Donor volatility: testing years of non-giving as a predictor for the next big gift

Guest post by Jessica Kostuck, Data Analyst, Annual Giving, Queen’s University

 

During my first few weeks on the job, my AD set me up on several calls with colleagues in similar, data-driven roles, at universities across the country. One such call was with Kevin MacDonell, keeper of CoolData, with whom I had a delightfully geeked-out conversation about predictive modeling. We ran the gamut of weird and wonderful data points, ending on the concept of donor volatility.

 

When a lapsed high-end donor has no discernable annual giving pattern, is it possible to use his or her years of non-giving to predict and influence their next big gift?

 

Our goal for our Annual Giving program was to identify these “volatile” donors (lapsed high-end donors with an erratic giving history), and reactivate (ideally, upgrade) them, through a targeted solicitation with an aggressive ask string.

 

(For more on volatility, see Odd but true findings? Upgrading annual donors are “erratic” and “volatile”, which describes findings that suggest the best prospects for a big upgrade in giving are those who are “erratic”, i.e. have prior giving but are not loyal, every-year donors, and “volatile”, i.e. are inconsistent about the amounts they give.)

 

I did some stock market research (see footnote), decided on a minimum value for the entry-point into our volatility matrix ($500), and, together with Senior Programmer Analyst, Kim Wilkinson, got cracking on writing a program to identify volatile donors.

 

 

 

Our ideal volatile donors had given ≥ $500 at least twice in the last 10 years, without any consecutive (“stable”) periods. Year over year, our ideal volatile donor would act in one of three ways – increase their giving by at least 60%, decrease their giving by at least 60%, or not give at all. Given the capacity level displayed by these volatile donors, we replaced years of very low-end giving <$99) with null values (“throwaway gifts”).

 

We had strict conditions for what would remove a donor from our table. If a donor had two years of consecutive giving within a ±60% differential from their previous highest giving point (v_value), we considered this a natural (or, at least, for this test, not sufficiently irregular) fluctuation in giving, and they were removed from the table. If the donor had two consecutive years of low-end (but not null) giving ($99-$499), this was considered a deliberate decrease, and they, too, were removed. Conversely, if a donor had two consecutive years of greatly increased giving, this was considered a deliberate increase, and they were also removed.

 

At any point, a donor could be admitted, or readmitted into our volatility matrix, by establishing, or re-establishing, a v_value and subsequent valid volatility point.

 

The difference between a lapsed donor and a volatile donor

 

Below is a sample pool of donors we examined.

 

 

Donor 1 is volatile all the way through, with greatly varying levels of giving, culminating in two years of non-giving. Donor 1 is currently volatile, and thus enters our test group.

 

Donor 2 is volatile for two years – FY07-08 and FY08-09 (v_value of $5,000 in FY07-08, followed by a valid volatile point in FY08-09 with a decrease of -80%), but then is removed from the table in FY09-10 with only a -50% decrease in giving. They do not establish a new v_value, even though their FY09-10 giving meets the minimum giving threshold for this test, because of their consecutive, only marginally decreased giving in FY10-11. This excludes Donor 2 from our test.

 

Donor 3 enters our volatility matrix in FY04-05, leaves in FY07-08, reenters in FY10-11, and maintains volatility to current day, and, thus, enters into our test solicitation.

 

While all three of these donors are lapsed, and are all SYBUNTs, only Donor 1 and Donor 3 are, by our definition, volatile.

 

Solicitation strategy and results

 

We now had a pool of constituents who were at least two years lapsed in giving, who all had a history of inconsistent, but not unsubstantial, contributions to the university. In an email solicitation, we presented constituents with both upgrade language and an aggressive ask matrix, beginning at a minimum of +60% of their highest ever v_value, regardless of where they were in the ebb and flow of their volatility cycle. Again, the goal of this test was to (1) identify donors with high capacity (2) whose giving to the university was erratic in frequency and loyalty and (3) encourage these donors to reactivate at greater than their previously-established high-end giving.

 

In our results analysis, we broadened our examination to include any gifts received from our testing pool within the subsequent four weeks, not just gifts linked to this particular solicitation code, to verify the legitimacy of tagging these donors as volatile – that is, having a higher-than-average probability to reactivate at a high-end giving level.

 

An important part of our analysis included comparing our testing pool to a control pool, pairing each of our volatile donors with a non-volatile twin who shared as many points of fiscal and biographic information as was possible.

 

Within the four-week time frame, our test group had about a 7% activity rate, whereas our control group had an activity rate of about 5% (average for the institution during this timeframe). Within our volatility test group, 50% of donors gave an amount that would plot a valid point on our volatility matrix.

 

Conclusion and next steps

 

Through our experiment, we sought to identify volatile donors, and test if we could trigger a reactivation in giving, ideally at, or greater than, their highest level on record.

 

Since not all of the donors within our test group made their gifts to the coded solicitation with the volatile ask matrix, it is indiscernible whether being presented with language and ask amounts that reflected their elusive giving behavior prompted a gift – volatile or otherwise. However, we do feel confident that we’re onto something when it comes to identifying and predicting the behavior of a particular, valuable set of donors to our institution.

 

Our above-average response rate (both versus the control group, and institution-wide) supports our “theory of volatility”, insofar as that volatile donors are an existing pool with shared behaviors within our donor population. We plan to re-run this test again at the same time next year, continuing our search to find a pattern within the instability.

 

Were we able to gather definitive results that will define and shape future annual giving strategy? Not exactly. But as far as data goes, this was definitely cool.

 

Jessica Kostuck is the Data Analyst, Annual Giving at Queen’s University in Kingston, Ontario. She can be reached at jessica.kostuck@queensu.ca.

 

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1. Varadi, David. “Volatility Differentials: High/Low Volatility versus Close/Close Volatility (HVL-CCV).” CSS Analytics. 29 Mar. 2011. Web. Winter 2015.

How to measure the rate of increasing giving for major donors

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

 

The lopsided nature of alumni giving

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.

What Some People in Advancement have to Say about All This

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.

Here are a few of the many helpful observations they made:

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.

Odd but true findings? Upgrading annual donors are “erratic” and “volatile”

In Annual Fund, Leadership giving typically starts at gifts of $1,000 (at least in Canada it does). For most schools, these donors make up a minority of all donors, but a majority of annual revenue. They are important in their own right, and for delivering prospects to Major Giving. Not surprising, then, that elevating donors from entry-level giving to the upper tiers of the Annual Fund is a common preoccupation.

It has certainly been mine. I’ve spent considerable time studying where Leadership donors come from, in terms of how past behaviours potentially signal a readiness to enter a new level of support. Some of what I’ve learned seems like common sense. Other findings strike me as a little weird, yet plausible. I’d like to share some of the weird insights with you today. Although they’re based on data from a single school, I think they’re interesting enough to merit your trying a similar study of donor behaviour.

First, some things I learned which you probably won’t find startling:

• New Leadership donors tend not to come out of nowhere. They have giving histories.
• Their previous giving is usually recent, and consists of more than one or two years of giving.
• Usually those gifts are of a certain size. Many donors giving at the $1,000 level for the first time gave at least $500 the previous year. Some gave less than that, but $500 seems to be an important threshold.

In short, it’s all about the upgrade: Find the donors who are ready to move up, and you’re good to go. But who are those donors? How do you identify them?

It would be reasonable to suggest that you should focus on your most loyal donors, and that RFM scoring might be the way to go. I certainly thought so. Everyone wants high retention rates and loyal donors. Just like high-end donors, people who give every year are probably your program’s bread and butter. They have high lifetime value, they probably give at the same time of year (often December), and they are in tune with your consistent yearly routine of mailings and phone calls. Just the sort of donor who will have a high RFM score. So what’s the problem?

The problem was described at a Blackbaud annual fund benchmarking session I attended this past spring: Take a hard look at your donor data, they said, and you’ll probably discover that the longer a donor has given at a certain level, the less likely she is to move up. She may be loyal, but if she plateaued years ago at $100 or $500 per year, she’s not going to respond to your invitation to join the President’s Circle, or whatever you call it.

Working with this idea that donor  loyalty can equate to donor inertia, I looked for evidence of an opposite trait I started calling “momentum.” I defined it as an upward trajectory in giving year over year, hopefully aimed at the Leadership level. I pulled a whole lot of data: The giving totals for each of the past seven years for every Annual Fund donor. I tried various methods for characterizing the pattern of each donor’s contributions over time. I wanted to calculate a single number that represented the slope and direction of each donor’s path: Trending sharply up, or somewhat up, staying level, trending somewhat down, or sharply down.

I worked with that concept for a while. A long while. I think people got sick of me talking about “momentum.”

After many attempts, I had to give up. The formulas I used just didn’t seem to give me anything useful to sum up the variety of patterns out there. So I tried studying some giving scenarios, based on whether or not a donor gave in a given year. As you might imagine, the number of possible likely scenarios quickly approached the level of absurdity. I actually wrote this sentence: “What % of donors with no giving Y1-Y4, but gave in Y5 and did not give in Y6 upgraded from Y5 to Y7?” It was at that point that my brain seized up. I cracked a beer and said to hell with that.

I tried something new. For each donor, I converted their yearly giving totals into a flag that indicated whether they had giving in a particular year or not: Y for yes, N for no. Imagine an Excel file with seven columns full of Ys and Ns, going on for thousands of rows, one row per donor. Then I concatenated the first six columns of Y/Ns. A donor who gave every year ended up with the string “YYYYYY”. A donor who gave every second year looked like “YNYNYN” — and so on.

I called these strings “donor signatures” — sort of a fingerprint of their giving patterns over six years. Unlike a fingerprint, though, these signatures were not unique to the individual. The 15,000 donors in my data file fit into just 64 signatures.

A-ha, now I was getting somewhere. I had set aside the final year of giving data — year seven — which I could use to determine whether a donor had upgraded, downgraded or stayed the same. All I had to do was take those 64 categories of donors and rank them by the percentage of donors who had upgraded in the final year. Then I could just eyeball the sorted signatures and see if I could detect any patterns in the signatures that most often led to the upgrading behaviours I was looking for. (This is much easier done in stats software than in Excel, by the way.)

All of the following observations are based on the giving patterns of donors who gave in the final two years, which allowed me to compare whether they upgraded or not. This cut out many possible scenarios (eg., donors who didn’t give in one of those two years), but it was a good starting point.

I confirmed that the more years a donor has given, the more likely they are to be retained. BUT:

• The more previous years a donor has given consecutively, the LESS likely they are to upgrade if they give again.
• A donor is markedly more likely to upgrade from the prior year if they have lapsed at least one year prior to giving again.
• Specifically, they are most likely to upgrade if they have one, two or three years with giving in the previous five. More than that, and they are becoming more loyal, and therefore less likely to upgrade.
• Donors who give every other year, or who have skipped up to two years at a time, are most likely to upgrade from last year to the current year.

I told you it was counter-intuitive. If it was just all obvious common sense, we wouldn’t need data analysis. Here’s more odd stuff:

• In general, the same qualities that make a donor more likely to upgrade also make a donor upgrade by a higher amount.
• By far, the highest-value upgrader is a last-year donor who lapsed the previous year but had three years of giving in the previous five.
• The next-highest donor signatures all show combinations of repeated giving and lapsing.
• As a general rule, the highest-value upgraders have about an equal number of years as a donor and as a non-donor.

The conclusion? Upgrade potential can be a strangely elusive quality. From this analysis it appears that being a frequent donor (three or four years out of the past six) is a positive, but only if those years are broken up by the odd non-giving year. In other words, the upgrading donor is also something of an erratic donor.

I thought that was a pretty nifty phenomenon to bring to light. I decided to augment it by trying another, similar approach. Instead of flagging the simple fact of having given or not given in a particular year, this time I flagged whether a donor had upgraded from one year to the next.

Again I worked with seven fiscal years of giving data. I was interested in the final year – year seven – setting that as the “result” of the previous six years of giving behaviour. I was interested only in people who gave that year, AND who had some previous giving in years 1 to 6. The result set consisted of “Gave same or less” or “Upgrade”, and if upgrade, the average dollar upgrade.

The flags were a little more complicated than Y/N. I used ‘U’ to denote an upgrade from the year previous, ‘S’ to denote giving at the same level as the year previous, ‘D’ for a downgrade, and ‘O’ (for “Other”) if no comparison was possible (i.e., one or both years had no giving). Each signature had five characters instead of six, since it’s not possible to assign a code to the first year (no previous year of giving in the data to compare with).

This time there were 521 signatures, which made interpretation much more difficult. Many signatures had fewer than five donors, and only a dozen or so contained more than 100 donors. But when I counted the number of upgrades, downgrades and “sames” that a donor had in the previous five years, and then looked at how they behaved in the final year, some clear patterns did emerge:

• Donors who upgraded two or more times in the past were most likely to upgrade again in the current year, and the size of their upgrade was larger, than donors who upgraded fewer times, or never upgraded. Upgrade likelihood was highest if the donor had upgraded at least four times in the previous five years.

• Donors who gave the same amount every year were the least likely to upgrade — this is the phenomenon people were talking about at the benchmarking meeting I mentioned earlier. Donors who never gave the same amount from one year to the next, or did so only once, had higher median upgrade amounts.

• And finally, the number of downgrades … this paints a strongly counter-intuitive picture. The more previous downgrades a donor had, the more likely they were to upgrade in the current year!

In other words, along with being erratic, donors who upgrade also have the characteristic that I started to call volatility.

I wondered what the optimum mix of upgrades and downgrades might be, so I created a variable called “Upgrades minus Downgrades”, which calculated the difference only for donors who had at least one upgrade or downgrade. The variable ranged from -4 (lots of downgrades) to plus 5 (lots of upgrades). What I discovered is that it’s not a balance that is important, but that a donor be at one extreme or the other. The more extreme the imbalance, the more likely an upgrade will occur, and the larger it will be, on average.

ERRATIC and VOLATILE … two qualities you’ve probably never ascribed to your most generous donors. But there it is: Your best prospects for an ambitious ask (perhaps a face-to-face one) might be the ones who are inconsistent about the amounts they give, and who don’t care to give every year.

By all means continue to use RFM to identify the core of your top supporters, but be aware that this approach will not isolate the kind of rogue donors I’m talking about. You can use donor signatures, as I have, to explore the extent to which this phenomenon prevails in your own donor database. From there, you might wish to capture these behaviours as input variables for a proper predictive model.

At worst, you’ll be soliciting donors who will never become loyal, and who may not have lifetime values that are as attractive as our less flashy, but more dependable, loyal donors. On the other hand, if you put a bigger ask in front of them and they go for it, they may eventually enter the realm of major giving. And then it will all be worth it.

Are we underestimating the generosity of our older alums?

Guest post by Peter Wylie and John Sammis

(Download a PDF version here: Are we underestimating the generosity of our older alums?)

I’m an older alum. I don’t want my generosity to be underestimated by my alma mater. Trouble is, if any of my old college buddies happen to read this, they’ll say, “Really? And what generosity would that be, Pete?”

Yeah, well, I’m not the kind of person we’re talking about in this piece. We’re talking about alums who graduated at least 30 years ago and have made substantial contributions to their colleges or universities. There are a heck of a lot of them out there, and more and more such alums are joining the ranks as our population ages.

So … let’s say you work in advancement in higher education or a secondary school. Does the data you have stored on these “senior” folks cause you to underestimate what they’ve given to your institution? John Sammis and I would offer a strong “yes” to this question. Why? Because of two phenomena that most of us rarely consider when we look at the lifetime hard credit dollars given by these alums: (1) inflation and (2) the fact that electronic giving records rarely go back much further than 1985.

In this piece we’ll take you through a series of examples from one college that has many older alums. The folks who work in advancement at that college agree with us. They think that inflation and electronic record keeping have caused them to underestimate the generosity of many of their older alums. We hope you find the examples intriguing, and we hope they cause you to think about how you may be doing the same kind of underestimating.

Let’s start off by looking at the lifetime hard credit giving at our example college. Table 1 shows that the oldest class year quartile (alums who graduated between 1931 and 1976) have given far more than the remaining three quarters of younger alums.

We see this kind of phenomenon nearly every week when we look at a new alumni database. The oldest 25% of alums almost always dwarf the cumulative giving of all other alums. And yet, in spite of that fact, we think the giving of these older alums is underrepresented. Bear with us.

Now let’s look at Table 2, which gives a picture of the inflation that has occurred in the United States over the last 60 years or so.

To make sure we’re being clear in the table, we’d ask you to indulge us and answer these three questions:

1. If an alum made a gift of $161 dollars in 1950, what would that gift amount to in 2011 dollars?
2. If an alum made a $50,000 gift in 2011, what would be the 1965 equivalent of that gift?
3. If an alum made a gift of $1,198 in 1975, what would that gift amount to in 2011 dollars?

If you came up with these answers, we’ve been clear:

1. $1,500
2. $6,914
3. $5,000

If you found the table a bit confusing, maybe a look at Figure 1 will help. It shows the same information conveyed in the leftmost column of Table 2. Whether you look at the table or the figure, the big picture is that there has been a good amount of inflation in this country over the last six decades. More to the point, what look like small gifts made decades ago look like very substantial gifts in today’s dollars.

What we’ll be doing now is speculative. We’ll be looking at the dollars that specific alums at our example school have contributed over many years. And then we’ll be estimating what those dollars are worth in terms of 2011 dollars. We should caution you: Our estimates could be pretty accurate, or they could be off the mark by quite a bit.

We’ll start by looking at the top five lifetime givers in each of the class year quartiles as laid out in Table 3. As you’d expect, the giving of the top five alums in the first quartile (those graduating between 1931 and 1976) greatly outdistances the giving of the second quartile top five alums (those graduating between 1977 and 1989) and so on down the line.

Now let’s check out something interesting for just the top five givers in class year quartiles 1 and 2. Notice in Figure 2 below that:

• The number one giver graduated in 1943 but didn’t make a first gift until 1983.
• The number two giver graduated in 1959 but didn’t make a first gift until 1984
• The number three giver graduated in 1967 but didn’t make a first gift until 1984.
• The number four giver graduated in 1949 but didn’t make a first gift until 1983.
• The number five giver graduated in 1965 but didn’t make a first gift until 1984.

Now take a look at Figure 3. Notice that:

• The number one giver graduated in 1983 and made a first gift in 1983.
• The number two giver graduated in 1985 and made a first gift in 1994. (This is the first time we’ve seen a first gift made after 1984.)
• The number three giver graduated in 1981 and made a first gift in 1983.
• The number four giver graduated in 1980 and made a first gift in 1983.
• The number five giver graduated in 1978 and made a first gift in 1984.

That was a lot of detail to offer you – maybe more than necessary. But by offering the detail we wanted to make at least two important points. The first is that our example college obviously has not recorded gift giving (electronically) before 1983. How do we conclude that (other than the fact that our contacts at the college confirmed it)? Because none of the ten alums we’ve looked at are listed as having made a first gift before 1983. No big surprise there.

But we think our second point is more attention-getting. In both quartiles there are alums listed as having graduated before 1983 (some of them long before then). What do we know about their giving prior to 1983? That’s our second point. We simply don’t know what their giving was prior to 1983. And here’s where our speculation comes in.

This is what we did. For each of the top three givers in the first and second class year quartiles, we made two inflation adjustments to their actual lifetime giving amounts: A conservative estimate, and a liberal estimate. As you read through how we made these estimates, you may disagree to some extent with our approach. We’d be surprised if you didn’t. But we’d like to defer discussion of such disagreements until the end of the piece.

The conservative estimate.

We took the year of each alum’s first gift and the year of each alum’s last gift, added them together, and divided that number by two. For example, let’s take the top giver in Quartile 1 whose lifetime hard credit giving is recorded as $11,286,872. That alum’s recorded year of first gift is 1983. His or her last gift was made in 2005. The average we computed was 1994. Using an inflation calculator, we arrived at an estimated lifetime giving amount of $17,097,560. In other words we converted $11,286,872 from 1994 dollars to 2011 dollars.

The liberal estimate.

We took each alum’s year of graduation and the year of each alum’s last gift, added them together, and divided that number by two. Let’s go back to our example of the top giver in Quartile 1 whose lifetime hard credit giving is recorded as $11,286,872. That alum’s year of graduation is 1943. His or her last gift was made in 2005. The average we computed was 1974. Using the same inflation calculator, we arrived at an estimated lifetime giving amount of $51,384,972. In other words we converted $11,286,872 from 1974 dollars to 2011 dollars.

In Figures 4-6 we compare the top three givers in class year quartile 1 and class year quartile 2 in terms of recorded lifetime giving, a conservative estimate of inflation adjusted giving, and a liberal estimate of inflation adjusted giving. In each figure you’ll see some dramatic giving differences between the older alum in class year quartile 1 (1931-1976) and the younger alum in class year quartile 2 (1977-1989). Since we’ve already covered a lot of information included in Figure 4, we’ll skip to Figure 5 and offer some reasons for why these differences are so large. To avoid overloading you with detail, we won’t do that for Figure 6, but if we did, the same kind of thinking would apply.

Here the older alum graduated in 1959, and the younger alum graduated in 1985. The older alums is electronically listed as having made his or her first gift in 1984 and his or her last gift in 2010 The younger alum made his or her first gift in 1994 and his or her last gift in 2008. Here’s what we think is going on:

• We’re certain that the actual giving amount for the younger alum ($3,127,000) is accurate. We’re far less certain about the actual giving amount ($10,150,030) for the older alum. We suspect that this amount is only the money in 2011 dollars that the alum contributed since 1984.
• How about the conservative estimate for each alum? The amounts for both are greater because we used the middle year between the first and last gift to adjust for inflation.
• How about the liberal estimate for each alum? Notice that this estimate for the younger alum is the same as the conservative estimate because the middle year for both estimates is the same. But not for the older alum. Here we picked the middle year between 1959 (the alum’s grad year) and 2010 (the last gift year) as the year to adjust for inflation. That year is 1984, thirteen years earlier than 1997, the year we used for the conservative estimate. That’s why we see the large jump from $14,196,156 to $20,777,384.

Closing Thoughts

The first thought we’d like to offer is that no one (including us) should make any hard and fast conclusions from what we’ve presented here. The data are only from one school, and our inflation estimates are certainly open to at least some healthy skepticism.

That said, we’d like you to consider these points:

• Inflation is something we’ve never seen figured into how lifetime giving is computed at fundraising institutions. In the almost six years the two of us have been working together, we’ve looked at giving data from at least 200 non-profits and schools. In all that time we’ve never had more than a fleeting discussion with anyone at those institutions about how both limited record keeping and inflation have distorted the giving picture they have of older donors. We could blame those folks for that shortcoming, but if we did that, we’d have to blame ourselves more. So we won’t do either. What we will do is start paying more attention to this issue and talking it up. What we’ve seen as a product of doing this paper is strong motivation to do just that.
• Depending on how accurately your gift data has been stored, it shouldn’t be hard to make more accurate inflation estimates than we have here. We know you’re busy, and we know your advancement services folks are stretched thin. But a little project that involved 20 or so of your major donors who have made multiple gifts might be enlightening. All that would be required is to make an inflation adjustment for each gift for each donor. Then simply add those adjusted gifts up for the donors and compare the recorded lifetime amounts with the inflation adjustment amounts. Maybe you’d have a ho-hum reaction to what you see, but we doubt it.
• Anything that might get folks in advancement to focus more on the value of their internal data can’t be a bad thing. In prospect research/major giving there is a huge emphasis on looking to the outside of an institution’s database to find evidence of giving capacity for the people stored in that database. There’s nothing wrong with that. Acquiring information about people’s wherewithal to make major gifts is important. However, and this is a big however, there is a huge under-emphasis in the field on looking at internal data – data that is often far more accurate than external data, and certainly far less expensive to access and look at. For example, you simply will not find external data that point to someone in your database who made a seemingly mid-size gift many years ago that is worth a huge amount in today’s dollars. But uncovering that kind of information in your own database could provide a strong hint (that won’t come from any outside source) about that person’s assets as well as his or her likelihood of sharing those assets with the place you work for.

As always, we’d love to get your reactions to what we’ve had to say here.

Does “Do Not Solicit” mean they won’t give?

Guest post by Peter Wylie

(Click here: Do Not Solicit – 071310 to download Microsoft Word version of this paper.)

I think studying higher education alumni databases is a lot like studying a foreign Language. Take French. I’ve been a student of it off and on for over 50 years. I’ve gotten pretty good at it. So much so that native speakers often compliment me on my fluency. When they do, I thank them graciously and quickly add “Plus je sais, plus je ne sais pas.” The more I know, the more I don’t know.

For over a decade I thought I “knew” that alumni who tell their alma maters not to ask them for money meant two things: (1) Don’t ask me, and (2) I’m not going to give you anything. Then a good friend and colleague sent me some data for a project that may end up being pretty cool. Of the 50,000 or so records she sent, over 7,000 had a “do not solicit” tag. Okay. That’s clear. Don’t call ’em. Don’t send ’em letters or e-mails. And definitely don’t go knocking on their doors just because you happen to be in the neighborhood. That’s all pretty clear.

But I’m nosy; I checked to see if any of those 7,000 or so had ever given the school any money. It turns out that a little over a third of them had given some money. I couldn’t tell how much because my friend had not given me dollar amounts. Just whether or not they’d ever given a hard credit gift to the school. (We’d agreed that actual amounts would distract us from the goals of the project.)

I couldn’t stop there. I started digging around to see what some of the differences were between the ones who’d given and the ones who hadn’t. I think I found some interesting stuff. Of course, that doesn’t mean what I found applies to other schools. Maybe it does. Maybe it doesn’t.

For me, that’s not so important. What’s important is that young folks (like Kevin MacDonell, the creator of this great blog) who carry the vanguard of data-driven decision making in our field will take this topic farther than I have. And that can’t be a bad thing.

In the rest of this piece I’ll cover three topics:

• Some of the interesting differences between the givers and non-givers (who say “Don’t solicit.”)
• The model I used to “predict” the givers
• Some concluding thoughts

Some Interesting Differences

In a moment we can take a look at Figures 1-6 that show some variables on which “Do not solicit” alums differ markedly when it comes to giving. Before we do that, however, I think it’s important to point out something we don’t know. We don’t know when or under what circumstances any of these alums told the school they did not want to be solicited. For example,

• Did they do it recently or a long time ago?
• Did they do it on a permanent basis, or just temporarily? It’s possible that some of them are parents of kids attending the school and their attitude for the next four years is, “Hey, I’m paying an arm and a leg for tuition. Until I’m done with that, please don’t go asking me for money while I’m in hock up to my underwear.”
• Are they bent out of shape over the beloved football coach who was finally let go after ten consecutive losing seasons – something they may eventually get beyond? Or is it simply a case of, “I’ll give you something, maybe a lot, when I’m ready. In the meantime, don’t bug me?”

Again, we don’t know. But before any advancement person uses the results of a predictive model like the one I lay out here, they should consider these sorts of possibilities. More about that later.

Let’s take a walk through the figures. After each one I’ve made a short comment or two to make it clear what the figure is conveying.

There are huge difference in the giving rates among three types of alums. Undergraduate alums are almost twice as likely to give as graduate alums and more than six times as likely to give as non-degreed alums.

Alums who have attended at least one reunion are two and a half times as likely to give as alums who have never attended a reunion.

Alums who have never attended an event are less than half as likely to give as alums who’ve attended one event and about a third as likely to give as alums who have attended two or more events.

Alums who are members of the online community are twice as likely to give as alums who are not members.

Alums who were members of a Greek organization as undergrads are almost twice as likely to give as alums who were not Greek members.

Alums who are children of alums are two and a half times as likely to give as alums whose parents are not alums.

Clearly, these figures (and several I haven’t included) show there are a number of variables in the alumni database at this school that can be used to predict which “do not solicit” alums may be more likely to give in the future. One way to find out if we’re right is to (a) build a model that yields a “likelihood of giving score” for each of these alums and (b) begin testing the model.

Building a predictive model

I have to admit that, here, I was a bit torn about how much technical detail to go into. If I take you minute step by minute step through the model building process, I risk both confusing you and boring you. That wouldn’t be good. On the other hand, if I go too light on the details, you may say, “Come on, Pete, you haven’t given me enough info to see if I can test your results at my own school.”

So … how about this as a compromise?

Using multiple regression (if you know what that is, great; if you don’t, not to worry), I created a score for each alum where EVER GAVE (0/1) was the outcome variable and where the variables you see above as well as the following were the predictor variables:

• Count of current volunteer activities
• Count of past volunteer activities
• Year the alum graduated (or should have if he or she had completed a degree “on time”)

The model generated well over 2,500 different score levels into which the 7,393 alums  could fall – way too many for anybody to get their conceptual arms around. The adjusted R squared for the model was about 36%. (Again, don’t worry if you don’t know what R squared means.)

I divided the 2,695 score levels into 10 groups of “deciles” containing about 740 alums each. As you look at Table 1, you’ll see these groups varied some in size. In Decile 1 (the lowest scoring 10% of alums) there are 726 people. In Decile 10 (the highest scoring 10% of alums) there are 739 people.

TABLE 1: Frequency Breakdown of “Do Not Solicit” Alums by Score Decile

If the model is to be useful in identifying “Do not solicit” alums who are likely to give, the number and percentage of givers should increase as the deciles increase. A look at Table 2 and Fig. 7 show that these numbers and percentages do just that. For example, in the first decile, of the 726 alums, only 12 (1.7%) have ever given anything to the school. In the tenth decile, of the 739 alums, 649 (87.8%) have given to the school.

TABLE 2: Number and Percentage of “Do Not Solicit” Alums Who Gave by Score Decile

Some Concluding Thoughts

You may have read other stuff I’ve written on data mining and predictive modeling. If you have, could be you’re tired of hearing me say that higher education advancement offices ignore most of the data they have on their alums as they go about the business of raising money from those alums.

Well, no rest for the weary here. This little study is a good example of what I’m talking about. Who would have thought there would be such striking differences between givers and non-givers who ask not to be asked? Not me. I just stumbled onto it because I was playing around with data that had been put together for a totally different reason. And I think that’s my point. When we’re talking alumni databases, there are oceans of data that could help us save a lot of money and generate a lot more revenue for some very worthy missions. But with the drops of analysis we’re currently doing on all that data, we’re not saving all that much money nor generating that much more revenue. We’re not. And that needs to change.

If you’re reading this, I suspect I’m preaching to the choir. It’s probably not you that needs the convincing on this matter. It’s probably the big bosses where you work that need the exhortation and cajoling. So the next time one of ‘em makes noises about spending big money on some product or service that’s designed to “prepare for the campaign” or whatever, you might say something like: “That’s cool. That’s great. But let’s not forget about all that data we’ve got just sitting there waiting to help us identify individuals who can play a major role in this project.” If they look intrigued, start pitching them. If they don’t, don’t give up. Take another run at them in a few months.

Back to the specific topic of this paper. Try to build a predictive giving model for your own “Do not solicit” alums. If you’re not proficient with using statistical software, find somebody in your school who is and get them to help you. If you have an Office of Institutional Research (or some similarly titled entity), that’s a good place to look. Just make sure the person you choose grasps the basic idea of what you’re trying to do and has the capacity to explain technical stuff in plain English.

Then do some in depth research on the high scoring alums that emerge from your model, especially those who’ve been generous givers over the years. Share the names with some of your colleagues, whether they’re involved in the annual fund, prospect research, or part of your cadre of gifts officers. My bet is that at least one alum is going to pop out of the mix who is teed up for a real nice appeal if your group comes up with the right strategy.

Good luck and let us know how it turns out.