Michael Peterman · Founder and CEO, VeraData: The Donor Science Company | VeraData, led by Michael Peterman, is a nonprofit fundraising consultancy turning insight into impact with their groundbreaking Donor Science.

Here at Funraise, we've long known that if only nonprofits were able to harness the power of data, they’d have the world changed in no time at all. In fact, nonprofits that embrace data tools like Funraise’s Fundraising Intelligence raise 7x more online annually and grow recurring revenue 1.5x faster on average.

Companies like VeraData are taking those results and multiplying them with Donor Science insights that result in even more funds raised and impact created. Today’s guest, Michael Peterman, is the Founder and CEO of VeraData: The Donor Science Company, a nonprofit fundraising consultancy turning insights into impact.

Listen in to hear Michael break down complex concepts like Donor Science and Predictive Analytics, give us a peek into the future of data-based fundraising, and send us off with ways any nonprofit can get started delivering results with data science today.

And for more VeraData + Funraise collaboration, check out VeraData’s webinar featuring Funraise CEO Justin Wheeler: Breaking Free From Tradition: How to Modernize Nonprofit Growth takes the concepts introduced here and takes them to the next level.

It's the combination of three things an enormous volume of very granular data, things that you'd never think about. Two, the multidisciplinary knowhow of mathematicians, of coders, of artists, writers, strategists. And three, leading-edge technology. And that's both hardware and software, purposefully architected to solve fundraising problems for charities. Commercial world, that, that answer may sound simple. It is anything but simple. And that I think that, in a nutshell is is what encapsulates donor science.

Hello and welcome to this episode of Nonstop Nonprofit!

Here at Funraise, we've long known that if only nonprofits were able to harness the power of data, they’d have the world changed in no time at all. In fact, nonprofits that embrace data tools like Funraise’s Fundraising Intelligence raise 7x more online annually and grow recurring revenue 1.5x faster on average.

Companies like VeraData are taking those results and multiplying them with Donor Science insights that result in even more funds raised and impact created. Today’s guest, Michael Peterman, is the Founder and CEO of VeraData Decision Labs, a nonprofit fundraising consultancy turning insights into impact.

Listen in to hear Michael break down complex concepts like Donor Science and Predictive Analytics, give us a peek into the future of data-based fundraising, and send us off with ways any nonprofit can get started delivering results with data science today. 

Justin Wheeler Michael, welcome to the podcast. How are you doing today?

Michael Peterman I'm outstanding. Thanks. Good to be here.

Justin Wheeler Great. Great. Well, thank you for for joining. Very excited to jump in and talk about donor science with you. But before we do that, I know you are the founder and CEO of Verra Data. And for those listening, could you tell us a little bit about, what their data does and why it's important?

Michael Peterman So Vera Data has been a passion project for me, spanning about 17 years. Our family of brands, our family of companies provides analytically driven fundraising solutions for all types and almost all sizes of charities. We provide end to end solutions anywhere from upfront strategy to execution and literally everything in between. Mission of the organization is to grow philanthropy as a sector charitable giving. In the United States, individual giving it has been pegged at just below 3% for, I think, last 50 years with AI and data. Our objective is to move that to 4%, and the impact that that has on our world is pretty significant. And one by one, we're helping more and more nonprofits every day, and we're getting closer and closer to making that mission come true, maybe making that a reality.

Justin Wheeler Awesome. And just for those listening, in case it wasn't clear, philanthropic giving is three has historically been 3% of the GDP. Yes. And your mission is to increase that to 4%. What are what are some of the challenges or roadblocks that you've observed that's that's kept philanthropic giving, you know, at this sort of stagnant percentage of the GDP?

Michael Peterman Well, it's a little bit of everything. I think it's a series of bad best practices, you know, quote unquote, best practices that this industry has been saddled with for a very long time. And I think part of that stems from the fact that charities are measured by a different yardstick than commercial enterprises. Right. They don't have the ability to invest in the future. They can't spend money to build AI and machine learning solutions, and to license two decades worth of ridiculous amounts of consumer data, because if they spend that money, they lose their charity rating. So they are, you know, from my perspective, charities are artificially constrained. And I think it's kind of you hear the charity defense counsel Dan Pallotta talks a lot about this, and his descriptions are much more eloquent and in depth than it's worth in other. There's a movie uncharitable. Yeah. For those that want to kind of see a little more in in that sphere, it's worth checking out. But ultimately I just think they're, they're at a they're at a disadvantage.

Justin Wheeler Right. Yeah. That that makes sense. And so taking one step back, how did you get involved or or how did this, this problem, you know, if we can call it that this the statistic sort of like growth compared to the GDP. How did this become your life's mission? Why is it important to you? I would love to hear that background.

Michael Peterman I've only been with two companies in my life. AQ data was I was a partner in Vero Data, which I founded. Everything that we did served commercial entities and I'm going to skip the part in the middle. But that led to Vero data. But ultimately we built a an analytics platform that was designed to leapfrog what everybody in marketing in general, this go back taking us back to 2007 AI, machine learning. Those weren't common terms. We were trying to we were we were trying we were on the leading edge of this evolution. And initially it was actually designed to serve commercial entities, travel and hospitality, multi-channel retail. But I was introduced through friend of a friend to a nonprofit agency, and we did a little work. I got introduced to a handful of nonprofits. Ultimately, we discovered that one nonprofits are woefully underserved. Just what we were talking about a moment ago for a number of reasons. And the people involved in this business at the charity level, at the fundraising agency level, for the most part, are just really good people. It's not the same level of cutthroat competition that you experience. And if you think back to the point in time when we started the business, data was then is now. It's the fuel for AI, it's the fuel for machine learning. Commercial enterprises have a different set of competitive rules. The there isn't a willingness for obvious regions to share data. You know, across all of the companies, all the competitive spaces. But in nonprofit, they do. They exchange information, they share data, they contribute to co-ops and are much more open minded about providing the data, the fuel. In order for us to do what we do. So it was a way to a work with altruistic, good human beings, which is just a wonderful way to go to work every day. A wonderful thing to be able to to work with good people. But we were able to accelerate our systems, our engines, our solutions, because we were able to get at all of the core data much, much faster than we otherwise would have. So we got a big head start in developing our machine learning capabilities over the rest of the industry. As a result of that. And it's it led to the development of a pretty impressive, I guess I'm not the right forum for this, but it's a it's a it's a defensive moat from the business because we started collecting a lot of this data at a, at a time when nobody was really thinking about how to use data to drive all of these, these very granular decisions. So that's a maybe that's enough of a.

Justin Wheeler An overview. Yeah. No, that's that's helpful. And so I know that there's a report I don't her science manifesto. And I've read the report and super fascinating. But for those listening maybe not familiar I've never heard of donor science. Can you can you share with the listeners what is that and why is why is donor science important?

Michael Peterman So I've always been told if I can't describe something on the back of a napkin, then I don't really understand it. So what is donor science is a it's a big question and maybe difficult to answer in a way that, you know, convincingly conveys the power in something short. But I think the back of the napkin answer is it's the combination of three things an enormous volume of very granular data, things that you never think about. Two, the multidisciplinary knowhow of mathematicians, of coders, of artists, writers, strategists, and three leading edge technology. And that's both hardware and software, purposefully architected to solve fundraising problems for charities and commercial world. That that answer may sound simple. It is anything but simple. And that I think that, in a nutshell is is what encapsulates donor science.

Justin Wheeler And so in terms of its application for nonprofits, how do you see nonprofits using this today and how is it how does it impact their their fundraising efforts?

Michael Peterman I think it functionally transforms how most charities view fundraising. And it's based on just simple tenets, right? Purity and completeness of data. And it's something that I think most fundraisers unknowingly misjudge. Right. The data that nonprofits use to guide their efforts is unnecessarily incorrect. It's incomplete. It's not standardized in our experience. It's it's riddled with these self-imposed inaccuracies that limit your ability to gain true insight. And as we go through today, I'm sure we'll be able to share some examples. Going back to our previous conversation, nonprofits don't have the budgets or access to this high end tech. They don't have access to world class data scientists. A more, in our case, a team of scientists across these these interrelated disciplines. So literally hundreds of models are operating in unison to create this symphony of algorithms that select and select audiences, that optimize the channel combinations that guide the the type of package details from the the weight of the paper, the font, the imagery, the messaging that resonates with the with the selected audience segments. You know. So data plus compute power plus world class data scientists fuzed with this season team of fundraising professionals to create these algorithms that far more accurately guide every decision that fundraisers need to make to to grow revenue, to increase the number of donors, to accelerate the financial relationship with those donors. It's it's human assisted artificial intelligence that I think will lead to us being able to change the world of philanthropy.

Justin Wheeler Interesting. Super interesting. It would be great if if, you know, how do you on the spot. But if you have an example or an application or use case of how a customer has used data science in a way that has driven, you know, whether that's revenue growth, whether that's donor, a number of new donors that have been acquired, you know, as a result. But based on what you you said there at the end, it sounds like one application is where we're getting all this information about your audience. And we're laying that on top of this enormous amount of data that we've compiled to get a better understanding of how well your audience responds to a direct mail piece, for instance, is that sort of tracking with a potential use case for how a nonprofit would use donor science?

Michael Peterman Yeah. I'll pick a really strong example. Might one of my favorite. Okay. We have a group that is in the veterans and first responder space. Wonderful charity. We started with them in 2017. They did not have a direct mail program at all. No direct mail fundraising at all. We proposed an AI driven design of experiments, you know. Yeah, I'll take a step back. Usually it works both ways. Usually we come into an organization that has a fairly mature set of best practices, and I'll give I can give multiple use cases here. But those best practices, the data, the decisions, the audience, everything has been skewed by things that they've done, by decisions that they've made. In this particular case, we were able to start with a blank slate. We were able to look at all of the cumulative data that we have as an organization and create from scratch everything from the creative, from the audience to the to the combination of channels. How do we do things? We ended up starting the program, and today that groups went from from 12,000,000 in 2017 are going to break 450,000,000 in 2024. Wow. We achieved things that I think charities have never seen before. And I'll give I'll give my favorite again. Great example. All charities, when they are doing acquisition work, they're trying to acquire new donors. There's a metric that everyone uses called cost to acquire. And it's it. It costs money. Charities have to spend money to get new donors in this instance, because we were able to use AI to guide all of this and made everything data driven. There were moments throughout the years where we would send out a million pieces of acquisition, and we would achieve a positive cost to acquire, so it was better than breakeven. Those are things that I at scale. I mean, sure, you might be able to have a little segment that does it here and there, but never at scale for large campaigns. And we were doing that regularly, and we've done it for a number of other groups that we've pivoted over time, and some that had existing programs. But when you use data to analyze the audience, to understand package preferences, to understand timing and cadence, to understand the force multiplier effect of a digital touch, maybe it's maybe it's social, maybe it's display, maybe it's email. They're not all created equal and everybody interacts with the channels differently. Being able to thread together the name and postal address to the social handles, the IP addresses, the email addresses, the mobile IDs, and understand people's relationship with the different channels gives us insight that is guiding a more holistic decision.

Justin Wheeler And is it, you know, obviously the sort of AI and the machine learning and the algorithms that are helping, like could you condense that down to, to essentially say it's like you've been able to personalize outreach or acquisition at scale so that the end donor or the end user is receiving something highly personal to them. Because of all of this aggregation and data crunching that's happening sort of behind the scenes is would that be a fair leap to make based off of, what you're accomplishing through through data science? Okay. And in regards to I want to go back to something you said earlier is, is, you know, that nonprofit, fundraisers in particular often make just wrong assumptions around their data or, you know, add anecdotes to the data that actually don't, you know, support the underlying sort of trends and themes. Maybe if you take a step back. So what is is it because nonprofits are under-resourced and don't have the right sort of data analysts? Is it a lack of technology? Is it all of the above? What is it in particular that makes it hard for nonprofits to work with accurate data and then therefore make good business decisions off of that?

Michael Peterman So I think the industry itself, and it's why they didn't do it intentionally, but they set this data sharing the data brokerage. I'll give you a silly example. Just a couple of examples. One, if if a charity rents 20 lists and there are duplicates in those lists, you know, Justin's on three list, Michael's on three list, they do something called random allocation of duplicates. So the first, you know, Justin gets Justin goes to list a michael's on those same three list. Michael goes to list B, and they do that for economic fairness of all the contributing sources. So that list A is able to generate income from the charity who's renting their name. The reality and the premise is that, oh, when we do it this way, it all balances out. In the end, it is 100%. I can tell you a certainty. Not true. It doesn't balance out for a lot of reasons. Donor science solves the problem, but stack coding everyone back to the point of origin retroactively lets you see the interaction between those lists. It can change the way you think about KPIs. Nonprofits typically focus on cost to acquire, and if you look at the reports that they're oftentimes review, it ranks the data sources or the packages in terms of CTA. But if you shuffle that deck differently, a lot of times some of the best CTAs have the lowest long term values. So if I look at a 24 month view, which I don't think enough people are doing, we can identify that maybe you're most expensive. Cost to acquire is your very is your most profitable list down the line. And because you're mis attributing this information because you're randomly attributing it, instead of gaining data purity by stack coding, all of the names that all of the the sources that Justin appears on you, there's a lot of intelligence that's just left on the table because the industry has said that's the way to do it. Another example is if I have a control package, that control package gets a cost to acquire going back to that of 20 bucks, and the test package you take in a test against the control that achieves a CTA of $75 immediately. The the reaction is that package is a failure. Absolutely wrong. All of the data that was used, has been tuned to fit that control package. Donor science looks at that audience at large, right? That was selected. And then it looks at the broader audience and determines, you know, looks at package preferences to understand what subsection of the audience would be successful with that package. And then they take that a step further to scope the full size of the audience that would be successful with that package, so that we can tell the client, hey, that test package that you thought was a failure can achieve a $17 cost to acquire, and the audience size is 3 million donors. We need to mail that again with this new machine learning define audience. So now instead of a failed test with a with sunk costs and nothing to show for it, donor science has identified a separate track with a new control package with new audiences that we hadn't previously tapped, so we just grew the universe for that charity. We increased the future file size and income and those things like that. There's so just simple. There's a lot more complexities, but those are examples of things that we see and we do over and over. For clients. It's that you see through that lens of donor science, it's just a different world. And when you think about it, yeah, really it's it's it's logic, it's math. And the obstacle to getting to these insights is how to get to the data that matters. That's our job, right? If we can if we grow your profitable universe, if we accelerate the financial relationship, those things ultimately lead to the ultimate growth in giving it large right that moves the needle.

Justin Wheeler Yeah.

Michael Peterman Sorry.

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Justin Wheeler No, I love the passion.

Justin Wheeler We were talking yesterday, briefly. And the conviction in sort of this offering or and what you provide to your clients, the conviction is so real that you actually put your own skin in the game. You put your money where your mouth is by removing all barriers of entry for non profit organizations to give this a go. So can you talk a little bit about how easy it is for an organization to actually get started to run, you know, whether it's a one campaign or multiple campaigns with you in terms of just the economics, because I thought that was really interesting. And from I, understanding what you shared is you're the only company in the space doing it this way. So can you, can you to, expand upon that a little bit?

Michael Peterman Yep. I probably shouldn't do this. And you can decide to edit this out if you want, but I'll take you back to. Okay, the why we did it. So we came out of the gate with this company in 2009. And if you think back to it, was deeply recessionary housing crisis problems all around. It was not the best time to launch a company that did things nobody understood. When we were trying to present our capabilities. We were using a vernacular that was foreign to, and still is actually to. Most surprisingly, that was foreign. When you talk about the, the methodologically, the things that we were doing. So we had to think fast and say, you know what? I'm pretty confident that we've got a solution that's going to materially improve what you're doing. We're going to build your models for free. And if it works, pass, if it doesn't, no harm. And if you think about, you know, from a recession standpoint, the easiest thing to buy is something that you don't pay for until it improves your results. So it turned out to me it was a stroke of luck, made us countercyclical. And because it worked so well and because all our head to heads are so successful, we continue that today. So if a charity what they're doing acquisition today even if they. Have models. They have solutions. If they want to test us. Will build acquisition models. Will build lapsed models. Will take your data, do all the work up front for free. We'll do a back test and we'll score a live file, something that you just put in the mail, and you can see the what are going to be the results with us and what are going to be the results without us. So you get to you get to see it before you pay us a nickel. And that comes with a sliding scale, pre-approved price, cost per thousand for analytics. It sets your your CPM, for our scoring, for our analytical work. I don't care what the KPI is that you want to solve for. It could even be a bundle of KPIs. Most often it's cost to acquire and on average will reduce that cost to acquire anywhere between, you know, 15% for the really smart, really capable groups. Up to, you know, 30, 40, 50% for the less sophisticated mailers happens every time. And where we fall on the continuum determines the price. So there's no risk to see what this can do for you. And I think that's a very unique proposition for these charities.

Justin Wheeler Yeah. No, absolutely. I mean, you know, the idea of free until the proof of concept, it speaks to the confidence and conviction you have in your own product, in your own offering, right, that you know it's going to work. You're willing to, you know, to prove that by saying, hey, we'll we'll show you the results before you have to have to pay. It's a great business model.

Michael Peterman It's a heavy lift, right? I mean, it's a lot of work to onboard the data, to set everything up, to build all the models, to get it done. If this wasn't working every time, we wouldn't be doing it this way.

Justin Wheeler Right? So yeah, you've got the confidence you can do it because it's working. So maybe your organizations listening who are like, no, we need to get our data in order. We need to take the first step in ensuring that our data is clean, pure and and so forth. Any practical sort of advice that you would provide to those listening who are serious about really cleaning things up?

Michael Peterman You know, anything valuable is going to require change. You know, Spencer Johnson wrote a book called Who Moved My Cheese, right? The premise is everything's in flux all the time. It works on how to anticipate, adapt, and enjoy change. It's not always a pervasive attribute of this industry. So when I say colvera data, it's not. We've kind of architected this blueprint to provide a paint by numbers copilot approach, where we do all of the heavy lifting with the assistance of the charity to get all the data established. But, you know, simple things. If you standardize the way that you code all of the source files, if you start to stack code all of the duplicates, if you immediately expand the layouts in your CRM system to capture the things that I talked about earlier, granular information is critical. The the the more data we have, the more impactful we will be. And it all starts with data, and we go through a fairly rigorous process to get all this data out of the charity's hands and kind of create the standard standardize things to help drag, drag the charity forward. Because again, it's a heavy lift. But if you code font size of font, weight of the paper, details of the package, outer envelope graphics, the unstructured data, the, yeah, all this stuff that if you don't know how to do it, it's overwhelming to think about all the stuff that is used to make decisions. Hundreds of decisions have to be made for every single campaign that is done in so many of it. So many of these decisions are made with a copy over from a spreadsheet. And they're just it's not pure. It's it's error prone. It's diluting your ability to be better. So if you really start to standardize the information to create standards for the organization, not only within. So oftentimes charities are siloed, right? There's the mass audience. That acquisition element is separated from the mid-level group, which may be separate from digital. Believe it or not, separate from major gifts in planned giving. It's surprising to me how many organizations don't integrate data and and analytics to begin with the end in mind. And if you think holistically or logically, you know the donor pyramid that that if you familiar with what I'm referencing, it's this living organism. And when you start to understand who's doing what. Across the spectrum, we can tune the math and or you can you can fuel people like us to do the math, to understand capacity and propensity from the point of acquisition, to drive stronger movement up that giving pyramid. You know, the the echelons are mass market at the bottom. It's the largest number of people. But as you move up, the number of people get smaller, but the dollars get much larger, especially in that major and plan giving sector. And it's the top of the pyramid. It's really important that. The charities are incorporating all of that data in their best practices, their standards. Right. So there's there's other factors that are probably too esoteric to dig into. But the chronological data, you know, over time gives us the ability to embed. Longitudinal modeling. And it will give them the same ability. Right. If you capture all of this stuff, you understand a donor's relationship with money and how it evolves or evolves. You're just able to see things differently. I can make more use of your data. Because of those silos, there's oftentimes infighting. There's not interdepartmental alignment around attribution. Digital might fight for something over Mayo, and the mid-level group might not be incented, or might not want to give up audience to major. Give them plan giving so the organizations don't necessarily have the systems or the structure or the architecture inside the organization to set this up correctly. Yeah. Part of the stuff that we do, part of the the consulting, is we help navigate that so that we're collecting all of it and providing a data driven, hey, let us show you the power of all of this so that you're in you can kind of recreate this infrastructure internally. Like I said, I gave you a long answer, and my colleagues are probably gonna be upset that I didn't do that a little better.

Justin Wheeler No. It's good. I mean, you know, points back to just like the need like technology is so critical, but without the right process or the right standardization. And in this case, technology can only go so far and can and only take you so far. You know, I think that just one we're running up on time here. And so like one more perspective I'd love to hear your thoughts on is when organizations, funders are start to get into the data and they start to bring in like, you know, some wealth data, for instance, like propensity. I think there's like this challenge in the nonprofit space where people mistake propensity for intent. And those two things are often very far misaligned. Or the goalposts are wider than I think most people think between that between propensity and intent. Meaning, yeah, I have the ability to give $100,000, but my intent towards your cause is just not there, right? So I'm not going to give $100,000. So with their data and with data science, does it help kind of break down sort of that bottleneck between propensity and intent?

Michael Peterman 100%. I refer to it as capacity and propensity, everybody. I think pretty much everybody can identify point in time today. They can go to any number of providers and get wealth. Just general what's income? What are my income producing assets? What's the wealth? I can get that today. But let's say we take the last five years, seven years of your promotional and transactional history across everything we're able to. And we by the way, this exists back about almost 20 years, every monthly update with thousands of pieces of information on every consumer, every adult consumer in the United States, including transactions. All this, the attitudinal in literally thousands of pieces of information. And I take that, you know, the mailings that they did or the campaigns, whether it's digital or mail seven years ago, and I map it back to that point in time. So what did Justin look like then? And every month or every time Justin was communicated with not only for your charity, but across this collaborative, this this co-op of charities, we're looking at that activity, how his relationship with money changes. How did the how long did it take him to make wealth? How long did it take him to lose wealth? What was his relationship with philanthropy? What is it today? How was it changed? How he got married? He had kids who get divorced, kids went to college. All these life change things. You got sick, something happened. And the way that he spent his money on consumer stuff that nobody has a view to. We do the way that they they give differently to different types of charities. Maybe it's based on the type of package. Maybe it's based on the combination of touch points, combination of packages, combination of, digital touches. It's impossible for any one charity to be able to know all of that stuff. And what we've built is an ecosystem that can map all of that back. And I can look at data in billions of dimensions in seconds and understand propensity likelihood to actually give a gift. And we can output that data at the time that we're providing an acquisition file. If you if you have a group that can use it and will, we can score people with capacity and propensity and guide how much to ask for so that you don't have to wait for someone to raise their hand. Maybe Justin donated $50 to your charity. It's not showing up on anything as hey, Justin could donate $1 million tomorrow and probably will because of his situation. We can shortcut that by giving you the propensity and capacity scores at the beginning so that you know, when Justin makes a $50, $20, whatever, a gift that you should be treating Justin differently right now, today. Put him in major gifts. Put him in mid level, put him where he belongs so that he's getting the right type of communication. To maximize the economic or the financial relationship between the charity and the donor.

Justin Wheeler And that's that is a great explanation. Yeah, absolutely. It's a great point. I think it's a great nugget for listeners to take away. Is that the size that someone gives today, is not necessarily a leading indicator of how much you're going to give tomorrow. And with this like added layer of data and information, understanding what their capacity is and if you get them, to care more intently about what you're doing, then they should be nurtured like a major donor or however you, you know, you class, you're sort of like your donors. I think that's like a really powerful nugget from individuals to walk away with, because so often you categorize your donors by the their last gift amount or their last annual gift amount, and they've therefore received that sort of touchpoint communication and so forth. So that's that is actually a great insight. Thank you for sharing that and for those listening and want to dig in more, in the show notes, we'll have a direct link to the Donor Science Manifesto, which is is a fascinating report on a lot of things we've discussed today that I think you will find helpful as a listener. I will also link directly to their data. And, Michael, I look forward to continuing our conversation and building a partnership with with the organization. And thank you for so much for for joining the podcast today.

Michael Peterman It was my pleasure. Thanks for having me. Hopefully the content was useful.

Justin Wheeler Absolutely.

Thanks for listening to this episode of Nonstop Nonprofit! This podcast is brought to you by your friends at Funraise - Nonprofit fundraising software, built for nonprofit people by nonprofit people. If you’d like to continue the conversation, find me on LinkedIn or text me at 714-717-2474. 

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