Predicting Employee Turnover in Consulting Firms: A Machine Learning Approach to Multi-Parameter Satisfaction Modeling

Abstract

This study employs a Random Forest classifier and a multi-parameter satisfaction model to predict employee turnover in consulting firms. By analyzing survey data from 2021 and 2024, we demonstrate that career progression and transparent communication consistently surpass pay and benefits as key retention drivers. Unlike traditional compensation-focused approaches, our model, trained on 2021 data and applied to 2024 responses, highlights the enduring stability of these priorities post-pandemic. These findings reveal that shifts in employee preferences reflect lasting changes rather than temporary disruptions. By integrating broader satisfaction factors and refining predictive analytics, consulting firms can proactively address turnover risks, empower HR teams to implement targeted interventions, and build a more resilient workforce for sustained organizational success.