Developing Models to Predict Giving Behavior of Nonprofit Donors

Abstract

Organizations in the nonprofit space are increasingly using data mining techniques to gain insights into their donors’ behaviors and motivations. Data mining can be costly but can also be valuable in retaining and obtaining donors. Throughout the course of this project, we have prioritized two objectives. One is to increase the ratio of funds raised to dollars spent on fundraising from current donors, making these efforts more profitable. The other is to determine how to most effectively solicit new donors. To accomplish these goals, we have used statistical modeling and data analysis to gain insights and create recommendations related to donor optimization and acquisition. To learn about the current donors, it is important to identify which unique traits make donors more likely to donate and whether those traits are related to an individual’s demographic information or giving history. Our team is classifying donors into "states" of giving based upon different metrics, including how recently, how much, how often, and for how long they have donated. We are using various data models to create actionable recommendations on how to tailor fundraising appeals specifically to different donors, which will increase the Inn’s overall donations and their return on fundraising investment. We are also mapping the transitions between these giving states so that donors dropping from higher states can be re-engaged, while donors with a high chance of moving into a more profitable state can be flagged and targeted. We will present these results in a dashboard that the Inn can use moving forward to better solicit each donor and maintain a steady fundraising revenue stream.