CoolData blog

25 August 2014

Your nonprofit’s real ice bucket challenge

It was only a matter of time. Over the weekend, a longtime friend dumped a bucket of ice water over his head and posted the video to Facebook. He challenged three friends — me included — to take the Ice Bucket Challenge in support of ALS research. I passed on the cold shower, but this morning I did make a gift to ALS Canada, a cause I wouldn’t have supported had it not been for my friend Paul and the brilliant campaign he participated in.*

Universities and other charities are, of course, watching closely and asking themselves how they can replicate this phenomenon. Fine … I am skeptical that central planning and a modest budget can give birth to such a massive juggernaut of socially-responsible contagion … but I wish them luck.

While we can admire our colleagues’ amazing work and good fortune, I am not sure we should envy them. In the coming year, ALS charities will be facing a huge donor-retention issue. Imagine gaining between 1.5 and 2 million new donors in the span of a few months. Now, I have no knowledge of what ALS fundraisers really intend to do with their hordes of newly-acquired donors. Maybe retention is not a goal. But it is a sure thing that the world will move on to some other craze. Retaining a tiny fraction of these donors could make the difference between the ice bucket challenge being just a one-time, non-repeatable anomaly and turning it into a foundation for long-term support that permanently changes the game for ALS research.

Perhaps the ice bucket challenge can be turned into an annual event that becomes as established as the walks, runs and other participatory events that other medical-research charities have. Who knows.

For certain is that the majority of new donors will not give again. Also for certain is that it would be irresponsibly wasteful for charities to spread their retention budget equally over all new donors.

Which brings me to predictive modeling. Some portion of new donors WILL give again. Maybe something about the challenge touched them more deeply than the temporary fun of the ice bucket dare. Maybe they learned something about the disease. Maybe they know someone affected by ALS. There is no direct way to know. But I would be willing to bet that higher levels of engagement can be found in patterns in the data.

What factors might be predictors of longer-term engagement? It is not possible to say without some analysis, but sources of information might include:

  • How the donor arrived at the site prior to making a gift (following a link from another website, following a link via a social media platform, using a search engine).
  • How the donor became aware of the challenge (this is a question on some giving pages).
  • Whether they consented to future communications: Mail, email, or both.
  • Whether the donor continued on to a page on the website beyond the thank you page. (Did they start watching an ALS-related video and if so, how much of it did they watch?)
  • Whether the donor clicked on social media button to share the news of their gift, and where they shared it.

Shreds of ambiguous clues scattered here and there, admittedly, but that is what a good predictive model detects and amplifies. If it were up to me, I would also have asked on the giving page whether the donor had done the ice bucket thing. A year from now, my friend Paul is going to clearly remember the shock of pouring ice water over his head, plus the positive response he got on Facebook, and this will bring to mind his gift and the need to give again. My choosing not to do so might be associated with a lower level of commitment, and thus a lower likelihood of renewing. Just a theory.**

Data-informed segmentation aimed at getting a second gift from newly-acquired donors is not quite as sexy as being an internet meme. However, unlike riding the uncontrollable wave of a social media sensation, retention is something that charities might actually be able to plan for.

* I would like to see this phenomenon raise all boats for medical charities, therefore I also gave to Doctors Without Borders Canada and the Molly Appeal for Medical Research. Check them out.

** Update: I am told that actually, this question IS asked. I didn’t see it on the Canadian site, but maybe I just missed it. Great!


I was quoted on this topic in a story in the September 4th online edition of the Chronicle of Philanthropy. Link (subscribers only): After Windfall, ALS Group Grapples With 2.4-Million Donor Dilemma

3 May 2010

The tough job of bringing in new alumni donors

Filed under: Alumni, Donor acquisition, John Sammis, Peter Wylie — Tags: , , — kevinmacdonell @ 8:48 am

Guest post by Peter Wylie and John Sammis

(Click here: Donor acquisition – Wylie and Sammis – 2 May 2010 – to download Microsoft Word version of this paper.)

Most alumni have never given a cent to their alma maters. “Whoa!” you may be saying, “What’s your evidence, fellows? That’s hard to swallow.”

We would agree. It’s not a pretty picture, but it’s an accurate one. For some documentation you can read “Benchmarking Lifetime Giving in Higher Education”. Sadly, the bottom line is this: In North America the lifetime hard credit alumni participation of at least half of our higher education institutions is less than 50%. If you look at only private institutions, the view is better. Public institutions? Better to not even peek out the window.

We do have a bit of optimism to offer in this paper, but we’ll start off by laying some cards on the table:

  • We’re specialists in data analysis. If we’re not careful, Abraham Maslow’s oft-quoted dictum can apply to us: “If you’re only tool is a hammer, every problem starts looking like a nail.” We don’t have all the answers on this complex issue. In fact, we believe that institutional leadership (from your president and board of trustees) is what’s most important in getting more alums involved in giving. Data driven decision making (the underpinning of all our work) is only part of the solution.
  • Donor acquisition is hard. If you don’t believe that, talk to anyone who runs the annual fund for a large state university. Ask them about their success rates with calling and mailing to never-givers. They will emit sighs of frustration and exasperation. They will tell you about the depressing pledge rates from the thousands and thousands of letters and postcards they send out. They will tell you about the enervating effect of wrong numbers and hang-ups on their student callers. They will tell you it isn’t easy. And they’re right; it isn’t.
  • RFM won’t help. (RFM stands for “Recency of Giving,” “Frequency of Giving,” and “Monetary Value of Giving.” It’s a term that came out of the private sector world of direct marketing over 40 years ago.) Applying that concept to our world of higher education advancement, you would call and mail to alums who’ve given recently, often, and a lot. Great idea. But if we’re focused on non-donors … call it a hunch … that’s probably not going to work out too well.

So … what’s the optimism we can offer? First, we’ve had some success with building predictive models for donor acquisition. They’re not great models, but, as John likes to say, “They’re a heck of a lot better than throwing darts.” In the not too distant future we plan to write something up on how we do that.

But for now we’d like to show you some very limited data from three schools — data that may shed just a little light on who among your non-giving alums are going to be a bit easier than others to attract into the giving fold. Again, nothing we show you here is cause for jumping up and down and dancing on the table. Far from it. But we do think it’s intriguing, and we hope it encourages folks like you to share these ideas with your colleagues and supervisors.

Here’s what we’ll be talking about:

  • The schools
  • The data we collected from the schools
  • Some results
  • A makeshift score that you might test out at your own school

The Schools

One of the schools is a northeastern private institution; the other two are southeastern public institutions, one medium size, the other quite small.

The data we collected from the schools

The most important aspect of the data we got from each school is lifetime giving (for the exact same group of alums) collected at two points in time. With one school (A), the time interval we looked at stretched out over five years. For the other two (B and C), the interval was just a year. However, with all three schools we were able to clearly identify alums who had converted from non-donor to donor status over the time interval.

We collected a lot of other information from each school, but the data we’ll focus on in this piece include:

  • Preferred year of graduation
  • Home Phone Listed (Yes/No)
  • Business Phone Listed (Yes/No)
  • Email Address Listed (Yes/No)

Some Results

The result that we paid most attention to in this study is that a greater percentage of new donors came from the ranks of recent grads than from “older” grads. To arrive at this result we:

  • Divided all alums into one of four roughly equal size groups. If you look at Chart 1, you’ll see that these groups consisted of the oldest 25% of alums who graduated in 1976 and earlier, the next oldest 25% of alums who graduated between the years 1977 and 1980, and so on.
  • For each class year quartile we computed the percentage of those alums who became new donors over the time interval we looked at.

Notice in Chart 1 that, as the graduation years of the alums in School A becomes more recent, their likelihood of becoming a new donor goes up. In the oldest quartile (1976 and earlier), the conversion rate is 1.2%, 1.5% for those graduating between 1977 and 1990, 3% for those graduating between 1991and 1997, and 7.5% for alums graduating in 1998 or later. You’ll see a similar (but less pronounced) pattern in Charts 2 and 3 for Schools B and C.

At this point you may be saying, “Hold on a second. There are more non-donors in the more recent class year quartiles than in the older class year quartiles, right?”


“So maybe those conversion rates are misleading. Maybe if you just looked at the conversion rates of previous non-donors by class year quartiles, those percentages would flatten out?”

Good question. Take a look at Charts 1a, 2a, and 3a below.

Clearly the pool of non-donors diminishes the longer alums have been out of school. So let’s recompute the conversion rates for each of the three schools based solely on previous non-donors. Does that make a difference? Take a look at Charts 1b, 2b, and 3b.

It does make some difference. But, without getting anymore carried away with the arithmetic here, the message is clear. Many more new donors are coming from the more recent alums than they are from the ones who graduated a good while back.

Now let’s look at the three other variables we chose for this study:

  • Home Phone Listed (Yes/No)
  • Business Phone Listed (Yes/No)
  • Email Address Listed (Yes/No)

Specifically, we wanted to know if previous non-donors with a home phone listed were more likely to convert than previous non-donors without a home phone listed. And we wanted to know the same thing for business phone listed and for email address listed.

The overall answer is “yes;” the detailed answers are contained in Charts 4-6. For the sake of clarity, let’s go through Chart 4 together.  It shows that:

  • In School A, 5.8% of previous non-donors with a home phone listed converted; 3.7% without a home phone listed converted.
  • In School B, 3.7% of previous non-donors with a home phone listed converted; 1.2% without a home phone listed converted.
  • In School C, 1.0% of previous non-donors with a home phone listed converted; 0.4% without a home phone listed converted.

Looking at Charts 5 and 6 you can see a similar pattern of differences for whether or not a business phone or an email address was listed.

What comes across from all these charts is that the variables we’ve chosen to look at in this study (year of graduation, home phone, email, and business phone) don’t show big differences between previous non-donors who converted and previous non-donors who did not convert. They show small differences. There’s no getting around that.

What’s encouraging (at least we think so) is that these differences are consistent across the three schools. And since the schools are quite different from one another, we expect that the same kind of differences are likely to hold true at many other schools.

Let’s assume you’re willing to give us the benefit of the doubt on that. Let’s further assume you’d like to check out our proposition at your own school.

A Makeshift Score That You Might Test at Your Own School

Here’s what we did for the data we’ve shown you for each of the three schools:

We created four 0/1 variables for all alums who were non-donors at the first point in time:

  • Youngest Class Year Quartile – alums who were in this group were assigned a 1; all others were assigned a 0.
  • Home Phone Listed — alums who had a home phone listed in the data base were assigned a 1; all others were assigned a 0.
  • Business Phone Listed — alums who had a business phone listed in the data base were assigned a 1; all others were assigned a 0.
  • Email Listed — alums who had an email address listed in the data base were assigned a 1; all others were assigned a 0.

For each alum who was a non-donor at the first point in time, we created a very simple score by adding each of the above variables together. Here’s the formula we used:

SCORE = Youngest Class Year Quartile (0/1) + Home Phone Listed (0/1) + Business Phone Listed (0/1) + Email Listed (0/1)

An alum with a Score of 0 was not in the Youngest Class Year Quartile, did not have a home phone listed, did not have a business phone listed and did not have an email address listed. An alum with a Score of 1met only one of these criteria, but not the other three, and so on up to an alum with a score of 4 who met all the criteria.

Charts 7-9 show the relationship of the Score to new donor conversion. We’d like you browse through them. After you do that we have a few concluding comments.

Some final thoughts:

  1. We think the charts are interesting because they show that using just a little information from an alumni database can point to folks who are far more likely to convert than other folks. Obviously, the score we created here (and suggest you try out at your own school) is very simple. Far more accurate scores can be developed using more advanced statistical techniques and the vast amount of information that’s included in almost all alumni databases.
  2. If you’ve taken the trouble to read this far, we’re, of course, pleased. We believe so fundamentally in data driven decision making that it brightens our day whenever someone at least entertains our ideas. But the problem may be with all the decision makers and opinion influencers out there who are not reading this piece and who would be, at best, bored by it. These are vice presidents and directors and bloggers and vendors who seem unwilling to make a commitment to the use of internal alumni database information — information that could save millions and millions of dollars on appeals (both mail and calling) to alums who are very unlikely to ever become donors.
  3. If you agree with us on point 2, the question becomes, “What can we do to change their minds, to get their attention?” First of all, we strongly encourage you to suppress the urge to grab them by the scruff of the neck and scold them. That won’t work. (Would that it did.) What we suggest is patience combined with persistence. New ideas and ways of doing things take a long time to take hold in institutions. How long has the idea of converting print based medical records to electronic form so they can be quickly shared among physicians and other who must make life altering decisions on the spot every day been around? If memory serves, it’s been a while. But don’t give up making the case and pushing politely but assertively. They’ll come around. We’re (all of us) a benevolent juggernaut whose opinions will eventually prevail.

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