Harnessing SERM: Deep Neural Networks for Strategic Employee Attrition Prediction and Management

This study conducts a response to the complexities of workforce dynamics. This paper presents the Strategic Employee Retention Model (SERM)—a novel deep learning construct dedicated to deciphering and preempting employee attrition. SERM represents a paradigm shift in attrition analytics, incorporating rich organizational behavior data into an intricate neural network informed by advanced natural language processing to probe the depths of structured and unstructured data. This study employs a comprehensive dataset featuring a plethora of variables, ranging from employee demographics to performance ratings and job satisfaction levels, to train and validate SERM. Tailored to refine recruitment and retention strategies, SERM exhibits exceptional predictive acumen with a training accuracy of 98.6 percent, testing accuracy of 88.1 percent, and validation accuracy of 86.02 percent. A rigorous comparative study against prevailing machine learning methodologies, including logistic regression, KNN, Naive Bayes, Random Forest, Ada Boost, and Gradient Boost, substantiates SERM’s dominance in prediction accuracy and actionable insights. This work, pivotal in the domain of predictive HR analytics, introduces a robust, empirically validated framework that considerably advances the current state-of-the-art.