I hope you’ve had a chance to read “The tough job of bringing in new alumni donors” by Peter Wylie and John Sammis. What did you think of it? I’m sure the most common reaction is “That’s very interesting.” There’s a big gap, though, between reaction and action. I want to talk about converting this knowledge into action.
The subject of that guest post is a lot more than just “interesting” for me. I’ve recently changed jobs, moving from prospect research for major gifts at a university with 30,000 living alumni to running an annual fund phonathon program at a university with more than three times as many alumni. For the first time, I will be responsible not only for mining the data, but helping to design a program that will make use of the results.
Like most institutions, my new employer invests heavily in acquiring new donors. Calling and mailing to never-donors yields a return on investment that may be non-existent in the short term and difficult to quantify in the (future) long term.
Yet it must be done. ROI is important, but if you write off whole segments based only on ROI in the current year, ignoring long-term value, your pool of donors will shrink through attrition. Broadening the base of new donors costs money — an investment we hope to recoup with interest when new donors renew in future years. (See “The Habit of Giving“, by Jonathan Meer, on the subject of a donor tendency to renew. Also see “Donor Lifetime Value“, by Dan Allenby, from his Annual Giving Exchange blog, for a brief discussion of the importance of estimating donor lifetime value vs. costs of continuing to solicit. I also owe thanks to Chuck McClenon at The University of Texas at Austin for helping me understand the dangers of focusing on short-term ROI.)
I have argued that in phonathon we ought to give highest priority to propensity to give (i.e., from our predictive model), and stop using giving history (LYBUNTs, etc.) to determine calling priority. (Previous post: Rethinking calling priority in your phonathon campaign.) The results of our predictive model will give us the ROI side of the equation. But I’m growing increasingly convinced that propensity to give must be balanced against that other dimension: Lifetime value.
Dan Allenby says, “Donor lifetime value means predicting the sum of donors’ gifts over the course of their lives,” and later cautions: “This kind of calculation is complicated and imperfect.” This is so true. I certainly haven’t figured out an answer yet. I presume it will involve delving into our past data to find the average number of years a new donor continues to give, and what the average yearly renewal gift is, to establish a minimum lifetime value figure.
And I suspect this as well: The age of the donor at conversion is going to figure prominently. In this life there are three things that are inescapable: death, taxes, and a call from the Annual Fund! The number of years a donor has left to give is a function of age. We can assume, then, that early conversion is more desirable than late conversion. (Not to mention death-bed conversion.)
The discussion by Wylie and Sammis (to return to that) really seals the deal. Not only do younger alumni have more time left to give, so to speak, but Wylie/Sammis have clearly demonstrated that younger alumni are probably also more likely than older alumni to convert.
If you’re already using predictive modeling in your program, think about the implications. Year after year, the biggest factor in my giving models is age (i.e., class year). Older alumni tend to score higher, especially if my dependent variable is ‘lifetime giving’ going back to the beginning of time. This flies in the face of the evidence, provided by Wylie and Sammis, that non-donor alumni are less and less likely to convert the older they get.
We need a counterbalance to raw propensity-to-give scores in dealing with non-donors. What’s the answer? One possibility is a propensity-to-convert model that doesn’t undervalue young alumni so much. Another might be a matrix, with propensity-to-give scores on one axis, and some measure of lifetime value (factoring in age) on the other axis — the goal being to concentrate activity on the high-propensity AND high-lifetime value prospects, and work outwards from there.
I don’t know. Today all I know is that in order to broaden the base of your donor pool and maximize returns over the long term, you must call non-donors, and you must call the non-donors with the most potential. That means embracing younger alumni — despite what your model tells you to do.