A decision-making framework for crowdfunding platform impact maximization

Crowdfunding platforms (CFP) are increasingly interested in enhancing their impact, by funding projects with positive environmental and social impacts. This article is the first that models the CFP strategies in this context. We compare two attitudes: a neutral (passive) facilitating attitude and an active one. In the latter case, the CFP implicitly favors a subset of the projects by promoting them to the crowd. We model the utility of the CFP, linked to its monetary outcome and the impact of the funded projects, and formulate an optimization problem that maximizes it by acting on the promotion strategy. We show how to solve this problem using real options and dynamic programming. For applying this framework, we perform an empirical study based on a large dataset that quantifies the attractiveness gain for projects when they are promoted by the platform. We then propose a novel hybrid empirical/analytical method, where platform data is processed and provides input parameters to the optimization tool. Our results show that substantial gains are expected when applying the optimization framework with respect to a platform adopting a classical passive attitude. Our study provides guidelines for platform managers on how to design and implement strategies that maximize their impact.