An agent-based simulation for a trade-off between frequency and depth in retail price promotion strategy

Abstract A good pricing strategy helps retailers generate profits, increase sales, and set a strategic position in the market. However, the interactions between retailers and customers add complexity to retailer pricing decisions. This study aims to model retail pricing complexity and analyse retail pricing strategies using an agent-based simulation approach. Two types of agents are modelled: customers and retailers. Customer buying decisions are influenced by several customer preferences factors, while product prices are set according to the retailer’s promotion strategy. The promotion is applied based on the frequency and depth of the price cut. A functional product market is considered in this simulation, representing daily necessities that are purchased regularly, such as foodstuffs and toiletries. The results show that the limited rationality and interactions of each agent drive the unique behaviour of the system, and that each pricing strategy has a different impact on retailer profit and market share. This study provides insights into pricing decision strategies related to price promotion.