Leveraging an Attention-Based Reflection Rotation Equivariant Quantum Prairie Dog Network for Marketing-Analytics in HRMS

Marketing analytics and human resource management systems (HRMS) have long been pivotal and cognitive aspects in organizational decision-making, yet the relationship between these two domains needs rather a modest analytical approach that accounts for both departmental dynamics and employee-related factors. The study introduces a novel framework, the Reflection Rotation Equivariant Invariant Quantum Prairie Dog Attention Network (2REI-QuaPDAN), which leverages advanced machine learning techniques to analyze employee data across departments such as Marketing, HR, Sales, IT, and Finance. The dataset, titled “marketing HR analysis,” contains 14,999 records with information about various employee metrics, including Marketing Spend, Recruitment Cost, Employee Satisfaction, Attrition Rate, and more. Pre-processing methods, namely Correlation Coefficients and Min–Max Normalization, pre-process the data for further analysis. Feature extraction is carried out using Inception Convolutional Vision Transformers, followed by predictions using the 2REI-QuaPDAN, which integrates reflection-equivariant quantum neural networks with rotation-invariant attention mechanisms. The Model's parameters are optimized with the Prairie Dog Optimization Algorithm (PDOA). The proposed 2REI-QuaPDAN framework achieved an impressive 99.9% prediction accuracy, representing an improvement of over 7–15% compared to existing neural network and analytical models. This significant enhancement demonstrates the Model’s superior capability in capturing complex interdependencies between marketing and HR parameters, ensuring more reliable and data-driven organizational insights. This method not only increases the marketing analytics and the human resource management system accuracy, but also provides substantial advantages in terms of scalability and model adaptability, enabling efficient decision-making with high accuracy.