A real-world test of DonorAtlas’s relationship mapping features — and what it means for researchers who never had time for this work before.
Traditionally, relationship mapping has been a long and tedious process reserved for high-stakes prospects. Could that be changing with advances in generative AI? I sure hope so.
I want to be clear upfront: I am a subscriber to DonorAtlas and I genuinely believe in what they’re building. I don’t get paid to say that — I just think it’s doing something the field has needed for a long time. And when I tested their new relationship mapping feature, I wanted to share what I found honestly, including what works, what doesn’t yet, and why I’m excited anyway.
There is so much hype around generative AI. I don’t want to fuel the binge on over-promising and under-delivering. But who isn’t at least curious about the possibility that relationship mapping could get easier and less expensive — and maybe start being used earlier in the prospecting process?
Why this matters right now
We are all under pressure to identify and move prospects who can make major and transformational gifts. The K-Shaped Economy shows no signs of abating, which means more giving will come from wealthier donors making larger gifts. If you’re not familiar with the term, the K-Shaped Economy describes an environment where the wealthy are experiencing growth in asset values while the less affluent are feeling the squeeze of higher prices and a tight job market.
What this means practically: engaging ultra-high-net-worth (UHNW) individuals requires an introduction. Even when an UHNW individual is already a donor to your organization, you might not have enough of a relationship to get them to respond to outreach. You need a door-opener. And that means you need to know who your existing donors and trustees know.
That’s exactly what relationship mapping is supposed to solve. The problem is that it’s historically been slow, expensive, and therefore rationed — reserved for only the most critical prospects.
What I tested: prospecting from a top donor’s connections
To try out DonorAtlas’s relationship mapping capabilities, I chose a well-known Tampa Bay philanthropist, Penny Vinik, and asked: who does she know that might be a new major gift prospect for a nonprofit she already supports?
Penny and her husband Jeffrey have given generously to the Tampa Museum of Art, which is currently in a publicly announced capital campaign. I asked DonorAtlas to show me her connections. At first, I was getting individuals already connected to the museum in some way — as donors or current or past trustees. That gave me 78 names, which was too many to evaluate meaningfully.
So I tried filtering out Florida connections entirely. That narrowed the list to 10 names. Much more manageable!
And that’s when I found an interesting lead: someone who had previously served with Penny on a Tampa private school board. The museum has extensive educational programming. This person has a high-profile business in Tampa Bay. Could they be a great prospect? Maybe! The point isn’t that DonorAtlas handed me a definitive answer — it’s that it helped me surface a name worth scrutinizing, quickly.
This exercise was about developing an initial list for scrutiny, not producing a finished prospect list. And it worked reasonably well. Relationship mapping is a new feature for DonorAtlas, and I’m expecting it to be refined and improved with user feedback — they have been extremely responsive to input, which is a delight.
The problem it also solves that I didn’t expect
One of the persistent challenges at Aspire has been developing a quick preliminary list for clients to review before we invest time in deeper research. When we go old-school — listing names of previous board members, for example — a name by itself isn’t enough. Development staff need context to assess whether a name is worth bringing to their top donor or trustee. But researching each name fully before the first review is inefficient.
DonorAtlas solves this problem in an interesting way. When you develop an early list, you automatically have full profiles for each name. That’s actually too much information for a first pass! But you can export only the specific data fields you want — a bio, a wealth indicator, a philanthropic summary — making it easy to present a meaningful list for initial review without overwhelming anyone.
And here’s the feature that changes everything for prospecting work: the ability to save a network of individuals and then filter any new list by connections to that saved network. So we can surface names and immediately know who in our existing network is connected to them. That is a giant improvement.
Why DonorAtlas is different for this work
DonorAtlas isn’t the first technology to help researchers mine for relationships. But it is specifically designed for fundraising — and that design intent shows. It makes it easy to prospect for new names based on philanthropy and wealth, filter for connections to a defined network, and quickly view profiles without leaving search results. Founded in 2024 and built entirely on generative AI, they keep adding features and — this matters — they actually listen to users.
Is it perfect? Not yet. But that’s not the point. The point is that relationship mapping, which used to feel like a luxury reserved for a handful of prospects per year, is starting to feel like something that could happen routinely. Earlier in the process. More systematically.
For researchers who have always known that relationships drive gifts — not wealth ratings, not capacity scores, not screenings — that’s a very exciting direction!
Have you experimented with relationship mapping tools in your research practice? I’d love to hear what you’re finding.
Additional Resources
- Of Pumpkins and Relationships: How to Use Relationship Mapping More Effectively | Aspire Research Group blog | 2024
- The Magic Rainbow of Prospect Identification l Jennifer Filla Blog | 2024
- 118: Sphere of Influence Research with Chris Mildner l Prospect Research #Chatbytes podcast | 2024



