A System for Analysis and Remediation of Attrition

With the increasing impetus on globalization, workplace modernization, and employee welfare, modern organizations are focusing more and more resources in developing their pool of human capital. Employee attrition poses a major challenge for organizations - be it in terms of operational cost or loss of talent. We present CLARA (CLustering for Analysis and Remedial of Attrition), a deployed end-to-end system applying descriptive, predictive, and prescriptive analytics, providing as output actionable remedial actions to be used by HR departments to improve employee retention. We propose a coupling between clustering and frequent pattern based scoring measure to identify candidate employees at a high risk of attrition, and subsequently recommend suggestive actions to improve their retention. Using publicly available IBM human resource (HR) dataset, we show that CLARA demonstrates comparable performance (compared to state-of-the-art techniques) in identifying such “high risk” employees. We further validate the quality of the recommendations provided by the framework in reducing the overall rate of human capital loss, and discuss the real-life implementation of the framework within our organization. CLARA attains up to 65% precision, on IBM HR dataset, in predicting employee attrition and, thanks to the remedial actions, up to 22.5% reduction in the predictive attrition score for the top-5 identified employees.