Optimal Targeting in Fundraising: A Machine Learning Approach

• URL: http://arxiv.org/abs/2103.10251v1
• Date: Wed, 10 Mar 2021 23:06:35 GMT
• Title: Optimal Targeting in Fundraising: A Machine Learning Approach
• Authors: Tobias Cagala, Ulrich Glogowsky, Johannes Rincke, Anthony Strittmatter
• Abstract summary: This paper studies optimal targeting as a means to increase fundraising efficacy. We randomly provide potential donors with an unconditional gift and use causal-machine learning techniques to "optimally" target this fundraising tool to the predicted net donors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper studies optimal targeting as a means to increase fundraising efficacy. We randomly provide potential donors with an unconditional gift and use causal-machine learning techniques to "optimally" target this fundraising tool to the predicted net donors: individuals who, in expectation, give more than their solicitation costs. With this strategy, our fundraiser avoids lossy solicitations, significantly boosts available funds, and, consequently, can increase service and goods provision. Further, to realize these gains, the charity can merely rely on readily available data. We conclude that charities that refrain from fundraising targeting waste significant resources.

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