Advancement consultant Peter B. Wylie and predictive modeling expert John Sammis have recently published a new paper, Data Mining and Alumni Association Membership. Like all of their work it’s written in a way anyone can understand. And like some of my recent posts have pointed out, it shows how data mining can be a powerful tool when used to predict all sorts of behaviours besides giving.
This time they’re showing you how you can use certain key pieces of information in your database to predict who will be most likely to want to join your dues-based alumni association. Their paper identifies the key variables that tend to be strongly related to active alumni association membership, and demonstrates how to create a predictive score. Their data came from four public higher-education universities with graduate and undergraduate enrollments that ranged from 4,500 to 27,000.
They believe schools should be using this information to save money on membership appeals, and boost membership.
And I do, too.
Addendum (20 Jan 2010): FYI, Peter Wylie is interviewed in the current issue of CASE Currents magazine. Will post link if it becomes available.