Optimal Targeting in Fundraising: A Causal Machine-Learning Approach

TL;DR

This paper presents a causal machine-learning method that enables charities to optimize their fundraising efforts by targeting individuals most likely to donate, significantly increasing donations while reducing costs.

Contribution

It introduces a novel causal machine-learning approach for optimal targeting in fundraising, demonstrating substantial improvements over uniform strategies using experimental data.

Findings

Optimal targeting increases net donations compared to uniform benchmarks.

Targeting should focus on past donors and exclude non-donors who were previously solicited.

Benefits persist even with limited publicly available data.

Abstract

Ineffective fundraising lowers the resources charities can use to provide goods. We combine a field experiment and a causal machine-learning approach to increase a charity's fundraising effectiveness. The approach optimally targets a fundraising instrument to individuals whose expected donations exceed solicitation costs. Our results demonstrate that machine-learning-based optimal targeting allows the charity to substantially increase donations net of fundraising costs relative to uniform benchmarks in which either everybody or no one receives the gift. To that end, it (a) should direct its fundraising efforts to a subset of past donors and (b) never address individuals who were previously asked but never donated. Further, we show that the benefits of machine-learning-based optimal targeting even materialize when the charity only exploits publicly available geospatial information or applies the estimated optimal targeting rule to later fundraising campaigns conducted in similar samples. We conclude that charities not engaging in optimal targeting waste significant resources.