Modelling of Consumer Goods Markets : An Agent Based Computational Approach

This article illustrates the use of multi-agent modelling and prediction of consumer goods markets. A behavioral model incorporating utility based rational choice enhanced with psychological drivers is presented to study a typical market, characterized by repeat purchase incidences by households. The psychological drivers incorporate purchase strategies of loyalty and change-of-pace, which affect the choice set of consumer agents in an agent based simulation environment. Agent specific memories of past purchases drive these strategies, while attribute specific preferences and prices drive the utility based choice function. Transactions data from a category in a supermarket is used to initialize, calibrate and test the accuracy of predictions of the model. Results indicate that prediction accuracy at both macro and micro levels can be significantly improved with the incorporation of purchase strategies. Moreover, increasing the memory length beyond a certain limit does not improve predictions in the model, indicating that consumer memory of past shopping instances is finite and low and recent purchase history is more relevant to current decision making than the distant past. The article the shows how simulation based methods can be used to model changes or interventions in the market, such as new product introductions, for which no past history exists