Optimal Targeting in Fundraising: A Machine-Learning Approach

Tobias Cagala (), Ulrich Glogowsky, Johannes Rincke and Anthony Strittmatter

No 9037, CESifo Working Paper Series from CESifo

Abstract: Ineffective fundraising lowers the resources charities can use for goods provision. We combine a field experiment and a causal machine-learning approach to increase a charity’s fundraising effectiveness. The approach optimally targets fundraising to individuals whose expected donations exceed solicitation costs. Among past donors, optimal targeting substantially increases donations (net of fundraising costs) relative to bench-marks that target everybody or no one. Instead, individuals who were previously asked but never donated should not be targeted. Further, the charity requires only publicly available geospatial information to realize the gains from targeting. We conclude that charities not engaging in optimal targeting waste resources.

Keywords: fundraising; charitable giving; gift exchange; targeting; optimal policy learning; individualized treatment rules (search for similar items in EconPapers)
JEL-codes: C21 C93 D64 H41 L31 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp and nep-exp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Working Paper: Optimal Targeting in Fundraising: A Machine-Learning Approach (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_9037

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