Document Type
Publication - Article
A Machine-Learning Tool-Supported Methodology for Modeling Nonprofit Donor Relations
Department
Computing
Date of Activity
7-2-2026
Abstract
Customer relationship analysis is common in the for-profit world, but it is less common for nonprofits. Moreover, the existing body of academic work that models donor interactions is generally specific to a particular nonprofit and does not provide tools that other nonprofits can use for their analysis. Our purpose is to provide an accessible and generalizable tool-supported methodology for nonprofits to understand their donor base. In this methodology, we present three aspects applicable to nonprofits. First, this methodology proposes the use of linear regression and supervised machine learning techniques to model donations as a function of marketing appeals and volunteer experiences with the organization. Second, we demonstrate the implementation of our approach using real nonprofit data. Third, we discuss actionable insights on donor behavior and marketing to draw conclusions about the most effective types of appeals for different groups of donors.
Recommended Citation
Weiss, C., Alférez, G.H. (2026). A Machine-Learning Tool-Supported Methodology for Modeling Nonprofit Donor Relations. In: Arai, K., Lorenz, P. (eds) Intelligent Computing. CC 2026. Lecture Notes in Networks and Systems, vol 1951. Springer, Cham. https://doi.org/10.1007/978-3-032-24810-7_3