Sometimes I think I have it too easy. Those of us working in post-secondary education advancement have so much biographical and other data to work with! There’s class year, abundant contact information, often employment information, and all the data generated by an individual’s willing engagement with a chosen institution and peer group. There is little need to collect anything external to provide additional predictors. (In fact, the external info I’ve been able to pair up with my data has had zero predictive value, in comparison.)
Not so, presumably, for those of you who work for other non-profits. Not only is your data more fragmentary, but you’re probably missing a key element: non-donors. You can’t distinguish the characteristics of a donor from those of a non-donor if both animals are not fully present in your database. (In a university database, a majority of constituents, usually, are non-donors.)
I admit, I have no idea what a donor database for, say, a hospital foundation looks like. If it’s strictly a DONOR database, then it’s got no non-donors in it. If it’s a PATIENT database (to which you append donor info), then obviously you’re in better shape. Still, given the understandable privacy restrictions that must be in place, predicting donor behaviour must be a bigger challenge.
I am not surprised, though, that some of the leaders in the field of predictive modeling work in hospital fundraising. I recently had the pleasure of having a long phone conversation with Kate Chamberlin at Memorial Sloan-Kettering Cancer Center, in New York. Her team of analysts (yes! her team! the idea makes one swoon) are not only proving that predictive modeling has a place outside of the alumni-type database, but they’re excelling at it.
The Chronicle of Philanthropy ran an excellent piece back in January, A New York Cancer Center Uses Technology to Predict Who Will Give, which begins: “Almost every charity’s pool of donors includes plenty of people who have both the means and the inclination to make a far bigger gift than they ever did in the past. The trick, of course, is to figure out just which people will make the leap. To that end, Memorial Sloan-Kettering Cancer Center, in New York, has become… ”
(And that’s it, unless you have a subscription. Unfortunately, I’m linking to it a little late.)
Sure, they’re big. But they’re successful because they’re smart. And they share their smarts at conferences. My advice to university data miners who plan to attend a conference this year: Seek out the non-university presenters and see what they have to say. Chances are they’ve got some of the most creative approaches, because they have to.
As for non-university readers who are drawn to what university advancement departments are doing: I wonder if you would be better off looking to the private sector for inspiration, rather than us university types?
I’m thinking of big retailers with data-rich selling environments such as an online bookstore, or a hardware chain with a strong loyalty-reward program. Most people in the Amazon.com database, for example, must have bought at least one book at some point in the past. Just as an arts-based nonprofit has no non-donors in their database, a bookseller would have no non-customers. The bookseller would attempt to predict who is most likely to make more purchases, versus who will buy once and abandon their account, and target accordingly. The store could withhold expensive incentives from the high scorers (since they will keep buying anyway), and from the low scorers (since they’re unlikely to respond in numbers), and target the middle instead.
Obviously, no charity would leave their high scorers untended that way – there is always some higher goal to reach. And of course there are big differences. But in terms of technique, maybe there are congruities to explore.