Rekabetçi piyasada fiyatlandırma ve promosyon stratejileri: ajan tabanlı yaklaşım

The fast-moving consumer goods (FMCG) market is highly competitive, marked by frequent purchases and price sensitivity, where pricing, promotion, and quality strategies critically shape consumer behavior and firm performance. For this purpose, in this study an agent-based simulation model is designed to analyze customer purchasing patterns in competitive market. The model includes two types of agents: customers and sellers. Customers make probabilistic purchase decisions using a logistic utility function, incorporating price, promotion, quality and personal characteristics like age and income. Based on historical performance data, sellers use regression-based learning techniques to update their price and promotion strategies dynamically. The research questions are addressed through three scenarios: S0, S1, S2, referring respectively to price-only competition, price and promotion competition, and quality-differentiated competition. The results show that in S0, competition lowers prices and profits but increases sales. In S1, promotion expands demand yet often reduces profits due to higher costs. In S2, quality differentiation raises prices and can yield profit gains when costs are manageable and customers value quality, while further lowering no-purchase outcomes. This study shows how agent-based modeling can extend traditional logit-oligopoly analysis by including dynamic seller learning and product differentiation. It contributes to marketing research methods and improves our understanding of customer behavior in competitive markets.