CoolData blog

9 October 2015

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.

 

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9 September 2015

Prospect Research, ten years from now

Guest post by Peter B. Wylie

 

(This opinion piece was originally posted to the PRSPT-L Listserv.)

 

As many of you know, Dave Robertson decided to talk about the future of prospect research in New Orleans via a folk song. It was great. The guy’s got good pipes and plays both the harmonica and the guitar really well.

 

Anyway, here’s what I wrote him back in March. It goes on a bit. So if you get bored, just dump it.

 

It was stiflingly hot on that morning in early September of 1967 when my buddy Bunzy sat down in the amphitheater of Cornell’s med school in Manhattan. He and the rest of his first year classmates were chattering away when an older gentleman scuffled through a side door out to a podium. The room fell silent as the guy peered over his reading glasses out onto a sea of mostly male faces: “I’ll be straight with you, folks. Fifty percent of what we teach you over the next four years will be wrong. The problem is, we don’t know which fifty percent.”

 

I’ve often thought about the wisdom embedded in those words. The old doc was right. It is very hard for any of us to predict what anything will be like twenty years hence. Nate Silver in both his book “The Signal and the Noise” and on his immensely popular website underlines how bad highly regarded experts in most fields are at making even short range predictions.

 

So when Dave Robertson asked me to jot down some ideas about how prospect research will look a decade or more from now, I wanted to say, “Dave, I’ll be happy to give it a shot. But I’ll probably be as off the mark as the futurists in the early eighties. Remember those dudes? They gave us no hint whatsoever of how soon something called the internet would arrive and vastly transform our lives.”

 

With that caveat, I’d like to talk about two topics. The first has to do with something I’m pretty sure will happen. The second has to do with something sprinkled more with hope than certainty.

 

On to the first. I am increasingly convinced prospect researchers a decade or more from now will have far more information about prospects than they currently have. Frankly, I’m not enthusiastic about that possibility. Why? Privacy? Take my situation as I write down my thoughts for Dave. I’m on the island of Vieques, Puerto Rico with Linda to celebrate our fortieth wedding anniversary. We’ve been here almost two weeks. Any doggedly persistent law enforcement investigator could find out the following:

 

  • What flights we took to get here
  • What we paid for the tickets
  • The cost of each meal we paid for with a credit card
  • What ebooks I purchased while here
  • What shows I watched on Netflix
  • How many miles we’ve driven and where with our rental jeep
  • How happy we seemed with each other while in the field of the many security cameras, even in this rustic setting

 

You get the idea. Right now, I’m gonna assume that the vast majority of prospect researchers have no access to such information. More importantly, I assume their ethical compasses would steer them far away from even wanting to acquire such information.

 

But that’s today. March 2, 2015. How about ten years from now? Or 15 years from now, assuming I’m still able to make fog on a mirror? As it becomes easier and easier to amalgamate data about old Pete, I think all that info will be much easier to access by people willing to purchase it. That includes people who do prospect research. And if those researchers do get access to such data, it will help them enormously in finding out if I’m really the right fit for the mission of their fundraising institution. I guess that’s okay. But at my core, I don’t like the fact that they’ll be able to peek so closely into who I am and what I want in the days I have left on this wacky planet. I just don’t.

 

On to the second thing. Anybody who’s worked in prospect research even a little knows that the vast majority of the money raised by their organization comes from a small group of donors. If you look at university alumni databases, it’s not at all unusual to find that one tenth of one percent of the alums have given almost a half of the total current lifetime dollars. I think that situation needs to change. I think these institutions must find ways to get more involvement from the many folks who really like them and who have the wherewithal to give them big gifts.

 

So … how will the prospect researchers of the future play a key role in helping fundraising organizations (be they universities or general nonprofits) do a far better job of identifying and cultivating donors who have the resources and inclination to pitch in on the major giving front? I think/hope it’s gonna be in the way campaigns are run.

 

Right now, here’s what seems to happen. A campaign is launched with the help of a campaign consultant. A strategy is worked out whereby both the consultants and major gift officers spread out and talk to past major givers and basically say, “Hey, you all were really nice and generous to us in the last campaign. We’re enormously grateful for that. We truly are. But this time around we could use even more of your generosity. So … What can we put you down for?”

 

This is a gross oversimplification of what happens in campaigns. And it’s coming from a guy who doesn’t do campaign consulting. Still, I don’t think I’m too far off the mark. To change this pattern I think prospect researchers will have to be more assertive with the captains of these campaigns: The consultants, the VPs, the executives, all of whom talk so authoritatively about how things should be done and who can simultaneously be as full of crap as a Christmas goose.

 

These prospect researchers are going to have to put their feet down on the accelerator of data driven decision-making. In effect, they’ll need to say:

 

“We now have pretty damn accurate info on how wealthy a whole bunch of our younger donors are. And we have good analytics in place to ascertain which of them are most likely to step it up soon … IF we strategize how to nurture them over the long run. Right now, we’re going after the low hanging fruit that is comprised of tried and true donors. We gotta stop just doing that. Otherwise, we’re leaving way too much money on the table.”

 

All that I’ve been saying in this second part is not new. Not at all. Perhaps what may be a little new is what I have hinted at but not come right out and proclaimed. In the future we’ll need more prospect researchers to stand up and be outspoken to the campaign movers and shakers. To tell these big shots, politely and respectfully, that they need to start paying attention to the data. And do it in such a way that they get listened to.

 

That’s asking a lot of folks whose nature is often quiet, shy, and introverted. I get that. But some of them are not. Perhaps they are not as brazen as James Carvel is/was. But we need more folks like them who will stand up and say, “It’s the data, stupid!” Without yelling and without saying “stupid.”

26 August 2015

Exploring associations between variables

Filed under: Book, CoolData, Predictor variables — Tags: , , , — kevinmacdonell @ 6:57 pm

 

CoolData has been quiet over the summer, mainly because I’ve been busy writing another book. (Fine weather has a bit to do with it, too.) The book will be for nonprofit and higher education advancement professionals interested in learning how to use multiple regression to build predictive models. Over the next few months, I will adapt various bits from the work-in-progress as individual posts here on CoolData.

 

I’ll have more to say about the book later, so if you’re interested, I suggest subscribing via email (see the box to the right) to have the inside track on this project. (And if you aren’t familiar with the previous book, co-written with Peter Wylie, then have a look here.)

 

A generous chunk of the book is about the specifics of getting your hands dirty with cleaning up your messy data, transforming it to make it suitable for regression analysis, and exploring it for interesting patterns that can strengthen a predictive model.

 

When you import a data set into Data Desk or other statistics package, you are looking at more than just a jumble of variables. All these variables are in a relation; they are linked by patterns. Some variables are strongly associated with each other, others have weaker associations, and some are hardly related to each other at all.

 

What is meant by “association”? A classic example is a data set of children’s weights, heights, and ages. Older children tend to weigh more and be taller than younger children. Heavier children tend to be older and taller than younger children. We say that there is an association between age and weight, between height and weight, and between age and height.

 

Another example: Alumni who are bigger donors tend to attend more reunion events than alumni who give more modestly or don’t give at all. Or put the other way, alumni who attend more events tend to give more than alumni who attend fewer or no events. There is an association between giving and attending events.

 

This sounds simple enough — even obvious. The powerful consequence of these truths is that if we know the value of one variable, we can make a guess at the value of another, as long as the association is valid. So if we know a child’s weight and height, we can make a good guess of his or her age. If we know a child’s height, we can guess weight. If we know how many reunions an alumna has attended, we can make a guess about her level of giving. If we know how much she has given, we can guess whether she’s attended more or fewer reunions than other alumni.

 

We are guessing an unknown value (say, giving) based on a known value (number of events attended). But note that “giving” is not really an unknown. We’ve got everyone’s giving recorded in the database. What is really unknown is an alum’s or a donor’s potential for future giving. With predictive modeling, we are making a guess at what the value of a variable will be in the (near) future, based on the current value of other variables, and the type and degree of association they have had historically.

 

These guesses will be far from perfect. We aren’t going to be bang-on in our guesses of children’s ages based on weight and height, and we certainly aren’t going to be very accurate with our estimates of giving based on event attendance. Even trickier, projecting into the future — estimating potential — is going to be very approximate.

 

Still, our guesses will be informed guesses, as long as the associations we detect are real and not due to random variation in our data. Can we predict exactly how much each donor is going to give over this coming year? No, that would be putting too much confidence in our powers. But we can expect to have plenty of success in ranking our constituents in order by how likely they are to engage in whatever behaviour we are interested in, and that knowledge will be of great value to the business.

 

Looking for potentially useful associations is part of data exploration, which is best done in full hands-on mode! In a future post I will talk about specific techniques for exploring different types of variables.

 

11 May 2015

A new way to look at alumni web survey data

Filed under: Alumni, Surveying, Vendors — Tags: , , , , — kevinmacdonell @ 7:38 pm

Guest post by Peter B. Wylie, with John Sammis

 

Click to download the PDF file of this discussion paper: A New Way to Look at Survey Data

 

Web-based surveys of alumni are useful for all sorts of reasons. If you go to the extra trouble of doing some analysis — or push your survey vendor to supply it — you can derive useful insights that could add huge value to your investment in surveying.

 

This discussion paper by Peter B. Wylie and John Sammis demonstrates a few of the insights that emerge by matching up survey data with some of the plentiful data you have on alums who respond to your survey, as well as those who don’t.

 

Neither alumni survey vendors nor their higher education clients are doing much work in this area. But as Peter writes, “None of us in advancement can do too much of this kind of analysis.”

 

Download: A New Way to Look at Survey Data

 

 

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

 

19 August 2014

Score! … As pictured by you

Filed under: Book, Peter Wylie, Score! — Tags: , , — kevinmacdonell @ 7:25 pm
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Left to right: Elisa Shoenberger, Leigh Petersen Visaya, Rebekah O’Brien, and Alison Rane in Chicago. (Click for full size.)

During the long stretch of time that Peter Wylie and I were writing our book, Score! Data-Driven Success for Your Advancement Team, there were days when I thought that even if we managed to get the thing done, it might not be that great. There were just so many pieces that needed to fit together somehow … I guess we each didn’t want to let the other down, so we plugged on despite doubts and delays, and then, somehow, it got finished.

Whew, I thought. Washed my hands of that! I expected I would walk away from it,  move on to other projects, and be glad that I had my early mornings and weekends back.

That’s not what happened.

These few months later, my eye will still be caught now and then by the striking, colourful cover of the book sitting on my desk. It draws me to pick it up and flip through it — even re-read bits. I find myself thinking, “Hey, I like this.”

Of course, who cares, right? I am not the reader. However, whatever I might think about Score!, it has been even more gratifying for Peter and I to hear from folks who seem to like it as much as we do. How fun it has been to see that bright cover popping up in photos and on social media every once in a while.

I’ve collected a few of those photos and tweets here, along with some other images related to the book. Feel free to post your own “Score selfies” on Twitter using the hashtag #scorethebook. Or if you’re not into Twitter, send me a photo at kevin.macdonell@gmail.com.

Click here to order your copy of Score! from the CASE Bookstore.

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jen

Jennifer Cunningham, Senior Director, Metrics+Marketing for the Office of Alumni Affairs, Cornell University. @jenlynham

Click here to order your copy of Score! from the CASE Bookstore.

While we would like for you to buy it, we would LOVE for you to read it and put it to work in your shop. Your buying it earns us each enough money to buy a cup of coffee. Your READING it furthers the reach and impact of ideas and concepts that fascinate us and which we love to share.

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