This document summarizes an exploratory data mining project on employee turnover conducted on a organization with approximately 1,500 employees. By analyzing data on compensation, performance, age, seniority and other variables for employees who left the organization in a year with high labor market activity, several key factors that influence turnover were identified. Application of simple data mining algorithms to the data was able to produce useful results for reducing turnover, such as identifying higher performing employees who may need better compensation to prevent their departure. The analysis suggests interventions based on the results could help lower turnover across the organization by an estimated 50%.