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.