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

Autor/inn/en	Durango-Cohen, Elizabeth J.; Balasubramanian, Siva K.
Titel	Effective Segmentation of University Alumni: Mining Contribution Data with Finite-Mixture Models
Quelle	In: Research in Higher Education, 56 (2015) 1, S.78-104 (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0361-0365
DOI	10.1007/s11162-014-9339-6
Schlagwörter	Alumni; College Graduates; Models; Data Analysis; Private Financial Support; Educational Finance; Fund Raising + Suchen Sie Ihr Suchwort? Hochschulabsolvent; Hochschulabsolventin; Analogiemodell; Auswertung; Private Investition; Bildungsfonds; Fundraising; Spendensammlung
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01