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A talent management tool using propensity to leave analytics

机译：使用倾向离开分析的人才管理工具

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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.
机译：现代组织在招聘，管理和留住高价值和人才的人们方面投入了大量资源。尽管过去五十年来，但是，由于知识经济中企业的不断变化的性质，该地区本身不断发展，因此没有银弹。在本文中，我们采用基于分析的方法来推进人才管理，特别是从员工承诺的角度来看。单独的员工的承诺是使用她的倾向定量离开公司的倾向，这是使用历史员工记录和其他特定组织的数据建模的。此外，还使用数据挖掘方法确定了这种预测的承诺水平背后的因素。通过特别关注倾向分数的准确性，随着时间的推移以及因素的可靠性，通过特别注意稳健和可操作的预测建模。倾向分数和确定的因素用于推断有助于员工的职业生涯，除了与本组织的业务目标相一致。我们在一个人才管理工具中纳入了所有这些，这是所有利益相关者的集成平台???员工，经理，前线管理和人力资源专业人士。此工具已成为大约100,000名员工的大型全球财富500强组织。部署结果非常有前途，具有显着的有形货币福利，以及可能的无形益处，如提高对员工承诺背后的因素的认识，增加员工与管理层的沟通以及改善员工参与的因素。

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