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Author Valle, M.A.; Varas, S.; Ruz, G.A.
Title Job performance prediction in a call center using a naive Bayes classifier Type
Year 2012 Publication Expert Systems With Applications Abbreviated Journal Expert Syst. Appl.
Volume 39 Issue 11 Pages 9939-9945
Keywords Employee turnover; Job performance; Naive Bayesian classifier; Call center
Abstract This study presents an approach to predict the performance of sales agents of a call center dedicated exclusively to sales and telemarketing activities. This approach is based on a naive Bayesian classifier. The objective is to know what levels of the attributes are indicative of individuals who perform well. A sample of 1037 sales agents was taken during the period between March and September of 2009 on campaigns related to insurance sales and service pre-paid phone services, to build the naive Bayes network. It has been shown that, socio-demographic attributes are not suitable for predicting performance. Alternatively, operational records were used to predict production of sales agents, achieving satisfactory results. In this case, the classifier training and testing is done through a stratified tenfold cross-validation. It classified the instances correctly 80.60% of times, with the proportion of false positives of 18.1% for class no (does not achieve minimum) and 20.8% for the class yes (achieves equal or above minimum acceptable). These results suggest that socio-demographic attributes has no predictive power on performance, while the operational information of the activities of the sale agent can predict the future performance of the agent. (c) 2012 Elsevier Ltd. All rights reserved.
Address [Valle, Mauricio A.] Univ Valparaiso, Fac Ciencias Econ & Adm, Santiago, Chile, Email: mauricio.valle@uv.cl
Corporate Author Thesis
Publisher Pergamon-Elsevier Science Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue (down) Edition
ISSN 0957-4174 ISBN Medium
Area Expedition Conference
Notes WOS:000303300900001 Approved
Call Number UAI @ eduardo.moreno @ Serial 213
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