Enhanced Revenue Prediction Model Using a Machine Learning-Based Data Warehouse Approach

Abstract

In this paper, we propose a novel machine learning-based technique to enhance the efficiency and accuracy of data warehouse operations, specifically targeting revenue optimization by integrating various data sources and applying machine learning algorithms to predict customer behavior and demand patterns. Our approach leverages the Extract, Transform, and Load (ETL) process to systematically prepare and optimize data that allows for more informed and proactive revenue management in cinema chains. Furthermore, we evaluate the model’s efficiency by comparing the predicted revenue data with actual figures and calculating the percentage error. Simulation results demonstrate that applying the proposed solution to real-time cinema data achieves up to 90.21% accuracy in intelligent revenue forecasting.