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Author (up) Valle, M.A.; Ruz, G.A. pdf  doi
  Title Turnover Prediction In A Call Center: Behavioral Evidence Of Loss Aversion Using Random Forest And Naive Bayes Algorithms Type
  Year 2015 Publication Applied Artificial Intelligence Abbreviated Journal Appl. Artif. Intell.  
  Volume 29 Issue 9 Pages 923-942  
  Abstract It is well known that call centers suffer from high levels of employee turnover; however, call centers are services that have excellent operational records of telemarketing activities performed by each employee. With this information, we propose to use the Random Forest and the naive Bayes algorithms to build classifiers and predict turnover of the sales agents. The results of 2407 sales agents' operational performance records showed that, although the naive Bayes is much simpler than Random Forest, both classifiers performed similarly, achieving interesting accuracy rates in turnover prediction. Moreover, evidence was found that incorporating performance differences over time increases significantly the accuracy of the predictive models up to 85%, with the naive Bayes being quite competitive with the Random Forest classifier when the amount of information is increased. The results obtained in this study could be useful for management decision-making to monitor and identify potential turnover due to poor performance, and therefore, to take a preventive action.  
  Address [Valle, Mauricio A.] Univ Finis Terrae, Fac Econ & Negocios, Santiago, Chile, Email:  
  Corporate Author Thesis  
  Publisher Taylor & Francis Inc Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0883-9514 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000363691000004 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 541  
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