Predicting Major Donor Prospects Using Machine Learning

Greg Lee, Aishwarya Sathyamurthi, Mark Hobbs

2024

Abstract

An important concern for many fundraising institutions is major gift fundraising. Major gifts are large gifts (typically $10,000+) and donors who give these gifts are called major donors. Depending upon the institution type, major gifts can constitute 80% of donation dollars. Thus, being able to predict who will give a major gift is crucial for fundraising institutions. We sought the most useful major donor prospect model by experimenting with 11 shallow and deep learning algorithms. A useful model discovers major donor prospects (i.e., false positives) without generating a similar number of false negatives, helping to preserve accuracy. The study also examined the impact of using different types of data, such as donation data exclusively, on the model’s utility. Notably, an LSTM-GRU model achieved a 92.2% accuracy rate with 110 false positive prospects and 40 false negatives for a religious fundraising institution. This model could assist major donor officers in identifying potential major donors. Similarly, for an education fundraising institution, an extra trees classifier was able to generate a major donor model with 92.5% accuracy, 71 false positives and 40 false negatives. False positives are prospects for fundraising institutions, providing major gift officers potential major donors.

Paper Citation

in Harvard Style

Lee G., Sathyamurthi A. and Hobbs M. (2024). Predicting Major Donor Prospects Using Machine Learning. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 462-470. DOI: 10.5220/0012422700003636

in Bibtex Style

@conference{icaart24,
author={Greg Lee and Aishwarya Sathyamurthi and Mark Hobbs},
title={Predicting Major Donor Prospects Using Machine Learning},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={462-470},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012422700003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Predicting Major Donor Prospects Using Machine Learning
SN - 978-989-758-680-4
AU - Lee G.
AU - Sathyamurthi A.
AU - Hobbs M.
PY - 2024
SP - 462
EP - 470
DO - 10.5220/0012422700003636
PB - SciTePress