CoolData blog

21 September 2010

A pauper’s guide to electronic screening

Filed under: Alumni, Major Giving, Peter Wylie — kevinmacdonell @ 5:20 am

Guest post by Peter B. Wylie

(This is a reprint of a paper previously published on the CASE website. Click here to download as a .pdf file: PAUPER_111306. — KMD)

You’re right. I cheated a little. I chose this title to grab your attention. (Actually I “borrowed” it from an old travel book about Paris.) However … even though you and the school you work for are not impoverished, your budget for electronic wealth screenings may not be as big as you’d like. If that’s the case, I want to offer some thoughts on how you can do a pretty good job of identifying major giving prospects – ones that may not be on your development officers’ radar screens – without doing an electronic screening.

I wrote this piece to build on another article I did several years ago (“Where the Alumni Money Is”). In that one I showed that the lion’s share of any school’s alumni donations come from the oldest 25% of alums who are not listed as ‘single’ or ‘missing’ in the marital status field. The data I used were from a group of eight schools that covered the gamut between public and private, large and small, and well known and less well known.

In this paper I used a similar but different group of four higher education institutions that allowed me to go a little farther than I did with the first article. Here’s a basic outline of what I’ll cover:

  • The main question I was trying to answer
  • The bet I was making
  • How I did the data analysis for the paper
  • What conclusions I think we can draw from the data analysis
  • Some specific suggestions for “action steps” you, your IT folks, and your development officers can take

The main question I was trying to answer

In the three years since I wrote the last paper I’ve learned a few things about major giving in general and prospect research in particular (things I had only an inkling of back then). One of the biggest things I’ve learned is this: When it comes to major giving in higher education, we rely too much on the data we gather on prospects from outside sources, and we don’t rely enough on the data we have in hand on these prospects.

Don’t get me wrong. I think getting data about prospects in your alumni database from outside sources is fine. The more you know about these folks (not just their wealth), the better. On the other hand, I’m bothered that most schools ignore the huge amount of internal data they already have on their alums.

At the risk of being a little too frank, here’s the kind of process I see go on far too often:

  1. In anticipation of a major campaign, a school will spend a considerable sum on an electronic screening of several thousand alums by one of the excellent firms that do that kind of work.
  2. The firm will return a huge amount of data to the school, most of which is designed to assess an alum’s capacity to make some type of major gift to the school. (Sadly, there is a good chance this data will not be used as part of the campaign. Why this is so often the case is a bit of a puzzle to me. But it happens far more frequently than any of us would like.)
  3. In the meantime the school will not delve into the huge store of data it keeps on its alums. Data that can point an accurate finger at which alums are most likely to give and which are least likely to give. Class reunion attendance is a good example. Unfortunately, most schools don’t store this information in their alumni databases. Those that do store it don’t use it to help them identify new major giving prospects — even though reunion attendance may well be the best single predictor of giving in higher education advancement. (At least I’ve never found a better one.)

Okay. Let’s say the scenario I’ve offered here is not atypical: A school isn’t going to do an electronic screening, or it’s going to ignore the screening data even if it does one. Beyond that, the school is not going to do any serious mining of its wealth of alum data to find good predictors of giving. Can we then come up with a reasonably quick and easy way for the school to still identify good prospects for major giving?

The bet I was making

I think the answer is an unequivocal yes. Why? Because for the last several years I have become more and more convinced that if you know an alum’s age along with a few other pieces of information about his/her marital status and contact information, you can do a pretty good job of determining whether that alum is a major giving prospect.

Does that sound farfetched? All I ask is that you withhold judgment until the end of the paper.

How I did the data analysis for the paper

For each of the four schools I mentioned above, I took these steps:

One. I gathered a representative sample (no less than 5,000) of alumni records for these fields:

  • Home phone listed (yes/no)
  • Business phone listed (yes/no)
  • E-mail address listed (yes/no)
  • Listed as something other than “single” or “missing” for the marital status (yes/no)
  • Preferred year of graduation

Two. I “guesstimated” the age of each alum by subtracting their preferred year of graduation from the present year and then adding 22 – the age that a lot of students receive their undergraduate degrees.

Three. I computed the percentage of total lifetime dollars by alums in each five year age interval.

Four. For every alum I created a simple score with a range from 1-5 based on whether or not that alum had a home phone listed, a business phone listed, an e-mail address listed, and whether or not the alum was listed as something other than “single” or “missing” for the marital status field. (For a further explanation of how this kind of score works, see the paper titled “A Simple Score.”)

Five. For the five year age interval with the highest percentage of lifetime giving, I computed the mean (average) lifetime giving for every alum by simple score level.

Let’s see what these steps yielded for School A. Take a look at Chart 1. (Click on chart for full-size view.)

Just to be clear, here’s how I constructed this chart. I used a sample of over 20,000 records from this school. For all of these records I computed the total amount these alums had contributed to the school at the time the sample was gathered. This value exceeded 100 million dollars. I then computed the total amount that alums in each five year age interval had contributed and converted these amounts to percentages of the total amount. For example, 34% of this lifetime amount of more than 100 million dollars had been contributed by alums aged 66-70.

A couple of things stand out for me about this chart:

  • Well over 80% of the lifetime giving comes in after alums reach the age of 60.
  • The under 50 crowd has contributed less than 10% of the total lifetime giving.

What do these facts say about where the focus ought to be on an upcoming capital campaign? We’ll talk more about issues like this a little later on.

Now let’s look at Chart 2 which shows the mean (average) lifetime giving by simple score level for this group of alums aged 66-70. (Click on chart for full-size view.)

Here’s the algorithm I used for this score: ‘HOME PHONE LISTED’ + ‘BUSINESS PHONE LISTED’ + ‘EMAIL LISTED’ – ‘SINGLE’ + 2. It’s pretty simple:

  • If an alum had a home phone listed, they got a 1 otherwise a 0.
  • If they had a business phone listed, they got a 1 otherwise a 0.
  • If they had any kind of e-mail address listed, they got a 1 otherwise a 0.
  • If they were listed as “single” in the marital status field, they got a minus 1 otherwise a 0 (people who are listed as single in a martial status field invariably give less than any other marital code).
  • Then, for each record, I added these numbers together and added a 2 to each number so there wouldn’t be any zero or negative scores. (Zero and negative scores tend to confuse people.)

So what we’ve got here is a very narrow age group of alums (66-70) who’ve given a huge amount of money to the school. Beyond that we’ve got a very simple score that sharply differentiates theses alums with respect to how much they’ve given.

What conclusions can we draw from the data analysis?

Let’s assume that the simple score is a rough measure of likelihood of giving to the school. Let’s call it affinity. And let’s say we isolate the alums in this group that have a score of 5 on this affinity scale. As it turns out there are just over 450 alums in this group that is between 66 and 70 years old and have a score of 5. Okay, now let’s look at the top ten lifetime givers in this group in descending order of giving:

$ 5,364,619

$ 5,038,707

$ 4,072,701

$ 1,644,404

$ 1,466,562

$ 979,197

$ 520,501

$ 496,978

$ 479,202

$ 256,006

What do you think? I think all these people are under stewardship – or at least they should be. You certainly don’t need to do a wealth screening on them. Right?

All right, now let’s look at the next ten alums in this group in descending order of lifetime giving.

$ 157,439

$ 110,621

$ 107,475

$ 88,834

$ 67,281

$ 60,017

$ 49,728

$ 41,825

$ 38,154

$ 35,728

Now it starts to get kind of interesting, doesn’t it? Finally, let’s look at the next ten alums in this group in descending order of lifetime giving.

$ 35,287

$ 35,202

$ 33,186

$ 32,190

$ 28,493

$ 28,366

$ 27,225

$ 25,401

$ 21,826

$ 21,359

You’ve probably anticipated me, but here’s where I’m going with this. As we get further and further down this list of 66-70 year olds who show high affinity to the school, we run out of people who are already on our radar screens, who are already assigned and being actively appealed to for a major gift. But I think a lot of these alums should be on our radar screens. So what if we don’t have wealth screening data on them? They’ve given something more than a pittance to the school already. They are by far in the highest giving age bracket of alums. And they have an affinity score that says they like to give to the school. Somebody should be reaching out to these folks. Why? Because some of them are poised to make a major gift. It’s that simple!

And how much did it cost us to identify them? A few hours of time from a talented IT person? I think it’s worth it.

At this point, I’ve either got you seriously interested in my line of reasoning or I’ve lost you. If I haven’t lost you, let me show you the equivalent of charts 2 and 3 for the remaining three schools whose data I looked at to prepare this article. Then we can step back and get some perspective on all this. (Click on each chart for full-size view.)

That’s a lot of charts and data to look at. But let’s go back to the basic question I was trying to answer. If a school doesn’t do an electronic screening, can we then come up with a reasonably quick and easy way for the school to still identify good prospects for major giving?

Again, I think the answer is yes. Why? Let’s summarize the logic I’m invoking from the data I’ve presented from these four schools:

  • Most of the lifetime giving in a school doesn’t start rolling in until alums reach at least the age of 55. So if you’re spending a lot of time looking for good prospects under that age, I don’t think you’re making great use of your time.
  • Within this older crowd of alums, you don’t need a lot of information beyond home phone listed, business phone listed, etc. to create a very simple score that sharply separates the big givers from the small or non-givers.
  • You can use the top end of that simple score to identify alums who aren’t yet on your development officers’ radar screens but who should be.

Now I’ve already said that the last thing I’m doing here is recommending that your school not do an electronic screening. Not at all. But I am saying that if you can’t do a screening, you have an option here that can help.

Some specific “action steps”

And finally, what about all you prospect researchers? After all, I wrote this thing for you guys. Here’s a suggestion on some steps you can take:

  1. Get your IT folks to do the same kind of analysis I’ve done here for your own school. Sure, you’ll get some push back from them. They’re overworked. But you can get them to do it for you.
  2. Look up those alums in a higher age category who have a high simple score but who aren’t yet assigned to a gift officer. Some of them will look very promising. I guarantee it.
  3. Take a few of the promising ones to a gift officer who thinks this data driven decision making stuff is sort of cool and ask him/her to contact these alums.

What do you have to lose?

A PAUPER’S GUIDE TO ELECTRONIC SCREENING
By Peter B. Wylie
You’re right. I cheated a little. I chose this title to grab your attention. (Actually I
“borrowed” it from an old travel book about Paris.) However … even though you and the
school you work for are not impoverished, your budget for electronic wealth screenings
may not be as big as you’d like. If that’s the case, I want to offer some thoughts on how
you can do a pretty good job of identifying major giving prospects – ones that may not be
on your development officers’ radar screens – without doing an electronic screening.
I wrote this piece to build on another article I did several years ago (“Where the Alumni
Money Is”). In that one I showed that the lion’s share of any school’s alumni donations
come from the oldest 25% of alums who are not listed as ‘single” or “missing’ in the
marital status field. The data I used were from a group of eight schools that covered the
gamut between public and private, large and small, and well known and less well known.
In this paper I used a similar but different group of four higher education institutions that
allowed me to go a little farther than I did with the first article. Here’s a basic outline of
what I’ll cover:
• The main question I was trying to answer
• The bet I was making
• How I did the data analysis for the paper
• What conclusions I think we can draw from the data analysis
• Some specific suggestions for “action steps” you, your IT folks, and your
development officers can take
The main question I was trying to answer
In the three years since I wrote the last paper I’ve learned a few things about major giving
in general and prospect research in particular (things I had only an inkling of back then).
One of the biggest things I’ve learned is this: When it comes to major giving in higher
education, we rely too much on the data we gather on prospects from outside sources, and
we don’t rely enough on the data we have in hand on these prospects.
Don’t get me wrong. I think getting data about prospects in your alumni database from
outside sources is fine. The more you know about these folks (not just their wealth), the
better. On the other hand, I’m bothered that most schools ignore the huge amount of
internal data they already have on their alums.
At the risk of being a little too frank, here’s the kind of process I see go on far too often:
1. In anticipation of a major campaign, a school will spend a considerable sum on an
electronic screening of several thousand alums by one of the excellent firms that
do that kind of work.
2. The firm will return a huge amount of data to the school, most of which is
designed to assess an alum’s capacity to make some type of major gift to the
school. (Sadly, there is a good chance this data will not be used as part of the
campaign. Why this is so often the case is a bit of a puzzle to me. But it happens
far more frequently than any of us would like.)
3. In the meantime the school will not delve into the huge store of data it keeps on
its alums. Data that can point an accurate finger at which alums are most likely to
give and which are least likely to give. Class reunion attendance is a good
example. Unfortunately, most schools don’t store this information in their alumni
databases. Those that do store it don’t use it to help them identify new major
giving prospects — even though reunion attendance may well be the best single
predictor of giving in higher education advancement. (At least I’ve never found a
better one.)
Okay. Let’s say the scenario I’ve offered here is not atypical: A school isn’t going to do
an electronic screening, or it’s going to ignore the screening data even if it does one.
Beyond that, the school is not going to do any serious mining of its wealth of alum data
to find good predictors of giving. Can we then come up with a reasonably quick and easy
way for the school to still identify good prospects for major giving?
The bet I was making
I think the answer is an unequivocal yes. Why? Because for the last several years I have
become more and more convinced that if you know an alum’s age along with a few other
pieces of information about his/her marital status and contact information, you can do a
pretty good job of determining whether that alum is a major giving prospect?
Does that sound farfetched? All I ask is that you withhold judgment until the end of the
paper.
How I did the data analysis for the paper
For each of the four schools I mentioned above, I took these steps:
One. I gathered a representative sample (no less than 5,000) of alumni records for these
fields:
Home phone listed (yes/no)
Business phone listed (yes/no)
E-mail address listed (yes/no)
Listed as something other than “single” or “missing” for the marital status (yes/no)
Preferred year of graduation
Two. I “guesstimated” the age of each alum by subtracting their preferred year of
graduation from the present year and then adding 22 – the age that a lot of students
receive their undergraduate degrees.
Three. I computed the percentage of total lifetime dollars by alums in each five year age
interval.
Four. For every alum I created a simple score with a range from 1-5 based on whether or
not that alum had a home phone listed, a business phone listed, an e-mail address listed,
and whether or not the alum was listed as something other than “single” or “missing” for
the marital status field. (For a further explanation of how this kind of score works, see the
paper titled “A Simple Score.”)
Five. For the five year age interval with the highest percentage of lifetime giving, I
computed the mean (average) lifetime giving for every alum by simple score level.
Let’s see what these steps yielded for School A. Take a look at Chart 1.
0.0% 0.1% 0.3% 0.8% 1.0%
3.2% 3.4%
6.1%
12.5%
34.0%
20.5%
18.1%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
25 AND
UNDER
26-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 66-70 71-75 76 AND
OLDER
CHART 1: SCHOOL A. PERCENTAGE OF LIFETIME GIVING BY FIVE YEAR
AGE INTERVALS
Just to be clear, here’s how I constructed this chart. I used a sample of over 20,000
records from this school. For all of these records I computed the total amount these alums
had contributed to the school at the time the sample was gathered. This value exceeded
100 million dollars. I then computed the total amount that alums in each five year age
interval had contributed and converted these amounts to percentages of the total amount.
For example, 34% of this lifetime amount of more than 100 million dollars had been
contributed by alums aged 66-70.
A couple of things stand out for me about this chart:
• Well over 80% of the lifetime giving comes in after alums reach the age of 60.
• The under 50 crowd has contributed less than 10% of the total lifetime giving.
What do these facts say about where the focus ought to be on an upcoming capital
campaign? We’ll talk more about issues like this a little later on.
Now let’s look at Chart 2 which shows the mean (average) lifetime giving by simple
score level for this group of alums aged 66-70.
$176
$1,694
$3,500
$13,323
$55,732
$-
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
1 2 3 4 5
SCORE
CHART 2: SCHOOL A. MEAN (AVERAGE) LIFETIME GIVING FOR ALUMS 66-70
BY SIMPLE SCORE LEVEL
Here’s the algorithm I used for this score: ‘HOME PHONE LISTED’ + ‘BUSINESS
PHONE LISTED’ + ‘EMAIL LISTED’ – ‘SINGLE’ + 2. It’s pretty simple:
• If an alum had a home phone listed, they got a 1 otherwise a 0.
• If they had a business phone listed, they got a 1 otherwise a 0.
• If they had any kind of e-mail address listed, they got a 1 otherwise a 0.
• If they were listed as “single” in the marital status field, they got a minus 1
otherwise a 0 (people who are listed as single in a martial status field invariably
give less than any other marital code).
• Then, for each record, I added these numbers together and added a 2 to each
number so there wouldn’t be any zero or negative scores. (Zero and negative
scores tend to confuse people.)
So what we’ve got here is a very narrow age group of alums (66-70) who’ve given a huge
amount of money to the school. Beyond that we’ve got a very simple score that sharply
differentiates theses alums with respect to how much they’ve given.
What conclusions can we draw from the data analysis?
Let’s assume that the simple score is a rough measure of likelihood of giving to the
school. Let’s call it affinity. And let’s say we isolate the alums in this group that have a
score of 5 on this affinity scale. As it turns out there are just over 450 alums in this group
that is between 66 and 70 years old and have a score of 5. Okay, now let’s look at the top
ten lifetime givers in this group in descending order of giving:
$ 5,364,619
$ 5,038,707
$ 4,072,701
$ 1,644,404
$ 1,466,562
$ 979,197
$ 520,501
$ 496,978
$ 479,202
$ 256,006
What do you think? I think all these people are under stewardship – or at least they
should be. You certainly don’t need to do a wealth screening on them. Right?
All right, now let’s look at the next ten alums in this group in descending order of
lifetime giving?
$ 157,439
$ 110,621
$ 107,475
$ 88,834
$ 67,281
$ 60,017
$ 49,728
$ 41,825
$ 38,154
$ 35,728
Now it starts to get kind of interesting, doesn’t it? Finally, let’s look at the next ten alums
in this group in descending order of lifetime giving.
$ 35,287
$ 35,202
$ 33,186
$ 32,190
$ 28,493
$ 28,366
$ 27,225
$ 25,401
$ 21,826
$ 21,359
You’ve probably anticipated me, but here’s where I’m going with this. As we get further
and further down this list of 66-70 year olds who show high affinity to the school, we run
out of people who are already on our radar screens, who are already assigned and being
actively appealed to for a major gift. But I think a lot of these alums should be on our
radar screens. So what if we don’t have wealth screening data on them? They’ve given
something more than a pittance to the school already. They are by far in the highest
giving age bracket of alums. And they have an affinity score that says they like to give to
the school. Somebody should be reaching out to these folks. Why? Because some of them
are poised to make a major gift. It’s that simple!
And how much did it cost us to identify them? A few hours of time from a talented IT
person? I think it’s worth it.
At this point, I’ve either got you seriously interested in my line of reasoning or I’ve lost
you. If I haven’t lost you, let me show you the equivalent of charts 2 and 3 for the
remaining three schools whose data I looked at to prepare this article. Then we can step
back and get some perspective on all this.
0.1%
0.7%
1.4%
2.4%
4.4% 4.6%
6.6% 6.8%
8.7%
10.6%
25.0%
28.8%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
25 AND
UNDER
26-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 66-70 71-75 76 AND
OLDER
CHART 3: SCHOOL B. PERCENTAGE OF LIFETIME GIVING BY FIVE YEAR
AGE INTERVALS
$67 $662
$8,794
$15,880
$59,809
$-
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
1 2 3 4 5
SCORE
CHART 4: SCHOOL B. MEAN (AVERAGE) LIFETIME GIVING FOR ALUMS 76
AND OLDER BY SIMPLE SCORE LEVEL
0.0% 0.2%
0.9%
1.3%
2.9%
7.1% 6.7%
12.0%
9.1%
18.3%
23.0%
18.6%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
25 AND
UNDER
26-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 66-70 71-75 76 AND
OLDER
CHART 5: SCHOOL C. PERCENTAGE OF LIFETIME GIVING BY FIVE YEAR AGE INTERVALS
$- $4,554
$25,016
$132,414
$63,280
$-
$20,000
$40,000
$60,000
$80,000
$100,000
$120,000
$140,000
1 2 3 4 5
SCORE
CHART 6: SCHOOL C. MEAN (AVERAGE) LIFETIME GIVING FOR ALUMS
71-75 BY SIMPLE SCORE LEVEL
0.2% 1.1% 1.4%
3.4% 3.8%
7.0% 6.6% 6.0%
2.7%
12.9%
43.4%
11.5%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
25 AND
UNDER
26-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 66-70 71-75 76 AND
OLDER
CHART 7: SCHOOL D. PERCENTAGE OF LIFETIME GIVING BY FIVE YEAR AGE
INTERVALS
$- $609 $557
$77,553
$53,877
$-
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
$70,000
$80,000
1 2 3 4 5
SCORE
CHART 8: SCHOOL D. MEAN (AVERAGE) LIFETIME GIVING FOR ALUMS
71-75 BY SIMPLE SCORE LEVEL
That’s a lot of charts and data to look at. But let’s go back to the basic question I was
trying to answer. If a school doesn’t do an electronic screening, can we then come up
with a reasonably quick and easy way for the school to still identify good prospects for
major giving?
Again, I think the answer is yes. Why? Let’s summarize the logic I’m invoking from the
data I’ve presented from these four schools:
• Most of the lifetime giving in a school doesn’t start rolling in until alums reach at
least the age of 55. So if you’re spending a lot of time looking for good prospects
under that age, I don’t think you’re making great use of your time.
• Within this older crowd of alums, you don’t need a lot of information beyond
home phone listed, business phone listed, etc. to create a very simple score that
sharply separates the big givers from the small or non-givers.
• You can use the top end of that simple score to identify alums who aren’t yet on
your development officers’ radar screens but who should be.
Now I’ve already said that the last thing I’m doing here is recommending that your
school not do an electronic screening. Not at all. But I am saying that if you can’t do a
screening, you have an option here that can help.
Some specific “action steps”
And finally, what about all you prospect researchers? After all, I wrote this thing for you
guys. Here’s a suggestion on some steps you can take:
1. Get your IT folks to do the same kind of analysis I’ve done here for your own
school. Sure, you’ll get some push back from them. They’re overworked. But you
can get them to do it for you.
2. Look up those alums in a higher age category who have a high simple score but
who aren’t yet assigned to a gift officer. Some of them will look very promising. I
guarantee it.
3. Take a few of the promising ones to a gift officer who thinks this data driven
decision making stuff is sort of cool and ask him/her to contact these alums.
What do you have to lose?
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3 Comments »

  1. Randy,

    Very interesting article on screening…

    Scott

    Comment by Randy — 2 October 2010 @ 11:29 am

  2. I’m wondering if this same algorithm can be used for non-higher-ed organizations…

    Comment by Dori Cavala — 18 October 2010 @ 2:35 pm

  3. Dori – Peter and I demonstrated exactly this exercise for a large performing-arts non-profit at a conference in Toronto last week. So yes, it can be done! Some of the predictors are different, but the technique is the very same. – Kevin

    Comment by kevinmacdonell — 18 October 2010 @ 3:15 pm


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