For healthcare organizations that need to increase charitable giving, artificial intelligence (AI) can change the game. AI programs handle tedious data searches and analysis faster and better than humans, giving people more time to make face-to-face connections. And the technology points fundraisers to the right donors for the right ask at the right time. Here’s how to make AI work for you:
Find High-Potential Donors Quickly
In many healthcare organizations, most of those who give $25,000 or more have made a smaller donation in the past. So most of the people a fundraiser wants to spend time with are already in the customer relationship management (CRM) system.
It’s difficult, however, to comb through all previous contributors and figure out who are the most likely to donate next. Wes Moon, co-founder and COO of Wisely fundraising software, notes that “AI is making an impact in this regard because it can systematically identify using a pattern who would be an excellent likely major gift donor and potentially who would be an excellent mid-level giving donor.”
Here’s how it works: The AI software will assess all the data in your CRM and with sophisticated algorithms, identify patterns which major donors share. Then it can find other people, who haven’t yet made a large gift but are likely to because they show similar patterns of behavior and have the net-worth required for a considerable gift.
Spend the Right Time with the Right Donors
Once the AI program has selected the best candidates, they can be assigned to a gift officer’s portfolio. However, officers still need to prioritize their meetings. They want to spend the most time with the most promising prospects.
AI programs help officers decide how to spend their time by predicting how much patrons are likely to donate. The software picks up characteristics of people who give at the mid-level and those who make major contributions; then it forecasts how much someone will give based on how closely they fit the model.
AI will predict when people are most likely to give, as well. For example, in some healthcare groups, major contributors follow a trend. They first give a small amount. Then about 7 years later, they donate around $1,000. Then about 2.5 years later, they make a major gift.
Each healthcare organization is a little different, but AI learns from the data so it defines the typical timeline for each group and uses it to position prospects within the journey. With that insight, gift officers can prioritize time with people who are closest to making significant donations.
Make the Right Ask
With a good sense of how much a donor is likely to give, fundraisers can more effectively match prospects with campaigns that fit their interests and their budgets. For example, a donor who is likely to make a mid-level donation and supports the organization’s brain research would probably love to help purchase new equipment for that department. While a major gift donor might want to leave a legacy and would prefer to fund a new wing.
This intelligence helps gift officers know which prospects want to hear about building plans and which want updates about innovations in research. And when the time comes, they can ask for a donation the patron is both willing and able to give.
It also helps reduce donor fatigue because officers can hone their outreach to a prospect’s interest. Patrons receive fewer requests, but each request aligns with their motivations so they’re more likely to respond.
Manage the Donor Journey
Not only does AI suggest which people will donate how much and when, it will also update the predictions based on recent interactions. Each day, as fundraisers enter the details of meetings and events into the CRM, the AI application will re-analyze the information.
Gift officers don’t have to spend hours elbow-deep in data to know how well prospects are moving toward a donation. The software will let them know each day who’s getting closer and who’s lost interest so they can adjust accordingly.
Start Relationships With Donors Early
Thousands of people make small donations to a healthcare organization and fundraisers cannot follow up personally with each of them. But some of those people will become major donors in the future. The AI system can pinpoint those small donors who have a high net worth and high probability of making a mid-level or large contribution going forward.
For example, the software may highlight a patient who recently had life-saving cancer treatment and has made a small thank-you gift but has the capacity for a major donation. Based on this insight, gift officers can add the donor to their portfolios, respond with personal thanks to their initial gift and begin developing a relationship. With early attention, high-value prospects will never fall through the cracks.
Adopt Reality-Based Planning
Organizations set new fundraising goals each year, and everyone wants to improve over last year. But often the new goals are arbitrary numbers, such as a 10 percent increase in contributions or a 20 percent increase in mid-level donors. There’s no data to suggest whether those targets are too high, too low or just right.
However, AI analysis of the organization’s current donors helps directors anticipate how many are likely to give in the coming year and how much income they’ll represent. Leadership may discover that it’s reasonable to expect only a 5 percent increase in donations or that 15 percent is within reach. Either way, with AI support, they have a reality-based plan with a higher probability for success.
As more and more groups compete within healthcare and across the board for donors’ dollars, those who simply work harder will not keep up. Giving USA reports that planned giving dropped 2.3 percent in inflation-adjusted dollars from 2017 to 2018.
Non-profits have to work smarter if they want to find and nurture the high-potential donors that will grow their programs. AI makes working smarter easy. It takes the guesswork out of fundraising and helps ensure that weeks, months and years of gift officers’ efforts will benefit donors and healthcare organizations alike.