A clusterwise linear regression model of alumni giving

We present a clusterwise regression model to analyse alumni contributions to a private, PhD-granting university in the Midwestern USA. The model provides a framework to simultaneously segment a population, and to explain the effect of various factors on the mean annual value of donations. We contribute a different approach to marketing studies in the university fundraising context, where segmentation is often based on intuitive, albeit possibly biased criteria. Instead, in clusterwise regression, individuals are assigned to segments with the objective of maximising the within-segment variation explained by a set of regression models. Our main finding is that individuals in different segments display systematic, but unobserved differences in their responses, i.e., the coefficients in the segment-level regression models exhibit differences in their magnitude, sign and level of significance. We discuss how characterising such differences can support tailored solicitation strategies.