Loot Box Pricing and Design

Ningyuan Chen (), Adam N. Elmachtoub (), Michael L. Hamilton () and Xiao Lei ()
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Ningyuan Chen: Rotman School of Management, University of Toronto, Toronto, Ontario M5S 3E6, Canada
Adam N. Elmachtoub: Department of Industrial Engineering and Operations Research and Data Science Institute, Columbia University, New York, New York 10027
Michael L. Hamilton: Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
Xiao Lei: Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027

Management Science, 2021, vol. 67, issue 8, 4809-4825

Abstract: In the online video game industry, a significant portion of the revenue is generated from microtransactions, where a small amount of real-world currency is exchanged for virtual items to be used in the game. One popular way to conduct microtransactions is via a loot box , which is a random allocation of virtual items whose contents are not revealed until after purchase. In this work, we consider how to optimally price and design loot boxes from the perspective of a revenue-maximizing video game company and analyze customer surplus under such selling strategies. Our paper provides the first formal treatment of loot boxes, with the aim to provide customers, companies, and regulatory bodies with insights into this popular selling strategy. We consider two types of loot boxes: a traditional one where customers can receive (unwanted) duplicates and a unique one where customers are guaranteed to never receive duplicates. We show that as the number of virtual items grows large, the unique box strategy is asymptotically optimal among all possible strategies, whereas the traditional box strategy only garners 36.7% of the optimal revenue. On the other hand, the unique box strategy leaves almost zero customer surplus, whereas the traditional box strategy leaves positive surplus. Further, when designing traditional and unique loot boxes, we show it is asymptotically optimal to allocate the items uniformly, even when the item valuation distributions are heterogeneous. We also show that, when the seller purposely misrepresents the allocation probabilities, their revenue may increase significantly, and thus, strict regulation is needed. Finally, we show that, even if the seller allows customers to salvage unwanted items, then the customer surplus can only increase by at most 1.4%.

Keywords: loot boxes; e-commerce; video games; bundling; probabilistic selling (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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