CoolData blog

4 November 2013

Census Zip Code data versus internal data as predictors of alumni giving

Guest post by Peter Wylie and John Sammis

Thanks to data available via the 2010 US Census, for any educational institution that provides us zip codes for the alums in its advancement database, we can compute such things as the median income and the median house value of the zip code in which the alum lives.

Now, we tend to focus on internal data rather than external data. For a very long time the two of us have been harping on something that may be getting a bit tiresome: the overemphasis on finding outside wealth data in major giving, and the underemphasis on looking at internal data. Our problem has been that we’ve never had a solid way to systematically compare these two sources of data as they relate to the prediction of giving in higher education.

John Sammis has done a yeoman’s job of finding a very reasonably priced source for this Census data as well as building some add-ons to our statistical software package – add-ons that allow us to manipulate the data in interesting ways. All this has happened within the last six months or so, and I’ve been having a ball playing around with this data, getting John’s opinions on what I’ve done, and then playing with the data some more.

The data for this piece come from four private, small to medium sized higher education institutions in the eastern half of the United States. We’ll show you a smidgeon of some of the things we’ve uncovered. We hope you’ll find it interesting, and we hope you’ll decide to do some playing of your own.

Download the full, printer-friendly PDF of our study here (free, no registration required): Census ZIP data Wylie & Sammis.

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.

13 November 2012

Making a case for modeling

Guest post by Peter Wylie and John Sammis

(Click here to download post as a print-friendly PDF: Making a Case for Modeling – Wylie Sammis)

Before you wade too far into this piece, let’s be sure we’re talking to the right person. Here are some assumptions we’re making about you:

  • You work in higher education advancement and are interested in analytics. However, you’re not a sophisticated stats person who throws around terms like regression and cluster analysis and neural networks.
  • You’re convinced that your alumni database (we’ll leave “parents” and “friends” for a future paper) holds a great deal of information that can be used to pick out the best folks to appeal to — whether by mail, email, phone, or face-to-face visits.
  • Your boss and your boss’s bosses are, at best, less convinced than you are about this notion. At worst, they have no real grasp of what analytics (data mining and predictive modeling) are. And they may seem particularly susceptible to sales pitches from vendors offering expensive products and services for using your data – products and services you feel might cause more problems than they will solve.
  • You’d like to find a way to bring these “boss” folks around to your way of thinking, or at least move them in the “right” direction.

If we’ve made some accurate assumptions here, great. If we haven’t, we’d still like you to keep reading. But if you want to slip out the back of the seminar room, not to worry. We’ve done it ourselves more times than you can count.

Okay, here’s something you can try:

1. Divide the alums at your school into ten roughly equal size groups (deciles) by class year. Table 1 is an example from a medium sized four year college.

Table 1: Class Years and Counts for Ten Roughly Equal Size Groups (Deciles) of Alumni at School A

2. Create a very simple score:

EMAIL LISTED(1/0) + HOME PHONE LISTED(1/0)

This score can assume three values: “0, “1”, or “2.” A “0” means the alum has neither an email nor a home phone listed in the database. A “1” means the alum has either an email listed in the database or a home phone listed in the database, but not both. A “2” means the alum has both an email and a home phone listed in the database.

3. Create a table that contains the percentage of alums who have contributed at least $1,000 lifetime to your school for each score level for each class year decile. Table 1 is an example of such a table for School A.

Table 2: Percentage of Alumni at Each Simple Score Level at Each Class Year Decile Who Have Contributed at Least $1,000 Lifetime to School A

 

4. Create a three dimensional chart that conveys the same information contained in the table. Figure 1 is an example of such a chart for School A.

In the rest of this piece we’ll be showing tables and charts from seven other very diverse schools that look quite similar to the ones you’ve just seen. At the end, we’ll step back and talk about the importance of what emerges from these charts. We’ll also offer advice on how to explain your own tables and charts to colleagues and bosses.

If you think the above table and chart are clear, go ahead and start browsing through what we’ve laid out for the other seven schools. However, if you’re not completely sure you understand the table and the chart, see if the following hypothetical questions and answers help:

Question: “Okay, I’m looking at Table 2 where it shows 53% for alums in Decile 1 who have a score of 2. Could you just clarify what that means?”

Answer. “That means that 53% of the oldest alums at the school who have both a home phone and an email listed in the database have given at least $1,000 lifetime to the school.”

Question. “Then … that means if I look to the far left in that same row where it shows 29% … that means that 29% of the oldest alums at the school who have neither a home phone nor an email listed in the database have given at least $1,000 lifetime to the school?”

Answer. “Exactly.”

Question. “So those older alums who have a score of 2 are way better givers than those older alums who have a score of 0?”

Answer. “That’s how we see it.”

Question. “I notice that in the younger deciles, regardless of the score, there are a lot of 0 percentages or very low percentages. What’s going on there?”

Answer. “Two things. One, most younger alums don’t have the wherewithal to make big gifts. They need years, sometimes many years, to get their financial legs under them. The second thing? Over the last seven years or so, we’ve looked at the lifetime giving rates of hundreds and hundreds of four-year higher education institutions. The news is not good. In many of them, well over half of the solicitable alums have never given their alma maters a penny.”

Question. “So, maybe for my school, it might be good to lower that giving amount to something like ‘has given at least $500 lifetime’ rather than $1,000 lifetime?”

Answer. Absolutely. There’s nothing sacrosanct about the thousand dollar level that we chose for this piece. You can certainly lower the amount, but you can also raise the amount. In fact, if you told us you were going to try several different amounts, we’d say, “Fantastic!”

Okay, let’s go ahead and have you browse through the rest of the tables and charts for the seven schools we mentioned earlier. Then you can compare your thoughts on what you’ve seen with what we think is going on here.

(Note: After looking at a few of the tables and charts, you may find yourself saying, “Okay, guys. Think I got the idea here.” If so, go ahead and fast forward to our comments.)

Table 3: Percentage of Alumni at Each Simple Score Level at Each Class Year Decile Who Have Contributed at Least $1,000 Lifetime to School B

 

Table 4: Percentage of Alumni at Each Simple Score Level at Each Class Year Decile Who Have Contributed at Least $1,000 Lifetime to School C

Table 5: Percentage of Alumni at Each Simple Score Level at Each Class Year Decile Who Have Contributed at Least $1,000 Lifetime to School D

Table 6: Percentage of Alumni at Each Simple Score Level at Each Class Year Decile Who Have Contributed at Least $1,000 Lifetime to School E

Table 7: Percentage of Alumni at Each Simple Score Level at Each Class Year Decile Who Have Contributed at Least $1,000 Lifetime to School F

Table 8: Percentage of Alumni at Each Simple Score Level at Each Class Year Decile Who Have Contributed at Least $1,000 Lifetime to School G

Table 9: Percentage of Alumni at Each Simple Score Level at Each Class Year Decile Who Have Contributed at Least $1,000 Lifetime to School H

Definitely a lot of tables and charts. Here’s what we see in them:

  • We’ve gone through the material you’ve just seen many times. Our eyes have always been drawn to the charts; we use the tables for back-up. Even though we’re data geeks, we almost always find charts more compelling than tables. That is most certainly the case here.
  • We find the patterns in the charts across the seven schools remarkably similar. (We could have included examples from scores of other schools. The patterns would have looked the same.)
  • The schools differ markedly in terms of giving levels. For example, the alums in School C are clearly quite generous in contrast to the alums in School F. (Compare Figure 3 with Figure 6.)
  • We’ve never seen an exception to one of the obvious patterns we see in these data: The longer alums have been out of school, the more money they have given to their school.
  • The “time out of school” pattern notwithstanding, we continue to be taken by the huge differences in giving levels (especially among older alums) across the levels of a very simple score. School G is a prime example. Look at Figure 7 and look at Table 8. Any way you look at these data, it’s obvious that alums who have even a score of “1” (either a home phone listed or an email listed, but not both) are far better givers than alums who have neither listed.

Now we’d like to deal with an often advanced argument against what you see here. It’s not at all uncommon for us to hear skeptics say: “Well, of course alumni on whom we have more personal information are going to be better givers. In fact we often get that information when they make a gift. You could even say that amount of giving and amount of personal information are pretty much the same thing.”

We disagree for at least two reasons:

Amount of personal information and giving in any alumni database are never the same thing. If you have doubts about our assertion, the best way to dispel those doubts is to look in your own alumni database. Create the same simple score we have for this piece. Then look at the percentage of alums for each of the three levels of the score. You will find plenty of alums who have a score of 0 who have given you something, and you will find plenty of alums with a score of 2 who have given you nothing at all.

We have yet to encounter a school where the IT folks can definitively say how an email address or a home phone number got into the database for every alum. Why is that the case? Because email addresses and home phone numbers find their way into alumni database in a variety of ways. Yes, sometimes they are provided by the alum when he or she makes a gift. But there are other ways. To name a few:

  • Alums (givers or not) can provide that information when they respond to surveys or requests for information to update directories.
  • There are forms that alums fill out when they attend a school sponsored event that ask for this kind of information.
  • There are vendors who supply this kind of information.

Now here’s the kicker. Your reactions to everything you’ve seen in this piece are critical. If you’re going to go to a skeptical boss to try to make a case for scouring your alumni database for new candidates for major giving, we think you need to have several reactions to what we’ve laid out here:

1. “WOW!” Not, “Oh, that’s interesting.” It’s gotta be, “WOW!” Trust us on this one.

2. You have to be champing at the bit to create the same kinds of tables and charts that you’ve seen here for your own data.

3. You have to look at Table 2 (that we’ve recreated below) and imagine it represents your own data.

Table 2: Percentage of Alumni at Each Simple Score Level at Each Class Year Decile Who Have Contributed at Least $1,000 Lifetime to School A

Then you have to start saying things like:

“Okay, I’m looking at the third class year decile. These are alums who graduated between 1977 and 1983. Twenty-five percent of them with a score of 2 have given us at least $1,000 lifetime. But what about the 75% who haven’t yet reached that level? Aren’t they going to be much better bets for bigger giving than the 94% of those with a score of 0 who haven’t yet reached the $1,000 level?”

“A score that goes from 0 to 2? Really? What about a much more sophisticated score that’s based on lots more information than just email listed and home phone listed? Wouldn’t it make sense to build a score like that and look at the giving levels for that more sophisticated score across the class year deciles?”

If your reactions have been similar to the ones we’ve just presented, you’re probably getting very close to trying to making your case to the higher-ups. Of course, how you make that case will depend on who you’ll be talking to, who you are, and situational factors that you’re aware of and we’re not. But here are a few general suggestions:

Your first step should be making up the charts and figures for your own data. Maybe you have the skills to do this on your own. If not, find a technical person to do it for you. In addition to having the right skills, this person should think doing it would be cool and won’t take forever to finish it.

Choose the right person to show our stuff and your stuff to. More and more we’re hearing people in advancement say, “We just got a new VP who really believes in analytics. We think she may be really receptive to this kind of approach.” Obviously, that’s the kind of person you want to approach. If you have a stodgy boss in between you and that VP, find a way around your boss. There’s lots of ways to do that.

Do what mystery writers do; use the weapon of surprise. Whoever the boss you go to is, we’d recommend that you show them this piece first. After you know they’ve read it, ask them what they thought of it. If they say anything remotely similar to: “I wonder what our data looks like,” you say, “Funny you should ask.”

Whatever your reactions to this piece have been, we’d love to hear them.

13 September 2012

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

Filed under: Annual Giving, Prospect identification, RFM — Tags: , , , , — kevinmacdonell @ 8:26 am

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.

13 December 2011

Finding connections to your major gift prospects in your data

Guest post by Erich Preisendorfer, Associate Director, Business Intelligence, Advancement Services, University of New Hampshire

(Thanks to Erich for this guest post, which touches on something a lot of prospect researchers are interested in: mapping relationships to prospects in their database. Actually, this work is more exciting than that, because it actually helps people find connections they may not have known about, via database queries and a simple scoring system. Is your Advancement Services department working on something like this? Why not ask them? — Kevin.)

Data miners often have an objective of exploring sets of data to determine meaningful patterns which can then be modeled for predictive patterning, hopefully to help meet their organization’s end goal(s).  However, there may be a time when the end behavior is not inherent in your database. Such a situation recently came up for my Advancement organization.

Our prospecting team recently started a program wrapped around peer recommendations: A prospect recommends new suspects to us based on the prospect’s interactions with the suspects. The question then became, what can we provide to the prospect to help get them thinking about potential suspects?

We currently do not have any type of data which would allow us to say, “Yes, this is what a relationship looks like,” outside of family relationships. We had to find a different way to identify potential acquaintances. I looked back at my own relationships to determine how I know the people I know. My friends and acquaintances largely come from some basic areas: school, work, places I’ve gone, etc.

Transforming my experience with relationships into what we have for useable data, I saw three key areas where relationships may exist: work history, education history, and extracurricular activities including one-time events. Fortunately, I was able to pinpoint our constituents’ time in each of the above areas to help isolate meaningful, shared experiences amongst constituents. Our work and extracurricular history includes to/from dates, and we have loads of educational history data that includes specific dates. Using this data, I am able to come up with potential relationships from a single prospect.

Prospect Profile (generated by entering a single prospect’s ID):

  • John Adams
  • Widget Factory, Employee 01/05/1971 – 06/16/1996
  • Student Activities: Football, Student Senate 09/1965-05/1966
  • Bachelor of Arts, Botany 1966

Potential Relationships (each item below is a separate query, using the Prospect Profile results):

  • Those employed by the Widget Factory who started before John ended, and ended after John began.
  • Those students who participated in Football and had a class year within +/-3 years of John.
  • Those students in Student Senate at the same time as John, similar to the Widget Factory example.
  • Those students who were in the same class year as John.
  • Those students who share John’s major.

Currently,since I have no way of proving the value of one point of contact over the other, each row returned in the potential relationships earns the constituent one point. Since my database stores historical records, I may get more than one row per constituent in any one category if they met more than one of John’s associated records – say they participated in Student Senate and played Football. This is great, because I want to give those particular constituents two points since they have more than one touch point in common with John.

I end up with a ranked list of constituents who share potential relationship contacts with my main prospect. The relationship lists provide our prospect researchers a starting point in putting together a solid list of high capacity constituents a single person may have some sort of relationship with, thus a greater insight into potential giving.

As of now, the report is in its infancy but looks to have high potential. As we grow the concept, there are multiple data points where further exploration could result in a higher level of functioning. As prospects use the lists to identify people they know, we can then deconstruct those choices to determine what is more likely a relationship. Should shared employment be ranked higher than shared class year? Should Football rank higher than Student Senate? I would guess yes, but I currently do not have supporting data to make that decision.

Another interesting concept, raised at the recent DRIVE 2011 conference, would be: “How are these two prospects potentially related by a third constituent?”  The result could mean the difference between two separate, forced conversations and one single conversation with three prospects shared over nostalgic conversations, drinks and, hopefully, money in the door!

Erich Preisendorfer is Associate Director, Business Intelligence, working in Advancement Services at the University of New Hampshire.

30 August 2011

After the data mining … prospect research asks, “what then?”

Filed under: Major Giving, Prospect identification — kevinmacdonell @ 5:38 am

I recently had a question from a prospect researcher who is taking on the task of learning data mining to predict propensity to make a major gift. (Yay!) She wanted to know, what happens “after” the data mining? Let’s say she ranks her prospects by score and now she’s got 100 or 200 names — what then? She writes: “I fear that I will then have to create 100 in-depth profiles on these prospects because the fundraisers will not have a plan or the confidence to move forward with these names.”

The situation is familiar: Too many names, not enough time to create full profiles for everyone on the list. My first instinct is to call this a prospect research problem and not a data mining problem.

When I was a prospect researcher, I had to create in-depth profiles for any prospect we were meeting with – even if it was the very first meeting and a gift would be years off, if it ever came at all. Today I work at a university with a much larger staff of development officers, but a research office that is (relatively) smaller. Full profiles for qualifying visits is unthinkable. DOs get no more than a summary briefing on prospects they’re meeting for the first time. This is for obvious practical reasons, but it’s my understanding that this is becoming the norm for many research shops – the full profile is produced only at an advanced stage of cultivation. So my first suggestion is, limit research to “top level” information only: Job title and company, giving history with the institution, maybe their Who’s Who profile if it exists … and not much more.

My second thought is that a data-related solution is possible. I would try an approach that Peter Wylie uses: Take the top several hundred prospects (that is, according to propensity score) and sort them in descending order by lifetime giving. Think of the propensity score as summing up the affinity that the prospect feels for the institution. The lifetime giving dollar amount also provides evidence of affinity, but capacity as well. If a prospect has a very high affinity score AND has given in five figures, they’re probably a good major-gift prospect. Take the top 10 or so names and do in-depth profiles on them alone, leaving the others for later. Or, if wealth screening data is available, one could use that instead of lifetime giving to cross with the list of top-scoring prospects.

But after thinking about it again, perhaps the real issue is contained in the original question: The researcher fears that fundraisers won’t have a plan, and they won’t have confidence in the process. That’s a fundamental problem, one that can only be addressed by communication, a certain amount of selling on the part of the data miner, and a lot of support from upper management.

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