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ERIC Number: EJ1050848
Record Type: Journal
Publication Date: 2015-Feb
Pages: 27
Abstractor: As Provided
Reference Count: 55
ISBN: N/A
ISSN: ISSN-0361-0365
Effective Segmentation of University Alumni: Mining Contribution Data with Finite-Mixture Models
Durango-Cohen, Elizabeth J.; Balasubramanian, Siva K.
Research in Higher Education, v56 n1 p78-104 Feb 2015
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.
Springer. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: service-ny@springer.com; Web site: http://www.springerlink.com
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A