The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Data Mining and Explorative Multivariate Data Analysis for Customer Satisfaction Study
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.
Related Content
Chaymaâ Boutahiri, Ayoub Nouaiti, Aziz Bouazi, Abdallah Marhraoui Hsaini.
© 2024.
14 pages.
|
Imane Cheikh, Khaoula Oulidi Omali, Mohammed Nabil Kabbaj, Mohammed Benbrahim.
© 2024.
30 pages.
|
Tahiri Omar, Herrou Brahim, Sekkat Souhail, Khadiri Hassan.
© 2024.
19 pages.
|
Sekkat Souhail, Ibtissam El Hassani, Anass Cherrafi.
© 2024.
14 pages.
|
Meryeme Bououchma, Brahim Herrou.
© 2024.
14 pages.
|
Touria Jdid, Idriss Chana, Aziz Bouazi, Mohammed Nabil Kabbaj, Mohammed Benbrahim.
© 2024.
16 pages.
|
Houda Bentarki, Abdelkader Makhoute, Tőkési Karoly.
© 2024.
10 pages.
|
|
|