Title
An Analytics Approach for Proactively Combating Voluntary Attrition of Employees
Abstract
We describe a framework for using analytics to proactively tackle voluntary attrition of employees. This is especially important in organizations with large services arms where unplanned departures of key employees can lead to big losses by way of lost productivity, delayed or missed deadlines, and hiring costs of replacements. By proactively identifying top talent at a high risk of voluntarily leaving, an organization can take appropriate action in time to actually affect such employee departures, thereby avoiding financial and knowledge losses. The main retention action we study in this paper is that of proactive salary raises to at-risk employees. Our approach uses data mining for identifying employees at risk of attrition and balances the cost of attrition/replacement of an employee against the cost of retaining that employee (by way of increased salary) to enable the optimal use of limited funds that may be available for this purpose, thereby allowing the action to be targeted towards employees with the highest potential returns on investment. This approach has been used to do a proactive retention action for several thousand employees across several geographies and business units for a large, Fortune 500 multinational company. We discuss this action and discuss the results to date that show a significant reduction in voluntary resignations of the targeted groups.
Year
DOI
Venue
2012
10.1109/ICDMW.2012.136
ICDM Workshops
Keywords
Field
DocType
large services arm,thousand employee,analytics approach,proactive retention action,proactively combating voluntary attrition,appropriate action,increased salary,high risk,voluntary attrition,key employee,employee departure,main retention action,data mining,investment,risk analysis
Turnover,Multinational corporation,Remuneration,Salary,Risk analysis (business),Computer science,Artificial intelligence,Attrition,Analytics,Marketing,Machine learning
Conference
ISSN
Citations 
PageRank 
2375-9232
3
0.52
References 
Authors
0
7
Name
Order
Citations
PageRank
Moninder Singh1381105.12
Kush R. Varshney236855.80
Jun Wang31858.60
Aleksandra Mojsilovic428839.15
Alisia R. Gill530.52
Patricia I. Faur630.52
Raphael Ezry730.52