Consumer Equilibrium, Demand Effects, and Efficiency in Group Buying

Abstract

Problem definition: We explore consumer equilibrium and efficiency in group buying events, in which the unit price for a good or service decreases with higher number of consumer sign-ups. Specifically, we study the following questions: (i) How does the dynamic consumer sign-up equilibrium evolve during these events? (ii) Is there empirical evidence that employing group buying increases demand? If so, by how much? (iii) Are there profit gains from employing this mechanism and how can they be improved? Academic/practical relevance: Group buying events are becoming increasingly popular in certain markets, especially in China. Our study contributes to better understanding of this innovative pricing mechanism and its effects on demand and profits, and can help with more effective implementation in practice. Methodology: We build a continuous-time dynamic game theoretical model to study consumer behavior and solve for its equilibrium. We then apply it to data obtained from group buying events employed by a large retailer and use structural regression methods and data clustering to estimate and evaluate demand and profit effects. Results: We demonstrate that our theoretical equilibrium is a good fit for the observed consumer behavior. We empirically show that group buying discounts are not very effective in boosting demand for very low and very high base demand levels but can significantly improve demand for intermediate levels. We estimate that employing group buying improved the retailer’s product demand and profits by 16.6% and 11.1%, respectively, and with better price and discount selections, it can improve profits by more than 30% on average. Managerial implications: Our theoretical model provides insights into dynamic consumer behavior during group buying events and can be a basis for researchers and practitioners in studying and designing these events. Our findings provide concrete support for managers on benefits of employing the mechanism, and our data-based method and analysis provide guidance for improving performance.