CoolData blog

19 April 2015

Planned Giving prospect identification, driven by data

Filed under: Planned Giving, Prospect identification — Tags: , , , — kevinmacdonell @ 6:28 pm

I’m looking forward to giving two presentations in my home city in connection with this week’s national conference of the Canadian Association of Gift Planners (CAGP). In theory I’ll be talking about data-driven prospect identification for Planned Giving … “in theory” because my primary aim isn’t to provide a how-to for analyzing data.


Rather, I will urge fundraisers to seek “data partners” in their organizations — finding that person who is closest to the data — and posing some good questions. There’s a lot of value hidden in your data, and you can’t realize this value alone: You’ve got to work closely with your colleagues in Advancement Services or with any researcher, analyst, or IT person who can get you what you need. And you have to be able to tell that person what you’re looking for.


For a shop that’s done little or no analysis of their data, I would start with these two basic questions:


  1. What is the average age of new expectancies, at the time they became known to your organization?
  2. What is the size of your general prospect pool?
The answer to the first question might suggest that more active prospect identification is required, of the type more often associated with major-gift fundraising. If the average age is 75 or older, I have to think that earlier identification of bequest intentions would benefit donor and cause alike, by allowing for a longer period for the conversation to mature and for the relationship to develop.


The answer to the second question gives an indication of the potential that exists in the database — but also the challenge of zeroing in on the few people (the top 100, say) in that universe of prospects who are most likely to accept a personal visit. Again, I’m talking about high-touch fundraising — more like Major Gifts, less like Annual Fund.


As Planned Giving professionals get comfortable asking questions of the data, the quality of the questions should improve. Ideally, the analyses will move from one-off projects to an ongoing process of gathering insights and then applying them. Along those lines, I will be giving attendees for both presentations a taste of how some simple targeting via data mining might work. As Peter Wylie and I wrote in our book, “Score!”, data mining for Planned Giving is primarily about improving the odds of success.


I said that I’m giving two presentations. Actually, it’s the same presentation, for two audiences. The first talk will be for a higher ed audience in advance of the conference, and the second will be for a more general nonprofit audience attending the conference proper. I expect the questions and conversations to differ significantly, and I also expect some of my assertions about Planned Giving fundraising to be challenged. Should be interesting!


Since you’ve read this far, you might be interested in downloading the handout I’ve prepared for these talks: Data-driven prospect ID for Planned Giving. There’s nothing like being there in person for the conversation we’re going to have, but this discussion paper does cover most of what I’ll be talking about.


If you’re visiting Halifax for the conference, welcome! I look forward to meeting with you.


7 July 2014

Mine your donor data with this baseball-inspired analysis

I’ve got baseball analytics on my mind. I don’t know if it’s because of the onset of July or because of a recent mention of CoolData on Nate Silver’s FiveThirtyEight blog, but I have been deeply absorbed in an analysis of donor giving behaviours inspired by Silver’s book, “The Signal and the Noise.” It might give you some ideas for things to try with your own database.

Back in 2003, Silver designed a system to predict the performance of Major League Baseball players. The system, called PECOTA, attempts to understand how a player’s performance evolves as he ages. As Silver writes in his book, its forecasts were probabilistic, offering a range of possible outcomes for each player. From the previous work of others, Silver was aware that hitters reach their peak performance at age 27, on average. Page 81 of his book shows the “aging curve” for major league hitters, a parabola starting at age 18, arcing smoothly upwards through the 20s, peaking at 27, and then just as smoothly arcing downwards to age 39.

My immediate thought on reading about this was, what about donors? Can we visualize the trajectory of various types of donors (major donors, bequest donors, leadership annual fund donors) from their first ten bucks right after graduating, then on into their peak earning years? What would we learn by doing that?

In baseball, the aging curve presents a problem for teams acquiring players with proven track records. By the time they become free agents, their peak years will have passed. However, if the early exploits of a young prospect cause him to resemble one of the greats from the past, perhaps he is worth investing in. The curve, as Silver notes, is only an average. Some players will peak far earlier, and some far later, than the average age of 27. There are different types of players, and difference types of curves, and every individual career is different. But lurking in all that noise, there is a signal.

Silver’s PECOTA system takes things further than that — I will return to that later — but now I want to turn to how we can visualize a sort of aging curve for our donors.

What’s the payoff? Well, to cut to the chase: It appears that some donors who go on to give in six figures (lifetime total) can be distinguished from the majority of lower-level donors at a very early age. Above-average giving ($200 or $250, say) in any one year during one’s late 20s or early 30s is a predictor of very high lifetime value. I have found that when big donors have started their giving young, they have started big. That is, “big” in relation to their similarly-aged peers – not at thousands of dollars, but at $100 or $500, depending on how old they were at the time.

Call it “precocious giving”.

Granted, it sounds a bit like plain common sense. But being analytical about it opens up the possibility of identifying a donor with high lifetime value when they’re still in their late 30s or early 40s. You can judge for yourself, but the idea appealed to me.

I’m getting ahead of myself. At the start of this, I was only interested in getting the data prepared and plotting the curve to see what it would look like. To the extent that it resembled a curve at all, I figured that “peak age” — the counterpart to baseball player performance — would be the precise age at which a donor gave the most in any given year while they were alive.


I wrote a query to pull all donors from the database (persons only), with a row for each year of giving, summing on total giving in that year. Along with Year of Gift, I pulled down Year of Birth for each donor — excluding anyone for whom we had no birthdate. I included only amounts given while the donor was living; bequests were excluded.

The next step was to calculate the age the donor was at the time of the gift. I added a column to the data, defined as the Year of Gift minus Year of Birth. That gave me a close-enough figure for age at time of giving.

As I worked on the analysis, I kept going back to the query to add things I needed, such as certain donor attributes that I wanted to examine. Here are most of the variables I ended up pulling from the database for each unique combination of Donor ID and Age at Time of Gift:

  • ID
  • Age at Time of Gift (Year of Gift minus Year of Birth)
  • Sum of Giving (total giving for that donor, at that age)
  • Donor Category Code (Alum, Friend, etc.)
  • Total Lifetime Giving (for each donor, without regard to age)
  • Deceased Indicator (are they living or dead as of today)
  • Current Age (if living, how old are they right now)
  • Year of Birth
  • Year of Gift

The result was a data set with more than 200,000 rows. Notice, of course, that a donor ID can appear on multiple rows — one for each value of Age at Gift. The key thing to remember is that I didn’t care what year giving occurred, I only wanted to know how old someone was when they gave. So in my results, a donor who gave in 1963 when she was 42 is much the same as a donor who gave in 2013 he was the same age.


Now it was time to visualize this data, and for that I used Tableau. I connected directly to the database and loaded the data into Tableau using custom SQL. ‘Age at Gift’ is numerical, so Tableau automatically listed that variable in the Measures panel. For this analysis, I wanted to treat it as a category instead, so I dragged it into the Dimensions panel. (If you’re not familiar with Tableau, don’t worry about these application-specific steps — once you get the general idea, you can replicate this using your tool of choice.)

The first (and easiest) thing to visualize was simply the number of donors at each age. Click on the image below to see a full-size version. Every part of the shape of this curve says something interesting, I think, but the one thing I have annotated here is the age at which the largest number of people chose to make a gift.


This chart lumps in everyone — alumni and non-alumni, living donors and deceased donors — so I wanted to go a little deeper. I would expect to see a difference between alumni and non-alumni, for example, so I put all degree and non-degree alumni into one category (Alumni), and all other donor constituents into another (Non-alumni). The curve does not change dramatically, but we can see that the number of non-alumni donors peaks later than the number of alumni donors.


There are a number of reasons for analyzing alumni and non-alumni separately, so from this point on, I decided to exclude non-alumni.

The fact that 46 seems to be an important age is interesting, but this probably says as much about the age composition of our alumni and our fundraising effort over the years as it does about donor behaviour. To get a sense of how this might be true, I divided all alumni donors into quartiles (four bins containing roughly equal numbers of alumni), by Birth Year. Alumni donors broke down this way:

  1. Born 1873 to 1944: 8,101 donors
  2. Born 1945 to 1955: 8,036 donors
  3. Born 1956 to 1966: 8,614 donors
  4. Born 1967 to 1991: 8,172 donors

Clearly these are very different cohorts! The donors in the middle two quartiles were born in a span of only a decade each, while the span of the youngest quartile is 24 years, and the span of the oldest quartile is 71 years! When I charted each age group separately, they split into distinct phases. (Reminder: click on the image for a full-size version.)


This chart highlights a significant problem with visualizing the life cycle of donors: Many of the donors in the data aren’t finished their giving careers yet. When Nate Silver talks about the aging curves of baseball players, he means players whose career is behind them. How else to see their rise, peak, and eventual decline? According to the chart above, the youngest quartile peaks (in terms of number of donors) at age 26. However, most of these donors are still alive and have many years of giving ahead of them. We will turn to them to identify up-and-coming donors, but as long as we are trying to map out what a lifetime of giving looks like, we need to focus on the oldest donors.

An additional problem is that our donor database doesn’t go back as far as baseball stats do. Sure, we’ve got people in the database who were born more than 140 years ago, but our giving records are very sparse for years before the early 1970s. If a donor was very mature at that time, his apparent lack of giving history might cause us to make erroneous observations.

I decided to limit the data set to donors born between 1920 and 1944. This excludes the following donors who are likely to have incomplete giving histories:

  • Anyone who was older than 50 in 1970, when giving records really started to get tracked, and
  • Anyone who is currently younger than 70, and may have many years of giving left.

This is a bit arbitrary, but reasonable. It trims off the donors who could never have had a chance to have a lifetime of giving recorded in the data, without unduly reducing the size of my data set. I was left with only 20% of my original data, but still, that’s more than 6,000 individuals. I could have gotten fussier with this, removing anyone who died at a relatively young age, but I figured the data was good enough to provide some insights.

The dramatic difference made by this trimming is evident in the following two charts. Both charts show a line for the number of donors by age at time of gift, for each of three lifetime giving levels: Under $1,000 in blue, $1,000 to $10,000 in orange, and over $10,000 in purple. What this means is that all the donors represented by the purple line (for example) gave at least $10,000 cumulatively over the course of their lifetime.

The first chart is based on ALL the data, before trimming according to birth year. The second chart is based on the 6,000 or so records I was left with after trimming. The first chart seems to offer an interesting insight: The higher the lifetime value of donors, the later in life they tend to show up in great numbers. But of course this just isn’t true. Although the number of donors with lower lifetime giving peaks at earlier ages, that’s only because that whole group of donors is younger: They’re not done giving yet. (I have added ‘Median Current Age’ to the high point of each curve to illustrate this.) Remember, this chart includes everyone — it’s the “untrimmed” data:

Contrast that three-phase chart with this next one, based on “trimmed” data. The curves are more aligned, presumably because we are now looking at a better-defined cohort of donors (those born 1920 to 1944). The oldest donor is 24 years older than the youngest donor, but that’s okay: The most important concern is having good data for that range of ages. Because the tops of these curves are flatter, I have annotated more points, for the sake of interest.


These curves are pretty, but they aren’t analogous to “performance curves” for baseball players — we haven’t yet looked at how MUCH donors give, on average, at each age. However, what general observations can we make from the last chart? Some that come to my mind:

  • Regardless of what a donor finally ends up giving lifetime, there are always a few (a very few) who start giving while they are in their 20s, and a few who are still around to give when they are in their late 80s and early 90s.
  • The number of donors starts to really take off at around age 40, and there is steady growth until about age 50, when the growth in number of donors begins to slow or plateau.
  • Donors start to drop out rapidly at around age 70. This is due to mortality of course, but probably the steepness of the drop is exaggerated by my trimming of the data at the older end.


Here is where things really get interesting. The whole point of this exercise was to see if we can spot the telltale signs of a future major donor while they are still relatively young, just as a baseball scout looks for young prospects who haven’t peaked yet. Do donors signal unusual generosity even when they are still in their 20s and 30s? Let’s have a look.

I zoomed in on a very small part of the chart, to show giving activity up until age 35. Are there differences between the various levels of donors? You bet there are.

As soon as a high-lifetime-value donor starts to give, the gifts are higher, relative to same-age peers who will end up giving less. The number of donors at these early ages is miniscule, so take this with a grain of salt, but a trend seems unmistakable: Up to the age of 30, donors who will end up giving in five figures and higher give about 2.5 to 3.5 times as much per year as other donors their age who end up giving $1,000 to $10,000 lifetime. AND, they give FIVE TIMES as much per year as other donors their age who end up giving less than $1,000 lifetime.


Later on, at ages 35 and 40, donors who will finish their giving careers at the high end are giving two to three times as much per year as donors in the middle range, and 5.6 to 7 times per year (respectively) as donors who will finish on the lowest end.

It might be less confusing to chart each group of donors by average giving per year, rather than by number of donors. This chart shows average giving per year up until age 65. Naturally, the averages get very spiky, as donors start making large gifts.



To temper the effect of extreme values, I log-transformed the giving amounts. This made it easier to visualize how these three tiers of donors differ from each other over a lifetime of giving:


What do I see from this? These are generalizations based on averages, but potentially useful generalizations:

  • Upper-end donors start strong relative to other donors, accelerate giving after age 40, and continue to increase giving throughout their lifetimes.
  • Middle- and low-range donors start lower. They also increase their yearly giving until their late 40s, but after that, they plateau and stay at the same level for the rest of their lives.


What’s the bottom line here? I think it’s this: Hundreds of donors were well on their way to being exceptional by the tender age of 40, and a few were signaling long before that.

Information like this would be interesting to Annual Fund as they work to identify prospects for leadership-level giving. But $10,000 in a lifetime is a little too low to make the Major Gifts folks take notice. Can we carve out the really big donors from the $10K-plus crowd? And can we also identify them before they hit 40? Have a look at this chart. For this one, I removed all the donors who gave less than $10,000 lifetime, and then I divided the high-end donors into those who gave less than $100,000 lifetime (green line) and those who gave more than $100,000 (red line).



The lines get a bit jagged, but it looks to me like the six-figure lifetime donors pull away from the five-figure donors while still in their 40s. And notice as well that they increase their giving after age 65, which is very unusual behaviour: By 65, the vast majority of donors have either long plateaued or are starting to wind down. (You can’t see this in the chart, but that post-65 group of very generous donors numbers about 50 individuals, with yearly average giving ranging around $25,000 to $50,000.)

When I drill down, I can see about a hundred donors sitting along the red line between the ages of 30 and 45, whom we might have identified as exceptional, had we known what to look for.

With the benefit of hindsight, we are now able to look at current donors who were born more recently (after 1969, say), and identify who’s sending out early signals. I have those charts, but I think you’ve seen enough, and as I have said many times in the past: My data is not your data. So while I can propose the following “rules” for identifying an up-and-comer, I don’t recommend you try applying them to your own situation without running your own analysis:

  • Gave more than $200 in one year, starting around age 28.
  • Gave more than $250 in one year, starting around age 29.
  • Gave more than $500 in one year, starting around age 32.

Does this mean I think we can ask a 32-year-old for $10,000 this year? No. It means that this 32-year-old is someone to watch out for and to keep engaged as an alum. It’s the donors over 50 or so who have exhibited these telltale patterns in their early giving that might belong in a major gift prospect portfolio.

Precocious giving certainly isn’t the only indicator of a good prospect, but along with a few other unusual traits, it is a good start. (See: Odd but true findings? Upgrading annual donors are “erratic” and “volatile”.)


Where do you go from here? That is completely up to you. I am still in the process of figuring out how to best use these insights.

Coming up with some rules of thumb, as above, is one way to proceed. Another is rolling up all of a donor’s early giving into a single score — a Precocity Score — that takes into account both how much a donor gave, and how young she was when she gave it. I experimented with a formula that gave progressively higher weights to the number of dollars given for younger ages. For example, $100 given at age 26 might be worth several times more than $200 given at age 44.

Using my data set of donors with a full life cycle of giving, I tested whether this score was predictive of lifetime value. It certainly was. However, I also found that a simple cumulative sum of a donor’s giving up to age 35 or 40 was equally as predictive. There didn’t seem to be any additional benefit to giving extra weight to very early giving.

I am shying away from using giving history as an input in a predictive model. I see people do this all the time, but I have always avoided the practice. My preference is to use some version of the rules above as just one more tool to use in prospect identification, distinct from other efforts such as predictive modelling.


That’s as far as I have gotten. If this discussion has given you some ideas to explore, then wonderful. I doubt I’m breaking new ground here, so if you’ve already analyzed giving-by-age, I’d be interested in hearing how you’ve applied what you’ve learned.

Incidentally, Nate Silver went on to produce “similarity scores” for pairs of hitters. Using baseball’s rich trove of data, he compared players using a nearest-neighbour analysis, which took into account a wide range of data points, from player height and weight to all the game stats that baseball is famous for. A young prospect in the minor leagues with a score that indicates a high degree of similarity with a known star might be expected to “age” in a similar way. That was the theory, anyway.

One can imagine how this might translate to the fundraising arena. If you identified groups of your best donors, with a high degree of similarity among the members of each group, you could then identify younger donors with characteristics that are similar to the members of each group. After all, major gift donors are not all alike, so why not try to fit multiple “types”?

I would guess that the relatively small size of each group would cause any signal to get drowned out in the noise. I am a little skeptical that we can parse things that finely. It would, however, be an interesting project.

A final note. The PECOTA system had some successes and for a time was an improvement on existing predictive tools. Over time, however, pure statistics were not a match for the combination of quantitative methods and the experience and knowledge of talent scouts. In the same way, identifying the best prospects for fundraising relies on the combined wisdom of data analysts, researchers and fundraisers themselves.

2 November 2010

Finding prospects flying under the radar: A nuts and bolts approach

Guest post by Peter Wylie and John Sammis

(Downloadable/printable version available here as a PDF: Flying Under the Radar)

Let’s say you’re a prospect researcher in higher education.  You’re getting some pressure – from your boss, from some of the gift officers you work with, maybe the campaign director – to come up with a list of new prospects. They use different words, but their message is clear:

“We’ve picked the low hanging fruit. We don’t want to keep going back to the same alums who’ve been helping us out in a big way for a long time. We need to find some new people who have the capacity and willingness to make a nice gift. Maybe not a huge gift, but a nice gift.”

If you’ve been working in the field awhile, this isn’t the first time you’ve faced this problem, nor is it the first time somebody has offered advice on how to solve it. Truth be told, you may have gotten too much advice:

  • “You haven’t done a screening for five years. You need to do a new one.”
  • “Our company has gotten very sophisticated about predictive modeling as well as gift capacity ratings. Use us.”
  • “You’re not using social media resources effectively. Facebook and MySpace are great places to find out about alums who have lots of financial resources and are philanthropically inclined.”
  • “You need to learn how to do data mining and predictive modeling or add somebody to your staff who already knows how to do it.”

It’s not that any of this advice is bad, even if it comes from a vendor whose goal is to get some of your business. The problem is that you, or the person you report to, has to sift through this advice and make some kind of decision — even if that decision is to do nothing different from what you’re currently doing.

Since John Sammis and I are some of the people out there offering this kind of advice to advancement folks, we often ask ourselves: “Are we making things too complicated for the people we’re trying to help?” Often the answer we come up with is, “Probably.” Why? That’s a whole can of worms we’d rather not get into. The short answer is that both of us grew up in an educational system where precious few of our teachers and authors of our textbooks were good at making things simple and clear. And like it or not, we’ve inherited some of their tendencies to obfuscate rather than elucidate. But we fight against it as best we can.

Hopefully we’ve won that battle in this piece. (You’ll decide if we have.) Anyway, what we’ve done here is use some data from a large public higher education institution to walk you through a simple process for finding new prospects.

Before we do that, let’s start off with three assumptions:

  • You have fairly recent gift capacity ratings for several thousand of your solicitable alums, some of whom you think may be good, untapped prospects.
  • You have ready access to someone who can develop a simple score for all those alums with respect to their affinity to give to your school.
  • You have reasonably good profiles on each of these alums. That is, those profiles include information like lifetime hard credit dollars given; date and amount of last gift; date and amount of first gift; what gift officers have ever been assigned to those alums and when; the most recent occupation of the alum; and so on.

Here are the steps we want to take you through:

1.     Look at the distribution of gift capacity ratings for the alums you’ve recently screened.

2.     Look at the giving data for these alums by gift capacity ratings.

3.     Have someone build you a simple affinity model using some very basic information stored on each alum.

4.     Pick a small group of alums who have a high capacity rating, a high affinity rating, and are not currently assigned to a gift officer.

5.     Look closely at the alums in this small group and identify some who may deserve more scrutiny.

We’ll go through each of these steps in detail:

Look at the distribution of gift capacity ratings for the alums you’ve recently screened.

Whenever you have a field of data (whether it comes from your own database or has been delivered to you by a vendor), it’s a good idea to make a frequency distribution of the field. (In statistics the term is “variable,” not “field,” so from here on out we’ll say “variable.”)

Here are a couple of reasons for doing this:

  • You get a big picture look at the variable. Our experience is that most people in higher education advancement don’t do this for the many variables they have in their alumni databases. For example, let’s say you asked the average associate vice president for advancement in a college or university this question: “What percentage of your solicitable alums have given $100 or less lifetime hard credit?” Our bet is that the vast majority would have no idea of what the correct answer was; moreover, they would be shocked if you told them.
  • You get a chance to see if there is anything out of the ordinary about the data that’s worth further exploration. Here’s a good example. When doing predictive modeling for a school, we look closely at the variable “preferred class year.” It’s a measure of how long alums have been out of school, and it’s a reasonably good measure of age. It’s not at all uncommon for us to encounter thousands of records coded as “0000” or, say, “1700.” Call it a hunch, but we’re pretty sure those folks didn’t graduate the year Christ was born, nor 75 years or so before the Declaration of Independence got signed. When we encounter a problem like this, of course, we ask the advancement services people we’re working with what those codes mean. The answers vary. Sometimes such codes indicate alums who are non-degreed. Sometimes they indicate alums who simply received a certain kind of certificate. Or they indicate something else. The important thing is that we ask; we clear up the mystery.

All right, Table 1 shows a distribution of the gift capacity ratings for a group of about 22,000 alums in the public higher education institution we mentioned earlier. Figure 1 displays the same distribution graphically. Take a minute or two to look at both of them. Then you can compare what you see with what we noticed.

Table 1: Estimated $Gift Capacity for Over 22,000 Alumni Divided Into 20 Groups of Roughly 5% Each

For us, two things about these data stand out:

1.     Some of the data are a little hard to believe. Let’s take a look at Group 1 in Table 1. There are 1123 records in this group. They comprise alums with the lowest five percent of gift capacity ratings. If you look at the “min” column, you’ll see that the lowest gift capacity rating is one dollar. Really? That alum must be down on his or her luck. You can’t see all the data in this distribution the way we can, but there are a total of 11 alums whose gift capacity is listed as being under $100. Obviously, you should be suspicious of such ratings. Contacting the vendor who generated them is a must. And politely staying after them until you get an acceptable answer is the right thing to do.

2.     The capacity ratings rise slowly until we get to the top ten percent of alums. There’s nothing particularly surprising about this. However, it is interesting (without showing you all the arithmetic) that, of the roughly one billion dollars of total gift capacity for these alums, over half a billion of that gift capacity resides with the top 10% of the alums.

Look at the giving data for these alums by wealth capacity ratings.

We’ve taken a look at the distribution of gift capacity ratings for the alums we’ve screened. Now let’s look at how those capacity ratings are related to the money the same alums have given to the school.

We’ll start with Table 2. The two columns on the right of the table (“Total$ given” and “Max$ given”) contain the most important pieces of information in the table. The “Total” column simply shows the total lifetime dollar amount given for the alums at each of the 20 gift capacity levels. The “Max” column shows the maximum amount any one alum has given at each of these levels.

Table 2: Giving Data for Over 22,000 Alumni Divided Into 20 Groups of Roughly 5% Each by Gift Capacity

We see a pattern that emerges from this table, but it’s a little hard to detect. So go ahead and take a look at Table 3 and Figure 2. Then we’ll offer our thoughts.

Table 3: Percentage of Alums Giving $50 or More Lifetime by Gift Capacity Level

When we look at these two tables and this one figure, two conclusions emerge for us:

1.     There is some relationship between gift capacity and giving, but it’s not a strong one.

2.     If we can believe the gift capacity ratings, there is a huge amount of untapped potential for giving, especially at the highest capacity levels.

Let’s start with the first conclusion, that there is not a strong relationship between capacity and giving. How do we arrive at the conclusion? Let’s go back to Table 2. Now if we just look at the five percent of alums with the lowest giving capacity (Group 1) and the five percent of alums with the highest giving capacity (Group 20), we see that the total lifetime giving goes from $34,062 to $2,396,810. That’s a big difference. The wealthiest alums have given about 70 times as much as the least wealthy alums. Also, the most generous alum in the lowest capacity group has given a lifetime total of $2,005 compared to the most generous alum in the highest capacity group who has given a lifetime total of $224,970. Again, we see a big difference.

But look at what happens in between these two extremes. Things bounce around a lot. For example, let’s compare the giving between capacity level 3 and capacity level 12. The total giving amount for the former group is $152,741; the total giving amount for the former group is $125,477. In other words, alums with a much higher giving capacity have given less than alums with a much lower giving capacity.

Further evidence of this “bouncing around” is apparent when you look at Figure 2 (a graphic version of Table 3). This chart shows the percentage of alums at each of the 20 giving capacity levels who have given $50 or more lifetime to the school. Notice how these percentages dip in the middle of the capacity range.

So let’s go back to our conclusion that there is some relationship between gift capacity and giving, but that it’s not a strong relationship. Yes, the overall trend of giving goes up with gift capacity, but we can in no way conclude that knowledge of an alum’s gift capacity is a good indication of how much he/she has given.

Okay, how about our second conclusion that there is a huge amount of untapped potential for giving, especially at the highest capacity levels? We think Figure 2 provides plenty of support for that conclusion. Look at the highest gift capacity level. Barely 50% of the alums in this category have given over $50 lifetime. Not as a single gift. No. Lifetime.

If that doesn’t convince you of the untapped potential for giving among such wealthy alums, we’re not sure anything will.

Have someone build you a simple affinity model using some very basic information stored on each alum.

Now comes the tricky part. Now comes the part where we risk losing you because we get a little too technical. We don’t want to do that. We want to avoid having you end up saying, “Geez, these guys said they were gonna make this simple, but they didn’t. Now I’m more confused than I was before I started reading this thing.”

This is not a perfect solution to the problem, but we think it might work. We’d like you to find someone who works at your school who can help you. Of course, it would be great if you already had someone on your advancement staff who fits that bill – someone whose job is focused on data mining and predictive modeling. Some schools have folks like that, but most don’t. (We’re assuming you don’t, otherwise there wouldn’t be a whole lot of need for you to be reading this piece.)

Anyway, the person you’re looking for is probably a stats professor in the psychology or education department, a graduate student pursuing a degree in that area, or someone who works in what is often called “institutional research.” Ideally, the person you find should be:

  • Someone who is helpful and accommodating. This seems obvious, but, sadly, a lot of people in higher education don’t meet these criteria. Maybe a quick and easy way to decide is to ask yourself: “If I walked into a high end department store, is this a person I would want to help me?” If your answer is not an unequivocal “yes,” you should keep looking.
  • Someone who is good at explaining things in clear, simple English. Candidly, a lot of people in the technical arena are not good at this. As we said earlier, the two of us (try as we might) struggle with making things clear to the people we work with. What we’d suggest is that you look for someone who is patient with you when you don’t understand something they say. You especially want to avoid someone who acts the least bit impatient and condescending if you don’t “get” something the first time they explain it.
  • Someone who knows at least a little about major giving. The person does not need to be an expert in prospect research. But he or she should have a sense of how an advancement office works and some of the pressure that prospect researchers and development officers endure, especially when they’re scrambling to meet campaign goals.
  • Someone who has good skills with a stats software package. We think this is a must. If the person only knows how to use Excel to analyze data, that’s not good enough. The person needs to be proficient in a package like SPSS, which is widely available on college and university campuses. John and I prefer a package called Data Desk, but the important point is that your person needs to be proficient with an application whose purpose is sophisticated data analysis.
  • Someone who’s had some experience with multiple regression. You may or may not have heard of multiple regression. Don’t worry about that. Just be able to confirm that the person who helps you has a solid working knowledge of the technique. A good way to find out if that’s the case is to ask the person to explain the technique using a simple example using some of your own data.

Let’s assume you’ve found someone to help you. As we said earlier, if you follow our plan, that person will build you a simple affinity model using some very basic information stored on each alum for whom you have a capacity rating.

For the benefit of that person, we’ve described below how we developed the model for the school we’re using as an example. We’ve tried to provide just enough detail to give your person a guide, but not so much that we bog the paper down with too many words.

Enclosed in the boxes below (so you can skip over it if you wish) is a summary of what we did:

We chose lifetime hard credit giving as our dependent variable. To each record we added one dollar of giving to arbitrarily rid the sample of zero givers. We then performed a log to the base 10 transformation on this variable to reduce as much of the positive skewness as possible. 

We chose the following predictors (independent variables) for entry into our multiple regression analysis:

  • MARITAL STATUS MISSING (the alum was given a 1 if there was no marital status listed for him/her in the database, otherwise a 0)
  • MARITAL STATUS SINGLE (the alum was given a 1 if he/she was listed as “single” in the database, otherwise a 0)
  • CLASS YEAR (the alum’s preferred year of graduation)



  • HOME PHONE LISTED (a 1 if a home phone was listed in the database for the alum, otherwise a 0)
  • BUSINESS PHONE LISTED (a 1 if a business phone was listed in the database for the alum, otherwise a 0)
  • EVENT ATTENDED (a 1 if an alum was listed as ever attending an event after graduation, otherwise a 0)
  • E-MAIL LISTED (a 1 if an e-mail address was listed in the database for the alum, otherwise a 0)

Table 4 summarizes the results of the regression analysis:

Table 4: Regression Analysis Table for the Simple Model Developed for This Paper

R squared = 24.9%     R squared (adjusted) = 24.9%
s =  0.9835  with  22446 – 9 = 22437  degrees of freedom
Source Sum of Squares df Mean Square F-ratio
Regression 7213.27 8 901.659 932
Residual 21701.5 22437 0.967218
Variable Coefficient s.e. of Coeff t-ratio prob
Constant -1603.28 218.2 -7.35  ≤ 0.0001
MARITAL STATUS MISSING -0.333961 0.01845 -18.1  ≤  0.0001
MARITL STATUS SINGLE -0.472877 0.01603 -29.5  ≤  0.0001
HP LISTED 0.243367 0.01496 16.3  ≤  0.0001
BP LISTED 0.685641 0.03374 20.3  ≤  0.0001
CLASS YEAR 1.64819 0.2196 7.51  ≤  0.0001
SQUARE OF CLASS YEAR -4.23E-04 5.52E-05 -7.66  ≤  0.0001
EVENT ATTENDED (YES/NO) 0.712603 0.05089 14  ≤  0.0001
EMAIL LISTED 0.422934 0.01487 28.4  ≤  0.0001

We divided the predicted scores from the regression for alums with the highest gift capacity into twenty roughly equal-sized groups where 1 was low and 20 was high.

Okay, where are we here? In the “boxed in” technical suggestion above, the last thing we said was: “We divided the predicted scores from the regression analysis for alums with the highest gift capacity into twenty roughly equal-sized groups where 1 was low and 20 was high.” Well, what does that actually mean?

Let’s start with the specific group of alums we’re most interested in looking at. These are the 1,123 alums who got the highest gift capacity ratings. If you go all the way back to Table 1 (which you don’t really need to do), you’ll see that their total gift capacity is $405,958,000 – a lot of money.

Our regression analysis created a very granular affinity score for this group. It had 408 different levels. The alums with the lowest of these scores (according to the regression analysis) are least likely to give a lot of money to the school; the alums with the highest of these scores are the most likely to give a lot of money to the school.

That’s terrific, but 408 score levels is a lot of levels to get your arms around. So what we did is take those scores and chop them up into 20 roughly equal sized groups from 1 to 20, and (again) 1 represents the lowest scores; 20 represents the highest scores. Detailed giving data on all these 1,123 alums is displayed in Table 5 below. We can look at those data in a moment, but let’s move on to our next step.

Pick a small group of alums who have a high capacity rating and a high affinity rating.

Table 5 gives us lots of information about where we’re likely to find this small group. Let’s see what looks interesting here. Remember, everyone in this total group of 1,123 alums has a gift capacity rating greater than $116,000. This is a wealthy bunch of folks – no question about that.

We’ll start with the lowest group, group 1. These 56 alums have the lowest affinity scores of the total group, and their giving data confirms that. Look at the value for these alums in the “sum” column: $430. That means that all 56 alums, as a group, have given less than $500 lifetime to the school. That works out to a mean (average) lifetime gift of less than $8 per alum. Our conclusion? These folks may be wealthy, but both their affinity score and their history of giving have them speaking loud and clear: “Our philanthropic interests are aimed at worthy causes other than our alma mater.”

Now let’s jump up to the top group, group 20. Notice that there are exactly the same number of alums in this group as in group 1 (56), but the giving data for this top group is quite different from the bottom group. Most notably, they’ve given a total of $483,789, well over a thousand times as much as the bottom group. So here we have a group of alums who (a) we know are wealthy; (b) have a high affinity rating developed from the regression analysis; and (c) have already given the school quite a bit of money.

Table 5: Giving Data for Over 1,123 Very High Gift Capacity Alumni Divided Into 20 Groups of Roughly 5% Each by Affinity Score

Look closely at the alums in this small group and identify some who may deserve more scrutiny.

Now we can take a very close look at this group in Table 6 (below, near the end of this post). It lists the total giving and gift capacity for each of these 56 records. (Remember, each of the 56 alums has a high gift capacity rating, and each has an affinity score that says they really like the school.)

We’ll start off with a couple of alums who have already given a considerable amount to the school. What’s particularly interesting about these two is how different they look from the perspective of the possibility of very large future gifts.

  • Record #1: From the looks of things, this person is probably well known to the research staff and to the gift officers. The person has given more than $100,000 and has a gift capacity that’s not a whole lot more than that amount. We’re pretty sure the school would like to have a lot more alums like this one.
  • Record #7: We find this one pretty interesting. The alum has given a bit over $21,000 lifetime, but their gift capacity is listed as well over $13 million. Since the alum clearly likes the school, and they have considerable wherewithal to give a lot more, why haven’t they given a lot more? Maybe there’s a good reason, maybe not. At the very least this is someone who deserves continued attention both from the prospect research side of the house and the gift officer side of the house.

Now we’ll move down to five alums (Records #15, 17, 18, 20, and 24) all of whom have given less than $6,000 lifetime but whose gift rapacity ratings all exceed $400,000. Here we are probably in the neighborhood of prospects who truly are flying under the radar. They may have been assigned to a gift officer. And when a prospect researcher looks at their profiles, the researcher may say, “Yeah, we know about him.” But our experience tells us that alums like these are worth a harder look. For example, we would ask:

  • Is the alum really assigned to a gift officer, or did the last gift officer simply write the alum off as not a “good prospect” with no good documentation as to why that decision was made?
  • What does the alum do for a living? Does that occupation (e.g., investment banker) jibe with the gift capacity rating?
  • Has the alum been an active volunteer or season ticket holder?
  • Is he or she at the age where a sizeable planned gift might be a possibility?

You get the idea. With folks like these we think you should dig a little. Some of them may be at what Malcolm Gladwell calls “the tipping point.” They may be right on the verge of making a much larger gift if you do a little more research on them and send the right gift officer out to meet with them.

By the way, take a look at Record #56. This person is really rich, the internal data says he/she really likes the school, but this person hasn’t given any money. We’d sure like to know the story about this person.

At the ending of Table 6 we offer some closing comments. We really appreciate your staying with us up to this point.

Table 6: Giving and Gift Capacity Data for All 56 Alums in the Highest Affinity Group

Some Closing Comments

We’ve put a lot of tables and charts in front of you. That’s a lot of information to absorb. Several thoughts that might be helpful:

  • If you found what we’ve said here intriguing but also a bit confusing, put the piece away for a few days. Then take another look at it. It should be clearer the second time around. If it’s not, please feel free to contact John Sammis at and Peter Wylie at
  • Share what we’ve written here with a colleague whose opinion you respect but who disagrees with you about a lot of things. That should make for an “interesting” discussion.
  • Whatever you do, we hope this piece encourages you and others in your advancement group to take a closer look at all the data you store on your alumni. The two of us will never back away from the importance of doing that when you’re trying to save money on appeals and generate more revenue for worthwhile projects.

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