Ready for a sobering look at your last five years of alumni giving?

Guest post by Peter B. Wylie and John Sammis

  

Download this discussion paper here: Sobering Look at last 5 fiscal years of alumni giving

 

My good friends Wylie and Sammis are at it again, digging into the data to ask some hard questions.

 

This time, their analysis shines a light on a concerning fact about higher education fundraising: A small group of donors from the past are responsible for the lion’s share of recent giving.

 

My first reaction on reading this paper was, well, that looks about right. A school’s best current donors have probably been donors for quite some time, and alumni participation is in decline all over North America. So?

 

The “so” is that we talk about new donor acquisition but are we really investing in it? Do we have any clue who’s going to replace those donors from the past and address the fact that our fundraising programs are leaky boats taking on water? Is there a future in focusing nearly exclusively on current loyal donors? (Answer: Sure, if loyal donors are immortal.)

 

A good start would be for you to get a handle on the situation at your institution by looking at your data as Wylie and Sammis have done for the schools in their discussion paper. Download it here: Sobering Look at last 5 fiscal years of alumni giving.

 

Final thoughts on Phonathon donor acquisition

No, this is not the last time I’ll write about Phonathon, but after today I promise to give it a rest and talk about something else. I just wanted to round out my post on the waste I see happening in donor acquisition via phone programs with some recent findings of mine. Your mileage may vary, or “YMMV” as they say on the listservs, so as usual don’t just accept what I say. I suggest questions that you might ask of your own data — nothing more.

I’ve been doing a thorough analysis of our acquisition efforts this past year. (The technical term for this is a WTHH analysis … as in “What The Heck Happened??”) I found that getting high phone contact rates seemed to be linked with making a sufficient number of call attempts per prospect. For us, any fewer than three attempts per prospect is too few to acquire new donors in any great number. In general, contact rates improve with call attempt numbers above three, and after that, the more the better.

“Whoa!”, I hear you protest. “Didn’t you just say in your first post that it makes no sense to have a set number of call attempts for all prospects?”

You’re right — I did. It doesn’t make sense to have a limit. But it might make sense to have a minimum.

To get anything from an acquisition segment, more calling is better. However, by “call more” I don’t mean call more people. I mean make more calls per prospect. The RIGHT prospects. Call the right people, and eventually many or most of them will pick up the phone. Call the wrong people, and you can ring them up 20, 30, 50 times and you won’t make a dent. That’s why I think there’s no reason to set a maximum number of call attempts. If you’re calling the right people, then just keep calling.

What’s new here is that three attempts looks like a solid minimum. This is higher than what I see some people reporting on the listservs, and well beyond the capacity of many programs as they are currently run — the ones that call every single person with a phone number in the database. To attain the required amount of per-prospect effort, those schools would have to increase phone capacity (more students, more nights), or load fewer prospects. The latter option is the only one that makes sense.

Reducing the number of people we’re trying to reach to acquire as new donors means using a predictive model or at least some basic data mining and scoring to figure out who is most likely to pick up the phone. I’ve built models that do that for two years now, and after evaluating their performance I can say that they work okay. Not super fantastic, but okay. I can live with okay … in the past five years our program has made close to one million call attempts. Even a marginal improvement in focus at that scale of activity makes a significant difference.

You don’t need to hack your acquisition segment in half today. I’m not saying that. To get new donors you still need lots and lots of prospects. Maybe someday you’ll be calling only a fraction of the people you once did, but there’s no reason you can’t take a gradual approach to getting more focused in the meantime. Trim things down a bit in the first year, evaluate the results, and fold what you learned into trimming a bit more the next year.

Sticking a pin in acquisition mail bloat

I recently read a question on a listserv that prompted me to respond. A university in the US was planning to solicit about 25,000 of its current non-donor alumni. The question was: How best to filter a non-donor base of 140,000 in order to arrive at the 25,000 names of those most likely to become donors? This university had only ever solicited donors in the past, so this was new territory for them. (How those alumni became donors in the first place was not explained.)

One responder to the question suggested narrowing down the pool by recent class years, reunion class years, or something similar, and also use any ratings, if they were available, and then do an Nth-record select on the remaining records to get to 25,000. Selecting every Nth record is one way to pick an approximately random sample. If you aren’t able to make this selection, the responder suggested, then your mail house vendor should be able to.

This answer was fine, up until the “Nth selection” part. I also had reservations about putting the vendor in control of prospect selection. So here are some thoughts on the topic of acquisition mailings.

Doing a random selection assumes that all non-donor alumni are alike, or at least that we aren’t able to make distinctions. Neither assumption would be true. Although they haven’t given yet, some alumni feel closer affinity to your school than others, and you should have some of these affinity-related cues stored in your database. This suggests that a more selective approach will perform better than a random sample.

Not long ago, I isolated all our alumni who converted from never-donor to donor at any time in the past two years. (Two years instead of just one, in order to boost the numbers a bit.) Then I compared this group with the universe of all the never-donors who had failed to convert, based on a number of attributes that might indicate affinity. Some of my findings included:

• “Converters” were more likely than “non-converters” to have an email in the database.
• They were more likely to have answered the phone in our Phonathon (even though the answer was ‘no pledge’)
• They were more likely to have employment information (job title or employer name) in the database.
• They were more likely to have attended an event since graduating.

Using these and other factors, I created a score which was used to select which non-donor alumni would be included in our acquisition mailing. I’ve been monitoring the results, and although new donors do tend to be the alumni with higher scores, frankly we’ve had poor results via mail solicitation, so evaluation is difficult. This in itself is not unusual: New-donor acquisition is very much a Phonathon phenomenon for us — in our phone results, the effectiveness of the score is much more evident.

Poor results or not, it’s still better than random, and whenever you can improve on random, you can reduce the size of a mailing. Acquisition mailings in general are way too big, simply because they’re often just random — they have to cast a wide net. Unfortunately your mail house is unlikely to encourage you to get more focused and save money.

Universities contract with vendors for their expertise and efficiency in dealing with large mailings, including cleaning the address data and handling the logistics that many small Annual Fund offices just aren’t equipped to deal with. A good mail house is a valuable ally and source of direct-marketing expertise. But acquisition presents a conflict for vendors, who make their money on volume. Annual Fund offices should be open to advice from their vendor, but they would do well to develop their own expertise in prospect selection, and make drastic cuts to the bloat in their mailings.

Donors may need to be acquired at a loss, no question. It’s about lifetime value, after all. But if the cumulative cost of that annual appeal exceeds the lifetime value of your newly-acquired donor, then the price is too high.

The tough job of bringing in new alumni donors

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?”

“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.