Effective Segmentation of University Alumni: Mining Contribution Data with Finite-Mixture Models

Abstract

Having an effective segmentation strategy is key to the viability of any organization. This is particularly true for colleges, universities, and other nonprofit organizations—who have seen sharp declines in private contributions, endowment income, and government grants in the past few years, and face fierce competition for donor dollars (Wall Str J p. R1, 2011). In this paper, we present a finite-mixture model framework to segment the alumni population of a university in the Midwestern United States based on the monetary value of annual contributions. A salient feature of the model is that it exploits longitudinal data, i.e., contribution sequences. Another important feature of the model is that it supports the identification of unobserved heterogeneities in the population’s giving behavior. Our empirical study presents substantive insights gained through the processing of the full contribution sequences, and establishes the presence of seven distinct segments of alumni in the population. Results provide a basis to support the design of segment-tailored solicitations, and guide the allocation of resources (e.g., telemarketing dollars) to fundraising activities.