Data Mining and Alumni Association...

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Data Mining and Alumni Association Membership

By Peter B. Wylie and John Sammis

It was a stifling hot day in Chicago in July of 2007. I was at the CASE Summit looking out at an audience

of some 100 folks clearly older than the 30-somethings John Sammis and I regularly work with. After

being introduced, I paced back and forth in front of them, not uttering a word for a good 20 seconds. Then

I stopped abruptly, peered out at them, and said:

“I work with the young professionals who report to you guys. Heads of the annual fund. Directors of

prospect research and alumni relations. And those overworked and underpaid IT folks in advancement

services.

“With them, I feel I have to be pretty nice. After all, they’re in the trenches working away every day

trying to keep the fundraising machinery of your schools running as smoothly as they can. But …”

I paused for a little dramatic effect as frowns and grimaces crawled onto their faces.

“But with you guys I can be more blunt and candid. That’s part of what you get paid for.”

The frowns and grimaces hardened.

“Here’s the deal. If your counterparts in the private sector—and I’m talking mostly people in direct

marketing—ignored the meager information they store on their customers the way most of you ignore the

vast information you store on your alums, ya know what would happen? They’d go out of business in six

months.”

Now I was getting some blank stares and more than a few expressions that said, “Who is this arrogant

geezer who has the gall to talk to us like this?” But I was also getting more than a few nods of approval

and agreement.

If John Sammis and I had both been giving that talk, I would have taken a softer approach. John is a

gentler, more diplomatic fellow than I who has successfully filed some of the crustiness off my

curmudgeon side. However, I know he agrees wholeheartedly with me that most schools do ignore too

much of their alumni data when they go about making appeals, whether for the annual fund or major



giving or planned giving or what have you. In fact, the two of us spend gobs of time empirically

demonstrating this fact with the data mining and predictive modeling work we do.

Fortunately, we think the tide is beginning to turn. More and more schools are starting to pay attention to

their alumni data as a way to save money and generate more revenue on appeals. (See “Does Data Mining

Really Work for Higher Education Fundraising?”).But is that the case with schools with dues-based

alumni associations? Are they using data mining and predictive modeling to thin down the hundreds of

thousands of calls they make each year trying to sign up new members? We don’t think so. We haven’t

done a scientific survey to support our opinion, but we’ve talked to enough schools with dues-based

associations to know that such data driven decision-making is barely on their radar screens.

Let’s say we’re right. Then a reasonable question becomes: Would data mining and predictive modeling

be useful in identifying alums who are more likely than their peers to respond positively to an invitation

to join? We think the evidence offered in this paper supports at least a tentative “yes” in answer to this

question.

Here’s what we’ll cover:

• The data we used to answer the question

• What the data showed us

• Some conclusions we’ve drawn from the data

The Data We Used

Before asking any schools to participate in this study, we needed to make a decision about what data to

request. Our hunch was that the basic variables we always look at as possible predictors (when we build

experimental sample giving models) made good sense:

• Whether or not a home phone was listed for the alum in the database

• Whether or not a business phone was listed

• Whether or not any kind of an e-mail address was listed

• Whatever code for the marital status field was listed for the alum in the database

• The alum’s preferred year of graduation

But building a model requires an outcome variable, sometimes called a “dependent” variable. More

simply put, it’s the variable you’re trying to predict. To build giving models, we almost always ask for the

lifetime hard credit dollars given by each alum. With this study, of course, we weren’t trying to predict

giving; predicting alumni membership was our goal. While alumni membership sounds concrete (an alum

http://www.case.org/Documents/Books/29502/Does_Data_Mining_Really_Work.pdf�





is a dues-paying member or not), we found things to be a bit more complicated when we started asking

questions. For example, alums can be classified as:

• Never having been a dues-paying member

• A lifetime member where one large fee takes care of all the dues

• A lapsed member (not an active member but someone who may have been an active member

several times over the course of a decade or more)

• An active member whose dues are up to date but who may or may not be a member next year

To arrive at a common definition of alumni membership across schools, we decided on this: An alum was

or was not an active member of the school’s alumni association. Not a perfect definition, but good

enough.

Four public higher education universities spread across the country agreed to provide us with the data for

the study. The graduate and undergraduate enrollment of each school ranged from approximately 4,500 to

27,000. Each school sent us a file that included all their solicitable alums (living and having good

addresses) with fields that allowed us construct the variables described above.

What the Data Showed Us

In this project we did a lot of number crunching and generated a bunch of charts, almost all of which we

found interesting. However, if we show you all those charts, we risk losing you, of having you exclaim,

“Guys, just show us the forest, we don’t need to see every maple, oak, and birch!” So we thinned the

herd. Let’s start with Table 1, whose title speaks for itself.

Table 1. Percentage of Current Active Alumni Association Members for Each of Four Schools

SCHOOL A

(TLDO)

SCHOOL B

(ECU)

SCHOOL C

(CSUMB)

SCHOOL D

(CSUSB)

% current active

alumni association

members

9.9% 3.7% 21.0% 3.1%

% nonmembers 90.1% 96.3% 79.0% 96.9%



It’s pretty clear that none of the schools, even School C, has a very high percentage of its alums as active

association members.

Now we’ll take you through the charts for School A, which show the relationship between each of our

candidate predictor variables and alumni membership. (These charts, in the figures that follow, don’t look

a whole lot different from the comparable charts for the other three schools.)

Figure 1. Percentage of Active Members by Home Phone Present for School A

The chart in figure 1 shows that there is a clear relationship between whether or not an alum has a home

phone number listed in the database and alumni membership. Notice that almost 11 percent of those

alums with a home phone listed are active members; only slightly more than 5 percent of alums without a

home phone listed are active members. To put this another way: An alum is more than twice as likely to

be an active member if he or she has a home phone listed than if no home phone number is listed.



Figure 2. Percentage of Active Members by Business Phone Present for School A

Figure 2 shows there is also a clear relationship between whether or not an alum has a business phone

number listed in the database and alumni membership. Of those alums with a business phone listed, 11

percent are active members; less than 5 percent of alums without a business phone listed are active

members. Again, we can say it this way: An alum is more than twice as likely to be an active member if

he or she has a business phone listed than if no business phone number is listed.

Figure 3. Percentage of Active Members by Home Email Present for School A

Figure 3 is kind of interesting. It certainly shows there is a clear relationship between whether or not an

alum has a home e-mail listed in the database and alumni membership. But the relationship appears to be

somewhat stronger than it does for home phone and business phone listed. Almost 23 percent of those



alums with a business phone listed are active members; less than 9 percent of alums without a business

listed are active members. So an alum is two and a half times more likely to be an active member if he or

she has a home e-mail present listed than if no home e-mail is listed.

Figure 4. Percentage of Active Members by Marital Status Is “Married” for School A

By now we’re pretty sure you’ve gotten the hang of these charts, so we’ll be brief here. An alum whose

marital status code is “Married” is two and a half times more likely to be an active member than an alum

who is not listed as married.

Figure 5. Percentage of Active Members by Class Year Quartile for School A

We found the chart in figure 5 intriguing. We expected to see much the same trend as we see with alum

giving. That is, the longer alums have been out of school, the more total money they’ve given. Therefore,



it seemed reasonable to assume that the longer alums had been out of school, the more likely it would be

that they’d be a member of the alumni association. As you can see here, that’s not necessarily the case.

Yes, the oldest 25 percent of alums (those who graduated in 1980 or earlier) have the highest membership

participation rate. But the youngest 25 percent (those who graduated in 1999 or later) have a higher

participation rate than the middle two quartiles (alums who graduated between 1981 and 1998). We found

similarity irregularities in the corresponding charts from the other three schools.

Our next step was to build a very simple score for each school where we expected to see a reasonably

strong relationship between score level and alumni association membership. Here’s the formula we used:

SCORE = Home Phone Present (0/1) + Business Phone Present (0/1) + Home E-mail Present

(0/1) + Marital Status Is “Married” (0/1) + Oldest Grad Class Quartile (0/1) + 1

We used the constant of 1 in the formula to avoid negative zero scores that can be confusing. The formula

may look a bit daunting, but it’s really pretty straightforward. We’ll work through a couple of examples.

Let’s say an alum:

• has a home phone present,

• does not have a business phone present,

• has a home e-mail present,

• has a marital status code other than “married,” and

• is not in the oldest class year quartile.

What’s the alum’s score? We say it’s 3. How did we arrive at that? The alum gets a 1 for having a home

phone present plus a 1 for having a home e-mail present plus a 1 for the constant. That adds up to 3.

Now let’s say an alum:

• has a home phone present,

• has a business phone present,

• has a home e-mail present,

• has a marital status code of “married,” and

• is not in the oldest class year quartile.

What’s the alum’s score? We say it’s 5. The alum gets a 1 for having a home phone present plus a 1 for

having a business phone present plus a 1 for having a home e-mail present plus a 1 for having a marital

status code plus a 1 for the constant. That adds up to 5.



We computed this score for every alumni record that each of the four schools sent us. Figures 6–9 below

show the relationship between score level and active alumni membership for the four schools. After figure

6 we’ll offer a detailed interpretation of what we see. For the remaining figures we’ll be briefer.

Figure 6. Percentage of Active Members by Mini Score for School A

The big picture in this chart, of course, is pretty clear: the higher the score, the greater the percentage of

alums who are active members of the association. If we go back to Table 1, we see that the overall

percentage of alums who are active members of the association for School A is 9.9 percent. Up to score

level 3 in figure 6, the participation rates are well below or just at this overall level. Then what do we see

at score levels 5 and 6? A pronounced jump up in participation rates: almost 25 percent for score level 5

and over 46 percent for score level 6. (We’ll talk about the implications of these trends in the next

section.)



Figure 7. Percentage of Active Members by Mini Score for School B

Compared to figure 6, this chart may not look all that impressive to you. However, remember that the

overall percentage of alums who are active members of the association for School B is only 3.7 percent.

Therefore, we think the participation rates for score levels 4, 5, and 6 look pretty good.

Figure 8. Percentage of Active Members by Mini Score for School C

What can we say? We’re impressed with this chart.



Figure 9. Percentage of Active Members by Mini Score for School D

Remember what we said about the chart in figure 7? If you don’t want to go back and look, we’ll say it

again here:

Compared to figure 6, this chart may not look all that impressive to you. However, remember that

the overall percentage of alums who are active members of the association for School B is only

3.7 percent. Therefore we think the participation rates for score levels 4, 5, and 6 look pretty

good.

Except for substituting “3.1 percent” for “3.7 percent,” we’d say exactly the same thing about chart 9.

Some Conclusions We’ve Drawn from the Data

We’ve drawn three major conclusions from this study:

• There is compelling evidence that some information in alumni databases is strongly related to

active alumni association membership.

• When these pieces of information are combined together into even a very crude score, this

relationship between information and active membership appears even more compelling.

• We think schools with dues paying alumni associations should be using this information to save

money on membership appeals and to substantially up their membership.



There is compelling evidence that some information in alumni databases is strongly related to

active alumni association membership.

For this project we looked at only a very few fields (we call them variables). Again, so you don’t have to

go back and hunt for them, they were:

• Whether or not a home phone was listed for the alum in the database

• Whether or not a business phone was listed

• Whether or not any kind of an e-mail address was listed

• Whatever code for the marital status field was listed for the alum in the database

• The alum’s preferred year of graduation

With the exception of preferred year of graduation, each of these variables showed a strong relationship

with active alumni membership. But what about all the other information that is stored in alumni

databases—things like number of reunions attended, state of residence, Greek affiliation as an

undergraduate, number of degrees received from the school, and on and on? We have to believe that some

of these variables will show an even stronger relationship with active alumni membership than the ones

we’ve displayed here.

The only way to find out if we’re right is to start foraging through these databases to hunt down these

predictive gems.

When these pieces of information are combined into even a very crude score, this relationship

between information and active membership appears even more compelling.

If you doubt our contention, take another gander at Charts 6–9. By the way, if we weren’t so concerned

about throwing too much information at you, we’d show you something else. We’d show you some

scores we developed using multiple regression (on these same data) to produce even more impressive

scores than the ones we’ve laid out here. (If anybody would like to see those scores, give us a shout and

we’ll e-mail them to you.)

We think schools with dues-based alumni associations should be using this information to save

money on membership appeals and to substantially increase their membership.



Here’s another look at figure 6:

Figure 6. Percentage of Active Members by Mini Score for School A

For each score level, there are more non-active members than there are active members. Let’s say you’re

in charge of the call center where students are making calls into the tens of thousands (maybe more) to

these nonmembers. Where do you think you’re going to get the most bang for your budget buck: calling

non-active members with scores of 1 or 2, or those with scores of 5 and 6?

Yeah, it really is that simple. Of course, when you get into all the logistical details, it doesn’t seem so

simple—and we appreciate that even though we never managed a call center. But conceptually, it is that

simple. You want to be calling non-active members who your internal data say “They look like active

members even though we haven’t yet converted them.”

As always, we appreciate feedback, even if it’s negative and critical and puts us back on our defensive

heels. So let us know what you think of what we’ve said here.

About the Authors

Peter Wylie is a national recognized advancement consultant who has worked with major colleges and

universities and taught many advancement professions how to mine donor data to find simple predictors

of giving. You can reach Peter at [email protected].

John Sammis is a predictive modeling expert who has helped all types of organizations around the world

analyze and understand their data, including the development organizations of many academic

institutions. You can reach John at [email protected].

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