Improving Decision-Making Processes in Retail Through Artificial Intelligence for Advanced Management Information Systems: A Study on Consumer Behavior in Qassim

Abstract: In today’s rapidly evolving retail environment, the sheer volume of consumer data presents both opportunities and challenges for businesses striving to maintain a competitive edge. This study explores the pivotal role of artificial intelligence and sophisticated data mining techniques within management information systems. The study aims to transform decision-making processes and deepen the understanding of consumer behavior in the Qassim region of Saudi Arabia, while also exploring implications for broader regional markets. By employing a dataset of 712 customers that encompasses demographic variables, lifestyle choices, and purchasing patterns, we implement leading machine learning algorithms, including Decision Trees, Random Forests, and Support Vector Machines. This allows us to uncover actionable findings that drive strategic initiatives. Additionally, we analyze the impact of artificial intelligence on retailers by comparing outcomes before and after implementing AI-enhanced analytics. The investigation reveals that retailers applying AI-enhanced analytics experience a remarkable 32% improvement in their responsiveness to market changes, a 28% increase in customer retention rates, and a 34.7% improvement in repeat customers. These results highlight the substantial impact of these technologies on operational efficacy and demonstrate how AI can enhance customer loyalty, satisfaction, and overall business performance. The Random Forest model achieved the highest accuracy at 96.91%. Furthermore, this research emphasizes the effectiveness of predictive analytics in identifying distinct consumer segments and tailoring marketing strategies to meet their specific needs. By enabling retailers to respond proactively to consumer trends, AI emerges as a crucial tool for enhancing customer engagement and satisfaction. The findings illustrate how data analysis empowers businesses to detect emerging trends and optimize inventory management practices, and boost profitability. This research underscores the transformative potential of integrating advanced algorithms into retail operations, fostering data-informed decision-making that cultivates sustainable growth and elevates customer satisfaction in an increasingly competitive marketplace. The observations gained from this study serve as a valuable resource for retailers eager to utilize the power of AI and data mining to navigate the complexities of modern consumer behavior.

Hussain Mohammad Abu-Dalbouh, Mushira Mustafa Freihat, Rayah Ismaeel Jawarneh, Osman Abdalla Mohamed Elhadi, Mortada Ibrahim Elimam, Leenah Sulaiman Almuhanna Abalkhail, Ghadi Mohammed Al Nafesah, Soliman Aljarboa and Sulaiman Abdullah Alateyah. “Improving Decision-Making Processes in Retail Through Artificial Intelligence for Advanced Management Information Systems: A Study on Consumer Behavior in Qassim”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.3 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170320

@article{Abu-Dalbouh2026,
title = {Improving Decision-Making Processes in Retail Through Artificial Intelligence for Advanced Management Information Systems: A Study on Consumer Behavior in Qassim},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170320},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170320},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {3},
author = {Hussain Mohammad Abu-Dalbouh and Mushira Mustafa Freihat and Rayah Ismaeel Jawarneh and Osman Abdalla Mohamed Elhadi and Mortada Ibrahim Elimam and Leenah Sulaiman Almuhanna Abalkhail and Ghadi Mohammed Al Nafesah and Soliman Aljarboa and Sulaiman Abdullah Alateyah}
}