Dynamic Resource Allocation on Multi-Category Two-Sided Platforms

Platform businesses are typically resource-intensive and must scale up their business quickly in the early stage to compete successfully against fast-emerging rivals. We study a critical question faced by such firms in the novel context of multicategory two-sided platforms: how to optimally make investment decisions across two sides, multiple categories, and different time periods to achieve fast and sustainable growth. We first develop a two-category two-period theoretical model and propose optimal resource allocation strategies that account for heterogeneous within-category direct and indirect network effects and cross-category interdependence. We find that the proposed strategy shares the spirit of the allocation rules for multiproduct nonplatform firms and single-product platform firms, yet it does not amount to a simple combination of the existing rules. Interestingly, the business model that platforms adopt crucially determines the optimal strategy. Platforms that charge by user should adopt a reinforcing rule for both within- and cross-category allocations by allocating more resources toward the stronger growth driver. Platforms that charge by transaction should also adopt the reinforcing rule for within-category allocation, but follow a compensatory rule for cross-category and intertemporal allocations by allocating more resources toward the weaker growth driver. We use data from the daily deals industry to empirically identify the network effects, propose alternative allocation strategies stemming from our theoretical findings, and use simulations to show the benefits of these strategies. For instance, we show that reallocating 10% of the average observed investment from Fitness to Beauty can increase profits by up to 15.5% for some cities.

Automated summary

Researchers focus on multcategory two-sided platforms and propose optimal resource allocation strategies across two sides in different time periods. The study shows that the business model of platforms determines the optimal strategy, allocating resources according to pricing method and growth driver. Empirical research and simulations demonstrate the benefits of adjusting investment allocation.