G-Lab Group's Five Data Pillars: How AI Predicts Nonprofit Donor Behavior
By Nick Leggiero-Silva | March 5, 2025 | 15 min read
Imagine being able to predict who's going to donate to a charity before they've even hinted at it. Sounds kind of crazy, doesn't it? At G-Lab Group, we're diving into how AI is reshaping fundraising by moving beyond broad appeals to tailor fundraising efforts to individual preferences and behaviors—creating real-time, accurate targeting, but for a good cause.
Traditional Fundraising Is No Longer Enough
At G-Lab Group, we aim to redefine how local Salvation Army units engage donors in a digital-first, AI-powered world. We hope this blog post becomes your go-to resource for AI, fundraising, and storytelling.
We must first examine traditional fundraising challenges to understand why this is such a big deal.
Traditional storytelling alone is no longer a competitive advantage. Nonprofits that rely on creative content and standard donor lists are falling behind. Digital has changed the game, and now AI is the next wave of disruption. Relying on those static donor lists is like navigating a maze with an outdated map. You're bound to get lost.
"The future of fundraising isn't mass messaging—it's personal, thoughtful, and data-driven. The nonprofits that embrace this shift will build stronger relationships, deeper engagement, and greater impact."
Everyone's bombarded with information these days. So, how does G-Lab cut through the noise and connect with potential donors? During our workshop at the CRD conference, we discussed the digital fundraising ecosystem and data advantage we are using in The ALM division. So, what's the secret behind it?
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The Five Pillars of Donor Data
Well, imagine having a 360-degree view of each donor and understanding every interaction, interest, and motivation. That's what this ecosystem aims to do. It's built on five key pillars of data, each providing a unique piece of the puzzle.
Picture a giant donor puzzle. Let's dive into these five pillars.
Pillar 1: Contributed Data
Okay, the first piece is contributed data, which comes straight from our client's CRM system. We pull donation data from client's platform, tracking historical giving patterns. This helps us understand how, when, and why donors give. It's like a donor's giving resume - it shows their track record of generosity.
Pillar 2: Captured Data
Next, we have captured data. We integrate paid social media engagement, email interactions, and website behavior, tracking donor signals across multiple channels. So we can analyze how donors are behaving online.
This data analysis is not any different from how any modern website works or how Netflix recommends shows based on your viewing habits. The key difference is we're using this data to connect people with causes they care about, not to sell products.
"By tracking donor signals across multiple channels, we can create personalized experiences that make giving more meaningful and impactful rather than overwhelming people with irrelevant or generic fundraising requests."
So, let's be honest—to this point, what we're doing isn't new. Digital tracking is already everywhere. You look at a blender on Amazon, and suddenly, it's stalking you across the internet.
And let's not forget—social media is already curating what we see based on what we engage with. This isn't some futuristic tech shift. It's just how digital ecosystems work now. The difference is that we're not using it to sell blenders. We're using it to connect people with causes they care about.
It's about making donor experiences better—more relevant, more meaningful.
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Personalization: The Game-Changer in Fundraising
This takes us straight into one of the biggest shifts in digital fundraising: hyper-personalization.
The numbers speak for themselves. According to Virtuous, personalized emails get 82% higher open rates than generic ones. More opens mean more engagement and more conversions—it all adds up. Instapage also found that personalized marketing boosts conversion rates by 25%. That's huge.
And donation amounts? This blew my mind. NonprofitPro found that donors give more when organizations personalize the ask—without hurting conversion rates. It's not just about getting more donors but maximizing every gift.
Show Image The proven impact of personalization on fundraising metrics
This isn't just in fundraising—this is a shift across all industries. A Virtuous survey found that 71% of donors feel more connected when outreach is personalized. And outside nonprofits? 80% of consumers prefer brands that tailor their experiences. Expectations have changed.
And nonprofits are catching on. Over 50% already use personalization in email and SMS, and NonprofitPro says it's boosted email open rates by 26%. It works.
The takeaway? The future of fundraising isn't mass messaging—it's personal, thoughtful, and data-driven. The nonprofits that embrace this shift? They'll build stronger relationships, deeper engagement, and greater impact. Fundraising isn't one-size-fits-all anymore—and that's a good thing.
You could say people actually like algorithms. Think of it this way: algorithms are like matchmakers. They're helping us discover what matters to us in a world of endless options. For digital fundraising, this means matching donors with causes they care about, creating a positive impact for both the donor and the charity. And ultimately, this leads to a better, more personalized giving experience.
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Pillar 3: Engagement Data
That leads to the third pillar, engagement data. We analyze donor interactions and real-time behavioral trends to determine which campaigns resonate. It's about figuring out what truly resonates and makes them tick. So now we've got their past giving, online behavior, and what they engage with.
Pillar 4: Derived Data
The fourth is derived data, where things get interesting. We officially launched this at the CRD Conference with the ALM division case study, and it's already making a significant impact. This is where we combine contributed, captured, and engagement data to generate deeper insights about donor intent—who is most likely to give and when.
This isn't something theoretical—this is already working. With derived data, The Salvation Army isn't just looking at individual donor actions anymore—we're identifying patterns that tell us when and how to engage donors at the right time. And we're already seeing results from this approach.
"To a certain extent, the Salvation Army ALM Division is already predicting donor behavior. "
To a certain extent, the Salvation Army ALM Division is already predicting donor behavior. By analyzing past giving behavior, engagement signals, and donor interactions, we can personalize outreach to ensure the right message reaches the right person at the right time.
So, instead of just sending out generic appeals, we can tailor outreach based on the donor's engagement. And this is already happening today, which powered the ALM case study. Let me give you an example.
Let's say a donor visits The Salvation Army's website and watches a video about their youth programs. That's our first signal—they're interested in that specific program area. Let's say they click on a donation link but don't complete it.
That's another signal. And if we see this happening with multiple donors, we don't just let that data sit there—we take action. Our system might automatically send them a personalized follow-up email featuring real-impact stories from the youth programs they showed interest in. If they still don't donate, we may follow up later with a testimonial video or a lower ask amount based on what we know about their past giving behavior.
So, instead of just hoping they return, we're re-engaging them in a relevant and personal way. This isn't a mass email blast—it's data-driven storytelling tailored to each donor. And that's what's already in place today.
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Pillar 5: Predictive Data
So what makes the fifth pillar, Predictive Data, different? Predictive Data will take it even further. While Derived Data helps us personalize outreach based on engagement, Predictive Data will allow us to forecast even further ahead—anticipating donor behavior before engagement happens.
Instead of waiting for donor signals, the AI will predict future giving patterns before interacting with a campaign. We're developing AI models that will go beyond engagement tracking and identify donors who are most likely to give—even if they haven't interacted with any recent content.
The Signals Behind AI-Powered Fundraising
Let's talk about the signals themselves. What exactly is the AI looking at when it makes these predictions?
The signals we use today—the ones that powered the ALM campaign—are all part of Pillar 4: Derived Data. These help us understand donor intent in real time and take immediate action to keep them engaged.
The AI is already analyzing these insights to personalize outreach:
Giving Behavior – Is this donor likely to give again?
Donor Segmentation – Which programs does this donor engage with?
Personalized Content Buckets – What messaging resonates with them?
Engagement Tracking – Have they clicked on our emails? Visited donation pages?
Social Signals – Are they interacting with The Salvation Army's content on social media?
These signals allowed us to dynamically adjust messaging, recommend relevant content, and convert more donors through personalization. But what makes this even more powerful is that we don't just personalize based on programs or interests—we go local.
"If I'm a donor, I don't just want to know that I'm helping a shelter somewhere. I want to know I'm helping the shelter in my city and neighborhood."
The Last Mile Engagement
The Salvation Army isn't just one organization—it's made up of many cities, many units, and many programs. So we don't just say, "Hey, this donor likes youth programs." We connect that preference to their specific location. If someone from Baton Rouge, Louisiana, engages with content about food insecurity, we don't just send them any food relief appeal—we send them stories and impact from Baton Rouge. That way, donors see how their gifts affect their own community.
So, instead of a generic Salvation Army message, the donor gets an email directly tied to the programs running in their city. And that's a huge deal because donors are more likely to give when they know their support is staying local. People in New Orleans care about New Orleans programs. People in Jackson, Mississippi, care about Jackson programs.
So we built a system ensuring every outreach piece matches the donor's location and services. That level of hyper-personalization is what made the ALM campaign such a success.
If I'm a donor, I don't just want to know that I'm helping "a shelter somewhere." I want to know I'm helping the shelter in my city and neighborhood. And that's what makes derived data so powerful—we're not just tracking what donors care about, we're matching it to where they live and how they can make a difference right there. That's already in place, and it's making a massive impact.
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The Foundation: Years of Media Organization
That level of personalization—that ability to connect donors to the right story, in the right place, at the right time—is only possible because of something we've been building for years. We didn't wake up one day and say, "Let's personalize content."
This precision only works if you've built the infrastructure for it. We've been collecting, categorizing, and structuring The Salvation Army's media archive for more than seven years. We have over 500 fully tagged and categorized stories. Thousands of transcripts from clients who have received help and services. Tens of thousands of photos, each properly archived. And we've had a dedicated team ensuring this content is searchable, structured, and ready for a moment like this.
Personalization isn't just about integrating data—it starts with clean, organized data. Without structure, AI can't make the right connections. And let's be honest, it's not only about helping local units tell their local story. For us, it's about giving local officers peace of mind—knowing they have this level of media organization supporting their fundraising. But maybe that's a topic for another blog post.
What's Next in AI-Powered Fundraising
We've covered a lot today—how AI is reshaping fundraising, how personalization is already driving real results, and what's coming next with predictive intelligence. If you're curious to see this in action, check out our latest blog post, where we discuss how The Salvation Army ALM Division implemented these strategies and their impact.
We're sharing this campaign's insights, challenges, and wins. So, if today's conversation sparked ideas for your fundraising efforts, visit our blog, subscribe to our newsletter, and keep the conversation going.
And if you enjoyed this article, share it. Send it to your team, post about it on LinkedIn, and let's get more people thinking about the future of fundraising.
"The future of digital fundraising is about knowing exactly which story to tell, to which donor, and at which time. That's the power of our Digital Fundraising Ecosystem."
Before we wrap up, I'd like to mention Eli Silva’s upcoming book: "Mission Intelligence: How AI Agents are Reshaping Decision-Making & The Cognitive Energy Economy." This book isn't just about how AI is transforming digital fundraising—it's about how AI reshapes decision-making.
Nonprofits are no longer just engaging with donors but with AI agents that analyze data, predict outcomes, and even recommend actions. This shift is redefining how organizations interact with both people and technology. If you want to understand what's next and how to stay ahead, this book is for you.
The future of digital fundraising is about knowing exactly which story to tell, to which donor, and at which time. That's the power of our Digital Fundraising Ecosystem.
About the Author:
Nick Leggiero-Silva is G-Lab Group’s Digital Fundraising Manager and a data-driven storyteller. As a leader at G-Lab Group, he works with organizations like The Salvation Army to implement data-driven approaches that connect donors with causes they care about.