CoolData blog

28 February 2011

Look beyond loyal donors to find Planned Giving prospects

Filed under: Alumni, Planned Giving, predictive modeling, Predictor variables — Tags: , — kevinmacdonell @ 9:17 am

According to conventional wisdom, the best Planned Giving prospects are donors who have consistently given small Annual Fund gifts over a long period of time. Rather than assume this is true always and everywhere, I think we should put the “loyal donor” rule of thumb to the test in the environment of our own data.

Here’s what I did recently. I picked a group of current Planned Giving expectancies, and pulled their giving totals for the 20 fiscal years prior to their identification. To select the group, I chose everyone identified as an expectancy in the year 2003 or later, so the years of giving that I pulled where 1983 to 2002. I also limited the group to people who are now at least 50 years old.  This ensured that everyone in the group was probably old enough to have participated in the Annual Fund during any of those years if they chose.

I didn’t look at how much they gave in any given year, only whether they gave. Expectancies who gave in 20 out of 20 years received a “score” of 20. Someone who had given in 10 years out of 20 got a score of 10, and so on. Non-donors were scored as zero.

Then I made a bar chart of their scores. The height of the bars corresponds to the percentage of the group that falls into each number of years of giving in that 20-year span.

What does this chart tell us? It’s clear these expectancies are indeed very loyal donors. A little under half of them have some giving in at least 10 of the 20 years. That’s wonderful.

I am struck that 15% of them have no giving at all. On the other hand, the proportion of alumni over 50 who are NOT Planned Giving expectancies and have no giving is 61%, so the expectancies compare well against them.

Here’s the same chart, but with all alumni over 50 who are not current expectancies:

Big difference! The scale is totally different, due to the disproportionate number of non-donors in this group. As a percentage of alumni, very loyal donors are scarce. Let’s look at it another way, excluding non-donors from both groups: In the chart below, the expectancy donors have giving in twice as many years as non-expectancy donors, on average:

No wonder, then, we’ve been told to focus on loyal Annual Fund donors in order to identify new prospects for Planned Giving. The connection is undeniable.

A couple of things interfere with the clarity of this picture, however. Have another look at the first chart above. Although all of these people are old enough to have contributed in every year since 1983, a significant percentage of them have given in only a handful of years. For example, 6 percent of current expectancies have giving in only ONE of the 20 years. They share that distinction with 10,000 alumni who are not expectancies.

In other words, if years of giving was your only metric for proactively identifying prospects, and no expectancies came in “over the transom,” so to speak, that 6 percent of the group would never be discovered. There are just too many individuals at the lower end of years-of-giving to get focused in any practical way. Donor loyalty is therefore a great predictor of Planned Giving potential, but it does not define the profile of a Planned Giving donor.

If donor data does not contain all the answers, where can you look? I have a few ideas.

Using the same group of current expectancies (age 50 or older, and identified in 2003 or later), I pulled some other characteristics from the database to test as predictors. I was careful to select data that existed before 2003, i.e. that pre-dated the identification of the individuals as expectancies.

Here’s a great one: Student activities. Participation in varsity sports, campus clubs and student government is coded in the database, and the chart below compares the proportions of the two groups who have at least one such activity code in their records.

Interesting, eh? Now, maybe ten or 15 years ago there was a big push on to solicit former athletes for Planned Giving, and that’s why they’re well represented in the current crop of expectancies — but I doubt that very much. The evidence indicates that student experience is a big factor even for decisions taken many years later. This is a great example of how even the oldest data is valuable in the present day.

Here’s another one: Alumni who hold more than one degree. The proportions on both sides are high, because I counted degrees from ANY university (we have that information in our database), and we have many graduate and professional degree holders. The chart would seem to indicate that expectancies are more likely to hold multiple degrees than non-expectancies. A little more digging would tell us whether a particular profession (doctors or lawyers, for example) are heavily represented among the expectancies group.

Here’s another one, for the presence of a Faculty or Staff code, which indicates whether someone is or at one time was employed by the university. This code is not uniformly applied (it does not directly correspond to actual employment or even HR data), so it’s not perfect, but as a rough indicator it works fine for data mining.

Next up is one of my very favourite predictors for Planned Giving potential: event attendance. I’ve seen this elsewhere, and it holds true here as well. Showing up at any kind of reunion or alumni-related event is highly predictive. I got a little lazy when I calculated this variable because I did not exclude events attended in 2003 or later; I would expect the percentages to change a bit, but probably not by much. I DID exclude attendance at any kind of donor-recognition event — if only donors are invited, attendance is merely a proxy for donor status.

I could do this for a dozen more variables, but you get the point. There are all sorts of additional indicators of Planned Giving potential sitting in your database. As well, my predictors are not necessarily your predictors. It’s up to you to do a little digging and find them.

From here, we could have niggling arguments about whether some of these predictors are really better than ‘donor loyalty’, or are even statistically significant, and so on. But if you are currently trying to identify prospects solely by identifying loyal donors, allow me to suggest this improvement in your methods: Devise a simple scoring system that gives one point for ‘donor loyalty’ (however you wish to define that — I’ve defined it as giving in at least 10 years out of 20), and one point for each of the other predictors that strike you as particularly powerful. Using the predictors I’ve presented here, my score would be calculated like so:

Loyal donor (0/1) + Student activity (0/1) + Multiple degrees (0/1) + Faculty or Staff (0/1) + Event attendance (0/1) = Maximum PG score of 5.

What happens when I apply this model to our database? Out of more than 30,000 living and addressable alumni over the age of 50 who are not already expectancies, only 89 have a perfect score of 5 out of 5. That’s a very manageable, high-quality list of individuals to provide for review by a Planned Giving Officer.

This model is far from the last word in data mining for Planned Giving, and it has some severe limitations. For example, focusing on these 89 individuals might essentially result in a campaign based on retired professors in the Faculty of Medicine! Your expectancies are not going to be one homogeneous group, so you’ll want to identify other clusters for solicitation. As well, almost 700 individuals in our database would have a score of 4 out of 5, so things get out of hand quickly when you have too few score levels.

Otherwise, it’s pretty nifty. This score is easy to understand, not terribly difficult to calculate, and is a useful departure from any single-minded focus on donor loyalty.


  1. Great post Kevin! To add another ingredient to your list, I would look at scholarship recipients as well. They have shown up in many PG models I have built.

    I also feel they intuitively/qualitatively make sense as they want to see future students receive the opportunities they had and were grateful for.

    Comment by Alex Oftelie — 28 February 2011 @ 10:11 am

  2. I love your analysis. Well put, when you say “donor loyalty predicts but does not define” the PG prospect pool.

    This is very consistent with, but more thorough than, what I teach in workshops and seminars. Thanks!

    Comment by Tony Martignetti — 28 February 2011 @ 1:09 pm

  3. Great post Kevin, and one word of caution: I heard recently about an institution that used an outside screening service to help look for PG suspects. Unfortunately, some current staff who were giving via payroll deduction were “over-represented” in the PG suspect pool given the frequency of their gifts!

    Comment by Fred Weiss, SunGard Higher Education — 28 February 2011 @ 7:23 pm

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