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Abstract
: This research develops and evaluates machine learning models for predicting sales compensation payouts based on key performance metrics. Using a comprehensive dataset of sales performance indicators, three regression algorithms were systematically compared to identify the optimal predictive model for compensation administration systems. Research Significance: Sales compensation prediction is critical for organizational budgeting, performance management, and ensuring fair compensation structures. Traditional manual calculation methods are prone to errors and inefficiencies, making automated predictive models essential for modern sales operations. This study addresses the need for accurate, data-driven compensation forecasting systems that can enhance transparency and reliability in sales management processes. Methodology: Algorithm Analysis Three machine learning algorithms were implemented and evaluated: Random Forest Regressor (RFR), AdaBoost Regressor (ABR), and Gradient Boosting Regressor (GBR). Models were trained on historical sales data and validated using standard train-test split methodology. Performance was assessed using multiple regression metrics including R², RMSE, MAE, and additional error measures to ensure comprehensive evaluation. Input Parameters: Sales Volume, Number of Deals, Average Deal Size. Output Parameter: Compensation Payout Results: Gradient Boosting Regressor demonstrated superior performance with perfect training accuracy and excellent generalization capability. The analysis revealed strong correlation between sales volume and compensation, validating performance-based incentive structures. All models showed acceptable predictive accuracy, with GBR providing the most reliable compensation forecasting.
Keywords: Sales compensation prediction, machine learning, gradient boosting, performance metrics, regression analysis, compensation modeling, sales analytics, predictive modeling.
