Computational Intelligence in Business Management: Strategies for Innovation and Optimization

In today's competitive corporate landscape, the successful integration of computational intelligence approaches provides a key edge for decision-making processes. Despite developments, there is a gap in understanding the subtle application of such methodologies in corporate management, particularly within retail environments. To address this gap, our study combines decision trees and artificial neural networks to evaluate retail transaction data, concentrating on customer behavior and purchase trends. Leveraging a comprehensive dataset spanning client demographics, transaction details, and product information, our study provides substantial insights. Results suggest an 87% accuracy rate reached by artificial neural networks in forecasting client preferences. This study contributes to bridging the research gap by giving actionable insights into the practical application of computational intelligence in boosting company strategies and decision-making processes within the retail industry.