A two-sided sales promotions modeling based on agent-based simulation

The growing competition in the retail market pushes retailers to optimize their sales and marketing strategies. In particular, in sectors where the profit margins are more restricted and customer loyalty depends heavily on the prices offered, in fact, understanding consumer reactions to sales promotions and providing them with the right deal at the right time is critical for retailers to survive. In this study, we propose an agent-based model that uses the Belief Desire Intention (BDI) concept to model how customers react to the various promotional offers they receive, and our study involves a seller agent that dynamically learns the appropriate promotion to give these customers on a customer-specific basis. Using empirical research findings in the literature, we developed the BDI part of the model based on the “Big Five-Factor Model”. Apart from this, we used the Q-learning algorithm to train the seller agent. Furthermore, one of our goals in this study is to show that learning-based decision-making agents can be more competitive on the market than rule-based decision-making agents. We therefore added a rule-based agent to the model and compared its efficacy to that of the learning-based decision-making agent. The model was tested in an artificial market through a series of experiments. The experiment results show that the proposed model can be used in actual applications to automate sales promotion decisions.