Shifting Paradigms in Employee Experience: A Predictive Diagnostic Model for Attrition and Burnout

In personnel management practices in high-performance corporate settings, human resource strategies are primarily guided by lagging indicators coming from exit surveys and attrition rate analysis. Such an approach is obviously limited, as it only allows getting insight into reasons for employee dissatisfaction after the loss has occurred. The current paper highlights the process of building an end-to-end Human Resources Diagnostic System for a high-tech IT company, Palo Alto Networks. The project involved designing innovative HR analytics Key Performance Indicators (KPIs) to engineer an Engagement Index, a Burnout Risk Score, and a new metric called Satisfaction Stability Score to facilitate the proactive control of worker engagement. The results were integrated into an interactive application designed with Streamlit that helped operationalize these ideas into daily human capital management practices and supported the transformation of raw data into actionable insights and retention strategies.