Modern organizations invest a lot of resources in recruiting, managing, and retaining people with high value and talent. In spite of several studies over the past fifty years, there is no silver bullet for talent management, since the area itself is constantly evolving due to the ever-changing nature of the enterprise in the knowledge economy. In this paper, we adopt an analytics-based approach to advancing talent management, particularly from the point of view of employee commitment. An individual employee's commitment is quantified using her propensity to leave the company, which is modeled using historical employee records and other organization-specific data. Furthermore, factors behind this predicted level of commitment are also identified using data mining approaches. The predictive modeling is made robust and actionable by paying special attention to the accuracy of the propensity scores, their stability over time, and the inter-pretability of the factors. The propensity scores and identified factors are used to infer meaningful recommendations that are helpful to an employee's career apart from being consistent with the business objectives of the organization. We have incorporated all of this in a talent management tool which is an integrated platform for all stakeholders — employees, managers, top-line management and human resource professionals. This tool has been deployed in a large, global, Fortune 500 organization for about 100,000 employees. The results of the deployment are very promising with significant tangible monetary benefits, as well as possible intangible benefits such as improved awareness of the management on factors behind employee commitment, increased communication of employees with the management, and improved employee engagement.
['Karthikeyan Natesan Ramamurthy', 'Moninder Singh', 'Yichong Yu', 'Jessica Aspis', 'Matthew Iames', 'Michael Peran', 'Qin S. Held']
A talent management tool using propensity to leave analytics