Modeling contribution behavior in fundraising: Segmentation analysis for a public broadcasting station

• Published in: European journal of operational research 2013-06, Vol.227 (3), p.538-551
• Main Author: Durango-Cohen, Elizabeth J.
• Format: Article
• Language: English
• Subjects: Broadcasting industry; Direct marketing; Expectation–maximization algorithm; Finite-mixture models; Fund raising; Fundraising; Market segmentation; Marketing; Markov analysis; Not-for-profit organizations; Radio stations; Segmentation analysis; Studies
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2013.01.008

► We conduct a segmentation analysis for a major not-for-profit organization. ► We employ finite-mixture models to estimate segments based on contribution histories. ► Models identify systemic differences not explained by socioeconomic traits. ► Markov Chain mixture models yield segments based on contribution patterns over time. Funding pressures have forced many not-for-profit organizations to reduce their reliance on mass-marketing efforts, e.g., pledge drives, and increase the volume and sophistication of their direct marketing activities. The efficiency of direct marketing, however, is linked to an organization’s ability to target population segments effectively, which, in turn, has motivated the development of methodological approaches for market segmentation. In this paper, we use finite-mixture models as a framework to analyze member contributions at a public radio station in the Midwestern United States between 2001 and 2008. We exploit the methodology’s flexibility, in a novel setting, to formulate segmentation models under different assumptions about the processes that generate the individual contribution sequences, i.e., longitudinal data. This differs from conventional models summarizing transactions with aggregate statistics, i.e., RFM data, that can introduce bias. We first consider generalized (Multivariate) Normal mixture models to obtain segmentations based on contribution frequencies and monetary value. To capture serial dependence in the contribution sequences, we present Markov Chain mixture models yielding segmentations based on behavioral patterns. We also derive instances of the Expectation–Maximization algorithm to estimate the associated parameters. Rather than modeling how segment size and membership evolve, we assume stable membership, and attribute manifest changes to the characteristics that define the segments. This, supports the identification of systematic, but unobserved, differences between individuals, and enables optimization of marketing policies for the ensuing segments. Analysis of the data from the public radio station shows significant heterogeneity in excess of that explained by residence location – a proxy for socioeconomic traits. In terms of managerial insights, we conclude that the contributions are largely driven by recommended membership levels, which suggests that the radio station can exert influence to increase revenue. We also find that the public radio members exhibit three distinct behavioral patterns: