Donor Segmentation: When Summary Statistics Don't Tell the Whole Story

Durango-Cohen, Elizabeth J.; Torres, Ramón L.; Durango-Cohen, Pablo L.

doi:10.1016/j.intmar.2013.04.002

Cited by 19 publications

(14 citation statements)

References 37 publications

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“…The donors' behavior could be described based on the RFM model. The RFM value is a preferred method of segmentation [35], and considered as an easy and effective technique for defining customer segmentation [36]. The RFM analysis proposed by Hughes [37] is a method that differentiates important customers from transaction data according to three attributes.…”

Section: B Rfmmentioning

confidence: 99%

“…Rupp et al [6] showed that charitable donors have been segmented by a range of demographic, psychographic and behavioral factors, as suggested by several studies related to customer segmentation [7,8]. In terms of behavior, RFM (Recency, Frequency, Monetary) is a powerful and well-known analytical method for segmentation due to its reflection of customer behavior (Durango-Cohen et al, 2013). Previous studies used customer transaction data to extract RFM and segment them for most marketing goals.…”

Section: Introductionmentioning

confidence: 99%

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Who Donates on Line? Segmentation Analysis and Marketing Strategies Based on Machine Learning for Online Charitable Donations in Taiwan

et al. 2021

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The reduction in government support and the rapid growth in the number of nonprofit organizations have made them face fierce competition for charitable donations. Identifying valuable donors and developing effective marketing strategies can contribute to online donation platforms. This study explored online donors' characteristics in Taiwan through the identification of different donor segments using a refined clustering algorithm. Furthermore, the marketing strategies based on the salient features of each segment are offered to retain donors and maximize their monetary donations. A real dataset derived from 14,029 donation records contributed by 7,432 donors during the years of 2016-2018 on an online donation platform were collected. A refined cluster analysis based on an improved particle swarm optimization algorithm was applied according to RFM (Recency, Frequency, and Monetary) values and donors' sociodemographic variables (e.g., Sex, Age, and Education). The results offered four segments of online donors in Taiwan. "Passive donors" were found to be the largest segment (38%), followed by "female active donors" (24%), "potential donors" (21%), and "male loyal donors" (17%). Most donors on the platform were female, highly educated, and aged between 30 and 40. The men's single donation amount was higher than women's; however, the women's total donations were higher than men's. We contributed the donor segmentation process with a refined clustering technique, which combines RFM and socio-demographic variables as criteria to compensate for the shortcomings of previous studies that only focused on RFM. Longitudinal online donation data instead of the questionnaire survey was used to analyze the profiles of online charitable donors in Taiwan.INDEX TERMS Online donor segmentation, donation marketing strategies, socio-demographic factors, RFM.

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Section: B Rfmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Who Donates on Line? Segmentation Analysis and Marketing Strategies Based on Machine Learning for Online Charitable Donations in Taiwan

et al. 2021

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“…Potential segmentation criteria range from extrinsic demographic measures through to intrinsic psychographic characteristics (Lee and Chang 2007). Between these extremes are a wide range of behavioral and value-based criteria, which include donation patterns, preferred methods of giving and levels of charitable interest that can be used to inform life time value (LTV) or recency, frequency, monetary value (RFM) analysis (Bennett 2006;Durango-Cohen et al 2013). Little wonder then that non-profit managers struggle to select the most appropriate segmentation criteria (Boenigk and Leipnitz 2016).…”

Section: Donor Segmentationmentioning

confidence: 99%

The post-Brexit donor: segmenting the UK charitable marketplace using political attitudes and national identity

Robson

Hart

2019

Int Rev Public Nonprofit Mark

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“…Unlike the more prevalent a priori segmentation method (where the number and types of segments are determined in advance by the fundraiser) a post-hoc segmentation model could be utilized where the number of segments and segment characteristics is inferred from data collected via questions and feedback from existing donors of Christian faith-related INGOs. Such inferences are drawn from statistical techniques (like K-means clustering) and is deployed in the works of Wedel and Kamakura [81], and Durango-Cohen, Torres, and Durango-Cohen [82]. Such post-hoc segmentation could be useful because the traits of donors of Christian faith-related INGOs are not known.…”

Section: Proposition Development: Christian Faith-related Ingos and Imentioning

confidence: 99%

Unpacking Donor Retention: Individual Monetary Giving to U.S.-Based Christian Faith-Related, International Nongovernmental Organizations

Ramanath

2016

Religions

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This article examines an important but relatively overlooked aspect in the field of international giving in the U.S.-individual monetary donations to Christian faith-related international nongovernmental organizations (INGOs)-and outlines the cognitive process influencing donors who choose to keep up their financial support to Christian faith-related INGOs. The propositions forwarded in this article draw on existing literature on Christian giving to international causes, INGO management, donor retention and finally, the logic of self-perception to highlight how existing donors might evaluate their repeat giving decision. The more existing donors of Christian faith-related INGOs can identify themselves with the INGO's identity-comprising its beliefs and values, its claims to legitimacy, and performance-the more likely it is for donors to be satisfied and decide to maintain a stable relationship with the specific INGO.

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Donor Segmentation: When Summary Statistics Don't Tell the Whole Story

Cited by 19 publications

References 37 publications

Who Donates on Line? Segmentation Analysis and Marketing Strategies Based on Machine Learning for Online Charitable Donations in Taiwan

Who Donates on Line? Segmentation Analysis and Marketing Strategies Based on Machine Learning for Online Charitable Donations in Taiwan

The post-Brexit donor: segmenting the UK charitable marketplace using political attitudes and national identity

Unpacking Donor Retention: Individual Monetary Giving to U.S.-Based Christian Faith-Related, International Nongovernmental Organizations

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