Tyler Hochman, CEO of FORE Enterprise, discusses their AI workforce analytics platform that predicts employee turnover before employees themselves are aware of their intentions to leave. The technology utilizes a combination of external and internal data sources to create predictive models on both aggregate and individual levels. External data sources include census information, demographics, and economic trends, while internal data encompasses employee performance metrics like utilization and schedule adherence.

Hochman highlights the importance of data organization and structuring for effective data analytics, emphasizing that manual data structuring can be cost-effective for small-scale operations. However, as organizations grow beyond a certain size, automation becomes more efficient. The discussion also delves into the privacy considerations surrounding employee data collection, with Hochman emphasizing the need to respect employees’ existing understanding of performance tracking metrics.
The conversation shifts to actionable insights derived from the predictive analytics, with Hochman identifying key factors that indicate employee turnover. For highly utilized employees, burnout, competitor risk, and upward mobility within the organization are significant predictors. In contrast, low-utilized employees may leave due to factors such as team composition, communication issues, and skills mismatch. Hochman stresses the importance of targeted intervention strategies tailored to the specific reasons driving employee turnover.

In conclusion, Hochman underscores the value of leveraging AI and machine learning techniques in data analytics pipelines to handle large volumes of data efficiently. By streamlining data acquisition, structuring, and analysis processes, organizations can gain valuable insights to optimize workforce retention strategies. The episode provides practical insights into utilizing data analytics to forecast employee turnover and implement targeted interventions for improved employee retention.