The cautionary tale of Mr. S. John Doe

A few years ago I met with an experienced Planned Giving professional who had done very well over the years without any help from predictive modeling, and was doing me the courtesy of hearing my ideas. I showed this person a series of charts. Each chart showed a variable and its association with the condition of being a current Planned Giving expectancy. The ultimate goal would have been to consolidate these predictors together as a score, in order to discover new expectancies in that school’s alumni database. The conventional factors of giving history and donor loyalty are important, I conceded, but other engagement-related factors are also very predictive: student activities, alumni involvement, number of degrees, event attendance, and so on.

This person listened politely and was genuinely interested. And then I went too far.

One of my charts showed that there was a strong association between being a Planned Giving expectancy and having a single initial in the First Name field. I noted that, for some unexplained reason, having a preference for a name like “S. John Doe” seemed to be associated with a higher propensity to make a bequest. I thought that was cool.

The response was a laugh. A good-natured laugh, but still — a laugh. “That sounds like astrology!”

I had mistaken polite interest for a slam-dunk, and in my enthusiasm went too far out on a limb. I may have inadvertently caused the minting of a new data-mining skeptic. (Eventually, the professional retired after completing a successful career in Planned Giving, and having managed to avoid hearing much more about predictive modeling.)

At the time, I had hastened to explain that what we were looking at were correlations — loose, non-causal relationships among various characteristics, some of them non-intuitive or, as in this case, seemingly nonsensical. I also explained that the linkage was probably due to other variables (age and sex being prime candidates). Just because it’s without explanation doesn’t mean it’s not useful. But I suppose the damage was done. You win some, you lose some.

Although some of the power (and fun) of predictive modeling rests on the sometimes non-intuitive and unexplained nature of predictor variables, I now think it’s best to frame any presentation to a general audience in terms of what they think of as “common sense”. Limiting, yes. But safer. Unless you think your listener is really picking up what you’re laying down, keep it simple, keep it intuitive, and keep it grounded.

So much for sell jobs. Let’s get back to the data … What ABOUT that “first-initial” variable? Does it really mean anything, or is it just noise? Is it astrology?

I’ve got this data set in front of me — all alumni with at least some giving in the past ten years. I see that 1.2% percent of all donors have a first initial at the front of their name. When I look at the subset of the records that are current Planned Giving expectancies, I see that 4.6% have a single-initial first name. In other words, Planned Giving expectancies are almost four times as likely as all other donors to have a name that starts with a single initial. The data file is fairly large — more than 17,000 records — and the difference is statistically significant.

What can explain this? When I think of a person whose first name is an initial and who tends to go by their middle name, the image that comes to mind is that of an elderly male with a higher than average income — like a retired judge, say. For each of the variables Age and Male, there is in fact a small positive association with having a one-character first name. Yet, when I account for both ‘Age’ and ‘Male’ in a regression analysis, the condition of having a leading initial is still significant and still has explanatory power for being a Planned Giving expectancy.

I can’t think of any other underlying reasons for the connection with Planned Giving. Even when I continue to add more and more independent variables to the regression, this strange predictor hangs in there, as sturdy as ever. So, it’s certainly interesting, and I usually at least look at it while building models.

On the other hand … perhaps there is some justification for the verdict of “astrology” (that is, “nonsense”). The data set I have here may be large, but the number of Planned Giving expectancies is less than 500 — and 4.6% of 500 is not very many records. Regardless of whether p ≤ 0.0001, it could still be just one of those things. I’ve also learned that complex models are not better than simple ones, particularly when trying to predict something hard like Planned Giving propensity. A quirky variable that suggests no potential causal pathway makes me wary of the possibility of overfitting the noise in my data and missing the signal.

Maybe it’s useful, maybe it’s not. Either way, whether I call it “cool” or not will depend on who I’m talking to.

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.

Address updates and affinity: Consider the source

Some of the best predictors in my models are related to the presence or absence of phone numbers and addresses. For example, the presence of a business phone is usually a highly significant predictor of giving. As well, a count of either phone or address updates present in the database is also highly correlated with giving.

Some people have difficulty accepting this as useful information. The most common objection I hear is that such updates can easily come from research and data appends, and are therefore not signals of affinity at all. And that would be true: Any data that exists solely because you bought it or looked it up doesn’t tell you how someone feels about your institution. (Aside from the fact that you had to go looking for them in the first place — which I’ve observed is negatively correlated with giving.)

Sometimes this objection comes from someone who is just learning data mining. Then I know I’m dealing with someone who’s perceptive. They obviously get it, to some degree — they understand there’s potentially a problem.

I’m less impressed when I hear it from knowledgeable people, who say they avoid contact information in their variable selection altogether. I think that’s a shame, and a signal that they aren’t willing to put in the work to a) understand the data they’re working with, or b) take steps to counteract the perceived taint in the data.

If you took the trouble to understand your data (and why wouldn’t you), you’d find out soon enough if the variables are useable:

• If the majority of phone numbers or business addresses or what-have-you are present in the database only because they came off donors’ cheques, then you’re right in not using it to predict giving. It’s not independent of giving and will harm your model. The telltale sign might be a correlation with the target variable that exceeds correlations for all your other variables.
• If the information could have come to you any number of ways (with gift transactions being only one of them), then use with caution. That is, be alert if the correlation looks too good to be true. This is the most likely scenario, which I will discuss in detail shortly.
• If the information could only have come from data appends or research, then you’ve got nothing much to worry about: The correlation with giving will be so weak that the variable probably won’t make it into your model at all. Or it may be a negative predictor, highlighting the people who allowed themselves to become lost in the first place. An exception to the “don’t worry” policy would be if research is conducted mainly to find past donors who have become lost — then there might be a strong correlation that will lead you astray.

An in-house predictive modeler will simply know what the case is, or will take the trouble to find out. A vendor hired to do the work may or may not bother — I don’t know. As far as my own models are concerned, I know that addresses and phone numbers come to us via a mix of voluntary and involuntary means: Via Phonathon, forms on the website, records research, and so on.

I’ve found that a simple count of all historical address updates for each alum is positively correlated with giving. But a line plot of the relationship between number of address updates and average lifetime giving suggests there’s more going on under the surface. Average lifetime giving goes up sharply for the first half-dozen or so updates, and then falls away just as sharply. This might indicate a couple of opposing forces: Alumni who keep us informed of their locations are more likely to be donors, but alumni who are perpetually lost and need to be found via research are less likely to be donors.

If you’re lucky, your database not only has a field in which to record the source of updates, but your records office is making good use of it. Our database happens to have almost 40 different codes for the source, applied to some 300,000 changes of address and/or phone number. Not surprisingly, some of these are not in regular use — some account for fewer than one-tenth of one percent of updates, and will have no significance in a model on their own.

For the most common source types, though, an analysis of their association with giving is very interesting. Some codes are positively correlated with giving, some negatively. In most cases, a variable is positive or negative depending on whether the update was triggered by the alum (positive), or by the institution (negative). On the other hand, address updates that come to us via Phonathon are negatively correlated with giving, possibly because by-mail donors tend not to need a phone call — if ‘giving’ were restricted to phone solicitation only, perhaps the association might flip toward the positive. Other variables that I thought should be positive were actually flat. But it’s all interesting stuff.

For every source code, a line plot of average LT giving and number of updates is useful, because the relationship is rarely linear. The relationship might be positive up to a point, then drop off sharply, or maybe the reverse will be true. Knowing this will suggest ways to re-express the variable. I’ve found that alumni who have a single update based on the National Change of Address database have given more than alumni who have no NCOA updates. However, average giving plummets for every additional NCOA update. If we have to keep going out there to find you, it probably means you don’t want to be found!

Classifying contact updates by source is more work, of course, and it won’t always pay off. But it’s worth exploring if your goal is to produce better, more accurate models.

Survey says … beware, beware!

I love survey data. But sometimes we get confused about what it’s really telling us. I don’t claim to be an expert on surveying, but today I want to talk about one of the main ways I think we’re led astray. In brief: Surveys would seem to give us facts, or “the truth”. They don’t. Surveys reveal attitudes.

In higher education, surveying is of prime importance in benchmarking constituent engagement in order to identify programmatic areas that are underperforming, as well as areas that are doing well and for which making changes therefore entails risk. Making intelligent, data-driven decisions in these areas can strengthen programming, enhance engagement, and finally increase giving. And there’s no doubt that the act of responding to a survey, the engagement score that might result, and the responses to individual questions or groups of questions, are all predictive of giving. I have found so myself in my own predictive modeling at two universities.

But let’s not get carried away. Survey data can be a valuable source of predictor variables, but it’s a huge leap from making that admission to saying that survey data trumps everything.

I know of at least one vendor working in the survey world who does make that leap. This vendor believes surveying is THE singular best way to predict giving, and that survey responses have it all over the regular practice of predictive modeling using variables mined from a database. Such “archival” data provides “mere correlates” of engagement. Survey data provides the real goods.

I see the allure. Why would we put any stock in some weak correlation between the presence of an email address and giving, when we can just ask them how they feel about giving to XYZ University?

Well.

I have incorporated survey data in my own models, data that came from two wide-ranging, professionally-designed, Likert-type surveys of alumni engagement. Survey data is great because it’s fresh, independent of giving, and revealing of attitudes. It is also extremely biased in favour of highly-engaged alumni, and is completely disconnected from reality when it comes to gathering facts as opposed to attitudinal data.

Let me demonstrate the unreliability of survey data with regard to facts. Here are a few examples of statements and responses (one non-Likert), gathered from surveys of two institutions:

• “I try to donate every year” — 946 individuals answered “agree” or “strongly agree” — but 12.3% of those 946 had no lifetime giving.
• “I support XYZ University regularly” — 1,001 individuals answered “agree” or “strongly agree” — but 18.7% of them had no lifetime giving.
• “Have you ever made a charitable gift to XYZ University (Y/N)?” — 1,690 individuals said “Yes” — but 8.1% of them had no lifetime giving.
• “I support XYZ University to the best of my capacity” — 1,498 individuals answered “agree” or “strongly agree” — but 39.6% of them had no lifetime giving!

And, even stranger:

• “I try to donate every year” — 1,371 answered “disagree” or “strongly disagree” — but 27.7% of those respondents were in fact donors!

Frankly, if I asked survey-takers how many children they have, I wouldn’t trust the answers.

This disconnect from reality actually works in my favour when I am creating predictive models, because I have some assurance that the responses to these questions is not just a proxy for ‘giving’, but rather a far more complicated thing that has to do with attitude, not facts. But in no model I’ve created has survey data (even carefully-selected survey data strongly correlated with giving) EVER been more predictive than the types of data most commonly used in predictive models — notably age/class year, the presence/absence of certain contact information, marital status, employment information, and so on.

For the purposes of identifying weaknesses or strengths in constituent engagement, survey data is king. For predicting giving in its various forms, survey data and engagement scores are just more variables to test and work into the model — nothing more, nothing less — and certainly not something magical or superior to the data that institutions already have in their databases waiting to be mined. I respect the work that people are doing to investigate causation in connection with giving. But when they criticize the work of data miners as “merely” dealing in correlation, well that I have a problem with.

Proving ‘event attendance likelihood’ actually works

In an earlier post I talked about what you need to get started to build an ‘event attendance likelihood’ model. Today I want to provide some evidence to back up my claim that yes, you can identify which segment of your alumni population is most likely to attend your future event.

To recap: Every living, addressable alumnus/na in our database is scored according to how likely he or she is to attend an event, whether it be a President’s Reception or Homecoming, whether they’ve ever attended an event or not.

The scores can be used to answer these types of questions:

• What’s the top 30% of alumni living in Toronto who should be mailed a paper invite to the President’s Reception?
• Who are the 50 members of the Class of 2005 who are most likely to come to Homecoming for their 5th-year reunion?

I built our first event-attendance model last summer. As I always do, I divided all our alumni into deciles by the predicted values that are produced by the regression analysis (the ‘raw score’). The result is that all alumni were ranked from a high score of 10 (most likely to attend an event) to 1 (least likely).

At that time, alumni were sending in their RSVPs for that fall’s Homecoming event. Because I use only actual-attendance data in my models, these RSVPs were not used as a source of data. … That made Homecoming 2009 an excellent test of the predictive strength of the new model.

Have a look at this chart, which shows how much each decile score contributed to total attendance for Homecoming 2009. The horizontal axis is Decile Score, and the vertical axis is Percentage of Attendees. Almost 45% of all alumni attendees had a score of 10 (the bar highlighted in red).

(A little over 4% of alumni attendees had no score. Most of these would have been classified as ‘lost’ when the model was created, and therefore were excluded at that time. In the chart, they are given a score of zero.)

To put it another way, almost three-quarters of all alumni attendees have a score of 8 or higher. But those 10 scores are the ones who really stand out.

Let me anticipate an objection you might have: Those high-scoring alumni are just the folks who have shown up for events in the past. You might say that the model is just predicting that past attendees are going to attend again.

Not quite. In fact, a sizable percentage of the 10-scores who attended Homecoming had never attended an event before: 23.1%.

The chart below shows the number of events previously attended by the 10-scored alumni who were at Homecoming in 2009. The newbies are highlighted in red.

The majority of high-scoring attendees had indeed attended previous events (a handful had attended 10 or more!). But that one-quarter hadn’t – and were still identified as extremely likely to attend in future.

That’s what predictive modeling excels at: Zeroing in on the characteristics of individuals who have exhibited a desired behaviour, and flagging other individuals from the otherwise undifferentiated masses who share those characteristics.

Think of any ‘desired behaviour’ (giving to the annual fund, giving at a higher level than before, attending events, getting involved as an alumni volunteer), then ensure you’ve got the historical behavioural data to build your model on. Then start building.

The 15 top predictors for Planned Giving – Part 2

It’s time to explore the two variables we created in Part 1. The first was ‘Years of Giving’, and the second was ‘Frequency of Giving’. Both of these things are generally assumed to be predictive of Planned Giving potential.

The key word is ‘assumed’. Based on assumption alone, you can go into your database right now and skim off the top alumni by years and frequency of giving, and call them your top Planned Giving prospects. That would fall into the category of data mining, and you might have some success doing this.

But why not kick it up a notch? If you can do data mining, you can do predictive modeling.

In modeling, characteristics such as years or frequency of giving are regarded as variables, just like ‘Class Year’, ‘Homecomings Attended’, ‘Business Phone Present’, and all the rest of them. And like any other variable, their relative power to predict Planned Giving potential is demonstrable.

Let’s explore ‘Years of Giving’ first.

For ease of visualizing this variable, I chopped it into ranges, as in the chart below. This chart shows how our Planned Giving expectancies (on the right) differ from all other alumni (on the left), with regards to the number of years they’ve made any gift.

Look at the blue parts (no giving) for both stacks: Our Planned Giving expectancies are far less likely than other alumni to be non-donors. That should not be a surprise.

Look at the purple parts (15 to 21 years of giving): Our expectancies are much more likely than all other alumni to give every year. Again, that’s perfectly in line with conventional wisdom. So far so good.

Our next chart shows the same side-by-side comparison for ‘Frequency’ of giving. This variable is quite closely related to ‘Years of Giving’, and we see the same dramatic differences. Our existing expectancies tend to be more frequent donors than the general alumni population.

There you have it, two solid characteristics associated with alumni who choose to enter Planned Giving commitments. These characteristics, and similar ones that might result from a standard RFM analysis (Recency, Frequency, Monetary value), might be enough to satisfy some.

But let me show you something else.

Here’s another side-by-side comparison. Now we’re looking at Homecoming Attendance. Have a look at this.

This is based on attendance data going back more than ten years. I am so glad we have that data, because as it turns out, Homecoming attendance is the second most powerful predictor of Planned Giving potential for our institution – after ‘Lifetime Giving’, but before any other variable related to giving history. Even more so: MULTIPLE Homecoming attendance!

I concede there is a significant age difference between these two groups – I did not take the extra step of limiting the population to older alumni when I made these charts. But the observation of difference between the groups still holds valid. (Only 16.4% of our living alumni from the Class of 1979 and earlier have ever attended Homecoming, but almost 56% of expectancies have.)

Prospecting for new Planned Giving commitments is hard enough. We make our jobs that much harder when we fail to add up the combined power of predictors such as event attendance and a dozen other things sitting in our databases.

If you remain unconvinced, if you still think that past giving behaviour is the only true predictor of future potential, then let me leave you with a final observation from my analysis of our own data: If all of our current, known Planned Giving expectancies were hidden in the database like needles in a haystack, and we were only allowed to use past giving patterns to find them again, we would miss two-thirds of them!

Ouch!

In Part 3, I will finally reveal my top 15 predictors of Planned Giving potential. I promise.