This episode of Research That Matters - A Global Deep Dive is based on the article “Machine Learning for Nonprofit Organizations” by Justin Holzer, Worcester Polytechnic Institute, published in the Journal of Nonprofit Innovation, Volume 4, Issue 2 (June 2024).

In this conversation, we break down how machine learning can help nonprofits increase efficiency, improve decision-making, and maximize limited resources. The article provides a practical, beginner-friendly overview of supervised, unsupervised, semi-supervised, and reinforcement learning—explaining how each approach can support fundraising, volunteer management, fraud detection, marketing analysis, and operational optimization.

A featured use case demonstrates how a neural network model was developed to predict whether donors would give again—achieving 86% accuracy in forecasting repeat donations. This example shows how nonprofits can move from reactive fundraising to data-driven strategy, enabling more targeted outreach and greater mission impact.

Designed for nonprofit leaders, data professionals, funders, and innovation-focused organizations, this episode translates technical research into actionable insights for modern nonprofit management.

This episode of Research That Matters - A Global Deep Dive is based on the article “Machine Learning for Nonprofit Organizations” by Justin Holzer, Worcester Polytechnic Institute, published in the Journal of Nonprofit Innovation, Volume 4, Issue 2 (June 2024), available at https://scholarsarchive.byu.edu/joni/vol4/iss2/6/. The audio version was created using Google’s NotebookLM to convert the published research into a podcast format, making advanced AI and machine learning concepts more accessible for nonprofit leaders through listening.