비영리 모금 및 기부자 관리 분야의 AI

Fundraising has used data for decades. The classic method is RFM segmentation, based on the recency, frequency and monetary value of past gifts, and it remains a strong baseline. Machine learning extends it with many more signals: giving channel, event attendance, volunteering, email engagement, length of relationship and response to past appeals. Models estimate a propensity score for a defined outcome, such as giving to the next appeal, upgrading, converting to monthly giving, or including the charity in a will. Major-gift prospecting adds wealth screening, in which vendors match donor records against public information such as property records, company filings and philanthropic history to estimate capacity. Capacity is not inclination: a wealthy person with no connection to a cause is rarely a good prospect, so strong programs weigh affinity alongside wealth. CRM platforms such as Salesforce Nonprofit Cloud and Blackbaud's Raiser's Edge NXT include predictive and generative features, and specialist tools such as Dataro focus on fundraising predictions. Generative AI is increasingly used to draft appeals, thank-you notes and call scripts, and to summarize donor notes before meetings. Data protection is the major risk. In the UK, the Information Commissioner's Office fined several large charities in 2016 and 2017, including the RSPCA and the British Heart Foundation, partly for wealth screening donors without telling them. The 2020 ransomware attack on Blackbaud exposed data from many nonprofit clients, showing that concentrated donor data means concentrated risk. A common misconception is that AI means sending more asks. Well-used models often do the opposite, identifying donors to contact less often or through a different channel, which reduces fatigue and cost. Another is that small charities lack enough data; simple models on a few thousand clean records can beat intuition, but messy, duplicated CRM data will undermine any tool.