Abstract: During an analysis technique, organization data for an organization (such as a company) and a set of potential predictors for retention are analyzed to generate Kaplan-Meier estimator curves. Then, clustering analysis is performed to determine natural groupings of Kaplan-Meier estimator curves. Note that the retention data may include, as a function of time, retention probabilities that the individuals remain in functions in an organization and a set of potential predictors for the retention probabilities. Moreover, the predictors for retention in the set of potential predictors are identified based on the determined natural groupings. For example, the identified predictors may be those for which at least two natural groupings have a large centroid separation. Furthermore, the identified predictors for retention may be used to determine remedial action to increase the retention probabilities.

Abstract: During an analysis technique, organization data for an organization (such as a company) and/or external data are used to calculate a performance metric and to determine retention risk for an employee. For example, the performance metric may be calculated based on revenue or productivity, and the retention risk may be determined based on an unemployment rate in a region that includes the organization or hiring trends in the region. The calculated performance metric and the determined retention risk are provided to the organization. In addition, a retention suggestion and an associated cost-benefit analysis are provided for the employee. This information may allow the organization to manage its employees, and to effectively apply limited resources to reduce retention expense and while increasing the likelihood of retaining employees.