Recency, Frequency, Monetary value. The calculation of scores based on these three transactional variables has a lot of sway over the minds of fundraisers, and I just don’t understand why.
It’s one of those concepts that never seems to go away. Everyone wants a simple way to whip up an RFM score. Yet anyone who can do a good job of RFM is probably capable of doing real predictive modeling. The persistence of RFM seems to rest on some misconceptions, which I want to address today.
First, people are under the impression that RFM is cutting-edge. It isn’t. In his book, “Fundraising Analytics: Using Data to Guide Strategy,” Joshua Birkholz points out that RFM is “one of the most common measures of customer value in corporate America.” It’s been around a long time. That alone doesn’t mean it’s invalid — it just isn’t anything new, even in fundraising.
Second, it’s often misconstrued as a predictive tool, and therefore the best way to segment a prospect pool. It’s not. As Josh makes clear in his book, RFM has always been a measure of customer value. It does not take personal affinities into account, nor any non-purchasing activities, he writes.
Note the language. RFM is borrowed from the for-profit world: retail and sales. Again, this doesn’t discredit it, but it does make it inappropriate as the sole or primary tool for prediction. Because it’s purely transactional in nature, all RFM can tell you is that donors will become donors. It CAN’T tell you which non-donors are most likely to be acquired as new donors. The RFM score for a non-donor is always ZERO.
It also can’t tell you which lapsed donors are most likely to be reactivated, or which donors are most likely to be upgraded to higher levels of giving. In the eyes of RFM, one person who gave $50 last year is exactly the same as any other person who gave $50 last year. They’re NOT.
Third, we’re often told that RFM is easy to do. RFM is easy to explain and understand. It’s not necessarily easy to do. Recency and Monetary Value are straightforward, but Frequency requires a number of steps and calculations, and you’re probably not going to do it in Excel. Josh himself says it’s easy to do, but the demonstration in his book requires SPSS. If you’re using a statistics software package such as SPSS and you’ve mastered Frequency, then true predictive modeling is easily within your grasp. Almost all the variables I use in my models are simpler to derive than Frequency.
Is RFM useless? No, but we need to learn not to pick up a hammer when what we really need is a saw. RFM is for ranking existing donors according to their value to your organization, based on their past history of giving. Predictive modeling is for predicting (who knew?), and answering the three hard questions I listed above (acquisition, reactivation, upgrade potential.)
You could, in fact, use both. Your predictive model might identify the top 10% of your donor constituency who are most likely to renew, while your RFM score set will inform you who in that top 10% have the highest potential value to your organization. A matrix with affinity (predictive model) on one axis and value on the other (RFM) would make a powerful tool for, say, segmenting an Annual Giving donor pool for Leadership giving potential. Just focus on the quadrant of donors who have high scores for both affinity and value.
If you want to use RFM in that way (that is, properly), then fill your boots. I recommend Josh Birkholz’s book, because he lays it out very clearly.
The real danger in RFM is that it can become an excuse for not collecting more and better data.
For any institution of higher education, the idea that RFM is the bee’s knees is patently untrue. Institutions with alumni have a trove of variables that are informative about ALL of their constituency, not just those who happen to be donors. Expand that to arts-based nonprofits, and you’ll find member-based constituencies and the very same opportunities to model in a donor/non-donor environment. Neither of these types of institutions should be encouraged to rely exclusively on RFM.
For the rest, who don’t have the data for their constituency but could, the idea that pure donor transaction data is all you need cuts off the chance of doing the work now to get more sophisticated about collecting and organizing data that will pay off in the years ahead.