The Future of Fundraising: Optimizing Impact with Large Language Models
It’s not news that peer-to-peer fundraising is a huge opportunity for charities. We’re more likely to respond (and be more generous) when a friend asks us to donate as opposed to a random stranger. Plus, peer endorsements promote trust in a complex system of requests for our money and attention.
It’s also not news that asking friends, family, and acquaintances to donate is hard. While charities try to reduce the burden on fundraisers by providing copy-paste templates, these don’t necessarily promote effective fundraising.
A selection of Movember’s fundraising resources for fundraisers’ social media campaigns
The trade-off here is that making fundraising easy is important to recruit fundraisers, but making fundraising look hard is important for actually raising the funds. Not only are we more willing to donate to someone that we see doing something difficult1 (think ALS’s viral ice bucket challenge), but we are also more likely to donate to someone that we see making a sustained effort with their fundraising such as regular and thoughtful posts.2
Thus, we face the age-old dilemma of harnessing the power of peer-to-peer fundraising: how can we make fundraising easy while making it look hard? The good news is, we do have a new tool available. Enter: Large Language Models (LLMs). Trained on immense pools of text data, these models understand, analyze, and generate human-like text and are the magic behind widely used artificial intelligence applications like ChatGPT.
Building off of what historically works for peer-to-peer fundraising, today we’ll break down the ways in which LLMs are an indispensable tool for charities seeking to tap into their golden network.
References
- Olivola, C. Y., & Shafir, E. (2018). Blood, sweat, and cheers: The martyrdom effect increases willingness to sponsor others’ painful and effortful prosocial acts. Available at SSRN 3101447. Retrieved from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3101447
- Chapman, C. M., Masser, B. M., & Louis, W. R. (2018). The Champion Effect in Peer-to-Peer Giving: Successful Campaigns Highlight Fundraisers More Than Causes. Nonprofit and Voluntary Sector Quarterly, 089976401880519. doi:10.1177/0899764018805196 Retrieved from: https://journals.sagepub.com/doi/abs/10.1177/0899764018805196
- Flynn, F. J., & Lake, V. K. (2008). If you need help, just ask: Underestimating compliance with direct requests for help. Journal of personality and social psychology, 95(1), 128. (2008). If you need help, just ask: Underestimating compliance with direct requests for help. Journal of personality and social psychology, 95(1), 128. Retrieved from: https://pubmed.ncbi.nlm.nih.gov/18605856/
- Silver, I., & Small, D. A. (2023). Put your mouth where your money is: A field experiment encouraging donors to share about charity. Marketing Science. Retrieved from: https://www.researchgate.net/publication/373001447_Put_Your_Mouth_Where_Your_Money_Is_A_Field_Experiment_Encouraging_Donors_to_Share_About_Charity
- Tan, J., Yan, L., & Pedraza-Martinez, A. (2020). How to share prosocial behavior without being considered a braggart? In Proceedings of the 53rd Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2020.482 Retrieved from: https://scholarspace.manoa.hawaii.edu/items/c60475e6-c6ff-4b64-b3a3-7dfb2ecc90ed
About the Author
Caitlin Spence
Caitlin Spence is a Senior Associate at The Decision Lab. Before joining The Decision Lab she worked in Aotearoa New Zealand’s justice sector as part of a team using behavioural science to create more accessible and culturally aware systems. Caitlin is interested in using data and experimental design to understand how systems can be designed or changed to favour positive and equitable outcomes. She holds a Bachelor of Arts, majoring in Statistics, from the University of Auckland.
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