CoolData blog

10 March 2011

Gifts of stock as a predictor of Major Gift potential

Filed under: Major Giving, Model building, predictive modeling, Predictor variables, regression — Tags: , — kevinmacdonell @ 6:09 am

(Image used via Creative Commons license. Click image for source)

In an earlier post, I wrote about giving-related variables and whether or not they’re okay to use in a model that is trying to predict giving itself. (My answer was “it depends”. See Giving-related variables: Keep or leave out?) Today I zero in on a specific example: gifts of securities as a predictor of major giving.

Following the logic of my earlier post, if the sample of people whom you intend to score includes non-donors, and you want non-donors to have a chance of making it onto the radar, then you must rule out ‘Gift of Stock’ as a predictor. Why? Because you want to keep any proxy for your outcome variable (the Y side of your equation) out of the predictors (the X side of the equation), as much as possible. A ‘yes’ for ‘Has made a gift of stock’ is possible ONLY for the donors in your sample, and will provide no insight into a non-donor’s potential for major giving.

But giving-related variables are frequently used to predict major gift potential. Gift count, first gift, recency, and stock gifts are all enticing predictors. You have a decision to make: Do you exclude non-donors, or leave non-donors in and forgo the potential predictive power of these variables?

For some the answer might be easy. If the vast majority of major donors to your institution had some prior giving before making their biggest gifts, and a major gift from a non-donor is extremely unlikely, then it makes sense to exclude non-donors. This makes most sense for alumni models: Alumni who are solicited every year and don’t give are rather unlikely to turn around and give a million dollars. (Although it happens!)

You can avoid having to make the decision, however, if you build two models: One including non-donors (and using no giving-related variables), and one excluding them (freeing your hand to use giving-related variables). That’s what I do. I test the output scores against a holdout sample of major donors, and whichever model outperforms in scoring the major donors will be my choice for that year.

Let’s say that at least one of your models is a donor-only model, and you’re itching to use ‘Stock gifts’ as a predictor. Hold on! You’re not done yet. You need to evaluate the degree to which ‘Stock gifts’ is independent of your DV. If the variable equates to major giving itself, it is not at all independent and should be excluded. It is merely a proxy for being a major donor.

It’s clear that stock givers are different from other donors. In the data set I have before me, alumni who have made at least one gift of stock have median lifetime giving of about $40,000, compared with all other donors’ median giving of about $170. More than 66% of stock donors have lifetime giving over $25,000, and more than 90% of them have made at least one gift of $1,000 or greater.

The fact of having given a gift of securities cannot seriously be considered “independent” of the DV, but the degree of non-independence varies with how the DV is defined. If I define it as “LT Giving over $25K”, I’m probably in the clear, because a considerable portion of stock donors (34%, in my data set) fall outside the definition of my DV. If my DV is “One or more gifts of $1K or greater,” however, I should steer clear of the stock-gifts predictor. True, not all stock donors are in the DV, but almost all of them are.

Stock donors probably represent a very small percentage of all your donors, so the variable may have little influence either way: Not a high-value predictor, but not a damaging one, either. (Given the limited number in your sample, the correlation coefficient is going to be pretty low.) Maybe if 85% of the stock donors were in my DV, instead of 90%, I might go ahead and use it. So in the end, it’s a judgment call based on what seems to make sense for your data and what you hope to get out of it.

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2 Comments »

  1. Can someone tell me how the abbreviation DV being used here?

    Comment by Linda — 16 March 2011 @ 2:24 pm

    • DV = dependent variable, a.k.a. response variable, outcome variable, “Y”, etc.

      Comment by kevinmacdonell — 16 March 2011 @ 2:27 pm


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