Turnover prediction in a call center: Behavioral evidence of loss aversion using random forest and naïve bayes algorithms

Mauricio A. Valle, Gonzalo A. Ruz

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

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 naïve 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 naïve 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 naïve 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.

Original languageEnglish
Pages (from-to)923-942
Number of pages20
JournalApplied Artificial Intelligence
Volume29
Issue number9
DOIs
StatePublished - 21 Oct 2015

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