Predicting Volatile Consumer Markets using Multi-Agent Methods

A behavioral model incorporating utility-based rational choice enhanced with psychological drivers is presented to study a consumer goods 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.