Empowering Nonprofit Organization to Reduce Donation Attrition with Machine Learning

Many Nonprofit organizations (NPOs) have a mission to empower vulnerable populations by providing safety and support services to build a healthier social community. The critical success factor for these organizations is generous and consistent donations from individuals, organizations, businesses, and governments. To remain financially viable and effective in mission, NPOs must achieve donation objectives. This demands a better understanding of donation activities and more specifically propensity/churn of existing donors. An Artificial Intelligence (AI) technique, Machine Learning can play a vital role in gaining insight into patterns of donors' response over the time and for various campaigns. Such data driven insights can help organizations design effective and personalized campaigns that result in reduced donor churn, attract new donors, and increase per donor donation amount. In this paper, we present an innovative application of unsupervised machine learning technique (K-Means) used with a Recency, Frequency, and Monetary (RFM) model to help improve outcomes of a US-based NPO with a mission to help families in need.