A multi-agent approach for the newsvendor problem with word-of-mouth marketing strategies

Abstract

Word-of-mouth (WOM) marketing is increasingly playing an important role in consumers’ purchase decision with the development of mobile Internet and various social media APP. We are particularly interested in such a problem as how to make decisions under effects of WOM campaigns? To answer this question, we develop a multi-agent model that emulates WOM or viral marketing process as spread of disease among people. Assume that each “infected” individual will purchase one unit of product. Then, the total “infected” people form the demand of the product, as an input of newsvendor problem. Besides finding the optimal order quantity of newsvendor problem, we also identify the most influential source node for kick off of the WOM marketing. The simulation results reveal that social network and WOM have a great influence on demand and profit of the firm. Even the source node has significant effect on output of WOM marketing. According to our simulation, the closeness centrality in social network analysis is the best measure to recognize the most influential source node, comparing to degree centrality, or betweenness centrality, etc. Finally, parameter analysis infers that profit of the firm will increase with higher the spreading probability or/and lower the resistant probability.