)
DOI: 10.4018/978-1-61692-865-0.ch013

Keywords: Information Science Reference / Information Security & Privacy / IT Security and Ethics / Security & Forensics

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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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Data Mining and Explorative Multivariate Data Analysis for Customer Satisfaction Study

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