Employee churn prediction

Authors:
V. Vijaya Saradhi;Girish Keshav Palshikar
Affiliations:
Tata Research Development and Design Center, Tata Consultancy Services, 54-B, Hadapsar Industrial Estate, Pune, Maharashtra, India;Tata Research Development and Design Center, Tata Consultancy Services, 54-B, Hadapsar Industrial Estate, Pune, Maharashtra, India
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Customer churn is a notorious problem for most industries, as loss of a customer affects revenues and brand image and acquiring new customers is difficult. Reliable predictive models for customer churn could be useful in devising customer retention plans. We survey and compare some major machine learning techniques that have been used to build predictive customer churn models. Employee churn (or attrition) closely related but not identical to customer churn is similarly painful for an organization, leading to disruptions, customer dissatisfaction and time and efforts lost in finding and training replacement. We present a case study that we carried out for building and comparing predictive employee churn models. We also propose a simple value model for employees that can be used to identify how many of the churned employees were ''valuable''. This work has the potential for designing better employee retention plans and improving employee satisfaction.