CoolData blog

13 June 2012

Finding predictors of future major givers

Guest post by Peter B. Wylie and John Sammis

(Download a print-friendly .pdf version here: Finding Predictors of Future Major Givers)

For years a bunch of  committed data miners (we’re just a couple of them) have been pushing, cajoling, exhorting, and nudging  folks in higher education advancement to do one thing: Look as hard at their internal predictors of major giving as they look at outside predictors (like social media and wealth screenings). It seems all that drum beating has been having an effect. If you want some evidence of that, take a gander at the preconference presentations that will be given this August in Minneapolis at the APRA 25th Annual International Conference. It’s an impressive list.

So…what if you count yourself among the converted? That is, you’re convinced that looking at internal predictors of major giving is a good idea. How do you do that? How do you do that, especially if you’re not a member of that small group of folks who:

  • have a solid knowledge of applied statistics as used in both the behavioral sciences and “business intelligence?”
  • know a good bit about topics like multiple regression, logistic regression, factor analysis, and cluster analysis?
  • are practiced in the use of at least one stats application whether it’s SPSS, SAS, Data Desk, or R or some other open source option?
  • are actively doing data mining and predictive modeling on a weekly, if not daily basis?

The answer, of course, is that there is no single, right and easy way to look for predictors of major giving. What you’ll see in the rest of this piece is just one way we’ve come up with – one we hope you’ll find helpful.

Specifically, we’ll be covering two topics:

  • The fact that the big giving in most schools does not begin until people are well into their fifties, if not their sixties
  • A method for looking at variables in an alumni database that may point to younger alums who will eventually become very generous senior alums

 

Where The Big Money Starts

Here we’ll take you through the steps we followed to show that the big giving in most schools does not begin until alums are well into their middle years.

Step 1: The Schools We Used

We chose six very different schools (public and private, large and small) spread out across North America. For five of the schools, we had the entire alumni database to work with. With one school we had a random sample of more than 20,000 records.

Step 2: Assigning An Age to Every Alumni Record

Using Preferred class year, we computed an estimate of each alum’s age with this formula:

Age = 2012 – preferred class year + 22

Given the fact that many students graduate after the age of 22, it’s safe to assume that the ages we assigned to these alums are  slight to moderate underestimates of their true ages.

Step 3: Computing The Percentage of  The Sum of Lifetime Dollars Contributed by Each Alum

For all the records in each database, we computed each alum’s percentage of the sum of lifetime dollars contributed by all solicitable alums (those who are living and reachable). To do this computation, we divided each alum’s lifetime giving by the sum of lifetime giving for the entire database and converted that value to a percentage.

For example, let’s assume that the sum of lifetime giving for the solicitable alums in a hypothetical database is $50 million. Table 1 shows both the lifetime giving and the percent of the sum of lifetime giving for three different records:

Table 1: Lifetime Giving and Pecentage of The Sum of All Lifetime Giving for Three Hypothetical Alums

Just to be clear:

  • Record A has given no money at all to the school. That alum’s percentage is obviously 0.
  • Record B has given $39,500 to the school. That alum’s percentage is 0.079% of $50 million.
  • Record C has given $140,500 to the school. That alum’s percentage is 0.280% of $50 million.

Step 4: Computing The Percentage and The Cumulative Percentage of The Sum of Lifetime Dollars Contributed by Each of 15 Equal-Sized Age Groups of  Alums

For each of the six schools, we divided all alums into 15 roughly equal-sized age goups. These groups ranged from alums in their early twenties to those who had achieved or passed the century mark.

To make this all clear we have used School A (whose alums have given a sum of $164,215,000) as an example. Table 2 shows the:

  • total amount of lifetime dollars contributed by each of these age groups in School A
  • the percentage of the $164,215,000 contributed by these groups
  • the cumulative percentage of the $164,215,000 contributed by alums up to and including a certain age group

Table 2: Lifetime Giving, Percent of Sum of Lifetime Giving, and Cumulative Percent of Sum of Lifetime Giving for Fifteen Equal-Size Age Groups In School A

Here are some things that stand out for us in this table:

  • All alums 36 and younger have contributed less than 1% of the sum of lifetime givng.
  • For all alums under age 50 the cumulative amount given is just over 7% of the sum of lifetime givng.
  • For all alums under age 62 the cumulative amount given is less than 30% of the sum of lifetime givng.
  • For all alums under age 69 the cumulative amount given is slightly more than 40% of the sum of lifetime givng.
  • Well over 55% of the sum of lifetime givng has come in from alums who are 69 and older.

The big news in this table, of course, is that the lion’s share of  money in School A has come in from alums who have long since passed the age of eligibility for collecting Social Security. Not a scintilla of doubt about that.

But what about all the schools we’ve looked at? Do they show a similar pattern of giving by age? To help you decide, we’ve constructed Figues 1 – 6 that provide the same information as you see in the rightmost column of Table 2: The cumulative percentage of all lifetime giving contributed by alums up to and including a certain age group.

Since Figure 1 below captures the same information you see in the rightmost column of Table 2, you don’t need to spend a lot of time looking at it.

But we’d recommend taking your time looking at Figures 2-6. Once you’ve done that, we’ll tell you what we see.

These are the details of what we see for Schools B-F:

  • School B: Alums 48 and younger have contributed less than 5% of the sum of lifetime giving. Alums 70 and older have contributed almost 40% of the sum.
  • School C: Alums 52 and younger have contributed less than 5% of the sum. Alums 70 and older have contributed more than 40% of the sum.
  • School D: Alums 55 and younger have contributed less than 30% of the sum. Alums 70 and older have contributed almost 45% of the sum.
  • School E: Alums 50 and younger have contributed less than 30% of the sum. Alums 61 and older have contributed more than 40% of the sum.
  • School F: Alums 50 and younger have contributed less than 20% of the sum. Alums 68 and older have contributed well over 50% of the sum.

The big picture? It’s the same phenomenon we saw with School A: The big money has come in from alums who are in the “third third” of their lives.

One Simple Way To Find Possible Predictors of The Big Givers on The Horizon

Up to this point we’ve either made our case or not that the big bucks don’t start coming in from alumni until they reach their late fifties or sixties. Great, but how do we go about identifying those alums in their forties and early fifties who are likely to turn into those very generous older alums?

It’s a tough question. In our opinion, the most rigorous scientific way to answer the question is to set up a longitudinal study that would involve:

  1. Identifying all the alums in a number of different schools who are in the forties and early fifties category.
  2. Collecting all kinds of data on these folks including giving history, wealth screening and other gift capacity information, biographic information, as well as a host of fields that are included in the databases of these schools like contact information, undergraduate activities, and on and on the list would go.
  3. Waiting about ten or fifteen years until these “youngsters” become “oldsters” and see which of all that data collected on them ends up predicting the big givers from everybody else.

Well, you’re probably saying something like, “Gentlemen, surely you jest. Who the heck is gonna wait ten or fifteen years to get the answers? Answers that may be woefully outdated given how fast society has been changing in the last twenty-five years?”

Yes, of course. So what’s a reasonable alternative? The idea we’ve come up with goes something like this: If we can find variables that differentiate current, very generous older alums from less generous alums, then we can use those same variables to find younger alums who “look like” the older generous alums in terms of those variables.

To bring this idea alive, we chose one school of the six that has particularly good data on their alums. Then we took these steps:

We divided alums 57 and older into ten roughly equal size groups (deciles) by their amount of lifetime giving. Figure 7 shows the median lifetime giving for these deciles.

Table 3 gives a bit more detailed information about the giving levels of these deciles, especially the total amount of lifetime giving.

Table 3: Sum of Lifetime Dollars and Median Lifetime Dollars for 10 Equal Sized Groups of Alums 57 and Older

We picked these eight variables to compare across the deciles:

  • number of alums who have a business phone listed in the database
  • number of alums who participated in varsity athletics
  • number of alums who were a member of a greek organization as an undergraduate
  • number of alums who have an email address listed in the database
  • number of logins
  • number of reunions attended
  • number of  years of volunteering
  • number of events attended

Before we take you through Figures 8-14, we should say that the method we’ve chosen to compare the deciles on these variables is not the way a stats professor nor an experinced data miner/modeler would recommend you do the comparisons. That’s okay. We were aiming for clarity here.

Let’s go through the figures. We’ve laid them out in order from “not so hot” variables to “pretty darn good” variables.

It’s pretty obvious when you look at Fig. 8 that bigger givers, for the most part, are no more likely to have a business phone listed in the database than are poorer givers.

Varsity athletics? Yes, there’s a little bit of a trend here, but it’s not a very consistent trend. We’re not impressed.

This trend is somewhat encouraging. Good givers are more likely to have been a member of a Greek organization as an undergraduate than not so good givers. But we would not rate this one as a real good predictor.

Now we’re getting somewhere. Better givers are clearly more likely to have an e-mail address listed in the database than are poorer givers.

This one gets our attention. We’re particularly impressed with the difference in the number of logins for Decile 10 (really big givers) versus the number of logins for the lowest two deciles. At this school they should be paying attention to this variable (and they are).

This figure is pretty consistent with what we’ve found across many, many schools. It’s a good example of why we are always encouraging higher ed institutions to store reunion data and pay attention to it.

This one’s a no-brainer.

And this one’s a super no-brainer.

Where to Go from Here

After you read something like this piece, it’s natural to raise the question: “What should I do with this information?”  Some thoughts:

  • Remember, we’re not assuming that you’re a sophisticated data miner/modeler. But we are assuming that you’re interested in looking at your data to help make better decisions about raising money.
  • Without using any fancy stats software and with a little help from your advancement services folks, you can do the same kind of analysis with your own alumni data as we’ve done here. You’ll run into a few roadblocks, but you can do it. We’re convinced of that.
  • Once you’ve done this kind of an analysis you can start looking at some of your alums who are in their forties and early fifiteies who haven’t yet jumped up to a high level of giving. The ones who look like their older counterparts with respect to logins, or reunion attendance, or volunteering (or whatever good variables you’ve found)? They’re the ones worth taking a closer look at.
  • You can take your analysis and show it to someone at a higher decision-making level than your own. You can say, “Right now, I don’t know how to turn all this stuff into a predictive model. But I’d like to learn how to do that.” Or you can say, “We need to get someone in here who has the skills to turn this kind of information into a tool for finding these people who are getting ready to pop up to a much higher level of giving.”
  • And after you have become comfortable with these initial explorations of your data we encourage you to consider the next step – predictive modeling based on those statistics terms we mentioned earlier. It is not that hard. Find someone to help you – your school has lots of smart people – and give it a try. The resulting scores will go a long way toward identifying your future big givers.

As always: We’d love to get your thoughts and reactions to all this.

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15 Comments »

  1. Hi guys, this is a wonderful, interesting post! Thank you for sharing what was obviously a great deal of work. One question: when you describe “giving,” are you including the value of outright gifts only, or gifts and pledges? Did you include gifts of property or assets such as art? Also, if the gifts were planned, were they discounted, or valued at full?

    Comment by Valerie Anastasio — 13 June 2012 @ 10:43 am

    • Thanks Valerie – to the best of my knowledge, the giving amounts used here are strictly hard credit and cash only, either outright gifts or payments on pledges, and not including the unpaid balance of pledges.

      Comment by kevinmacdonell — 13 June 2012 @ 9:14 pm

    • Valerie, Kevin is right. All hard credit. Peter

      Comment by Peter wylie — 15 June 2012 @ 10:30 am

  2. Cool stuff. Just found a typo, Age = 2012 – [preferred class year + 22]. If preferred class year is 2000, then age=2012-[2000+22]=-10? Correction: Age = 2012 – preferred class year + 22.

    Comment by Hu — 13 June 2012 @ 12:12 pm

    • Right you are. I will remove the offending brackets. Funny, you read something over ten times …. Thanks.

      Comment by kevinmacdonell — 13 June 2012 @ 12:33 pm

  3. What do you mean by “login”? What do visitors typically do after logging in: make a donation, change address, volunteer for events, register for reunions, view information?

    Comment by Andrew Ziem — 13 June 2012 @ 3:45 pm

  4. I am not a statistician but I’m getting my PhD so have to dabble in it! I am currently looking at our group of known major gift donors (living and deceased) to see what predictors they may have for my class project. We are well aware of what characteristics make someone a major gift prospect once they are there, but I want to know how to identify those individuals now that could be ready to “pop up” as you put it. Did you actually run statistics and determine if any of the factors were statistically signficiant as predictors? This was a timely piece and very useful.

    Comment by Tonia Ferrell — 14 June 2012 @ 8:41 am

    • Hi Tonia. Unfortunately, with the large N’s we use in data mining, relationships between variables that look very weak almost always end up being statistically significant. That’s why there has been a strong movement in the behavioral sciences over the last 20 years to move away from tests of statistical significance and towards what are called “effect sizes.” There is lot of literature on that. The writing is pretty lousy but, being a doctoral student, so much of what you have to read is not very well written.

      If you’d like to call me at 202-332-7571 for a more extended discussion, please feel free. I love talking about this stuff. Peter Wylie

      Comment by Peter wylie — 15 June 2012 @ 10:44 am

  5. This is a great starting point for teaching a person the thought process behind data mining and variable selection. 🙂 I did the same thing 2.5 years ago when I first started “dabbling.”

    Speaking of deceased constituents, is it a good idea to include them and former major givers (from more than 25 years ago)? If society is changing so fast, the qualities of a major donor are also likely to change every few years.

    Comment by Lara Tewes — 14 June 2012 @ 10:39 am

    • We have a predicable major gift donor profile that hasn’t changed for many years, so including deceased donors at this point would not be problematic for my institution. The pace of change among donors may be different at other institutions. But I could see running the statistics with and without those individuals to see if there is a difference to be sure.

      Comment by Tonia Ferrell — 14 June 2012 @ 2:33 pm

    • I believe including deceased individuals would indeed be problematic, especially the farther back in time you go. If any of your variables have a recency factor in them (event attendance comes to mind), then automatically there’s going to be a confounding element (i.e., ‘death’) that prevents that portion of the sample from participating in the characteristic. I think it’s good practice to stick with living constituents. As well, if you’re not going back very far in time, you’re probably not adding many additional major donors to the training set to make much of a difference anyway. That’s my take on it.

      Comment by kevinmacdonell — 15 June 2012 @ 12:32 pm

  6. But if you are measuring cummulative giving, then isn’t it tautological to say that people give more the older they get?

    Comment by Andrea — 14 June 2012 @ 12:22 pm

  7. Very interesting article. Curious how you chose the 8 variables to compare across the deciles? Did you look at any other variables

    Comment by Katie Edwards — 18 June 2012 @ 11:46 am


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