Authors:
Greg Lee
;
Aishwarya Sathyamurthi
and
Mark Hobbs
Affiliation:
Jodrey School of Computer Science, Acadia University, Wolfville, Canada
Keyword(s):
Fundraising Institutions, Major Donors, Machine Learning.
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.
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