Optimal Design of Default Donations

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 algorithm is a readily implementable tool for nonprofits, and our theoretical findings translate into rules of thumb for the design of fundraising websites: (i) suggested amounts should typically exceed the most common donation amount; (ii) with sufficient market segmentation, a single suggestion is optimal and can significantly improve on nonpersonalized suggestions; and (iii) larger menus are valuable in the absence of segmentation. As a case study, we apply our optimization framework to publicly available experimental data. Our model predicts that the optimal menu could increase revenue by more than 3%.

Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2024.1300.