[append] about them that says, “Hey, they might be able to turn into a really good major gift donor,” or what might make them stand out as a major gift prospect?
And lastly, we’re going to turn it over to all of you for some questions and answers.
Before I fully hop into this, it looks like Wendy said the sound quality is poor. Wendy, can you hear me okay or is it still goofy?
Steven Shattuck: I think you’re sounding pretty good, Ryan. I’m going to tell people to dial in by phone if you have any trouble. It’s usually a little bit better audio quality.
Ryan Woroniecki: Okay. Awesome! All right. Thanks Steven. So let me go ahead and let me jump into it. We’re going to go right into the quiz. Okay? So I want you to take out some — right down what you think the answers are to these questions, and we’re going to go through the questions, and then I’ll give you the answers.
So, between 2011 and 2012, what do you thing total U.S. contributions were? Do you think that it’s A, less than $100 billion; B, $300-$400 billion; C, $500-$700 billion or D, less than $300 billion? So between 2011-2012, all money donated to nonprofits — which of those brackets do you think it falls into?
The next question — do you think that there has been a growth or a decrease of charitable donations between 2011 and 2012, not adjusting for inflation? So do you think it’s A, an increase of 2%; B, an increase of 3 1/2%; C, a decrease of 1% or D, a decrease of 3 1/4%? So think about that for a second.
Then I’m going to go into the next slide, which is a graphic that’s actually going to give you those answers. If you look down below, this is the trend in giving from 2003-2012. So what you’re looking at here is — there is the growth as the economy started to grow, and that it dips back down as the bubble burst, and there it is starting to make a recovery around 2010. So this is the giving, but as I just mentioned, it kind of mirrors the way the economy was doing.
So if you take a look all the way back to 2003, it was right around $300 billion, and in the high point was right around 2005, 2007, when it was close to $350 billion, but to answer the two questions, a lot of you are correct. It is B and B. So if we hop back for a second here. We snagged the mouse. There we go.
So total U.S. charitable contributions between 2011 and 2012, it was B, between $300-$400 billion, and the growth between 2011 and 2012 was an increase of 3 1/2%. So all of you that guessed B and B, congratulations, you were right on the money. So the thought is that, look, we’re on an upward growth. This is good for anyone in the development world.
So now there’s two more questions — the last part of the quiz here. Between 2011-2012, what do you think that total individual giving was as a percentage of all giving? So imagine that there’s foundation giving like grants, there’s individual giving, there are requests and there’s corporate giving. So do you think it was A, 50% with some individuals; B, 98% with some individuals; C, 83% for individuals and D, 72% was for individuals? So take a minute. Think about that, then we’ll actually go to the next part of the question.
This is less A, B, C, D, and it’s more like when you’re back in grade school and you have to draw the line to connect one thing to the next. So there are four different sets of percentages. Okay? Each one of these percents — A, would be 15%, B, is six, C, is 72, D, is 7%. This actually gives away the answer to question number-one — where do you think everything matches up? Do you think foundations were 15%? Do you think foundations were 6%? Do you think requests were 7%? Do you think they were 72%? So sit there, think about it, write it down, and then I’ll go ahead and I’ll break down the answers on the next page.
So who gave in 2012? This is out of that $313 billion that was donated. What falls where? If you look, the largest part of the circle, the big yellow thing right here, this is all individual giving. What constitutes individual giving? Let’s just talk about that for second. So that’s when I donate $20 by the mail to the [Asian] Conservancy.
That is also when a very large donor makes a pledge and pays $200,000 a year by check to get $1 million over a five-year period. So these are small gifts and very large gifts.
So the overwhelming percent giving, not too surprisingly, is done by individuals. So the answer to question-one there was D. It’s 72%. Outside of that, we’re matching the rest of it. Foundations are right behind. Probably not too surprising, a lot of wealthy folks stashed their money away in foundations like the Bill and Melinda Gates Foundation, and that’s how they give.This is also community foundations giving, that kind of a thing.
Individuals, again, are 72%. Foundations are 15%. Right behind that is bequests. So this is a bequest being realized. This is, “Hey, I told the A.S.P.C.A. that when I pass on they’d get 10% of my assets, you know, what’s there and will.” So that’s a bequest. It’s when they’re actually being realized when somebody passes away.
Then over here we have corporate giving. This is McDonald’s — you know, smaller companies as well — the mom-and-pop computer store down the street actually making gifts. So that’s the breakdown. What this says is, if you’re going to figure out who you can go after to get most of the money available — it’s saying that individuals are the way to go.
Here’s another neat little sheet. If we take a look to see what kinds of organizations received the most money in 2012, this is going to break it down. Pretty interesting over here — religion gets about a full third of most of the money donated elsewhere. They do a really, really good job of getting folks to donate. A lot of that might have to do with tithing or the way the religion is set up.
Anyway, right below that you have education. Education includes independent schools. It includes higher education. So you might often think that the very large universities and colleges have these very advanced prospect research and development offices with multiple major gift officers, possibly multiple prospect researchers. There’s probably a pretty tight correlation between the fact that that’s going on and they’re getting a very large amount of money.
Pretty much tied with them are human service organizations. There are so many human service organizations out there, and a lot of them do such great work, so they’re kind of right there behind education.
Behind that you have gifts to foundations. So if somebody made a gift to the local community foundation, that’s where that counts. Health, these are hospitals, hospices, public society benefit, internal affairs. So right after the first three, four, it really starts to fall off into smaller amounts of the whole pie, when it comes to giving.
Right below that, let’s just take a quick walk through “What’s up? What’s down?” 2012 versus 2011. Giving increased by about 3 1/2% between 2012 and 2011. What’s even better is that the biggest piece of the pie, individual giving — individual giving grew by almost 4%, 3.9%, in that time period.
Foundations were giving more money away as well, 4.4%. Giving by bequest — excuse me — that dropped an estimated 7%. We can expect that number to vary. It should actually go up in the next few years as the generations start to shift more.
Then at the very bottom, probably one of the best markers of “Hey, the economy is growing!” is corporations gave away 12.2%, more in 2012 than they did in 2011.
As we take a look over here — so you have GDP. I don’t want to get too far advanced in economics, but GDP is one of the best ways to examine how a nation’s economy is doing. It looks at a number of different variables. At any rate, as we look at what percentage — how do we compare giving to the gross domestic product or GDP — if you look back around boom, it was a little bit higher, over 2%, but now it’s kind of leveled off at about 2%. We don’t expect it to drop anymore, but a Michael backup a little bit, but you wouldn’t expect a whole lot of variance there.
So now we’re going to go ahead and we’re going to get into the part of “What’s a good major gift prospect? How do you go about identifying that? What you look for? What happens?” So there’s a funny little meme here. It’s called “the condescending Wonka meme,” and I’ve worked with a lot of small organizations and large organizations, and the small organizations that the board is finally realizing — they’ve realized, “All right, if you want to grow the programs, we have to grow the fundraising.” The easiest way to do that, in a sense, is getting more major gifts.
And somebody might say, “All right. That’s it! Director of development, I know you’re a one-man shop. You’ve got to go get more major gifts. Go to town!” “So the Board wants more major gifts. Tell me again how they say ‘it’s so easy’?” So I’m going to try to help you say, “All right . . . ” Not major gift fundraising is easy necessarily, because that’s all relationship building, but in terms of identifying the folks that you want to target, that’s what we’re going to focus on.
So good prospects have two key things. They have the capacity to get at the level that you ask. Think about yourselves, you could love an organization and immense amount, but no matter how much you want to support them, if you don’t have $1 million to give them, you can’t do it, so in that sense, you have to have the capacity, and the other part, the part I just mentioned, is you have to have inclination and the understanding.
Just because you might have $1 million, it doesn’t mean that you have any desire to give it away; it doesn’t mean you understand philanthropy or a specific organization’s mission. So good prospect is going to have to meet — those two criteria.
Below that, development is building the relationship with those prospects. So what are the markers you should look for to find those prospects? Now if you take a quick look here, with the markers of wealth and philanthropy, what has been shown time and time again, in this number may even be 15% and 85%, but when you look at giving, it’s a pyramid, and the majority of the money is given from a smaller subset of individuals, and it’s often referred to as the 20/80 rule, so 20% of an organization’s donors typically will give 80% of the money to the organization.
All right. So, how do we go around and how do we find the 20%? The thought here with the pictures is, “Hey, here are all these people. They could be a big donors. He could be your big donor.” This is your standard “This guy has to be the best donor, because he’s this old monocled man.” Right? He looks very wealthy and dapper. But really it could be any of these normal looking folks over here too.
What we’re going to do is I’m going to very quickly explain — we did a back test where we took information that 400 nonprofits provided us, where they told us about 2 million different donors to them, and they told us those donors gave them $5 billion — and as a company, what we do is we [append] wealth and philanthropic data and we said, “You know, we have a good idea of what we think some good markers might help self identify, but let’s actually test it. Let’s see what we can figure out.”
So down here, we’re going to go through “Who are the top 20?” The best thing you can do, and this is very common; this is something you hear on any kind of Bloomerang webinar — is your best major gift prospects are the people that already donate to you. So the first thing that you might want to do is analyze the previous giving to your organization.
So what are the pros? Data you have is free if you make sure to keep track of who gives. The predictive capabilities are not usually impacted by average reporting errors or inconsistencies, so there’s a few errors, you’d imagine under the uniform, it shouldn’t be too terribly tough, and it’s very simple. You can run something such as R.F.M. which stands for “recency, frequency and money,” to identify who those folks with the best affinity are, and I’ll go ahead and I’ll show you a simple R.F.M. example in a second.
What are the cons associated with that? Well the cons are that it’s limited to the success or failure of your previous marketing efforts. Let’s say that you treat everybody the same and you always give them a “Thank You” letter, regardless of if they gave you $1000 or $20, and they get just an email, [you’re probably likely to] program that way.
The other thing is, what if your a small organization, you don’t have a lot of donors. Then it’s tough to kind of take the mile-high view with a very small set of data. Outside of that, it can be difficult to project opportunity outside of those previous performances.
So again this kind of says, “This is a great place to start, but you have to take a little bit of a look outside.” So download that.
I’m going to show you what’s a very simple R.F.M. What this is is it’s saying, “All right. If you take every record in your file and you want to look at some summary giving information, what are the three variables you can pull?” All right. So let’s say you take a look at when’s the last time somebody made a gift?
So if you have 500 people in your database, you compare their last gift date across the board, everyone’s going to fall in a percentile. So in the case of this person, they fell into the 82nd percentile. They are fairly recent donors, as compared to the rest of the file.
In terms of frequency, do the same thing. How often are they giving? In this case, they might have given recently, but as opposed to the rest of the file, they’re not giving too often.
And then money — how much money have donated compared to the rest of the file? Maybe most of the file has given away $40 and these people have given away $60. Right question marks on that case, the right around the 65th percentile. When you’re trying to do this [to do], you take all of those scores, each percentile score, and you added up, and everybody gets a 0-300 score, and that way you’re just looking across the board “Hey! Who has the best affinity with us based on this summary giving information?”
Again, this is free. There’s function as an Excel that will let you do these competitive rankings. All you have to do is a very simple export out of your donor management software to do this. And again, that’s a good starting point, because these folks are the ones that have a good relationship with you. All right?
So outside of that, this is where you’re actually going to get into the back study part. When you’re not looking at your own data, what’s the next best thing to look at that’s predictive of somebody being a good major gift prospect? Well, if you look at over here, it’s not too surprising really, it’s when you can find people that are making big gives elsewhere.
If you can identify somebody that’s philanthropic in a big way at other organizations, ideally within the last five years — and some people think it’s organizations with similar interests or other interests. That really depends greatly on what school of thought you come from. But nevertheless, if you can find somebody making a gift at a donor on a roll of the local organization, of $5000 or more, they are a great prospect for you, assuming again that there’s a bit of a relationship.
So what are the pros and cons associated with this data? The pros: much of the data is free and easy to understand. You can get annual reports from a lot of local organizations. They might be printed. You might be able to find them out on the web, or if you go to the theater, the symphony, typically in the back of the playbill, you’ll actually see that list of donors, and you can just kind of scan it and say, “Wait a minute, I know that lady.” She’d probably be a good major gift prospect for you if there’s, again, a relationship with your organization.
Know what are the cons associated with that data? Well after a while, if it’s on the Internet, an organization might take it down. Additionally, not all of that information is public. Organizations are required to list out all of their donors for the world to see. So it’s dependent on each organization publicizing their donors.
Going along with that, the older annual reports are the older donor honor rolls, they can be harder to find. Furthermore, some of the printed ones — those might only be sent to the donors or board members. Sometimes you can find them at community organizations or community foundations, because — not always — but sometimes they get the annual reports of organizations they give gifts to.
Additionally, some organizations might just show a name. They might not show gift amounts or defined giving levels. They could just have, you know, the angels circle and the platinum levels
or something like that.
And then the last — just because there’s a name on the list, it doesn’t necessarily mean it’s the person that you have a relationship with. It could be somebody else with the same name. So those are the cons associated with that charitable giving data.
If you actually want to see how predictive it is, though, when you’re comparing that giving elsewhere, this is how it breaks down. If you can find — this is going back to the study with the 2 million individuals and $5 billion. So when we found that somebody made a gift of $5-$10,000 somewhere else, that isolated a little over 1% of the file, and they were responsible for 5% of the giving.
As a number grows, you’d expect the percentage of money to grow as well, and more often than not, it does. So somebody that’s responsible for a $10-$25,000 give somewhere else, that’s about 7/10 of a percent of the file, and they were responsible for giving 7% of the money, and he keeps growing. It just kind of starts to get ridiculously predictive down here. At $50,000 or more, you have half a percent of the file that gave 13% of the money.
Then when we find somebody making a six-figure gift elsewhere, they’re responsible for about a full quarter of the $5 billion. So if you can find somebody that made a $100,000 or larger gift to a neighboring organization and they have a relationship with you, that’s probably — you want to do a little research before you pick up the phone — but that’s probably the first person you want to call and say, “Hey, thanks! Why is it that you’re giving to us? What do you like about our mission?”
So down below, free do-it-yourself — here, this is how you can actually build a data set of charitable giving internally. Sit down. Think about who are your peer or competitive organizations in the area? So literally sit down and make a list of the other organizations that do something similar or the large institutions in your backyard. Go to their websites. See if you can’t find an annual report or a donor list.
If you get the paper documents, those are great too. Then combine them into a large PDF. Search the document is often as you want. You probably want to try to update it, you know, just always keep on the lookout. Maybe set Google alerts, or maybe just, you know, once a month or every three months, go out and try and find some of that giving data.
Down below this is looking at it not so graphically, but more Excel-like, if you will, and again, it’s just showing “Look! 1% of the file gave about 5% of the money.” If you multiply it there, the predictive strength is right under 5%. So all things being equal — let’s say all of your donors have the same relationship with you — if you can find one that had made a $5000 give somewhere else, there are about five times more likely to make a major gift to you than anybody else in the file.
Then it goes all the way up to — you know, where it started to get ridiculous in terms of just how predictive it is. You can find that somebody had made a $50,000 or larger gift elsewhere. They’re 25 times more likely to give you a big gift, again, assuming that the relationship is equal.
So down below that, what’s the third best marker? What’s the next best thing? As it turns out, people that sit on the board of a grant giving foundation. So if you can find that somebody sits on the board of, let’s say, The Lily Fund or a local community foundation, or anything like that, where they volunteer their time to help decide how the money gets distributed, that person is more likely to be a good major gift prospect for you then one of the old standards in the industry — looking at somebody that has $2 million worth of real estate.
So what are the pros and one of the cons associated with this? While the pros are is that the IRS — you know, every organization, if you work at a nonprofit, you fill out a 990 or somebody there does, maybe a lawyer or an accountant — but nevertheless, that 990 has to be filled out. It will list the board members. Additionally, a lot of foundations will list their trustees on their website.
And the last part is GuideStar offers a free service. I mean, you have to sign up, but you don’t really have to pay to actually access the 990 data. You can also look at the Foundation Center, and these are all places where you can actually see “Hey! Who are those foundation trustees?”
What are the cons? Well the 990 reports, if you try to get them through GuideStar or the other providers, they’ll lag behind a few years, but the reason is the IRS gives it away in a really wonky format, and each different page of the 990 is actually a different document with that format, so it can be tough to kind of peace through them. But again, if you go to GuideStar and you sign-up, you can actually go ahead and you can look at those documents.
All right. So moving along, what’s the next best marker? As somebody that studied political science, I never would’ve guessed that your prospect’s lifetime giving to federal political campaigns — that’s any F.E.C. gift — is the next best predictor of future philanthropy. The way it works is the F.E.C. regulates political giving, but they make sure that one person is in funding somebody’s campaign for a specific motive, that kind of thing.
So because that data is publicly available, you can get it through the F.E.C. Here are the pros and cons associated with that. So pro-, a one-time gift of $250 — and F.E.C. gift again. This isn’t state giving. That puts your constituent or donor — your prospect — in the top 6% of the U.S. population. That’s a small group.
A one-time gift of $1000 puts your constituent in the top 1/10 of 1% of the country. We’re looking at a really small group of people at this point. There’s something unique about them. They’re starting to self identify. They could just be very politically motivated, but more likely than not, you’re starting to see some signs of wealth there.
Now we’re not going to look at individual gifts now. We’re going to start looking at lifetime worth of giving. Pretty much anybody who’s lifetime F.E.C. giving, when you aggregate all those gifts, they’re going to go ahead and they will be a wealthy person. It’s really unlikely you’re looking at somebody that’s made that many non-tax-deductible donations that has a lot of wealth.
Here’s the part where it’s just — this crazy light turns on. If you can find that somebody’s lifetime F.E.C. giving is equal to or greater than $15,000, there is essentially a one-to-one correlation that they have made a four- or five- or six- or evening seven-figure charitable gift somewhere. So if you can find somebody who’s lifetime political giving his $15,000 or more, it’s really unlikely that they haven’t made at least a $10,000 charitable donation somewhere.
So again, the F.E.C. data is free. You can easily look that up at OpenSecrets, or you could just type in somebody’s name “F.E.C. giving,” into Google or Yahoo. That’s another way to try to find it.
Now what are the cons? Not all charitable donors are political donors. Every rule you say — you know, that there’s always someone that’s not going to get along with it, so it doesn’t always hold true, but more often than not it does.
The other thing is that there’s a lot of speculation as to why large political donors are also large charitable donors. We haven’t really studied why that’s the case. We just know that it is, so when you can’t always fully explain something, it can cause people to be a little bit skeptical, but I can assure you [that] if you talk to any very seasoned prospect researcher, they will explain . . .
In fact, there’s a lady that used to work at Buffalo State named Rachel Link who just said, “it’s a simple as this, if I find somebody who’s lifetime . . . ” And this is her. This isn’t everybody else. This isn’t fact, but if she found somebody making a $5000 political get somewhere, the ask for the major gift officer would be $50,000 — no other variables — that piece of information alone. So it is quite predictive.
Down below, this is a quick look — if you’re looking to add the charitable and political giving. When we looked again at those 2 million people and $5 billion and saying, “Hey, what can we append?” When we were able to find that their lifetime giving, charitably, politically, elsewhere, was above $500,000, then isolated 14% of the file that was responsible for about 75% of the giving. Again, if you can find folks giving elsewhere, it just gets wildly predictive.
If you look at the top here, when somebody’s total giving elsewhere was $2500, that was a little under 4% of the file that was responsible for over half the $5 billion donated. So again, I’m going to stop after this in a little bit, but the giving, when you can find folks giving elsewhere, just as his pie chart show, it’s incredibly predictive.
If you want to go ahead and here’s the next slide. Come on. Well how about that? All right. Well we’ll just jump ahead to this graph. This is looking at real estate. This is saying, “How predictive is real estate?” Real estate is actually the fifth the best marker. After you look at your internal giving, external charitable giving, foundation trustees giving, then you get to the old kind of standard, real estate.
So the way it works is the darker blue shows what percentage of the file we were able to append the real estate to, and the lighter blue says, “Hey! How much giving are they actually responsible for?” So if you look — absolutely, if somebody has $2 million in real estate, that is an excellent marker that they themselves are a good prospect for you.
If you look down over here, this is where it’s not quite predictive — 250, I half $1 million in real estate — that’s not kicking off a large return on your time with these folks, if you will. So really the goal is, if you want to shift towards that $750,000 or more piece of real estate, once you get to $1 million, it starts to get more predictive, them into million dollars or more in total holdings, it gets even more predictive.
There’s an easy way to access this data. You can go to your local tax assessor’s office and look up individual’s records, if you know their address. You can also go out to websites like Zillow, which are free. Those are different. They don’t work on tax assessments. Some people like them because they don’t work on tax assessments, so you can say, “Hey! What’s the estimated market value?” and other people don’t like them so much because it’s prone to the way the market is going, and it doesn’t mean any one person’s house might work that way, but Zillow, the tax assessor’s office, that’s a great free place to get his real estate data.
Down below that — what are the next best markers when we back tested? Looking at SEC insiders, so somebody that’s a decision maker in a publicly-traded company, or they own 10% or more of that company. The Securities and Exchange Commission, they’re the ones that regulate what these decision-makers do so and run stuff doesn’t happen, or if it does, they get in trouble, because you can see what they were doing. Not that they’re all bad people. Actually, most of them a good people, but looking at it, if we can get a “Yes” “Confirmed”, “Exact Match” or “Maybe,” we were able to isolate 2% of those 2 billion people who were responsible for giving about 15% of the money. That’s predictive at about 7%. And MarketGuide, that’s the data set. They’re like Forbes profiles. Similar figures for that.
Down below, Dun & Bradstreet, that’s private businesses. So this is going to be based on “Hey! How big is the business where we can identify them as an executive?” So if we get all the maps together, that’s about 6 1/2% of the file, they were responsible for 21% of that $5 billion. It has a predictive strength of 3, about 3 1/3. So if you find — again, all things previously mentioned, if they’re all equal.
Let’s look at all those business owners, not necessarily the size, but if they are a business owner or an executive — you know, private company — they’re three-times more likely to be a good major gift prospect. Not too surprising — as the size of the revenue of that company increases, it becomes more and more predictive. You know, you imagine larger companies — they can pay their executives more money, so that’s about half percent of the file was responsible for giving way 5% of the money. It has predictive strength of about  percent.
And back down there you asked, “Is farm, ranch land, included in real estate?” Absolutely. Any real estate holdings altogether.
All right. So over here, this again is just a visual going through “All right. How predictive is that business affiliation or business ownership in terms of owning the public companies and then being executive of the private company level?”
All right. So now we know who gives. How do we find them? I’ve been talking about this all throughout. Some of the easiest places to look, folks — Google. Put in someone’s name. If you have their address, try that. And then type in what you’re looking for, F.E.C. giving, business. You can have a lot of luck finding information on LinkedIn. So there’s the search engines, public directories. So FreeEDGAR — that will tell you the information about F.E.C. insiders. [SC] Edgar.com — that will go ahead and give you Canadian stock info.
We’re private databases. So these are ones that are not — you know, kind of out there openly, but they’re free to use.
Hoovers — there’s a free version of Hoovers that will tell you the business information. Zillow will get you real estate data. OpenSecrets and Follow the Money, that will give you some of the political giving. GuideStar — that will give you information about those board members, the grant-giving and grant-seeking nonprofits.
And again, I guess I didn’t spend too much time on this, but when we found that somebody sat on the board of a grant-receiving organization, like the ones that many of you may work for, that wasn’t too terribly predictive in and of itself to say “Hey! This person’s a really good donor.” You know, they’re volunteering their time. They’re doing a lot for you, sometimes, not always, but more often than not — absolutely — and that doesn’t necessarily mean that they give though.
And then down below there’s actually . . . I’m not sure if this is still available, but please feel free to try it. Donorseries.com/integratedsearch — you can actually test the prospect research service that the company I work for has, so that’s another place.
And then social media. Now you have to have a social media account to get a lot of information on these people, but LinkedIn, Facebook, twitter, People, MyLife, these are different ways you can try to get info about these folks. Keep in mind, though, that if you’re looking them up, it’s highly possible that they can see you look them up.
Now who are the pay services? So here online kind of one-off searches — there’s Capital IQ, Wealth-X, DonorSearch, iWave, WealthEngine, LEXIS-NEXIS, GG+A, DonorScape. Target Analytics — there a Blackbaud division. They have a product called WealthPoint, where you can do that.
Screening analytics and automated review lists, these are ones — you know, they’re up at the top in saying, “Oh. I wonder what Steve’s doing.” You could check out Steve or Lauren Johnson at this address.
Down here, this is kind of a more proactive en masse review, where you take a look at everyone in the database. Hopefully all these folks are doing and internal and an external review on everybody, but it varies based on what you’re getting from the provider. We’re all different.
So DonorSearch, we do the best reviews and the analytics, as does WealthEngine, Target Analytics, GG+A. iWave also does that, and various fundraising, consulting firms, have their own products.
Really quickly, this is the kind of a profile that we’re capable of delivering, and a lot of the other vendors do something quite comparable, where “Hey! This is the biggest gift we found in the making. Here’s an estimated 10% of their net worth,” and then what are the quality matches for everything that’s possible?
Go ahead, and I’m going to turn it out to you for questions.
Steven Shattuck: Great. Well thanks, Ryan. That was a lot of information, as promised. I know that was a lot of data. Really valuable stuff there, so thanks for sharing that with us. I know people were chatting that they were enjoying what they were seeing, and I even — I don’t know if you saw this Ryan, but I sent out a few of the blog post that you’ve written about the subject, just fill in the gaps a little bit, but that was great.
We do have about probably 12 or 13 minute for questions. I know some folks have asked questions already, and we’re going to get to those, but if you were maybe sitting on your hands there and wondering a few things, do a reach out and ask Ryan a question here. He’s available for the next 13 minutes or so, and Ryan has also agreed to answer some questions off-line. So we’ll definitely share his contact info a little later on at the end.
So I’m going to roll through a question I actually got through my email. May in here was wondering about slide 15, Ryan, and I’m going to bounce back to that if you don’t mind. Could you just maybe explain this methodology a little bit, maybe drill down into it? There were a couple of questions about how this is actually calculated. I know it’s a little technical, maybe you could dig into this just a little better.
Ryan Woroniecki: Sure. I’m happy to. So what you’re trying to do is you’re trying to gauge affinity by looking at three different data sets, which you should have in your database. Maybe you’re using Bloomerang, and you can pull it on out, but you want to see, “All right. When was the last time somebody’s given you a gift?” and actually, I’m going to explain this, and then I’m going to go ahead and and I’m going to talk about it — hanging out with a friend, and use that as an example to try and gauge “Who are your better friends?”
So recency — how recent was the last gift? When is the last time that they actually sent the check, picked up the phone, donated via credit card, and said, “Here. I want you to take this money because I know you’ll do a good job with it at meeting your mission.”
So frequency — how many gifts? How many times have they given to you? So maybe it’s an in-kind gift. Maybe it’s a check. Maybe it’s somebody that has a monthly pledge, and actually, just not to get too deep into it, but if you can get somebody to give you a monthly gift automatically, that’s a great way to solidify somebody that’s more likely to remain as a good loyal donor to you.
The last part is money. Have they given you a lot of money? Or have given you not so much? Now I’m looking at all of this, it’s comparing it to the folks in the rest of your file. So what I was explaining before was saying, “Hey! Who’s your best friend?” Well if you think about it, is that somebody that you hang out with all the time — so that would be frequency — or is it somebody that you see once a month? Well they could both be really good friends — maybe only see them once a year — they could both be really good friends. So in trying to build this affinity score that feeds off all three variables, you’re trying to isolate if any of them are kind of minimal.
So recency — when is the last time you saw that person? Frequency — how often do you hang out with them? Money — think about that as time. How much time do you hang out with them?
The way I always try to explain this and kind of what I do to people not in the fundraising world is I say, “Look. Is your best friend the person who’s helped you move three different times or the person that brought stakes to cookout and then you never really see him?” Think about it. The same holds true of a good, loyal donor. They might not give you a whole lot of money, but maybe they give to you three times a year, every year.
While what that’s going to do is that’s going to [surface them] as saying, “Hey! They’re quite often going to be recent, and they’re quite often going to be frequent. What if it’s somebody that gives to you, let’s say, once year, but they always give you $5000?” So that’s kind of the basic just of it.
Now if I go ahead and I explain how these numbers translate, in order to build an R.S.M., what you want to do is you want to pull out these three sets of summary giving data, and you want to rank them against everybody else in the file.
So let’s just say that this person is John Smith and there’s only 100 people that donate to this organization. So I’m looking at it, and I want to say “Who gave most recently?” It could be last month. It could be last week. It’s all relative, based on that organization’s giving, but John Smith, as compared to the rest of my other 99 donors — well, he scored 82. He has given more recently to me than 82 people or 81 people. So in terms of how recent of a donor, he’s quite recent.
How frequently does he give? How many gifts has he given me over my lifetime as an organization compared to the rest of the folks in the file? Well he falls in the 43rd percentile. So that means that 57 people have given more frequently than he does. So in terms of recency, he’s fairly recent. How often does he give? He could be doing a fair bit better, because most people give more frequently than he does.
The last part is “How much money has he donated?” This is kind of like going back to “Hey! The guy brought some stakes to the cookout.” So John Smith actually falls somewhat close in that category. He’s given more money than 65 people in the rest of the file, so he gets a 65. So the thought of all of this is, “Where do they rank compared to everybody else?” and again, if you do some quick Google searches, there are very easy formulas you can use in Excel when you do the export, and then you can compare everybody to everyone else and get this kind of a score.
So just at a very basic level, each of these 0-100% scores, 82 is a good score. 43 isn’t a great score.
Well here’s the last part. Here’s how you level at all out so it’s on an even playing field — you know, because maybe you been hanging out with your one buddy since you were a kid, but the two of you are on opposite ends of the country. So maybe only seen each other once a year now, “Hey! We spent so much time together in the past, he’s not too terribly lapsed as a friend, as opposed to a lot of other friends, and you know, I still see him once a year.”
So you’re going to add it all up and you’re going to say, “Let’s take this 100 score right here for recency, and this one here for frequency, and this one here for money,” and is compared to everybody else, they’re going to get 0-300 scores. 300 is your best friend. Zero — maybe that’s an acquaintance. They never give you a gift. It’s somebody your friend told you about, but you never actually met the person.
Does that answer your question, everybody?
Steven Shattuck: I think so. That question came into my email, actually, so I think Megan will follow up, but hopefully that answers it pretty well. That seems like a really good exercise to do anyway, kind of outside the context of all this. So it’s a pretty neat calculation, Ryan. So thanks for that.
I’m going to jump to the next question in the chat room, from Ann, and Ann was wondering something that was actually on my mind. She’s asking about privacy, Ryan. So you shared all these things that you can kind of do research on people. Ann’s got a board member who feels a little antsy maybe about doing these kinds of searches. Maybe they think it’s an invasion of privacy. What would you say to that? And how do you kind of deal with this privacy issue as you’re doing this kind of research?
Ryan Woroniecki: Sure. Sure. So it certainly can be a touchy subject. The whole N.S.A. thing brought it right to the forefront in the not-too-distant past. The fact of the matter is, the organizations that are very efF.E.C.tive at individual giving, you’re looking at your larger higher ed institutions; you’re looking at healthcare. All of these organizations are doing that.
That means, in advance of a major gift officer talking to somebody, they’ve seen a profile, and they’re always going to be different, based on who does them, but they’ll have some highlights about the person. There’s a few different ways that you can think about that. Number-one, I wouldn’t go ahead and bring it with me and show it to the donor, because that could very easily offend them.
But that said, some of the higher-end donors, they know this is going on. I spoke with a board member at a maritime museum not too far from here that actually funded their organization doing it. So yes, it’s touchy, but that said, all of the information looking up is in the public realm. Right? Real estate records, those are public. A lot of donors want to be recognized as having made big gifts. That’s public data if the organization recognizes it. What people put on Facebook, based on their privacy settings, that’s public.
So we’re not doing anything unethical here. In fact, there’s an organization that’s kind of like [A.F.P.], but it’s called APRA. It’s specifically for people that do prospect research, and they have their own very specific code of ethics, and if you sit down and you look at it, nothing they’re doing is wrong.
The other thing is that if you go to somebody who’s a really big major gift prospect and you know almost nothing about them, that’s one way you might actually kind of pee them off. They might not be too keen if you haven’t done your homework and figured out who they are. So yes, there’s a fine balance.
Am I suggesting you talk to their bank or and try to figure out what the bank account is? Absolutely not. But knowing before you go talk to the CEO of a local company who’s been giving you $50 a year and before you never picked up the phone and said “Thanks,” you probably want to find out what he did before you as the CEO, and if you can figure out what his giving is, I mean in advance of going to talk to him about what he likes about your mission, you might know he already likes the arts, or you might know that he’s given tons of money to muscular dystrophy.
You might be able to find the part of your mission and service that as an opportunity to her and say, “Wait a minute! Laura, I see that you funded a lot of multiple dystrophy initiatives. While this is what we’re doing at our hospital. This is what we’re trying to accomplish. Do you want to add any input? Is there something that you like? Is her something that you would like to see?”
Yes it’s all completely ethical. Again, I wouldn’t hand them their profiles. If they do ask to see their profiles, you really should show it to them. So be careful what you write in your notes, because they should really — according to A.F.P. — they should be able to see the contact reports too, if I’m not mistaken. But yes, it’s all public data. You’re not doing anything unethical.
Steven Shattuck: I think that’s pretty for advice. I mean, don’t be creepy. Right? Just don’t be creepy.
Ryan Woroniecki: Completely.
Steven Shattuck: Beth here in the chat room has a question about real estate. Ryan, I know that was one of your five markers. She’s wondering, “Is farm and ranch land included in real estate?” I think the answer is “Yes.”
Ryan Woroniecki: Yes. Absolutely. So again, you have to keep in mind, everything that we do is in the context of our back study. We didn’t try to isolate it to see how real estate was coded and figure out whether or not that was predictive, so we didn’t actually want to say “Hey! If we only look at real estate holdings where its farmland, how does that translate amongst the $5 billion?”
We kind of looked at it — at all real state — from mile-high. So in that sense, yes, real estate is included. Do we know how predictive farmland is? No we don’t. I am sure . . . In fact, good friend of mine, her family owns a whole bunch of farmland in Indiana. I don’t know what it’s worth on paper, but if it’s over $1 million, it would say “Wait a minute. These folks are good major gift prospects,” where in fact, they might not be.
Conversely, if you look at a guy like John Bon Jovi, he has one and a half million dollars — maybe it’s more — in real estate in New Jersey that’s classified as “farmland.” That was a nifty little way for him to say some tax money, to say “Yes. This is my farm.” So it could go either way. I don’t really know, when you hone in on something that specific, how it pans out, but more or less, yes, there’s a lot of money in real estate. It’s somebody that you shouldn’t overlook.
Steven Shattuck: Cool. Well great. I think we covered a lot of the questions in the chat room that were focused on wealth screening and research and things. There were a couple of questions about fundraising that I answered with some blog posts, and hopefully that was helpful to folks.
We got about five minutes left, and Ryan, since you were so nice to share all this information with us, I kind of want to give you the last word to talk about what you do and maybe a little bit about DonorSearch, if you want, but here’s Ryan’s contact info, and Ryan, I think it’s safe to say that you’d be happy to answer any more questions that maybe we didn’t get in in time or that are maybe a little more in depth. Is that a fair statement?
Ryan Woroniecki: Yes. Yes. It is. So folks, DonorSearch is a prospect research company. We help organizations and consultants with their kind of stuff all the time. Don’t be shy. Go ahead and give me a call if you have questions — 410-702-4223, and that’s about it.
There were two other questions I saw down at the bottom.
Steven Shattuck: Yes.
Ryan Woroniecki: Was it . . .
Steven Shattuck: We had one for Monica. Maybe you could answer. “An organization like A.F.P., but for prospect researchers. That was on your list.”
Ryan Woroniecki: Yes. Yes. That organization is called APRA, and actually I don’t think it is here.
Steven Shattuck: Okay.
Ryan Woroniecki: APRA — if you just Google A-P-R-A . . . I wish I could remember what it stood for, but it’s essentially the . . .
Steven Shattuck: Is that an association, Professional Researchers for Advancement?
Ryan Woroniecki: Yes. Thank you, Steven. You’re correct. That’s exactly what it is.
Ryan Woroniecki: And if you’re interested, you can actually go ahead and you can join their blog. It’s called “Prospect L.” It’s not really a blog. It’s an open forum where people right questions and other researchers help them. So really, it’s a great community. You don’t have to be a researcher to sign up.
I wouldn’t go there and ask very specific questions about how to use their database. That really tees people off. Excuse me. But if you do have questions like, “Hey! I’m new to prospect research, and I want to try to figure out a way to say — and easy free way to get F.E.C. giving data for my folks. Where can I go do that?” they’ll tell you; they’ll help you out with that.
There was another question up above, were somebody said, “If I enter information about my folks into Bloomerang, does that mean it’s public information?” And I’m going to turn it over to Steven, but the short answer is “No.” That information is private, and that’s true of pretty much all fundraising platform providers.
Steven Shattuck: Yes. Absolutely. Honestly, the long answer is also “No.” Yes. And that will definitely not be available to the public. That will stay inside your database. That will be private for sure. So no worries there.
Ryan Woroniecki: Just to be clear, along the same lines, the giving information that DonorSearch has as a company — when I was telling you about that back test — we don’t share information when — let’s say one of you were to work with us and you sent over some records that — and you provided you giving information — that never gets shared. The databases that we have of charitable giving, that all comes exactly from the same way that I explained. You all could build your own charitable giving databases about looking at publicized donor honor rolls.
Steven Shattuck: Great. Cool. Well great, Ryan. This is a lot of fun. I hope people enjoyed it as much as I did. It’s always nice to see this data, because I’m kind of a data nerd, but I enjoy it. Hopefully everyone also enjoyed it.
We do do these webinars once a week. Next week we’re going to be talking about capital campaigns. So if that’s a subject that interests you, head on over to our webinar page there at the link at the bottom and register for that totally free webinar — totally educational. We’re going to be talking about “A Voiding Common Capital Campaign Mistakes.” So that will be a pretty interesting conversation. So check that out if it interests you.
Like I said, we will be sending out a recording of this presentation a little later on this afternoon, so look for an email from me, and with that, will call it a day here. So Ryan, thanks again for joining us, and thanks to all of you for taking an hour or so out of your morning to hang out with us. Great for you to be here. Thanks everyone who chatted in some questions, and we will talk to you next time. So have a great rest of your day. Bye now.