Leveraging Machine Learning to Predict Donor Behavior

This white paper discusses the application of machine learning in predicting donor behavior to enhance fundraising outcomes. It outlines the methodology employed by GiveCampus, which includes a comprehensive predictive modeling approach utilizing five years of giving data from partner educational institutions. The paper details the data collection and preprocessing stages, emphasizing the importance of avoiding data leakage and ensuring model accuracy. It explains how features were derived from historical data and how principal component analysis was used to improve model performance. The modeling process is described, highlighting the use of various algorithms and cross-validation techniques to optimize predictive power. Additionally, the paper presents the significance of propensity scores in identifying potential donor converters. The findings suggest that while predictive models are not infallible, they can significantly improve fundraising strategies by allowing institutions to tailor their outreach efforts and allocate resources more effectively. The iterative nature of these models is also noted, indicating that continuous data collection can lead to progressively better predictions.