In fundraising management, the assessment of the expected gift is a key point. The availability of accurate estimates of the number of donations, their amounts, and the gift probability is relevant in order to evaluate the results of a fundraising campaign. The accuracy of the expected gift estimation depends on the appropriate use of the information about Donors. In this contribution, we propose a non-parametric methodology for the prediction of Donors' behavior based on Artificial Neural Networks. In particular, Multi-Layer Perceptron is applied. In the numerical experiments, the expected gift is then estimated based on a simulated dataset of Donors' individual characteristics and information on donations history.

Machine Learning and Fundraising: Applications of Artificial Neural Networks

Diana Barro
;
Luca Barzanti;Marco Corazza
;
Martina Nardon
2023-01-01

Abstract

In fundraising management, the assessment of the expected gift is a key point. The availability of accurate estimates of the number of donations, their amounts, and the gift probability is relevant in order to evaluate the results of a fundraising campaign. The accuracy of the expected gift estimation depends on the appropriate use of the information about Donors. In this contribution, we propose a non-parametric methodology for the prediction of Donors' behavior based on Artificial Neural Networks. In particular, Multi-Layer Perceptron is applied. In the numerical experiments, the expected gift is then estimated based on a simulated dataset of Donors' individual characteristics and information on donations history.
2023
Working Paper - Department of Economics, Ca' Foscari University of Venice
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/5050280
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