Traditional donor modeling uses one-size-fits-all equations that simply sort constituents into lists. However, modern fundraising demands a more sophisticated approach to understand and improve the donor journey.

This introductory course teaches you how to use machine learning techniques to uncover hidden patterns in your donor data and identify distinct audience segments for more effective engagement.

What you'll learn:

Core concepts of machine learning and how it differs from traditional linear modeling
Practical experience with multiple decision tree methods
Testing methods and presenting findings effectively

Through practical examples and exercises, you'll learn how to transform your fundraising analytics from simple sorting to sophisticated segmentation that drives real results.

Prerequisites: Experience in building linear models, including data preparation, significance testing, and modeling; and ability to use R for data science.

Hey there—welcome in!