Does “no children” really mean Planned Giving potential?

I gave a presentation to fundraising professionals and other nonprofit types recently, and I spent a little time discussing my work with predicting Planned Giving potential. One of the attendees asked if I was aware of a recent study that found that the most significant predictor for Planned Giving was the absence of children.

I had, and in my (not very coherent) response I said something to the effect that although this was interesting, I had reservations about taking an observation based on other institutions’ populations and applying it to ours. I would prefer to test it, I said. (I believe that someone else’s valid observation about their own data is only an assumption when applied blindly to mine.) And then I said that we don’t have the data to begin with.

But as I was talking, a thought occurred to me: Yes, in fact we DO have child data! I had even used that data in my PG model, but it had never occurred to me to study it very closely.

Back in the spring of 2009, our school conducted an extensive online survey of alumni as part of a national benchmarking study of alumni engagement. One of the core questions (supplied by the study firm, Engagement Analysis Inc.) asked specifically about likelihood to consider a bequest. Another question, which we added ourselves, asked respondents how many children they had under the age of 18. (We had a purpose in asking about “under 18”, and it wasn’t Planned Giving. Had I specifically been seeking a PG predictor, I would not have qualified the statement. Presumably the positive “childless effect” is explained by the lack of need to divide an estate up among children, regardless of their age.)

Our response rate was very high, and quite representative of our alumni population. Standing there in the midst of my presentation, I realized I had enough information to test the ‘childless’ theory in the environment of our own data.

The chart below shows survey responses to the PG question on the horizontal axis. The question was actually a scale statement which indicated that the responder was very likely to leave a bequest to our institition. Possible answers ranged from 1 to 6, with a one meaning “strongly disagree” and a six meaning “strongly agree”. If the respondent did not answer the question, I coded it as zero so it would show up on my chart.

In the chart, each group of respondents (i.e., each vertical bar) is segmented according to their answer on the “children” question. Notice the relative size of the blue segments, the responders who have no children under 18. For the proportion of this segment, there is a difference of approximately ten percentage points between the “strongly agree” group and the “strongly disagree” group.

In other words, childless alumni in our survey data set ARE more receptive to considering Planned Giving.

I said earlier that the survey response was representative of our alumni population. Therefore, many of the responders are far too young to be considered prospects. So I made another chart, which shows only alumni in the older half of the population: Class year 1990 and earlier. The difference between these two charts will seem subtle because they’re busy-looking, so let me point it out to you: Now the gap between the “strongly disagree” and the “strongly agree” for people with no kids has widened to 15 percentage points. This is a vote of confidence in favour of using “number of children” as a predictor of PG receptivity.

But here’s a question: Can you use child data to segment your prospect pool, and thereby avoid having to engage in predictive modeling? My answer is “No.” In both of the charts above, a majority of respondents answered “no children”, regardless of their attitude to Planned Giving. Yes, there’s a difference among the groups, but although it is significant, it is not definitive.

Others may quibble, saying that the data is suspect because we only asked about children under 18. But I really think this predictor is a lot like certain other conventional predictors, the ones related to frequency and consistency of giving: Alone, they are not powerful enough to isolate your best PG prospects. Only when you combine them with the full universe of other proven predictors in your database (event attendance, marital status, etc.) will you end up with something truly useful.

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The 15 top predictors for Planned Giving – Part 3

Okay, time to deliver on my promise to divulge the top 15 predictor variables for propensity to enter a Planned Giving commitment.

Recall the caveat about predictors that I gave for Annual Giving: These variables are specific to the model I created for our institution. Your most powerful predictors will differ. Try to extract these variables from your database for testing, by all means, but don’t limit yourself to what you see here.

In Part 2, I talked about a couple of variables based on patterns of giving. The field of potential variables available in giving history is rich. Keep in mind, however, that these variables will be strongly correlated with each other. If you’re using a simple-score method (adding 1 to an individual’s score for each positively-correlated predictor variable), be careful about using too many of them and exaggerating the importance of past giving. On the other hand, if you use a multiple regression analysis, these related variables will interact with each other – this is fine, but be aware that some of your hard-won variables may be reduced to complete insignificance.

Just another reason to look beyond giving history!

For this year’s Planned Giving propensity model, the predicted value (‘Y’) was a 0/1 binary value: “1” for our existing commitments, “0” for everyone else. (Actually, it was more complicated than that, but I will explain why some other time.)

The population was composed of all living alumni Class of 1990 and older.

The list

The most predictive variables (roughly in order of influence) are listed below. Variables that have a negative correlation are noted N. Note that very few of these variables can be considered continuous (eg. Class Year) or ordinal (survey scale responses). Most are binary (0/1). But ALL are numeric, as required for regression.

1. Total lifetime giving
2. Number of Homecomings attended
3. Response to alumni survey scale question, regarding event attendance
4. Number of President’s Receptions attended
5. Class Year (N)
6. Recency: Gave in the past 3 years
7. Holds another degree from another university (from survey)
8. Marital status ‘married’
9. Prefix is Religious (Rev., etc.) or Justice
10. Alumni Survey Engagement score
11. Business phone present
12. Number of children under 18 (from survey) (N)

Like my list of Annual Giving predictors, this isn’t a full list (and it isn’t 15 either!). These are the most significant predictors which don’t require a lot of explanation.

Note how few of these variables are based on giving – ‘Years of giving’ and ‘Frequency of giving’ don’t even rate. (‘Lifetime giving’ seems to take care of most of the correlation between giving and Planned Giving commitment.) And note how many variables don’t even come from our database: They come from our participation in a national survey for benchmarking of alumni engagement (conducted in March 2009).

Scholarship/bursary recipients and alumni giving

I’ve attended a number of conference presentations on data mining given by David E. Robertson of Syracuse University (Twitter), and a question I’ve heard him ask more than once is: Are alumni who were the recipients of financial assistance while they were students more likely to give to their alma mater later in life?

A great question, but one we haven’t been able to answer for our own institution, as the historical data on student assistance just isn’t in Banner. So I was itching to get at this when our institution conducted a far-ranging survey of alumni this past spring. The result was a little surprising.

The survey was in fact a national benchmarking study of alumni engagement offered by Engagement Analysis Inc. (I will have more to say about that in the coming months. For now, I’ll say that if you’re at a university in Canada that hasn’t looked into this yet, then do so!)

The survey features a core set of questions which are held constant from university to university, but with the option of adding one’s own institution-specific questions. The results of these questions are outside the benchmarking analysis, so no peer comparisons are possible. But we found some of these questions were definitely worth asking.

Alumni were asked to respond Yes or No to the statement, “I received a scholarship or bursary while attending university.” (David Robertson, if I recall correctly, was specifically interested in students who received need-based assistance rather than merit-based awards, but we chose to lump both groups together. Mistake?)

A little more than 43% of alumni said they’d received one or the other. What was interesting was that they are no more engaged with the university than alumni who answered ‘No’.

The survey was non-anonymous, so we were able to match up survey responses to giving and demographic data in Banner. We found that:

• average and median lifetime giving were only marginally higher for the recipients versus the non-recipients.
• the segment of ‘Yes’ alumni who had given any amount in any year was only 1.6 percentage points higher than that of the ‘No’ alumni (51.7% for the Yes group, and 50.1% for the No group).

When the survey results were presented to Advancement staff earlier this winter, we were asked to look at this question again. What about giving specifically designated for scholarships and bursaries? If there is any “gratitude effect,” we would naturally expect it to see it in this area of giving. However, once again, average and median lifetime giving for scholarships and bursaries were essentially equivalent for both groups.

We certainly hear about alumni, often older alumni, who say they are thankful for the chance they received early in life and want to give back. No doubt it is a real force at work somewhere in the psyche of the donor. But it seems to be too subtle an influence to be detected at the macro level, at least for our institution, using the tools described above.

On the negative side, a potential predictor variable has been struck off the list. On the positive side, well, now we know.

Have others tested this? Gone about it differently?

• David E Robertson Jr
• Location Syracuse, NY
• Web http://fullpacker…
• Bio Syracuse University researcher and graduate student interested in the philanthropic contributions of the marginalized and disenfranchised.