Transforming peer donors into organizational donors: understanding and managing peer donor transformation

• In Event: Trust in philanthropy and fundraising

Abstract

Peer-to-peer fundraising has emerged as a popular funding approach for nonprofit organizations, generating fast revenue and a promising opportunity for donor base expansion by transforming peer donors into organizational donors following their peer donation (Hesse & Boenigk, 2023). The paper discusses the “transformation likelihood,” which is the probability of peer donors directly contributing to the nonprofit organization beyond their initial peer donation. Thereby, the aim of the paper is twofold. First, it aims to identify determining factors of the transformation likelihood of peer donors to become organizational donors after a peer donation. Second, it evaluates if using artificial intelligence (machine learning) can assist in identifying peer donors most likely to transform.

This paper builds on significant relationship fundraising research (Beldad et al., 2015; Bennett & Ali-Choudhury, 2009; Merchant & Sargeant, 2010; Naskrent & Siebelt, 2011; Sargeant, 2001; Xiao & Yue, 2021) to theorize peer donor transformation. It focuses on factors influencing one-time organizational donors' transition into repeat donors. Findings suggest that positive experiences and perceived efficacy (Beldad et al., 2014), perception of the nonprofit organization (Beldad et al., 2015; Naskrent & Siebelt, 2011), post-donation follow-up communication (Khodakarami et al., 2015), and donor characteristics (Wiepking & Bekkers, 2012) play crucial roles. Considering the unique nature of peer-to-peer fundraising, this paper derives a set of applicable factors for determining transformation likelihood and examines their capacity of predicting transformation.

The data was collected in July 2023 through an online survey targeting U.S. donors that have contributed to a peer-to-peer fundraising campaign before. The survey was administered using the Prolific Panel Provider. The final sample comprises 706 participants. The study’s data analysis is divided into two parts. To test the factors affecting the transformation likelihood, and considering the binary nature of the outcome variable, logistic regression is performed. Second, to evaluate the effectiveness of employing machine learning to identify which of the previously mentioned factors accurately predicts transformation likelihood at a donor-level, supervised learning is applied, which is commonly used for predictive tasks by leveraging existing labeled data to predict future events in new datasets (Casado et al., 2023).

One of the most striking findings is that, among peer donors with no prior affiliation with the nonprofit organization, the transformation likelihood stands only at 14.1%. By applying a random forest classifier, the study was able to predict the likelihood of transformation with an accuracy of 77%, identifying several factors being particularly suitable for prediction. The results of the logistic regression indicate systematical explanation for the occurrence of transformation. For example, post-donation communication with peer donors after their initial contribution doubles their likelihood of transformation. Interestingly, the study challenges some preconceived notions about transformation behavior in traditional fundraising contexts. Established factors such as trust in the organization and mission alignment were found to have no significant influence on the transformation likelihood.

References

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Author

• Laura Hesse, Universitat Hamburg