{"title":"A two-sided sales promotions modeling based on agent-based simulation","authors":"","doi":"10.1007/s11403-024-00404-4","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>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.</p>","PeriodicalId":45479,"journal":{"name":"Journal of Economic Interaction and Coordination","volume":"73 1","pages":""},"PeriodicalIF":0.8000,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Economic Interaction and Coordination","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1007/s11403-024-00404-4","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍：
Journal of Economic Interaction and Coordination addresses the vibrant and interdisciplinary field of agent-based approaches to economics and social sciences.
It focuses on simulating and synthesizing emergent phenomena and collective behavior in order to understand economic and social systems. Relevant topics include, but are not limited to, the following: markets as complex adaptive systems, multi-agents in economics, artificial markets with heterogeneous agents, financial markets with heterogeneous agents, theory and simulation of agent-based models, adaptive agents with artificial intelligence, interacting particle systems in economics, social and complex networks, econophysics, non-linear economic dynamics, evolutionary games, market mechanisms in distributed computing systems, experimental economics, collective decisions.
Contributions are mostly from economics, physics, computer science and related fields and are typically based on sound theoretical models and supported by experimental validation. Survey papers are also welcome.
Journal of Economic Interaction and Coordination is the official journal of the Association of Economic Science with Heterogeneous Interacting Agents.
Officially cited as: J Econ Interact Coord
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