Prospect research is a game change for fundraisers, especially major gift officers. However, getting the most out of your wealth screening tool can be difficult if you don’t know what to look for.
Ryan Woroniecki of DonorSearch recently joined us for a webinar, in which he gave a brief overview of how prospect search works. He explained a few philanthropic markers, why they are predictive, what can be extrapolated, and how everything is designed as information gathering for a first meeting where you can learn more about the individual. In case you missed it, you can watch a full replay here:
Steven: Well, good afternoon to everyone listening in. Thanks for
joining us for today’s webinar. This is our DonorSearch deep
dive. My name is Steven Shattuck and I’m the VP of Marketing
here at Bloomerang and I’ll be monitoring today’s discussion.
And today, I’m joined by my colleague, Chris, from Bloomerang
and my friend, Ryan, from DonorSearch. Hey there guys.
Chris: Hey, how are you Steven?
Steven: Thanks for both of you for spending some time to chat some
Bloomerang customers today. And thanks to all of you for
registering and taking a half hour or so out of day to join us.
I know all of you are Bloomerang users and some of you are also
DonorSearch users. And what’s going to happen today is Ryan from
DonorSearch has a short presentation. He’s also going to show
you some of his tips and tricks for using DonorSearch. So, if
you’re not already using it, hopefully, you’ll find it
interesting. And if you’re already using DonorSearch, I think
you’ll have a lot to take away from this presentation to help
you use it better.
So, Ryan’s going to run through a few slides and then he’s going to
jump right over to the tool. And as soon as he’s done with that,
we’re going to jump into a Q&A session. So, if you see something
during his presentation or maybe hear something you’d like
explained, we’ll try to answer just as many questions as
possible within the 30 minutes.
We wanted to keep this short since it is during the middle of the
workday and we don’t want to keep folks from their busy tasks
for too long. But feel free to send anything over in that chat
box. I’ll see those questions and I’ll fill those to Chris and
Ryan a little later on.
And just so everyone knows, just some housekeeping things, I will be
sending out a recording and Ryan’s slides a little later on. So,
if you have to bounce early or disconnected for some reason or
just wanted to review the content again later, look for an email
from me, probably, tomorrow with all those things. So, Ryan, I’m
not going to take up anymore time. Why don’t you get us started?
Ryan: Thank you, Steven. Good afternoon everybody. My name’s Ryan
Woroniecki. I work at DonorSearch. I’ve been here for, about,
three years. A little bit of background, DonorSearch is a
prospect research company. If you don’t know, we partner with
your good friends at Bloomerang. Our service actually works from
inside your database.
The purpose of this deep dive, today, is telling you a little bit
more about prospect research – what it is, the basic principles
of it, what you try and find out about an individual, and then
how our product actually works within the software that you
subscribe to. So, if there are any questions during the
presentation as Steven mentioned, please, go ahead and put them
in the chat box and we’ll try to get to them at the end of the
So, really quickly, here’s the first slide. It’s an overview. This is
what we’re going to cover. First off, what is prospect research?
And secondly, it will be the statistical markers of a
philanthropist who did a back study to actually figure out what
can we find out about somebody that proves that they’ve made a
big gift somewhere. The third part’s the integrated search.
That’s the online service that you can use from within
Bloomerang. The fourth part is research tips. What these will
actually do is, this will give you some insight in what you can
do to have a more effective search. What can you do to get more
out of the service that you subscribe to or you could subscribe
if you don’t already. And the last part is going to be direct
DonorSearch data acquisition. I’ll get into that in just a
So, here we go. What is prospect research? In order to get the answer
to this, I went to a place that a lot of people go for just
about everything. I went to Wikipedia. And the Wikipedia page
says, “Prospect research, also known as development research or
fundraising research, is a process in fundraising where a
researcher, identifies and provides relevant information about
potential donors to an organization.”
What exactly does that mean? That means somebody tries to find out
information about a prospect, perhaps look at their real estate,
figure out if they’re a business executive, try and identify
gifts to other organizations, perhaps, or see if they have a
grant giving foundation. Basically, identify any piece of
information that could surface somebody as the major gift
prospect or get you a little bit more concrete information about
So, as a gift asker, you, actually, have a good idea of what you
might go in and ask for prior to meeting them. So, a few little
tips about what it can do, what it can’t do, that kind of a
thing. The first tip you’ll see there is it’s ideally used prior
to cultivation meeting. So, what this means is you’re going to
want to go out and try and figure out what you can about the
individual before you actually pick up the phone or call them
and meet them for lunch. Or if you know you will be speaking
specific people at an event, at either the event or sometimes
afterwards, you’re going to try and set up a meeting for a gift
to ask, you want to do it then. So, the point of this is, ahead
of time. Before you actually meet with the person.
Second thing again, identify someone as a major gift prospect and
identify philanthropy elsewhere. So, if you find somebody making
big gifts somewhere else, you get a sense of this is the amount
of money the person has not only been asked for but said, “yes”
to. That’s in your favor. And then, identify markers as well.
The one thing that it can’t do, just to be really clear, is that it
will not tell you everything about the individual. There’s a lot
of information that you will learn when you do go to meet with
the individual in person that you can’t get off of public data.
So, imagine you’re going to meet with somebody for lunch or tea at
their house and when you walk in, you discover that there’s a
never going to be figured in prospect research and frankly,
that’s more valuable than most things we would be able to tell
So, the purpose is you have to come to an understanding that what
these services and Google searches and anything like this you
can find, they’re always going to be incomplete. They are a
starting point, okay.
So, the next part is the statistical markers of a philanthropist.
Back in 2012, we back tested our models to figure out what we
could find, what data could DonorSearch append that was
predictive of a known philanthropist.
So, what we did was we took data from 400 different nonprofits where
they told us about two million individuals that had donated over
five billion dollars to those four different organizations. And
what we did is ran them through our standard wealth and
philanthropic reviews to see when we append a piece of real
estate to a known donor, how predictive is that real estate of
the known dollars donated?
So, from that, we came up with some numbers. So, what we did was we
looked at all the places you’d expect us to find predictive
information. And we actually found places that were a little
If you’ll see here on the left, this is a benchmark database. That
means what dataset are we appending to the individuals. To the
right of that is the percent of the records. To the right of
that is how much money did that subset of people find and then
you have the predictive strength.
For example, when we looked across the file to find SCC insiders,
from among the two million people, we found that was two percent
of the file. And that two percent of the two million people were
responsible for giving away 15.5 percent of those 5 billion
dollars. So, it has a predictive strength of about seven and
The next most predictive marker we found was somebody with real
estate over a million dollars. One of the things you can try and
look for when you’re looking at a profile to say, hey, do you
think this person actually is a good nature [gift] prospect or
not, is if you find them having a piece of real estate over a
million dollars, not too big of a surprise there.
The next most predictive marker is find somebody as the executive of
a business where the company earns five million or more in
revenue. So, when we did that, we found six-tenths of a percent
of the file that was responsible for five percent of the money.
So, the predictive strength there is nine percent.
When it really, really starts to tilt the scales in for you favor
though, is when we’re able to append an individual making a
charitable gift elsewhere of size. So, we found seven-tenths of
a percent of those two million people gave seven percent of the
five billion dollars. The really tallying part is actually when
we can find somebody making a gift of $50,000 somewhere else.
Not a big surprise, but believe it or not, the industry standard
in using the services isn’t actually to look for that.
So, we know though when we find somebody making a gift of 50,
100,000, or $1 million, that’s actually your home run. That’s
the best thing you’re looking for. As you can see here, that
tiny subset of the group, 1.2 percent was responsible for over a
full third of the money donated. So, again, when you try to find
somebody that’s a good nature gift prospect, if you can find
them making big gifts elsewhere, that’s really your home run.
From there, I’m going to show you the new integrated search. I’m
going to give you a quick walk-through of what it does and then
I’m actually going to go out, hop into Bloomerang, and show you
a few quick searches from there.
So, what the integrated search does – it’s going to save you time. It
will go out and search multiple data sets and bring them all
back into one comprehensive, editable profile. So, it’ll search
wealth, it’ll search for philanthropy, and it’ll also search a
free source that a number of you are probably familiar with
So, outside of that, what it’ll show you, it’ll show you a DS score
which is like an overall rating. The overall quality which says,
how accurate is this data going to be and then, two different
ask amounts. Again, these aren’t concrete, you want to use them
to guide you. But when you meet the person, you certainly want
to take the information gained from that one-on-one meeting and
use that to change it.
For example, if somebody tells you that they’re just starting to send
their kids off to college, and let’s say they’re going to go to
a private school, that might change how much you’re about to ask
them for. So, keep that in mind.
Let me explain the different data sets that it looks at. So, it’ll go
off and look across, LexisNexis, and Zillow. That’s going to
find real estate. It’s going to look across Dun & Bradstreet and
[Cortec] which will find you private business information. It’s
going to look for boats, it’s going to look for airplanes, it’s
going to look across SCC insiders, and it’ll also look across
another data set that you’re probably familiar with called,
Guide Star. That will try and identify those people as board
members and either grant-seeking organization or grant-giving
If we go back to what’s predictive of a known philanthropist, if you
find that somebody is in fact a board member at a grant-giving
foundation, it’s not a big surprise that they themselves are
likely to have made a big gift somewhere.
At any point, I’m going to pop out of here and I’m going to go into
Bloomerang really quickly. Over here, what we’ll do is we’ll go
ahead and take a look for a very known wealthy individual in the
Indianapolis area name Mel Simon. So, if I type in, Simon under
constituents right here, is going to go ahead and it’s going to
try and find Mel Simon for me. Once it does, I’ll be able to
bring up his profile. I have to log back in.
So, again this is the dashboard once you log into Bloomerang. And
then from there, when I click this, and I go ahead and type in,
Simon, it’ll bring up any of the accounts that could be him and
here we have Mel Simon in Indianapolis, Indiana. The important
part here is that’s the information that we have on Mel.
When I click perform search, it’s going to send over his name and the
address information that we have and it’ll try and field a
profile on it. So, what happens is it’s opened up this tab and
what you’re looking at here, this is a preview of all of the
different gifts that could be attributed to Mel. In a few
seconds, these tabs will switch over so that I can actually see
the profile once it’s built and right on queue, there it is.
So, when I click it, it’s going to bring back a brief summary. So,
the way it plays is this is the data that was presented to
DonorSearch at the time of query. You get the two different ask
amounts. One’s based on the biggest gift, so, when it took the
information here, it found 1 gift of $20,000 or more that had
attributed to him confidentially. And it was actually unable to
rate the wealth information.
If you look up here, what was put into it was, simply, a city and
state, with that much information, oftentimes we’re not able to
find a whole lot of data and confidently match it. So, I’ll show
you a way around that in a minute. As we keep scrolling down,
you might notice there’s blue question marks everywhere.
The whole point of this is we want it easy for you to use and
understand. So, if you say, “Gee, I don’t understand what this
DS rating is,” and you click it, up comes a brief little
description that will say, “A DS-1 is actually somebody made a
gift of 5,000 or more somewhere else” – the high rating. Whereas
you have somebody that’s a DS-4 or 1-5, we couldn’t find that
they were a good nature gift prospect. It doesn’t mean that they
aren’t, we just couldn’t attribute anything to them. So, they’re
somebody that we think could be a good club level donor for you
annual fund – $100, $500, $1,000 a year gift kind of thing.
So, as we scroll down, here’s what we found for Mel in terms of the
type gifts. They’re the ones with the white backgrounds. So, we
have this gift to the Indianapolis Art Museum in the name of
both Mel and Bren Simon and it was made back in 2006 and the
range was $20,000 or more. Outside of that, if you look, there’s
a few zero dollar gifts. That doesn’t mean he gave a zero dollar
gift, it means when the nonprofit told us about the gift, they
didn’t tell us how big it was.
To show you, another thing you want to do when you’re doing these
searches, is anytime you see a blue link, that will actually
take you off into another search. So, when I click DNFR over
here, it’s, actually, going to download that donor recognition
document. Let’s open it in Adobe. And I was, actually, able to
pull up where his and his wife’s foundation were listed as
having made that gift.
So, if I do control S-I-M-O-N, this case brings up the charitable
foundation in their name. The point of this is to be able to
say, hey, can we find that there’s a wing of building named
after them. Can we find there’s an endowed scholarship?
So, without digressing too much, if you look at this for somebody
that’s the owner of a basketball team and say that, “Gee, this
profile’s kind of lean,” in fact, you’re right. So, one of the
things that we can do to enhance it is we can try and figure
out, all right, well, where is it that Mel lives? Because, if
you notice here, all we had was that he lives in Indianapolis,
So, what we can do is we can, actually, go out and edit his address.
First, let’s see if we can’t find it. So, what I’m going to do
is I’m going to go out to a website you’re all familiar with,
the White Pages. And what I’m going to do is I’m going to type
in Mel Simon and Indianapolis, Indiana and hit search. And in a
second, it’s going to bring back here, we found a few different
ones that have ads. So, once I click it, it’s going to bring
back the address. And make sure you have a better pop-up blocker
than what I’ve been using.
So, it’s 110 Ditch Road in Carmel, Indiana. So, if I go back here
under his address, I drop that in, Indianapolis, Carmel, and
then his ZIP code over here and I hit save, it’ll switch his
information. And then when I want to go back and run another
profile, I’m going to get much better results.
In fact, there’s one other thing that we’re going to want to do too
while this is looking. It, actually, shows you his name is
listed as Melvin. So, let me go back into his profile and switch
it over to Melvin and you, actually, see an even bigger increase
in the results. So, here we go, edit, Melvin, save, and we’ll go
back to the summary and perform another search.
So, while the two searches are running, you see this one found eight
gifts again. Once it, actually, has it, there’s a lot more data
that’s it’s bringing back. So, something as simple as just
making sure you have the full name is going to greatly enhance
the results of your search. This will take a few seconds. So,
while this is thinking, I’m, actually, going to bring back the
presentation over here. And these are some research tips right
here. So, when you’re doing the look up, make sure you have a
few things. Make sure you have the person’s name right. If you
have their name right, you’re going to get a lot more results.
For example, when we looked up Melvin as opposed to Mel, it brought
back the 420 plus possibilities as opposed to the 8 or 10
possibilities. Try and get a spouse name. If you can get a
spouse name, the data is going to be even better. Try and get
the correct address. When you can plug an address in, it’s
always going to do better. Frankly, we’re not going to find a
piece of real estate with at least the ZIP code. Because
otherwise, it’s a whole bunch of guess work. Click the
hyperlinks for more data. I’ll show you that in a little bit.
And perform multiple searches. Don’t get discouraged by doing the
first one. In fact, when you do a look up, perhaps, you find
more information. For example, we found his wife’s name, Bren.
And we could, actually, have used that to plug in more data back
into Bloomerang and get even more data out of the next search,
So, at this point, that should be ready. And here is his profile.
When I click the integrative search profile, look at the
difference that we have, okay? This, over here, is breakdown of
interest. So, he likes to give society benefit, really likes the
arts, behind that, we have the Democrats, and then, independent
schools. Over here, we found seven different pieces of real
estate valued at just under two million dollars. We found over
four hundred million dollars’ worth of insider stocks. There are
13 million dollars’ worth of confirmed gifts. The biggest one
that we’re really confident of is 10 million or more. Then we
found political gifts off the charts – three quarters of a
To see all the data, you can open the individual tabs. So, here’s the
gift from Mel and Bren Simon to the Indianapolis Museum of Art.
In fact, if you want to change it, all these are editable. So,
down here, there’s the $50 million campaign gift to Indiana
University – you see it has a gray background. If I click it,
and over here in the action tab, I hit, mark very high, the
profile’s, actually, going to recalculate. So, suddenly, the
target [inaudible 22:18] based on his biggest gift is, actually,
50 million instead of 10.
And you see that gift is listed up here with a very quality score of
20. If you want to see more data, such as the political giving,
this is a great place to do more research. So, when I look at
this $25,000 gift to the DNC or the Democratic Senatorial
Campaign in ’09 and I click this, it, actually, does a search
for Simon, property owner, Mel Simon. This guy’s a big deal. It
brings up his company’s Wikipedia page. The goal for that really
though is more often times than not, you’re going to pull up
somebody’s LinkedIn which is quite telling.
Down below that, LexisNexis. So, these are the individual site
matches where we found them linked to the address that we point
in. So, they pay taxes from 110 Ditch Road on this address 5301
Lee Avenue in Little Rock, Arkansas. In fact, what we do from
there, is we, actually, run anything we get a full address for.
So, if you want to know more about that property he has in
Little Rock, Arkansas, I click this and it’ll take me to the
overview, over here the map’s going to load, and if I want to
see even more, there’s the birds-eye, and we can, actually, do
one better. This is probably a [rental] property of his. That’s
the street view. You, actually, get a really good look at the
If we want to go back, it’ll take us to the profile and apparently, I
need some much better firewall stuff. We’ll go back this way. If
we take a look here, Dun & Bradstreet, these are private
businesses. So, the way it works is here’s a $5.9 million
company, Simon Enterprises, where he is listed as Chairman of
the Board, that alone isn’t going to cause us to have a really
big ask amount, because we don’t we he’s compensated. But that
gives you a much better idea of who is and he probably makes a
fair bit of money for doing that. You’re going to make some
assumptions with this.
Over here, these are all the different grant giving boards that he
was listed on. Down below that, we have the grant seeking
nonprofit Jewish Federation of greater Indianapolis. He’s been
on their board for quite some time. And then, if we look down
below, these are, actually, his SCC insider holdings where he
has exercised options and on the same date, you can, actually,
see that he sold shares. So, that’s windfall of cash.
Down below that is going to be summary information about him as an
SCC insider. And this will be some biographical stuff, the
Marquis Who’s Who where it lists him as the partial owner of the
One last things right here is Who Knows Who, and we, apparently find
him on board, such as the Jewish Federation of Greater
Indianapolis. We’re, actually, going to show you all the other
board members so, we get those potential connections. If you
know him, he could introduce you to John Ackerman. If you don’t
know him, but you happen to know John or Gerald or Bradley,
perhaps, one of them could provide an introduction.
So, those are some tips about how to do the search, the data that you
can, and here’s one last part of the presentation before I hand
it over to Chris. Data acquisition, on a daily basis,
DonorSearch adds anywhere between 50 to 100,000 charitable gift
records to our databases. So, as it stands now, we pulled in a
little under 50 million gift records from annual reports – donor
honor rolls. You go to the play, you get the play bill and in
that, includes the donors. We pull that donor recognition list
out and that’s how we build our database.
So, it all comes from public data. So, what that means is, you know
the organizations in your backyard better than we do. If you are
clients, you can, actually, say, “Hey, I want you to try and get
the data for this theater and that school and the university in
my backyard,” and we can go out and find it. If it’s out on the
web, we’ll get that data back into the database and when you
look folks up, we’re going to do a much better job for you. In
fact, you can send those lists to firstname.lastname@example.org.
So, without any further hesitation, I going to pass this off to
Chris. I think we have about five minutes for Q&A here.
Steven: Yes. Thanks, Ryan. That was awesome. Chris is here, he’s
standing by, he’s waiting for any questions from folks. So, if
there was something that maybe you want Chris or Ryan to
explain, feel free to send those over on the chat box. We’ve got
about five minutes before the half hour is up. We don’t want to
keep people too long, but happy to answer any questions. So,
Chris, is there anything that you wanted to cover?
Chris: Only that I want all of our Bloomerang customers and in this
case, particularly, our DonorSearch customers, to know who I am,
because of my job. For those of you who haven’t met yet, my
title is, Customer of Success Advocate. And I’m, ultimately,
responsible for making sure that all of our Bloomerang customers
and those using some of our partner tools like DonorSearch, that
they’re happy and successful users.
So, I’ll follow up with everybody after this show, at least, by
email to introduce myself whether you’re a current DonorSearch
user or whether you’re interested in DonorSearch, to see if you
need any help to make your Bloomerang experience better, your
DonorSearch better, then that’s what I want to do. So, you can
expect to hear from me by email. And if you have any feedback
questions for me, then I’ll be happy to engage in a conversation
Ryan: Yes, Chris is here for you guys. He is your man with boots on
the ground at Bloomerang. So, don’t hesitate to reach out to him
if you ever need anything.
Steven: Hey, Ryan, it looks like we’ve got a couple questions here in
the chat room. So, if you don’t mind, I’m just going to toss
them you way. Christina here was wondering if you can
automatically save the donor search information to a
constituent’s profile in Bloomerang. And I think the answer is
no to the automatic part. But may you could share some ways
folks can put that information in Bloomerang.
Ryan: Yes. That’s a great question. As of right now, there is not a
way to automatically send the data back into Bloomerang. What we
are doing, however, is we are working on building the technology
so that there will be another button to click to get that data
back in. And I don’t have a time frame for you, but that is
available outside of Bloomerang. However, you can, certainly,
save these profile. And, actually, Chris, perhaps, you might
know this is are where are the notes fields in Bloomerang so
that you could enter information in about, let’s say, the
biggest gift or what their DS rating is or that kind of a thing?
Where could we find that?
Chris: Well, there should a constituent note field on their profile
page. We put a lot of stuff in here. If there are no notes for
this donor, they wouldn’t show up. But there is a note field
donor. You can put notes on the constituent record itself or you
can put it on notes in their timeline or both depending on what
your protocols are. So, the short answer is, yes. You can create
a summary of their DonorSearch profile and put that on a note so
that anybody who looked at their record could see what you had
Ryan: I think that’s what most of our users do. They get their info
from DonorSearch and then they just create a note on the
constituent profile. I think that’s what most folks do.
Steven: We’ve got another question here from Rita. She’s asking if
there’s a separate Bloomerang entry which is marked as a
relationship to another person. So, if Bren has a relationship
with Melvin, will DonorSearch include the person that is in the
relationship with the person you’re researching? I think the
answer is, no. I think that button just does a search on the one
person. Isn’t that right, Ryan?
Ryan: Yes, that’s correct. So, it won’t bring over any relationships.
The way Who Knows Who works is it looks across any of the known
data sets that match. So, when we identify that he is an
executive at Clay Terrace and Simon Property Group, it will go
out and it will look to see who at those organizations is also
at the board level and list all of them. It doesn’t, actually,
bring in previous relationships. That’s not the search is
Steven: So, Rita, go ahead and do a search on Bren separately, also and
you’ll get the information that way. We’ve got one more question
and I think, then, we’ll call it a day. So, thanks, again, to
everyone who stuck around and who listened for about a half
Ryan, Jolie, here was wondering if DonorSearch offers predictive
modeling services based on outside data and a donors giving to
the client’s organization. Do you guys do any sort of predictive
modeling over at DonorSearch?
Ryan: Jolie, excellent question. We, absolutely, do predictive
modeling. We also do what’s known as a wealth screening wherein,
in both of those instances, they’re, actually, quite similar.
But predictive modeling is the back-end of wealth screening.
What we would do is we would take a look at your database and
identify who are the better donors by looking at your giving
information. And then, independent of whether or not they’re
good donors or not donors at all, we’ll, actually, go out and
try to identify if we can find them making gifts anywhere else.
Can we find that they sit on the boards of grant giving
foundations, or their SCC insiders looking for their real
And then, on the back end, we, actually, come up with a few different
scores. One of them would be an annual fund likelihood where we
reconcile that known eternal giving that we pulled from
Bloomerang. And we reconcile that with the giving elsewhere and
the wealth elsewhere. Wealth is a little is a little less
predictive in that scenario.
And we do another one for the major gift likelihood where we’ll,
actually, go out and let’s say, somebody has ten million dollars
in real estate and gave a five million gift somewhere, that will
cause them to score highly in a predictive model, but nothing
will cause them to score better than if they’ve already been a
good donor to you.
And, actually, when we see those services, we, typically, return
these profiles on at least ten percent if not more of the entire
file. So, a long answer to a short question.
Steven: Great. Well, I know we went a little bit over so, thanks
everyone for hanging around. I think we’ll call it a day there.
I know Chris is always standing by for anymore additional
questions. So, don’t hesitate to reach out to him. You’ll
probably hear from him in the next few days or so. You’ll also
be receiving an email from me tomorrow that will have a
recording link to this presentation as well as all of Ryan’s
slides. So, [inaudible 35:28] little later or had to leave
early, if you left early, you’re not going to hear this. But, I
will be sending those out so you can watch them later.
So, Ryan, I’ll say thanks to you. Thanks for showing us all this good
stuff. And Chris thanks for putting this together and being here
as well. It was great to have you both here.
Chris: Thanks guys. Thanks customers.
Ryan: Steven, Chris, everybody, thank you much. And you have a great
rest of the day.
Steven: All right guys, take it easy. We’ll be in touch soon.