CoolData blog

30 May 2016

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

Filed under: Annual Giving, Coolness — Tags: , , , , — kevinmacdonell @ 5:02 am

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.

 

volatile sql clip

 

 

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.

 

volatile donor history image

 

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.

 

————-

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

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.

 

18 February 2014

Save our planet

Filed under: Annual Giving, Why predictive modeling? — Tags: , , — kevinmacdonell @ 9:09 pm

You’ve seen those little signs — they’re in every hotel room these days. “Dear Guest,” they say, “Bed sheets that are washed daily in thousands of hotels around the world use millions of gallons of water and a lot of detergent.” The card then goes on to urge you to give some indication that you don’t want your bedding or towels taken away to be laundered.

Presumably millions of small gestures by hotel guests have by now added up to a staggering amount of savings in water, energy and detergent.

It reminds me of what predictive analytics does for a mass-contact area of operation such as Annual Giving. If we all trimmed down the amount of acquisition contacts we make — expending the same amount of effort but only on the people with highest propensity to give, or likelihood to pick up the phone, or greatest chance of opening our email or what-have-you — we’d be doing our bit to collectively conserve a whole lot of human energy, and not a few trees.

With many advancement leaders questioning whether they can continue to justify an expensive Phonathon program that is losing more ground every year, getting serious about focusing resources might just be the saviour of a key acquisition program, to boot.

30 April 2013

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.

18 April 2013

A response to ‘What do we do about Phonathon?’

I had a thoughtful response to my blog post from earlier this week (What do we do about Phonathon?) from Paul Fleming, Database Manager at Walnut Hill School for the Arts in Natick, Massachusetts, about half an hour from downtown Boston. With Paul’s permission, I will quote from his email, and then offer my comments afterword:

I just wanted to share with you some of my experiences with Phonathon. I am the database manager of a 5-person Development department at a wonderful boarding high school called the Walnut Hill School for the Arts. Since we are a very small office, I have also been able to take on the role of the organizer of our Phonathon. It’s only been natural for me to combine the two to find analysis about the worth of this event, and I’m happy to say, for our own school, this event is amazingly worthwhile.

First of all, as far as cost vs. gain, this is one of the cheapest appeals we have. Our Phonathon callers are volunteer students who are making calls either because they have a strong interest in helping their school, or they want to be fed pizza instead of dining hall food (pizza: our biggest expense). This year we called 4 nights in the fall and 4 nights in the spring. So while it is an amazing source of stress during that week, there aren’t a ton of man-hours put into this event other than that. We still mail letters to a large portion of our alumni base a few times a year. Many of these alumni are long-shots who would not give in response to a mass appeal, but our team feels that the importance of the touch point outweighs the short-term inefficiencies that are inherent in this type of outreach.

Secondly, I have taken the time to prioritize each of the people who are selected to receive phone calls. As you stated in your article, I use things like recency and frequency of gifts, as well as other factors such as event participation or whether we have other details about their personal life (job info, etc). We do call a great deal of lapsed or nondonors, but if we find ourselves spread too thin, we make sure to use our time appropriately to maximize effectiveness with the time we have. Our school has roughly 4,400 living alumni, and we graduate about 100 wonderful, talented students a year. This season we were able to attempt phone calls to about 1,200 alumni in our 4 nights of calling. The higher-priority people received up to 3 phone calls, and the lower-priority people received just 1-2.

Lastly, I was lucky enough to start working at my job in a year in which there was no Phonathon. This gave me an amazing opportunity to test the idea that our missing donors would give through other avenues if they had no other way to do so. We did a great deal of mass appeals, indirect appeals (alumni magazine and e-newsletters), and as many personalized emails and phone calls as we could handle in our 5-person team. Here are the most basic of our findings:

In FY11 (our only non-Phonathon year), 12% of our donors were repeat donors. We reached about 11% participation, our lowest ever. In FY12 (the year Phonathon returned):

  • 27% of our donors were new/recovered donors, a 14% increase from the previous year.
  • We reached 14% overall alumni participation.
  • Of the 27% of donors who were considered new/recovered, 44% gave through Phonathon.
  • The total amount of donors we had gained from FY11 to FY12 was about the same number of people who gave through the Phonathon.
  • In FY13 (still in progess, so we’ll see how this actually plays out), 35% of the previously-recovered donors who gave again gave in response to less work-intensive mass mailing appeals, showing that some of these Phonathon donors can, in fact, be converted and (hopefully) cultivated long-term.

In general, I think your article was right on point. Large universities with a for-pay, ongoing Phonathon program should take a look and see whether their efforts should be spent elsewhere. I just wanted to share with you my successes here and the ways in which our school has been able to maintain a legitimate, cost-effective way to increase our participation rate and maintain the quality of our alumni database.

Paul’s description of his program reminds me there are plenty of institutions out there who don’t have big, automated, and data-intensive calling programs gobbling up money. What really gets my attention is that Walnut Hill uses alumni affinity factors (event attendance, employment info) to prioritize calling to get the job done on a tight schedule and with a minimum of expense. This small-scale data mining effort is an example for the rest of us who have a lot of inefficiency in our programs due to a lack of focus.

The first predictive models I ever created were for a relatively small university Phonathon that was run with printed prospect cards and manual dialing — a very successful program, I might add. For those of you at smaller institutions wondering if data mining is possible only with massive databases, the answer is NO.

And finally, how wonderful it is that Walnut Hill can quantify exactly what Phonathon contributes in terms of new donors, and new donors who convert to mail-responsive renewals.

Bravo!

22 January 2013

Sticking a pin in acquisition mail bloat

Filed under: Alumni, Annual Giving, Vendors — Tags: , , — kevinmacdonell @ 6:45 am

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.

Older Posts »

The Silver is the New Black Theme. Create a free website or blog at WordPress.com.

Follow

Get every new post delivered to your Inbox.

Join 1,243 other followers