Optimal Design of Default Donations

Optimal Design of Default Donations

Many nonprofit organizations rely on suggested donation amounts to incentivize giving, yet there is little consensus on how to set those amounts. In this article, Castro and Rodilitz develop a data-driven framework for designing optimal donation menus that maximize fundraising revenue while accounting for differences in donor preferences and responsiveness to suggestions. The authors show that commonly used heuristics can perform poorly and instead introduce a dynamic-programming algorithm that efficiently identifies revenue-maximizing donation menus. Their analysis also quantifies the value of donor information and demonstrates when personalized suggestions, larger menus, or a single targeted default are most effective. Applying the framework to data from a large fundraising experiment, the authors find that an optimally designed menu could substantially outperform the default amounts tested in practice. The results provide actionable guidance for nonprofits seeking to improve online fundraising through more effective choice architecture.