Which analytics type assists in understanding the drivers of turnover?

The correct choice is predictive analytics because it plays a crucial role in analyzing historical data to identify patterns and trends that could signal potential turnover. Predictive analytics uses statistical models and machine learning techniques to forecast future events, making it particularly effective in understanding the factors influencing employee turnover. By examining various indicators, such as employee engagement scores, performance metrics, and job satisfaction levels, organizations can anticipate which employees may be at risk of leaving and understand the underlying causes.

In this context, other types of analytics provide valuable insights but do not directly focus on predicting future turnover based on those drivers. Descriptive analytics primarily summarizes historical data to provide a snapshot of what has happened in the past without exploring the reasons behind these trends. Diagnostic analytics seeks to explain why certain events occurred by analyzing relationships and causes, but it does so after the fact rather than providing foresight into future turnover. Prescriptive analytics goes a step further by recommending actions based on possible outcomes but requires insights gained from predictive analytics to be effectively implemented.

Therefore, predictive analytics stands out as the most suitable type for understanding what drives turnover, equipping organizations with the insights necessary to retain talent effectively.