Base-level giving programs have long relied on broad segmentation and historical giving patterns to guide outreach.
As an industry, the discussion for evolving this approach has been constant for years with limited progress.
Donor expectations are rapidly evolving. A personalized giving experience with curated outreach and relevant messaging should not be considered a goal—it is a must have. Recent technological innovation has empowered fundraisers to meet this moment. Data science can not only offer predictions of who will make a gift, it can also predict when the gift will be made, how much to ask, and what message will be most compelling. These predictions present a holistic perspective of potential as well as detailed instructions on how to best connect with supporters’ passions. In addition, this detailed level of prediction can be automated by employing personas and mapped journeys to begin to approach the ideal N=1 approach to broad support.
This shift represents a meaningful evolution in annual giving strategy. Rather than treating donors as static profiles, predictive models scaled with AI enable organizations to recognize that readiness to give is fluid and meet emerging opportunity. A donor’s likelihood to contribute can change based on recent activity, engagement, life events, communication cadence, and even broader economic signals.
For base-level giving programs, where efficiency and scale are critical, this level of precision is especially valuable. Instead of sending the same message to an entire segment, teams can align their outreach along two key dimensions:
- Meeting the moment: Identifying when a donor is most receptive increases the likelihood of conversion and reduces fatigue from poorly timed solicitations. For example, a habitual year-end donor may not be worth soliciting in April, or at least not without a special strategy.
- Ask amount: Tailoring gift amounts based on holistic historical giving, as well as predicted capacity and inclination, helps ensure asks feel both appropriate and achievable. For example, a donor who recently gave $50 but has a history of giving much more in recent years can be asked for more.
At BWF, this approach is reflected in the Ready to Give score within our Donor AI platform, powered by IBM Watson. The score synthesizes multiple data points to assess a donor’s current position within an engagement flywheel—moving from awareness to engagement to action. Rather than relying solely on past giving, it considers how donors are interacting with an organization today and how those interactions signal future behavior.
This framework helps teams think beyond isolated campaigns and toward sustained engagement. A donor who is not ready to give today may still be highly valuable—requiring different messaging, stewardship, or touchpoints to build readiness over time. Conversely, donors who are primed to give can be engaged with timely, relevant asks that align with their demonstrated interests.
Nonprofits that use AI well aren’t replacing human judgment, they’re strengthening it, using data not just to predict behavior but to show up in ways that feel timely and relevant. The organizations that succeed will be those that move beyond one-size-fits-all outreach to building more thoughtful, personalized relationships at scale.
