Finding Donors by Relationship Fundraising

Our research utilizes revenue–business-based relationships and data to expand the donor bases of non-profit organizations. Fundraisers desire to predict who will donate and how much to allocate their marketing resources effectively. To answer both questions, we develop the Spatial Tobit Type 2 (ST2) model that integrates the auto-Logistic (AL) and auto-Gaussian (AG) models into the Tobit type 2 framework. The AL component is used to predict who is likely to donate by inferring inter-client similarities based on the clients' transaction information from the revenue businesses. Similarly, the AG component is used to predict how much based on a similar measure of inter-client similarities. The Tobit type 2 framework combines both components into the single framework of ST2. Our empirical application linking a veterinary school's medical treatment records to its donation records demonstrates that clients' relationships built through their medical treatments at the school hospital positively contribute to their donation decisions.

► This research applies relationship fundraising to expand donor bases.
► Fundraisers predict who will donate and how much to allocate resources.
► Our Spatial Tobit Type 2 model answers both questions.
► Our empirical results demonstrate that the proposed model performs well.
► Non-profit organizations can apply this research to find prospective donors.