First Faculty Advisor
Statistics; Higher Education; Philanthropy; Modeling; Predictive; Alumni; Donor;
In recent years, college and universities have relied increasingly upon the charitable contributions of its previous graduates; as the costs of tuition rise substantially, development offices are facing the challenge of creating annual fund campaigns that are minimally expensive while providing the maximum potential for return. This study addresses the available constituent database at one University in particular in an effort to identify what criteria are the strongest predictors of donor response at a small, private university located within New England. The analysis utilized predictive modeling and data-mining largely within the software program Rapid Insight to build several models in an effort to streamline the soliciting process and identify constituents with the highest propensity to donate at a variety of levels.
The analysis includes statistical models intended to identify which characteristics make an individual likely to transition from non-donor to donor status, what ask techniques are most successful for a philanthropic campaign, which individuals are most likely to provide large donations, and which individuals will give consecutive gifts over several years. Statistical modeling builds on current research within the field of university development office data mining; it serves as an evaluation of several studies that indicate that a negative growth rate in giving occurs around the retirement age; this does not appear to be the case at this particular institution. In addition, it builds upon evidence suggesting which majors at predominantly business colleges have the strongest likelihood of providing large gifts to their alma mater. Several models within the study suggest which solicit techniques have the strongest success rate for a philanthropic campaign, including the use of telefund calls, direct mail solicits, e-mail solicits, and several other possibilities.