Optimal Targeting in Fundraising: A Machine Learning Approach

Fundraising is a costly activity: the largest 25 US charities spend between 5% and 25% of total donations on fundraising expenses (Andreoni and Payne, 2011). These numbers are a matter of concern for two reasons. First, high fundraising costs imply that a smaller proportion of overall donations can finance charitable projects. This effect can lead to an underprovision of the provided goods and services and may, thus, lower welfare if the donors' utility depends on provision levels (Rose-Ackerman, 1982; Name-Correa and Yildirim, 2013). Second, high fundraising costs also matter from the charities' perspectives: it is well documented that donors are averse to financing overhead costs (Tinkelman and Mankaney, 2007; Gneezy et al., 2014). Hence, charities with excessive fundraising expenses will be less successful in raising donations. In conclusion, reducing disproportional fundraising costs can be crucial, both from a welfare and a charity-management perspective. However, while there is a broad literature studying how fundraising instruments such as matching grants and unconditional gifts affect donors' behavior (surveyed by Andreoni and Payne, 2013), previous research has paid less attention to how charities could increase the cost efficacy of fundraising. This paper shifts focus to a novel approach to increase a fundraising campaigns' efficacy: optimal targeting of fundraising activities based on causal machine learning.