TY - GEN
T1 - Data-Driven Sales Prediction and Strategic Insights for E-Commerce Through ML and BI Tools
AU - Al-Etaibi, Badr
AU - Ahmad, Abdulrahim
AU - Al-Naymat, Ghazi
AU - Al-Betar, Mohammed Azmi
N1 - Publisher Copyright:
©2025 IEEE.
PY - 2025
Y1 - 2025
N2 - —Accurate sales forecasting is crucial for e-commerce businesses to optimize inventory, improve customer satisfaction, and maintain competitiveness. Motivated by the need for data-driven decision-making in small and medium-sized enterprises, this study integrates machine learning (ML) and Business Intelligence (BI) to enhance short-term sales prediction and strategic planning for a real-world clothing store. Using transactional data from October 2022 to April 2025, three supervised models—Linear Regression, XGBoost, and Random Forest—were applied to predict daily sales trends. Linear Regression outperformed the others in RMSE, MAE, and R2, providing accurate forecasts with minimal overfitting. An interactive Power BI dashboard with a star schema design offered insights into customer behavior, product performance, and sales projections, enabling informed strategic adjustments. The research demonstrates the value of combining ML and BI for data-driven forecasting and operational decision-making in e-commerce.
AB - —Accurate sales forecasting is crucial for e-commerce businesses to optimize inventory, improve customer satisfaction, and maintain competitiveness. Motivated by the need for data-driven decision-making in small and medium-sized enterprises, this study integrates machine learning (ML) and Business Intelligence (BI) to enhance short-term sales prediction and strategic planning for a real-world clothing store. Using transactional data from October 2022 to April 2025, three supervised models—Linear Regression, XGBoost, and Random Forest—were applied to predict daily sales trends. Linear Regression outperformed the others in RMSE, MAE, and R2, providing accurate forecasts with minimal overfitting. An interactive Power BI dashboard with a star schema design offered insights into customer behavior, product performance, and sales projections, enabling informed strategic adjustments. The research demonstrates the value of combining ML and BI for data-driven forecasting and operational decision-making in e-commerce.
KW - Business Intelligence
KW - E-commerce Analytics
KW - Machine Learning
KW - Power BI Dashboard
KW - Sales Forecasting
UR - https://www.scopus.com/pages/publications/105033693660
U2 - 10.1109/ITT69610.2025.11352921
DO - 10.1109/ITT69610.2025.11352921
M3 - Conference contribution
AN - SCOPUS:105033693660
T3 - 2025 10th International Conference on Information Technology Trends, ITT 2025
SP - 152
EP - 157
BT - 2025 10th International Conference on Information Technology Trends, ITT 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Information Technology Trends, ITT 2025
Y2 - 6 November 2025 through 7 November 2025
ER -