# CoolData blog

## 13 November 2014

### How to measure the rate of increasing giving for major donors

Filed under: John Sammis, Major Giving, Peter Wylie, RFM — Tags: , , , , , , — kevinmacdonell @ 12:35 pm

Not long ago, this question came up on the Prospect-DMM list, generating some discussion: How do you measure the rate of increasing giving for donors, i.e. their “velocity”? Can this be used to find significant donors who are poised to give more? This question got Peter Wylie thinking, and he came up with a simple way to calculate an index that is a variation on the concept of “recency” — like the ‘R’ in an RFM score, only much better.

This index should let you see that two donors whose lifetime giving is the same can differ markedly in terms of the recency of their giving. That will help you decide how to go after donors who are really on a roll.

You can download a printer-friendly PDF of Peter’s discussion paper here: An Index of Increasing Giving for Major Donors

## 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

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.

## 21 March 2013

### The lopsided nature of alumni giving

Filed under: Alumni, Major Giving, Peter Wylie — Tags: , , , — kevinmacdonell @ 6:06 am

## Guest post by Peter B. Wylie

(Printer-friendly PDF download of this post available here: Lopsided Nature of Alum Giving – Wylie)

Eight years ago I wrote a piece called Sports, Fund Raising, and the 80/20 Rule”. It had to do with how most alumni giving in higher education comes from a very small group of former students. Nobody was shocked or awed by the article. The sotto voce response seemed to be, “Thanks, Pete. We got that. Tell us something we don’t know.” That’s okay. It’s like my jokes. A lot of ’em don’t get more than a polite laugh; some get stone silence.

Anyway, time passed and I started working closely with John Sammis. Just about every week we’d look at a new alumni database, and over and over, we’d see the same thing. The top one percent of alumni givers had donated more than the other ninety-nine percent.

Finally, I decided to take a closer look at the lifetime giving data from seven schools that I thought covered a wide spectrum of higher education institutions in North America. Once again, I saw this huge lopsided phenomenon where a small, small group of alums were accounting for a whopping portion of the giving in each school. That’s when I went ahead and put this piece together.

What makes this one any different from the previous piece? For one thing, I think it gives you a more granular look at the lopsidedness, sort of like Google Maps allows you to really focus in on the names of tiny streets in a huge city. But more importantly, for this one I asked several people in advancement whose opinions I respect to comment on the data. After I show you that data, I’ll summarize some of what they had to say, and I’ll add in some thoughts of my own. After that, if you have a chance, I’d love to hear what you think. (Commenting on this blog has been turned off, but feel free to send an email to kevin.macdonell@gmail.com.)

The Data

I mentioned above that I looked at data from seven schools. After some agonizing, I decided I would end up putting you to sleep if I showed you all seven. So I chopped it down to four. Believe me, four is enough to make the point.

Here’s how I’ve laid out the data:

• For each of the four schools I ranked only the alumni givers (no other constituencies) into deciles (10 groups), centiles (100 groups), and milliles (1,000 groups), by total lifetime hard credit giving. (There is actually no such word as “milliles” in English; I have borrowed from the French.)
• In the first table in each set I’ve included all the givers. In the second table I’ve included only the top ten percent of givers. And in the third table I’ve included only the top one percent of givers. (The chart following the third table graphically conveys some of the information included in the third table.)

To make sure all this is clear, let’s go through the data for School A. Take a look at Table 1. It shows the lifetime giving for all alumni donors at the school divided into ten equal size groups called deciles. Notice that the alums in decile 10 account for over 95% of that giving. Conversely, the alums in decile 1 account for two tenths of one percent of the giving.

Table 1: Amount and Percentage of Total Lifetime Giving in School A for all Alumni by Giving Decile

Moving on to Table 2. Here we’re looking at only the top decile of alumni givers divided into one percent groups. What jumps out from this table is that the top one percent of all givers account for more than 80% of alumni lifetime giving. That’s five times as much as the remaining 99% of alumni givers.

Table 2: Amount and Percentage of Total Lifetime Giving at School A for Top Ten Percent of Alumni Donors

If that’s not lopsided enough for you, let’s look at Table 3 where the top one percent of alumni givers is divided up into what I’ve called milliles. That is, tenth of a percent groups. And lo and behold, the top one tenth of one percent of alumni donors account for more than 60% of alumni lifetime giving. Figure 1 shows the same information in a bit more dramatic way than does the table.

Table 3: Amount and Percentage of Total Lifetime Giving at School A for Top One Percent of Alumni Donors

What I’d recommend is that you go through the same kinds of tables and charts laid out below for Schools B, C, and D. Go as fast or as slowly as you’d like. Being somewhat impatient, I would focus on Figures 2-4. I think that’s where the real punch in these data resides.

Table 4: Amount and Percentage of Total Lifetime Giving in School B for all Alumni by Giving Decile

Table 5: Amount and Percentage of Total Lifetime Giving at School B for Top Ten Percent of Alumni Donors

Table 6: Amount and Percentage of Total Lifetime Giving at School B for Top One Percent of Alumni Donors

Table 7: Amount and Percentage of Total Lifetime Giving in School C for all Alumni by Giving Decile

Table 8: Amount and Percentage of Total Lifetime Giving at School C for Top Ten Percent of Alumni Donors

Table 9: Amount and Percentage of Total Lifetime Giving at School C for Top One Percent of Alumni Donors

Table 10: Amount and Percentage of Total Lifetime Giving in School D for all Alumni by Giving Decile

Table 11: Amount and Percentage of Total Lifetime Giving at School D for Top Ten Percent of Alumni Donors

Table 12: Amount and Percentage of Total Lifetime Giving at School D for Top One Percent of Alumni Donors

When I boil down to its essence what you’ve just looked at for these three schools, here’s what I see:

• In School B over the half of the total giving is accounted for by three tenths of one percent of the givers.
• In School C we have pretty much the same situation as we have in School B.
• In School D over 60% of the total giving is accounted for by two tenths of one percent of the givers.

Over the years I’ve gotten to know a number of thoughtful/idea-oriented folks in advancement. I asked several of them to comment on the data you’ve just seen. To protect the feelings of the people I didn’t ask, I’ll keep the commenters anonymous. They know who they are, and they know how much I appreciate their input.

Most of the big money in campaigns and other advancement efforts does not come from alumni. I’m a bit embarrassed to admit that I had forgotten this fact. CASE puts out plenty of literature that confirms this. It is “friends” who carry the big load in higher education fundraising. At least two of the commenters pointed out that we could look at that fact as a sad commentary on the hundreds and hundreds of thousands of alums who give little or nothing to their alma maters. However, both felt it was better to look at these meager givers as an untapped resource that we have to do a better job of reaching.

The data we see here reflect the distribution of wealth in society. The commenter said, “There simply are very few people who have large amounts of disposable wealth and a whole lot of hard working folks who are just trying to participate in making a difference.” I like this comment; it jibes with my sense of the reality out there.

“It is easier (and more comfortable) to work with donors rather than prospective donors.” The commenter went on to say: “The wealthier the constituency the more you can get away with this approach because you have enough people who can make mega-gifts and that enables you to avoid building the middle of the gift pyramid.” This is very consistent with what some other commenters had to say about donors in the middle of the pyramid — donors who don’t get enough attention from the major giving folks in advancement.

Most people in advancement ARE aware of the lopsidedness. All of the commenters said they felt people in advancement were well aware of the lopsided phenomenon, perhaps not to the level of granularity displayed in this piece. But well aware, nonetheless.

What you see in this piece underestimates the skew because it doesn’t include non-givers. I was hoping that none of the commenters would bring up this fact because I had not (and still have not) come up with a clear, simple way to convey what the commenter had pointed out. But let’s see if I can give you an example. Look at Figure 4. It shows that one tenth of one percent of alumni givers account for over 48% of total alumni giving. However, let’s imagine that half of the solicitable alumni in this school have given nothing at all. Okay, if we now double the base to include all alums, not just alum givers, then what happens to the percentage size of that top one tenth of one percent of givers? It’s no longer one tenth of one percent; it’s now one twentieth of one percent. If you’re confused, let’s ask someone else reading this thing to explain it. I’m spinning my wheels.

One More Thought from Me

But here’s a thought that I’ve had for a long time. When I look at the incredible skewness that we see in the top one percent of alumni donors, I say, “WHY?!” Is the difference among the top millile and the bottom millile in that top one percent simply a function of capacity to give? Maybe it is, but I’d like to know. And then I say, call me crazy, LET’S FIND OUT! Not with some online survey. That won’t cut it. Let’s hire a first rate survey research team to go out and interview these folks (we’re not talking a lot of people here). Would that cost some money to go out and get these answers? Yes, and it would be worth every penny of it. The potential funding sources I’ve talked to yawn at the idea. But I’ll certainly never let go of it.

As always, let us know what you think.

## Guest post by Peter B. Wylie and John Sammis

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 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.

## 30 August 2011

### After the data mining … prospect research asks, “what then?”

Filed under: Major Giving, Prospect identification — kevinmacdonell @ 5:38 am

I recently had a question from a prospect researcher who is taking on the task of learning data mining to predict propensity to make a major gift. (Yay!) She wanted to know, what happens “after” the data mining? Let’s say she ranks her prospects by score and now she’s got 100 or 200 names — what then? She writes: “I fear that I will then have to create 100 in-depth profiles on these prospects because the fundraisers will not have a plan or the confidence to move forward with these names.”

The situation is familiar: Too many names, not enough time to create full profiles for everyone on the list. My first instinct is to call this a prospect research problem and not a data mining problem.

When I was a prospect researcher, I had to create in-depth profiles for any prospect we were meeting with – even if it was the very first meeting and a gift would be years off, if it ever came at all. Today I work at a university with a much larger staff of development officers, but a research office that is (relatively) smaller. Full profiles for qualifying visits is unthinkable. DOs get no more than a summary briefing on prospects they’re meeting for the first time. This is for obvious practical reasons, but it’s my understanding that this is becoming the norm for many research shops – the full profile is produced only at an advanced stage of cultivation. So my first suggestion is, limit research to “top level” information only: Job title and company, giving history with the institution, maybe their Who’s Who profile if it exists … and not much more.

My second thought is that a data-related solution is possible. I would try an approach that Peter Wylie uses: Take the top several hundred prospects (that is, according to propensity score) and sort them in descending order by lifetime giving. Think of the propensity score as summing up the affinity that the prospect feels for the institution. The lifetime giving dollar amount also provides evidence of affinity, but capacity as well. If a prospect has a very high affinity score AND has given in five figures, they’re probably a good major-gift prospect. Take the top 10 or so names and do in-depth profiles on them alone, leaving the others for later. Or, if wealth screening data is available, one could use that instead of lifetime giving to cross with the list of top-scoring prospects.

But after thinking about it again, perhaps the real issue is contained in the original question: The researcher fears that fundraisers won’t have a plan, and they won’t have confidence in the process. That’s a fundamental problem, one that can only be addressed by communication, a certain amount of selling on the part of the data miner, and a lot of support from upper management.

## 10 March 2011

Filed under: Major Giving, Model building, predictive modeling, Predictor variables, regression — Tags: , — kevinmacdonell @ 6:09 am

(Image used via Creative Commons license. Click image for source)

In an earlier post, I wrote about giving-related variables and whether or not they’re okay to use in a model that is trying to predict giving itself. (My answer was “it depends”. See Giving-related variables: Keep or leave out?) Today I zero in on a specific example: gifts of securities as a predictor of major giving.

Following the logic of my earlier post, if the sample of people whom you intend to score includes non-donors, and you want non-donors to have a chance of making it onto the radar, then you must rule out ‘Gift of Stock’ as a predictor. Why? Because you want to keep any proxy for your outcome variable (the Y side of your equation) out of the predictors (the X side of the equation), as much as possible. A ‘yes’ for ‘Has made a gift of stock’ is possible ONLY for the donors in your sample, and will provide no insight into a non-donor’s potential for major giving.

But giving-related variables are frequently used to predict major gift potential. Gift count, first gift, recency, and stock gifts are all enticing predictors. You have a decision to make: Do you exclude non-donors, or leave non-donors in and forgo the potential predictive power of these variables?

For some the answer might be easy. If the vast majority of major donors to your institution had some prior giving before making their biggest gifts, and a major gift from a non-donor is extremely unlikely, then it makes sense to exclude non-donors. This makes most sense for alumni models: Alumni who are solicited every year and don’t give are rather unlikely to turn around and give a million dollars. (Although it happens!)

You can avoid having to make the decision, however, if you build two models: One including non-donors (and using no giving-related variables), and one excluding them (freeing your hand to use giving-related variables). That’s what I do. I test the output scores against a holdout sample of major donors, and whichever model outperforms in scoring the major donors will be my choice for that year.

Let’s say that at least one of your models is a donor-only model, and you’re itching to use ‘Stock gifts’ as a predictor. Hold on! You’re not done yet. You need to evaluate the degree to which ‘Stock gifts’ is independent of your DV. If the variable equates to major giving itself, it is not at all independent and should be excluded. It is merely a proxy for being a major donor.

It’s clear that stock givers are different from other donors. In the data set I have before me, alumni who have made at least one gift of stock have median lifetime giving of about \$40,000, compared with all other donors’ median giving of about \$170. More than 66% of stock donors have lifetime giving over \$25,000, and more than 90% of them have made at least one gift of \$1,000 or greater.

The fact of having given a gift of securities cannot seriously be considered “independent” of the DV, but the degree of non-independence varies with how the DV is defined. If I define it as “LT Giving over \$25K”, I’m probably in the clear, because a considerable portion of stock donors (34%, in my data set) fall outside the definition of my DV. If my DV is “One or more gifts of \$1K or greater,” however, I should steer clear of the stock-gifts predictor. True, not all stock donors are in the DV, but almost all of them are.

Stock donors probably represent a very small percentage of all your donors, so the variable may have little influence either way: Not a high-value predictor, but not a damaging one, either. (Given the limited number in your sample, the correlation coefficient is going to be pretty low.) Maybe if 85% of the stock donors were in my DV, instead of 90%, I might go ahead and use it. So in the end, it’s a judgment call based on what seems to make sense for your data and what you hope to get out of it.

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