Optimal Design of Default Donations

Abstract

Nonprofit fundraising websites often display a set of suggested donation amounts, allowing prospective donors to effortlessly select an amount from this menu of suggestions instead of manually inputting their ideal donation. Although this strategy is effective at shaping behavior, it can also backfire: suggested amounts attract donors with both lower and higher ideal donations, potentially leading to a net decrease in revenue. To address this challenge, we present a comprehensive framework for designing a  menu of suggestions to maximize fundraising revenue in the presence of heterogeneous donors. Our analysis reveals the limitations of a greedy approach. Instead, we design an algorithm based on dynamic programming principles that efficiently finds an optimal menu. Additionally, we shed light on the value of information by comparing against a benchmark that knows the largest amount that each donor would select. If the nonprofit has information about each donor’s ideal donation, it can obtain a constant-factor guarantee with respect to this full-information benchmark. If the nonprofit only has distributional information, we characterize how the guarantee depends on donor heterogeneity and the size of the menu. Our results highlight the value of market segmentation as well as the potential advantages of larger menus. As a case study, we apply our optimization framework to experimental data from Altmann et al. (2019). Our counterfactual analysis suggests that the optimal menu could increase revenue by more than 3%.

Keywords: fundraising, defaults, online platforms, dynamic programming, analytics for social good